How to Put AI at the Service of People in Cities, and Not Otherwise: in Conversation with Prof. Flavio S Correa da Silva

Prof. Flavio S Correa da Silva is a researcher in Artificial Intelligence, with over 30 years of experience in theoretical advancements of AI, as well as the development of AI applications for problem-solving, with a particular interest in human-centric AI and applications related to urban mobility, healthcare, wellbeing, and the ageing society. After completing his PhD in 1992 at the University of Edinburgh (UK), he returned to Brazil to become a faculty member at the University of Sao Paulo (USP), where he created the Laboratory of Artificial Intelligence, Logics and Formal Methods and the Laboratory of Interactivity and Digital Environments. He is currently Associate Professor at USP and Honorary Research Fellow at the University of Aberdeen (UK), having held the position of short-term visiting scholar at the University of Milano-Bicocca (Italy) in 2018 and 2019. He has published over 100 articles in peer-reviewed journals and conferences and written eight academic books.

Q: There seems to be a growing interest in building Intelligent Cities. From your perspective, what could truly bring intelligence to cities?

FS: The concept of Intelligent (or Smart) Cities (IC) is usually tied to the use of computational technologies to improve the efficiency of urban services. Even though this perspective of IC can be traced back to the late 1960s, it is commonly assumed to have gained prominence in the 21st century. It is rooted in the notion of cities as machines, in which citizens are components in a large mechanism. Frequently, it induces the search for solutions to specific and specialised problems (such as over-consumption of natural resources, environmental pollution, and efficiency in the delivery of public services), without challenging relevant sociotechnical imaginaries (e.g., What causes the consumption of natural resources and environmental pollution? What services are perceived by citizens as most relevant and based on what presuppositions?). In contrast, Human-centric Urban Design (HCUD) is based on the principle that cities (and related technologies) exist to serve people, hence citizens are not components of urban systems, rather they constitute the raison d’être of cities. HCUD has its origin connected to the works of Jane Jacobs and particularly of Jan Gehl and has proven to be very effective at getting to the root of problems and issues to be solved, by giving priority to human and environmental needs and valuing local knowledge. In my view, this is the right approach to designing and building truly intelligent cities.

Q: How can AI be useful to design truly intelligent cities?

FS: The goal of AI is to build systems that can sense, reason about, and act upon the states of complex environments, based on computations that enable complex behaviour to the point of deserving to be classified as intelligent. Since its origin in the mid-1950s, research in AI has been structured in two lines: systems to replace human agency and systems to augment human capabilities, more recently coined Human-centric AI (HCAI). The former aligns well with the mechanistic approach to the analysis and design of complex systems, whereas the latter aligns with the human-centric approach. HCAI can provide urban designers, planners, and managers with effective tools to preserve, support, enrich, and evolve local knowledge. To name a few possibilities, route-finding systems for pedestrians can take into consideration the health and wellbeing attributes of all citizens on a street, au pair with the optimisation of distance, speed, and safety of individual pedestrians, and generative design can be enriched by large scale, location-aware empirical data to provide designers with innovative design alternatives.

Q: There has been growing debate about ensuring ethical behaviour of AI-based systems. How does this issue impact the use of AI in the design of truly intelligent cities?

FS: Broadly speaking, Ethics is about the identification of attitudes that can take humans – and Humanity as a whole – to a life worth living. The field of Normative Ethics is commonly divided into three main approaches, namely (1) Ethics of Virtues, based on the view that the development and practice of virtue (generosity, courage, compassion etc.) forms the basis of a life worth living; (2) Ethics of Duties, based on the view that ethical behaviour must obey socially established duties and obligations; and (3) Ethics of Consequences, based on the view that what is right or wrong must be judged by a balance between positive and negative consequences of actions. Monitoring and assessment of alignment with respect to norms and duties, as well as assessment of short-term consequences of actions, are relatively easy to implement and automate in computational systems. However, they work best to identify misalignment with respect to norms and expected positive balance of consequences. Nevertheless, positive outcomes of intelligent systems are more naturally connected to the individual and collective nurturing of virtues. Recently, I have proposed the development of Positive AI, specifically aiming at this ethical issue with respect to HCAI to support truly intelligent cities. In my view, this approach to the development of AI can lead to tangible quality metrics for systems design to support the design of truly intelligent and human-centric cities.

Analysing ‘Twitter Conversation’ of Tube Stations: The Case of the Covid-19 Pandemic

Covid-19 pandemic has deeply affected urban mobility: the social-distancing strategies adopted to cope with the virus transmission pushed the majority of city users into avoiding public transport services in favour of safe and contactless travel options. To investigate this phenomenon, this research proposes an Urban Informatics approach to understand passengers’ opinions and expressed polarity through user-generated social-media data (Twitter) in the Greater London Area. The analysed corpus consists of 27,700 tweets posted between January 13th, 2020 and May 17th, 2021, and geolocated in immediate proximity of a short sample of selected Tube stations. Data is segmented in several phases, based on the restriction measures enforced by the UK National Authorities. Each subset is then analysed through texts structuring and semantic analyses, with LDA topic modelling and Sentiment Analysis. Finally, the outcomes of these processes are interpreted in their chronological succession and compared to demand data and service-disruption data.

User-generated Social-media Data as a Tool for Understanding the City

Data is at the foundation of modern planning practice and among other tools that enable collection of mobility patterns and flows. In particular, user-generated social-media data has the potential to represent both national-scale (Jurdak et al., 2015) and urban-scale mobility patterns (Plunz et al., 2019).

This opportunity has become even more crucial considering the need to investigate the unprecedented effects of disruption of the Covid-19 pandemic on urban mobility. In particular, the nation-wide lockdown and post-lockdown phases have drastically changed citizens behaviours and mobility patterns related to the use of public transport services (Buhrmann et al., 2020), due to the need to avoid crowded transport infrastructures in favour of safe and contactless travel options (e.g., private vehicles, cycling, walking, etc.).

In this context, the article presents an extended analysis of a Twitter-data corpus, collected by geographical location and specific timeframes, and further filtered by removing non-relevant tweets and non-human posting. The acquired corpus consists of 27,700 tweets geolocated in immediate proximity of selected Tube stations of the Greater London Area (UK) and posted between January 13th, 2020 and May 17th, 2021. The social-media data is segmented in 9 phases, shown in Figure 1, to compare the outcomes of the analyses in timeframes that have a mobility-demand significance, but also a social connotation.

Figure 1 Distribution of reference set words during the phases

The goal of these analyses is to offer a comprehensive view of the relation between public transport and user-generated data. Hence, the process is twofold: firstly, the analysis is focused on understanding the impact of the pandemic on the public transport mobility patterns through daily demand data and daily service-disruption data. Secondly, the social-media data is investigated with two different methodologies based on texts structuring and semantic analyses, specifically: (i) identification of tweets’ subjects through LDA Topic Modelling, and (ii) assessment of the conversation emotional polarity through Sentiment Analysis.

Social Media Data Gathering

In accordance with the research scope, the phasing structure is derived from the evolution of the pandemic; the phases were selected by considering the national and local policies imposing restrictions affecting Greater London. The calendar, comprising of 25 policy milestones, was reduced to 9 phases in order to avoid an excessive segmentation of the outputs, considering only the major self-distancing measure changes. In addition to the pandemic period, a Baseline Phase was added for the purpose of providing a reference scenario for the assessed indicators (see Figure 2). This practice is common in a before-after analysis and the timeframe chosen was in line with the Coronavirus publications produced by Transport for London (TfL).

Figure 2 Restriction and phasing definition

Similarly, a selection of Tube stations (see Figure 3) was performed in order to identify the locations to look for geo-located tweets. This process was achieved by using demand data from 2019 published by TfL, which represents the daily entry/exit for the typical weekday of a pre-pandemic year. The stations were selected by considering the 85th percentile of the demand, resulting in 40 stations located in the Greater London Area.

Figure 3 Location of selected Tube stations

The selected stations’ locations are used as centroids to calculate buffers of 400 meters of radius. The measure of the proposed catchment area around stations was chosen since it can be considered as a 5-minute walking distance in the analysis setting. Moreover, the small catchment area represents a location bias able to represent the content posted in proximity of public transport infrastructure.

The raw outcome of this process is a dataset comprising of 95,463 tweets, which was then post-processed in several steps in order to harmonize tweets’ text to be input in further semantic analyses, removing noise and superfluous information, by:

  • Detecting repetitive and non-human posts in the overall corpus, the presence of which could lead to biased and inaccurate topic modelling;
  • Text content pre-processing with characters-based simplifications removing punctuation signs, emojis, URLs and converting all the text to lowercase format;
  • Natural Language Processing (NLP) methodologies, such as stop words removal and lemmatizations (Bird et al., 2009).

All these processes contribute to the harmonization of text and the removal of words with no semantic meaning, enhancing the performance of the topic modelling, specifically: (i) stop words removal was based on the NLTK English stop-words list (e.g. a, an, the, etc.) (Bird et al., 2009), which was extended with custom words (e.g., London, etc.) that appeared ubiquitously in the corpus; (ii) lemmatization consists of a reduction of each word to its root and allows a further selection of allowed tokens, in this case leading to the selection of nouns, verbs, adjectives and adverbs in each tweet. The data cleaning process was differentiated for topic modelling and sentiment analysis and it’s shown in Figure 4.

Figure 4 Data collection and cleaning process – From left to right: collection in stations’ buffer; filtering of non-user posts; text content pre-processing; Natural Language Processing (NLP)

Text Semantic Analysis

The two analyses performed to comprehend the content of the corpora were (see Figure 5):

  • Topic modelling: to gain knowledge on the main tweets’ subjects
  • Sentiment analysis: to investigate the expressed message polarity.
Figure 5 Text semantic analysis – From left to right: LDA topic modelling; Reference set models querying; Sentiment analysis

Topic Models are statistical language models used to understand the theme structure of a document. In particular, the used model was Latent Dirichlet Allocation (LDA), which is a probabilistic generative model that takes as input a collection of lemmatized texts and outputs topics’ probability distribution over each document (Kapadia, 2019). For each phase of the analysis timeframe, topic modelling was used to identify 10 topics per period.

The model parameters were hypertuned to obtain best fitting topics, looping over LDA’s main parameters. Subsequently, the significance of the results was quantified using the topics’ coherence metric, which measures the degree of semantic similarity between high-scoring words in the topic, helping to distinguish artifacts from semantically valid outcomes. In order to measure the influence of pandemic-related posts in the analysed corpora, a reference control set built around Covid-19 related words was defined and compared against the previously obtained topics.

By using the reference set as an unseen document to query the models, the output was the probabilistic distribution of the queried control words among the phases’ topics set. Furthermore, Sentiment Analysis was performed using Vader (Valence Aware Dictionary and sEntiment Reasoner) (Hutto et al., 2014), a lexicon and rule-based analysis tool developed to estimate the polarity of social media texts. The tool outputs three values divided into positive, neutral and negative scorings and a compound value to summarize the sentiment’s results.

Topic Modelling Analysis

Since the topic modelling analysis was performed on diverse corpora and the LDA model is generative, the reference was devised to infer the relation between Covid-19 related terms during the phases.

The set consists of 17 terms that also include variation of the same lemma, divided thematically into five categories:

  • Pandemic: covid, corona, coronavirus, virus, pandemic;
  • Lockdown: lockdown, quarantine, staysafe, stayhome;
  • Prevention: distance, distancing, mask, facemask;
  • Vaccine: vaccine, covidvaccine, vaccination;
  • Other: nhs.

This set was built considering the overall corpus content by manually evaluating a random subset of tweets, Figure 1 shows the distribution of these words.

The relation between the reference set and the phases’ corpora is expressed by measuring how many terms of the set fall into the dominant topic (i.e., the topic with highest semantic similarity with the queried text) and by the overall topic weight (i.e., the probability distribution of the topics with respect to the queried text).

Demand Data and Tweets

In addition to the demand used to select the stations, which was referred to the pre-pandemic year (2019), the daily demand of the selected timeframe was analysed in order to understand the actual impact of Covid-19 on the Tube’s mobility. The variation between the phases is substantial to the extent that during the last phase (P8) the average daily demand is -67% compared to the Baseline Phase (see Figure 6).

Figure 6 Timeseries of number of tweets per day (left); Timeseries of passeggers demand per day (right)

Service Disruption Analysis

In order to structure a comprehensive view of the public transport usage during the pandemic evolution, the service disruption was taken into consideration. In this regard, the disruptions caused by diverse sources were categorized and aggregated per phase. The service interruptions and delays were considered for all the Tube lines since the selected stations are distributed among all the subway lines and a service quality index was calculated in term of minutes of delay per day, as shown in Figure 7.

Figure 7 Service quality index per phase

Sentiment Anylysis

While the Topic Modelling Analysis is performed separately on each corpus, the Sentiment Analysis is used to interpret each single tweet, then the results are aggregated and interpreted per phase. VADER tool expresses the polarity of each item by three values, defined as positive, neutral and negative and a normalized weighted Compound Score to summarize the overall results (Hutto et al., 2014). For the scope of this research the value considered is the Compound Score, which expresses a single comparable score among the entries.

Findings

While the demand and service data describe a currently well-known phenomenon, the text semantic analysis allows the gathering of more insights on the perception of the pandemic. The distribution of covid-related terms in the different phases allow to understand the evolution of the conversation: during the Lockdown 1 phase, the conversation was more focused on the actual situation, with the largest part of the conversation focused on the “Pandemic” theme. This was followed by an interest more related to the “Lockdown” theme and the restriction measures, during Lockdown 2 phase. Finally, the discussion converged towards the “Vaccine” theme during the last analysed phases.

Figure 8 Variation of the Dominat Topic Weight and included reference set terms (up); Variation of the average Compound Score in comparison with the Baseline Phase (bottom)

By comparing these considerations to the topic modelling outputs, it is possible to confirm some of these insights. During the Lockdown 1 phase (P1), the dominant topic relates to “Pandemic” and “Lockdown” themes and it shifts toward “Lockdown” in P4 and to “Vaccine” during the last phases. However, the Dominant topic weight metric shows how relevant the topic is in relation to the generated model. A more or less relevant topic can be influenced by several factors, such as an effective topic modelling process or the semantic significance of the analysed corpus. However, for the scope of this research, it can be considered as a reference-set independent weight metric describing the relevance of the outputs.

Finally, the sentiment analysis can be interpreted using the threshold proposed by the tool creators (Hutto et al., 2014): typical compound limits are positive (score ≥ 0.05), neutral ( – 0.05 > score > 0.05) and negative (score ≥ – 0.05). Considering these values, all the phases are characterized by a positive average compound score. However, social-media data is usually influenced by a positivity bias (Waterloo et al., 2018) because negative emotions are often considered as not appropriate to publicly share on social media. For this reason, the trend metrics are proposed, and it is possible to appreciate how the general sentiment per phase is always lower than the Baseline Phase and especially how the variation closely follows the demand data, hence the restrictions implementation calendar.

Even if in a qualitative fashion, these sets of data manage to add information that is otherwise difficult to obtain without the utilization of surveys or time-consuming and cost-impactive methods. For this reason, the analysis of social media data is steadily increasing its applications, ranging from sociological and medical studies to mobility-related analyses. Crowd-sourced data could be one of the potential data feeds needed to develop a comprehensive model of an urbanized area (digital twin) able to describe and simulate phenomena happening in the urban environment, because in addition to location-based information, they allow to identify social and cultural patterns.


The results of this research work has been presented at the 49th European Transport Conference 2021 (ETC 2021, 13-15 September 2021 – Online), organized by AET, and published on ZENODO: Messa, F., Ceccarelli, G., Gorrini, A., Presicce, D., Deponte, D. (2021). Analysing ‘Twitter Conversation’ of London Tube Stations: The Case of the COVID19 Pandemic. In: Proceedings of the 49th European Transport Conference 2021 (ETC 2021), 13-15 September 2021 – Online. https://doi.org/10.5281/zenodo.6493631

References

Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media, Inc.

Buhrmann, S., Wefering, F., Rupprecht, S. (2019). Guidelines for Developing and implementing a sustainable urban mobility plan – 2nd edition. Rupprecht Consult-Forschung und Beratung GmbH. Available at: https://www.eltis.org/mobility-plans/sump-guidelines

Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media 8 (1). Available at: https://ojs.aaai.org/index.php/ICWSM/article/view/14550

Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., & Newth, D. (2015). Understanding human mobility from Twitter. PloS one, 10 (7), e0131469. https://doi.org/10.1371/journal.pone.0131469

Kapadia, S. (2019). Topic Modeling in Python: Latent Dirichlet Allocation (LDA). Available at: https://towardsdatascience.com/end-to-end-topic-modeling-in-pythonlatent-dirichlet-al location-lda-35ce4ed6b3e0.

Plunz, R. A., Zhou, Y., Vintimilla, M. I. C., Mckeown, K., Yu, T., Uguccioni, L., & Sutto, M. P. (2019). Twitter sentiment in New York City parks as measure of well-being. Landscape and urban planning, 189, 235-246. https://doi.org/10.1016/j.landurbplan.2019.04.024

Waterloo, S. F., Baumgartner, S. E., Peter, J., & Valkenburg, P. M. (2018). Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp. new media & society, 20 (5), 1813-1831. https://doi.org/10.1177/1461444817707349

Nature-based Solutions to Mitigate Vehicular Traffic Pollution Beyond SUMPs

The research was aimed at defining an innovative nature-based approach to effectively tackle vehicle emissions by considering the role of urban green areas, relying on the capacity of trees to absorb pollutants. The project combined two datasets describing the city of Milan (Italy) in relation to public trees inventory and private transport pollutants. A series of open and proprietary data was collected and analyzed to develop a GIS model, including green areas and traffic volumes. The main outcomes of the research are: (i) developing a GIS model linking public greenery environmental performance with vehicle-related PM2.5 emissions, allowing analysis, mapping and visualizations of datasets; (ii) defining green spaces provision in terms of trees necessary to absorb the rate of pollutants emitted by vehicles. Such integrated data-driven research could support decision makers in the integrated planning of SUMPs and Urban Green Infrastructures (UGI) to leverage on their co-benefits for climate change mitigation and adaptation.

Conceptual Framework

Most of the EU cities are faced with increasing levels of traffic congestion, energy dependency, and air pollution. According to the World Health Organization, this represents the main environmental risk in Europe, affecting mainly people who live in urban areas. Encouraging sustainable mobility strategies through public transport, shared and micro mobility, and active modes of travel is one of the most essential but challenging tasks for these cities (United Nations, 2018; Buhrmann et al., 2019), among which is the development of strategies directly linked to the UN’s SDG11-Sustainable Cities and Communities and the SGD 13-Climate Action (see Figure 1).

Figure 1 The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015

Urban mobility indicators (e.g., road network, distance to major roads, traffic density, etc.) explain a large proportion of the variability over time in the levels of environmental exposures such as air pollution, noise, temperature, and green space (Nieuwenhuijsen, 2016). Together with Ozone, fine particulate matter (PM2.5) resulting from vehicular traffic is considered the most threatening secondary air pollutants in cities, causing the most substantial health problems and premature mortality (European Environment Agency, 2019).

This has become even more crucial considering the unprecedented long-term effects of the Covid-19 pandemic on urban mobility, as characterized by a drastic decrement in the usage indicators of public transport systems. The guidelines recently proposed by the European Platform on Sustainable Urban Mobility Plans (2020) provided examples of short-term transport planning interventions to face this critical situation. This includes the implementation of Nature-based Solutions to mitigate air pollution.

Increasing urban green areas and promoting urban forestry strategies have significant potential to decrease the vulnerability and enhance the resilience of cities in light of climatic change, thanks to capability of plant species (especially trees) to sequester huge amounts of carbon dioxide and remove PM2.5 and PM10 particulate matter and other pollutants such as NOx, while producing oxygen and lowering the temperature of the surrounding environment. Urban green spaces, from point trees to large natural spaces, provide significant ecosystem services to humans, capable of delivering significant benefits to the psycho-physical health of communities. The environmental performances of green areas are directly related to their size, condition, and quality, together with the characteristics of the tree species. Finally, the capacity of tree species to mitigate atmospheric pollutants directly depends on their physiological (i.e., stomatal conductance) and morphological (i.e., trichomes, waxes) leaf and canopy traits (Baraldi et al., 2019). Balancing urban forest density, particularly in areas of high traffic density, would greatly improve both local and city-wide urban air quality, which is why traffic and transport management issues are strictly linked to Nature-based Solutions as a strategy to improve the air quality in our cities.

From a general point of view, the investigation of air quality issues is a complex field of study, that requires knowledge from a cross-disciplinary approach considering the variety of relevant skills (e.g., environmental science, applied mathematics, urban planning, traffic engineering, etc.). The use of advanced satellite/ sensing applications is becoming a consolidated and successful domain, thanks to its scientific relevance and its capability to provide practical solutions for supporting public institutions. However, there is still a lack of knowledge about air quality modelling techniques due to the absence of standard guidance for data collection and practical restrictions in performing more extended/ granular studies relying on multiple monitoring stations.

In this framework, one of the most innovative examples of air quality data collection campaign is represented by the study recently proposed by the non-profit association Cittadini per l’Aria and the consultancy ARIANET S.r.l. (Calori et al., 2018). This was based on a modeling system to estimate the impact of emissions from diesel-powered vehicles circulating in Milan on annual average NO2 concentrations (see Figure 2). The model took into account the dispersion of emitted pollutants and the chemical transformations that occur in the atmosphere as a function of meteorology. Emissions from vehicular traffic in the city of Milan were estimated using information provided by AMAT-Agenzia Mobilità, Ambiente e Territorio of the Municipality of Milan and the COPERT methodology (European Environment Agency, 2019). The study was based on participatory science actions carried out by citizens of Milan in 2017. In particular, 219 passive NO2 samplers were deployed in front of homes, or schools, or workplaces, on a pole at a height of 2.5 meters. Such experimental information was combined with the one provided by the monitoring network operated by ARPA Lombardia.

Figure 2 Monthly NOx concentration in Milan (Cittadini per l’Aria, 2020)

Relevant Policy Guidelines, Data Portals & Research Projects

Objectives

The proposed research was aimed at selecting typologies of Nature-based Solutions (European Commission, 2021a) capable to mitigate over time the traffic emissions in order to have a healthy and livable city where pollutants generated by vehicle emissions are reduced by both developing integrated Sustainable Urban Mobility Plans (SUMPs) and increasing tree species able to absorb them. The research combined two main typologies of data: (i) trees in public urban areas and their characteristics and performance (e.g., dimensions, localization, species, capacity to remove atmospheric pollutants, etc.); (ii) estimated level of transport-related PM2.5 emissions (e.g., traffic flows, vehicle characteristics, estimated emissions, etc.).

According to the methodological framework proposed by the European Commission (2021b), the research was based on GIS-Geographic Information Systems analysis on green areas and transport pollutants. GIS-based techniques were applied to produce cartographic analysis and thematic maps based on geo-referenced structured data sets regarding the environmental and mobility characteristics of urban areas, focusing on the case study of Milan, Italy. The study originated from the goal to find a feasible GIS-based methodology to calculate air pollutants produced by traffic and compare them with the air pollutant removal capacities provided by the different tree species on site.

Starting from an overview of the type of trees already planted in the city of Milan and considering their performances in terms of ecosystem services (e.g., Kg/year of carbon stored, Kg/year of pollutants removed, etc.), the methodology links air pollutants by transport modes to the capacity of trees to remove them both on neighborhood and city wide scale.

Considering air quality parameters related to the fine particles (i.e., PM2.5), as one of the most harmful pollutants affecting both respiratory and cardiovascular system functions, the main objective of the research was to obtain a projection of green spaces (especially trees) necessary to absorb the rate of pollutants generated by on-road vehicles. The calculation of pollutant emission of traffic flows took into account diesel-powered trucks and cars (from EURO 0 to EURO 6), as one of the most harmful category of vehicles in terms of pollutant emissions in urban centers.

Enabling Data

The research started from the analysis of the census database concerning the trees present in Milan (SIT-Geoportal of the City of Milan, 2021), which includes the following information:

  • Data on the species and location of more than 250.000 trees, implemented and regularly updated by the Green Maintenance Unit of the Municipality of Milan;
  • Data regarding the biometric values of green areas (i.e., height, trunk, canopy diameter);
  • Additional values and parameters calculated by using the i-Tree Eco software (see Table 1) including but not limited to the following: (i) Carbon storage, the amount of carbon stored in the tissues of a tree; (ii) Pollutants removal, the amount of grams of pollutants removed; (iii) Oxygen production – amount of oxygen produced by each tree.
Figure 3 The urban green areas of Milan (SIT-Geoportal of the City of Milan, 2021)

The above-mentioned data has therefore served as the basis for the research. Each single species was exported from the general database (see Figure 3). Data analysis showed that the five best performing tree species from the point of view of pollutant uptake are the following:

  • Acer platanoides (Norway maple) – more than 27,400 units;
  • Betula pendula (Silver birch) – more than 1,180 units;
  • Celtis Australis (European nettle tree) – more than 14,600 units;
  • Tilia cordata (Small-leaved lime) – more than 19,400 units;
  • Ulmus minor (Field elm) – more than 12,200 units.
Table 1 The average performances of the top five tree species in Milan (SIT-Geoportal of the City of Milan, 2021)

It is interesting to highlight that the performances of the previously mentioned species are remarkable, to such an extent that some exemplars of Tilia cordata (Small-leaved lime) reach almost 3000 kg of carbon sequestrated, several Betula Pendula (Silver birch) reach 5900 kg, while some Celtis australis (European nettle tree) reach the extraordinary amount of 7300 kg.

The research work was expanded thanks to the collaboration with LAND Research Lab®, the research and innovation unit of LAND Group which focuses on emerging trends and technologies for landscape development. The Lab aims to identify collaborative procedures and data-driven methodologies to make cities and rural areas more liveable, climate-proof and resource-efficient by reconnecting people with nature. Special services provided are design and assessment of Nature-based Solutions, green infrastructure planning, sustainability strategies, participative public space ideation and cooperation within European research networks.

Research projects and strategies have been developed also in-house, such as the LIM landscape information modelling® (see Figure 4), a landscape approach to Building Information Modeling (BIM) which supports the design of Nature-based Solutions and steers informed decisions for greener and healthier cities based on a data-driven approach. LIM was co-financed by the Chamber of Commerce of Milano, Lodi, Monza and Brianza under the call “Bando Qualità dell’Aria-Imprese 2030”.

Figure 4 Schematic representation of the LIM landscape information modelling®

The application of the LIM was aimed at systematising date from the census of the green areas in Milan (SIT-Geoportal of the City of Milan, 2021) and the other data sources described above, allowing to create a spatial information model to quantify environmental parameters, and opening up the possibility of simulating future green growth scenarios and building a pre-assessment of sustainability to support design decisions. In particular, the use of LIM was focused on the definition of the average performance of each tree species to absorb the rate of pollutants emitted by vehicles, with reference to PM2.5-fine particulate matter. Thus, a new database was created containing the annual PM2.5 sequestration of each species according to DBH-Diameter at Breast Height classes. Moreover, the use of LIM allowed the easy data visualization of the environmental performance on the existent scenario, compatible with GIS integration.

The second part of the research was focused on the calculation of pollutants resulting from motorized traffic in Milan. This was based on a proprietary dataset of Systematica S.r.l., which includes the bidirectional vehicular flows of every single link of the road network during a morning peak hour (see Figure 5). The multimodal model, based on Cube Voyager software, has been calibrated at both the private traffic level and the public transport level through count databases provided by public entities and spot traffic survey campaigns. The model includes only aggregate information about traffic volumes, not referring to the type of vehicle (e.g., cars, commercial vehicles, heavy vehicles, etc.), nether less to the power supply (e.g., gasoline, diesel, etc.). This data were obtained from official information about the car fleet circulating in Milan (Automobile Club d’Italia, 2021).

Figure 5 Traffic volumes in Milan (Systematica S.r.l., 2020)

Then, the proposed methodology was based on the definition of the yearly traffic volumes on the road network (10 hours per day, 290 days per year) and on the calculation of the pollutants emitted by diesel-powered vehicles (trucks and cars), with particular reference to PM2.5. To this end, the methodology relied on standard metrics for road infrastructures implementation proposed by Regione Lombardia (2014) and on the Air Pollutant Emission Inventory Guidebook of the European Environment Agency (2019), as follows:

  • E: emission of pollutant i, for each type of vehicle k [g/year];
  • I: pollutant (i.e., PM2.5);
  • K: type of vehicle;
  • VKT: vehicle activity [km/year];
  • EFi,k: emission factor [kg/year].

Results and Future Work

Figure 6 shows the average performance of each tree species to absorb the rate of fine particles emitted by vehicles (and their DBH-Diameter at Breast Height values) and the annual PM2.5 emissions of all diesel-powered cars (from EURO 0 to EURO 6). It is interesting to highlight that the highest values are understandably concentrated along the peripheral routes connecting Milan to the municipalities of the metropolitan area. On the other hand, emissions are significantly lower in the central part of the city, thanks also to the introduction of Area C, which is a Limited Emission Zone established to discourage the use of the most polluting private motor vehicles, reduce pollution, and derive funds for investment in public transportation.

Figure 6 On the left, the average performance of each tree species to absorb the rate of PM2.5 fine particles emitted by diesel-powered vehicles. On the right, the annual PM2.5 emissions of diesel-powered trucks and cars (interactive map). The map also shows the future green infrastructures defined in the Territorial Administration Plan of the Municipality of Milan (2020)

Trees, through their physiological processes, provide a variety of ecosystem services. This term basically denotes the multiple benefits that humankind and the environment receives, directly or indirectly, from ecosystems. These includes provisioning services such as food and water; regulating services that affect climate, floods and air quality, and supporting services such as pedogenesis and nutrient cycling (Millennium Ecosystem Assessment, 2005). Plants are also able to capture huge amounts of carbon dioxide, produce oxygen, reduce PM2.5 particulate matter, and lower the temperature of the surrounding environment during the hottest and sultry summers. It is important to highlight that there are plants that are better suited than others to reduce air pollution. In this sense, their presence should be increased in large urban centres such as Milan, from urban parks to private  green areas.

The future developments of the research work will be focused on:

  • Extend the research to quantify others traffic-related pollutants and the capacity of public green space to mitigate them, in order to have an even more integrated tool to support public authorities in tackling air quality issues in urban areas.
  • Enable the projection of urban trees characteristics according to different future scenarios, allowing to assess the potential increase in ecosystem services delivery due to vegetation growth. This will support local authorities to achieve short, medium and long-term goals related to the Sustainable Development Goals (SDGs);
  • Estimate the extent and composition of urban green spaces necessary to remove the rate of pollutants emitted by vehicles in the given scenarios (Testi, 2022). The calculation of pollutant emission will consider the mobility strategies defined by the SUMP of the Municipality of Milan (2018) and the Piano Aria Clima of the Municipality of Milan (2022);
  • Identify new urban areas or particularly polluted urban areas where to suggest the planting of tree species based on their specific absorption capacity. This will consider the open space development defined by the Territorial Administration Plan of the Municipality of Milan (2020);
  • Promote the creation of new afforestation areas, on the outskirts of the city of Milan, by deploying most performing trees in terms of air quality improvement and climate adaptation. This will consider indications provided by Municipal Ecological Network, Metropolitan Strategic Plan and Regional Landscape Plan.

As a final step of the research, a neighborhood scale project could be produced to measure the effects of forestation into the city, focusing on:

  • Finding an innovative method to control over time the emissions in order to have a clean and sustainable urban environment where traffic pollutants are reduced and green areas increased in order to leverage on their co-benefits for climate adaptation and mitigation;
  • Contributing through existing monitoring platforms to involve civil society (e.g., citizens, local organizations, NGOs) in the measuring of air pollutants, assessment of environmental quality, co-creation of mapping and promotion of sustainable mobility measures;
  • Producing co-created intra-sectoral planning guidelines and tools to support city authorities and practitioners in the decision making process of urban green infrastructures integrated into SUMPs.

The results of this research activity have been presented at the International Conference Smart and Sustainable Planning for Cities and Regions: Gorrini, A., Messa, F., Presicce, D., Attardi, V., Deponte, D., Balestrini, A., Schiavo, F., Pirosa, L.C., and Pallotta, D. (2022). Nature-based Approach to Mitigate Vehicular Traffic and Air Pollution Beyond SUMPs. In: 4th International Conference Smart and Sustainable Planning for Cities and Regions 2022 (SSPCR 2022), 19-22 July 2022, Bolzano (Italy). https://doi.org/10.5281/zenodo.6778749

Acknowledgment

The research was conducted in collaboration with the LAND Research Lab®. We thank the Municipality of Milan and Cittadini per l’Aria for sharing data. We also thank Arantxa DeLaHoz and Matteo La Torre for their contributions in data analysis. The analyzed data were treated according to the GDPR-General Data Protection Regulation (EU, 2016/679). This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

Baraldi, R., Chieco, C., Neri, L., Facini, O., Rapparini, F., Morrone, L., Rotondi, A., Carriero, G., (2019). An integrated study on air mitigation potential of urban vegetation: From a multi-trait approach to modeling. Urban Forestry & Urban Greening, 41, 127-138. https://doi.org/10.1016/j.ufug.2019.03.020

Buhrmann, S., Wefering, F., Rupprecht, S. (2019). Guidelines for Developing and implementing a sustainable urban mobility plan – 2nd edition. Rupprecht Consult-Forschung und Beratung GmbH. Available at: https://www.eltis.org/mobility-plans/sump-guidelines

Calori, G., Nanni, A., Pepe, N., Silibello, C. (2018). Effettuazione di simulazioni relativi a scenari di riduzione dei veicoli diesel nella città di Milano. ARIANET S.r.l. Available at: https://www.cittadiniperlaria.org/wp-content/uploads/2019/01/RapportoARIANET-compressed.pdf

European Environment Agency (2019). Air Pollutant Emission Inventory Guidebook. Technical guidance to prepare national emission inventories. Available at: https://www.eea.europa.eu/publications/emep-eea-guidebook-2019

European Commission (2021a). Evaluating the Impact of Nature-based Solutions: A Handbook for Practitioners. European Commission, Directorate-General for Research and Innovation, Directorate C – Healthy Planet, Unit C3 – Climate and Planetary Boundaries, Brussels. Available at: https://op.europa.eu/en/publication-detail/-/publication/d7d496b5-ad4e-11eb-9767-01aa75ed71a1

European Commission (2021b). Evaluating the Impact of Nature-based Solutions: Appendix of Methods. European Commission, Directorate-General for Research and Innovation, Directorate C – Healthy Planet, Unit C3 – Climate and Planetary Boundaries, Brussels. Available at: https://op.europa.eu/en/publication-detail/-/publication/6da29d54-ad4e-11eb-9767-01aa75ed71a1

European Platform on Sustainable Urban Mobility Plans (2020). COVID-19 SUMP Practitioner Briefing. CIVITAS SATELLITE CSA. Available at: https://www.eltis.org/sites/default/files/covid-19_sumppractitionersbriefing_final.pdf

Kabisch N., Korn H., Stadler J., Bonn A. (2017). Nature-Based Solutions to Climate Change Adaptation in Urban Areas—Linkages Between Science, Policy and Practice. In: Kabisch N., Korn H., Stadler J., Bonn A. (eds). Nature-Based Solutions to Climate Change Adaptation in Urban Areas. Theory and Practice of Urban Sustainability Transitions. Springer, Cham.

Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-being: Synthesis. World Resources Institute, Washington. Available at: https://www.millenniumassessment.org/documents/document.356.aspx.pdf

Nieuwenhuijsen, M.J. (2016). Urban and transport planning, environmental exposures and health-new concepts, methods and tools to improve health in cities. Environmental Health, 15, S38. https://doi.org/10.1186/s12940-016-0108-1

Regione Lombardia (2014). Linee guida per la redazione di studi di Fattibilità. Available at: https://www.regione.lombardia.it/wps/wcm/connect/219e7f85-4823-468f-a4e4-4a60547b5a83/231015_Linee+Guida+Studi+Fattibilità.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-219e7f85-4823-468f-a4e4-4a60547b5a83-moWlwz3

Testi, I. (2022). Urban Forestry Science. Machine Learning and the City: Applications in Architecture and Urban Design, 517-520. https://doi.org/10.1002/9781119815075.ch43

United Nations (2018). World urbanization prospects, the 2019 revision. Department of Economic and Social Affairs, Population Division, United Nations Secretariat, New York. Available at: https://population.un.org/wup/

Zhang, J., Ghirardo, A., Gori, A., Albert, A., Buegger, F., Pace, R., Georgii, E., Grote, R., Schnitzler, J.P., Durner, J. and Lindermayr, C. (2020). Improving air quality by nitric oxide consumption of climate-resilient trees suitable for urban greening. Frontiers in plant science, 11, 1491. https://doi.org/10.3389/fpls.2020.549913

Zupancic, T., Westmacott, C., & Bulthuis, M. (2015). The impact of green space on heat and air pollution in urban communities: A meta-narrative systematic review (Vol. 61). Vancouver: David Suzuki Foundation. Available at: https://davidsuzuki.org/wp-content/uploads/2017/09/impact-green-space-heat-air-pollution-urban-communities.pdf

How Covid-19 is Affecting Pedestrian Modeling and Simulation: The Case of Venice

The relevance of computer-based pedestrian crowd simulations has become crucial in transport planning considering the unprecedented effects of the Covid-19 pandemic on urban mobility. However, there is still a lack of knowledge regarding the impact of social distancing on crowding, queuing, route choice, and other pedestrian crowd phenomena. In this context, the current study was aimed at applying the Social Force Model of the pedestrian simulation platform PTV Viswalk to investigate the effects of disruption of social distancing on pedestrian dynamics. First, a descriptive set of metrics and parameters was applied for calibrating the dynamic regulation of interpersonal distances among pedestrians. Then, the plausibility of the proposed Social Distancing Model has been evaluated against the so-called fundamental diagram to calibrate pedestrian Volume-Delay Functions. Finally, the proposed model has been integrated into the PTV Visum simulation platform to evaluate the effect of social distancing on large-scale pedestrian route choice. To do so, a macroscopic static model of the City of Venice was developed to test the effectiveness of alternative crowd management strategies related to pedestrian dynamics, in a predictive scheme.

Introduction

Pedestrian movements are one of the most complicated items when approaching transport planning, since they are strongly based on people behavior and personal perceptions of environment, social distancing and reasons at the basis of the trip. The use of computer-based systems for the simulation of pedestrian dynamics (e.g., Legion, MassMotion, PTV Viswalk, etc.) is a consolidated and successful domain, thanks to its scientific relevance and its capability to provide practical solutions for supporting transport planners in managing crowded facilities (Gorrini et al., 2018).

The relevance of computer-based simulations in transport planning has become even more crucial considering the unprecedented effects of the Covid-19 pandemic on pedestrian dynamics, and urban mobility more in general. The activities of transport planners and decision makers have been projected ahead towards promoting pedestrian mobility and walkability in order to reduce risk of contagion associated with high contact and crowding conditions.

This research approaches the problem of pedestrian flow assignment under social distancing in a multi-level scale. First, a microscopic pedestrian model was developed to evaluate flow conditions at different demand levels. The microscopic model evaluated both BAU (Business-As-Usual) and SD (Social Distancing) pedestrian flow conditions by means of the Social Force Model (SFM) (Helbing and Molnar, 1995). By measuring flow, density and speed under different demand levels, the fundamental diagrams for both scenarios were built (BAU and SD). Then, Pedestrian Volume-Delay Functions (pVDFs) were calibrated by estimating the parameters that better fit the fundamental diagrams. Such functions were implemented in a large-scale macroscopic static pedestrian assignment model to evaluate the effect of social distancing on route choice.

Microscopic Model

In a preliminary work previously presented by the authors (Deponte et al., 2020), a modification to the parameters of the default social force model implemented in the pedestrian simulation platform PTV Viswalk was proposed in order to simulate social-distancing conditions. The study focused on calibrating the dynamic regulation of interpersonal distances among pedestrians (i.e., social isotropic parameters related to repulsive force), to avoid conditions of inappropriate proximity and spatial restriction due to high density situations. The calibration process consisted of the iterative variation of the Social Force Model parameters until reaching a flow condition in which pedestrians would respect the majority of the time a predefined maximum density threshold (1 ped/sqm), with a regular behavior and avoiding back and forth oscillations of pedestrians when approaching others (Kretz, 2015). This predefined maximum density is a proxy for a minimum interpersonal distance of 1 m, considering squared cells. Figure 1 shows a comparison of the time duration in which the density threshold is exceeded for a given input flow for the standard Social Force Model (BAU) and the Social-Distancing Model (SDM).

Figure 1 Simulation results achieved through the Social Force Model and the proposed social distancing model (Deponte et al., 2020).

The calibrated SFM under social distancing (SD), and the default model (Business as Usual: BAU), were used to build the pedestrian fundamental diagrams for both conditions. The straight corridor set-up did not allow to observe the complete domain of densities of the fundamental diagram, since the flow input was limited by the release (loading) capacity. To overcome this limit, a closed loop set-up was implemented, as done by Seyfried et al. (2005), so it was possible to observe also hypercritical flow conditions (i.e., densities exceeding the density-at-capacity). The following image shows the simulation set-up (see Figure 2):

Fundamental Diagrams and Volume-Delay Functions

From the results of the microscopic model, the fundamental diagrams for the BAU Model and the SD Model were built (see Figure 3). A significant reduction in the capacity is observed under social-distancing conditions, going down from 63 ped/m/min in the BAU model to 21 ped/m/min in the SD model. The maximum density falls down from 4 ped/sqm to 1 ped/sqm, which was indeed the maximum density used as input for the calibration of the social-force model parameters (Deponte et al., 2020).

Figure 3 SDModel Fundamental Diagrams: Flow-Density (a) and Speed-Density (b)

In order to apply the findings from the microscopic simulation model into a large-scale macroscopic pedestrian assignment model, both the hypo- and hypercritical parts of the fundamental diagrams were used to calibrate Pedestrian Volume-Delay Functions for BAU and SD models, through the least-squares method. In this work the standard BPR (Bureau of Public Roads, 1946) function formulation was used.

Case Study

The calibrated Pedestrian Volume-Delay Functions were then used in a large-scale macroscopic pedestrian assignment developed in the software PTV Visum (PTV Group, 2021). The chosen case study was the City of Venice, Italy. The main island of the Venetian Lagoon is certainly a suitable example of a dense urban pedestrian network to apply and analyze people flow distribution across a complex urban context through the use of User-Equilibrium Pedestrian Assignment. The model implements an OD matrix including both external trips (between the island and the dry land) and internal trips (Origin and Destination within the island) for the morning peak hour of an average working day. The total estimated pedestrian OD matrix consists of 15,700 trips.

Figure 4 (a) shows the pedestrian assignment flows in the network for both the BAU and SD models, while Figure 4 (b) shows volume differences in the SD assignment compared to the BAU model. Significant volume differences are observed in the network, not only at link-level (localized) but also at paths-level (large scale). Looking at the large-scale, in the SD assignment the pedestrian flows increase along the northern and southern sectors of the island, while the flow along the central connection (The Rialto Bridge) decreases significantly (-15%). This is the result of the redistribution of flows coming from Piazzale Roma and the Railway Station and going to the eastern part of the island. Detouring through the Cannaregio district (north) or through Dorsoduro district (south), allows to reach a new equilibrium by maintaining lower densities.

Figure 4 Link Volume (a) and Volume Difference SD vs. BAU (b)

Concluding remarks

Beside the ability to support the definition and the operational assessment of effective responsive strategies to face the impact of the Covid-19 pandemic on urban mobility, the proposed analytical approach demonstrates the importance of pedestrian modeling and simulation. The use of advanced and robust pedestrian macroscopic models capable of capturing and replicating a larger variety of pedestrian dynamics allows to enrich the definition of innovative city walking plans with further valuable and evidence-based insights. In fact, the main goal of state-of-the-art walking plans, which consist of operational and actual implementation programs, is to understand, analyze, foresee and manage people movements in and around cities, allowing people to get access to urban opportunities, meet, and move safely and easily, by ensuring permeability, access for all abilities and planning for future growth.


The results of this research activity have been published in the following scientific Journals: Espitia, E., Gorrini, A., Vacca, A., Deponte, D., Sarvi, M. (2022). How Covid-19 is Affecting Pedestrian Modelling and Simulation: The Case of Venice. Transportation Research Record. https://doi.org/10.1177/03611981221088224

Acknowledgments

We thank Prof. Majid Sarvi (University of Melbourne, VIC, Australia) for his contribution as co-author of the paper. We also thank the team of PTV for their collaboration and for sharing information about how to calibrate the model of the Viswalk platform, and Simone Castelnuovo (Senior Consultant of Systematica Srl) for his fruitful contribution in calibration of the model. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

Gorrini, A., L. Crociani, L. Vizzari, G., and S. Bandini. (2018). Stress estimation in pedestrian crowds: Experimental data and simulations results. Web Intelligence, 17(1), 85-99. http://dx.doi.org/10.3233/WEB-190403

Helbing, D. and P. Molnar (1995). Social force model for pedestrian dynamics. Phys. Rev. E, 51(5), 4282-4286. https://doi.org/10.1103/PhysRevE.51.4282

Deponte, D., G. Fossa, G., and A. Gorrini (2020). Shaping space for ever-changing mobility. Covid-19 lesson learned from Milan and its region. TeMA – Journal of Land Use, Mobility and Environment, 2020, 133-149. https://doi.org/10.6092/1970-9870/6857

Kretz, T. (2015). On oscillations in the social force model. Physica A: Statistical Mechanics and its Applications, 438, 272-285. https://doi.org/10.1016/j.physa.2015.07.002

Seyfried, A., B. Steffen, W. Klingsch, and M. Boltes (2005). The fundamental diagram of pedestrian movement revisited. Journal of Statistical Mechanics: Theory and Experiment, 2005, 10, P10002. https://doi.org/10.1088/1742-5468/2005/10/p10002

Bureau of Public Roads (1946). Traffic Assignment Manual, Department of Commerce, Urban Planning Division, US Department of Commerce, Washington, DC.

PTV Group (2021). PTV Visum. Karlsruhe: PTV Group.

Looking with Machine Eyes: Understanding Patterns in Urban Spaces

The development of new technologies is shaping the growth of cities in many ways. Among these, the Internet of Things (IoT), Artificial Intelligence (AI), the high-resolution global positioning system (GPS), big data and new building materials and techniques are expected to transform cities’ core functioning elements, affecting all aspects of our lives (Joint Research Centre, 2019; Geospatial Commission, 2020). When technology is combined with the growing availability of open data, new possibilities emerge to develop a novel understanding of the urban fabric and use.

Deep learning algorithms are a subset of machine learning algorithms that process and combine their input in ever growing abstractions to obtain meaningful outputs. This project focuses on the analysis of pre-trained deep learning models for object detection, image segmentation and crowd counting and on their applications on research topics linked to the understanding of temporal patterns related to the development of Covid-19, climate analysis and thermal comfort, and their integration with transport modelling.

Deep learning algorithms are of particular interest in the field of computer vision, enabling the manipulation of large datasets in an automatic way. In the past years, the rising availability of deep learning techniques led to new frontiers in the automatic understanding of images and estimating the number of objects within an image. Deep learning methods obtained state-of-the-art results for image classification, object detection (Zhao et al., 2019) and instance/semantic segmentation tasks. Furthermore, in the past years, the availability of pre-trained weights for deep learning algorithms has grown.

Dataset and First Steps

The study area centers around Corso Buenos Aires, Milan, one of the longest and most well-known retail streets in Europe (Transform Transport, 2018). The dataset consists of a collection of images (from MilanoCam) depicting the street with a strong perspective view, showing the flow of vehicles and people from Piazza Lima to Porta Venezia. These are taken at one-hour intervals, with a high potential for comparative analyses of changes in the street use behavior at peak times and different seasons.

The current study builds on previous research presented in Shifting Paradigm 1st edition, which was structured in two phases. In the first phase, a set of algorithms with pre-trained weights were selected based on their popularity and tested on a selection of 16 images. The results were then measured against the manual counts performed on each image. The goal was to define the usability of these models out-of-the-box, for the given dataset. Table 1 outlines algorithms tested within Phase 1 of the research and Table 2 lists the mean deviation between automatic counts and manual ones for each algorithm, computed as:

Table 1 Algorthms tested within phase 1
Table 2 Mean deviation between automatic
and manual counts for each algorithm

In the second step, the analysis of the images for a year-long period was carried out. The initial focus was on the performance of the algorithms with respect to weather and light conditions as well as business activity of the street.

Finally, the study aimed to identify the patterns in the street use every day at 19:00, the moment when people leave their offices, with particular attention to the Covid-19 lockdown period. Figure 1 outlines the main temporal trends and patterns for each mode.

Figure 1 Main temporal trends and patterns for each mode.
Data is available here: https://datastudio.google.com/reporting/1750a9ff-7a98-43ca-b385-5dcea733016c

Data and Tools

In the last phase, an extensive dataset of 14,956 images was constructed, representing 12 daily timeslots (6am – 10pm) between January 2019 and May 2021. These were then analyzed on the fly using Yolov5 (You Only Look Once) (Jocher et al., 2021) as an object detection algorithm. The goal was to recognize pedestrians and locate them in an image.

YOLOv5 represents an evolution of the YOLO architecture, which enables faster and more accurate class predictions. This architecture relies on CSPDarknet53 as the backbone and PAN as an aggregator level. Furthermore, it uses image augmentation methods in training to improve accuracy in results (Gutta, 2021).

An open-source model was used to detect objects in the images, which was trained on CCTV cameras in Montreal. The dataset represents vehicles, pedestrians, constructions, cyclists, and bus instances, obtaining a mean Average Precision (mAP) equal to 0.809 in pedestrians’ recognition on the original training set (City of Montreal, 2021). Figure 2 shows results on a sample image of Corso Buenos Aires.

Figure 2 Sample image of Corso Buenos Aires

Analyses

Building on knowledge obtained from previous research, the study was then structured in four steps, which focus on the analysis of pedestrian patterns in Corso Buenos Aires with increasing temporal granularity (i.e., months, day types, hours) and spatial accuracy (i.e., whole image, sidewalks).

First, an exploratory analysis was performed on the dataset (see Figure 3). The goal was to visualize overall trends in registered pedestrian footfall in 2019. Figure 3 shows average monthly profiles for weekdays and weekends. These present expected patterns, with anomalies in June and September, when extraordinary events (i.e., Fashion week, Giro d’Italia, etc.) occurred in the street. Then, the internal validity of the dataset was assessed from two perspectives.

Figure 3 Preliminary analyses & data visualization.
Workdays and Weekends, Pedestrians, 2019

The first one aimed at quantifying the deviations of the model’s performance for different weather and lighting conditions, identifying systematic inaccuracies in the detections. Here, a manual validation methodology was implemented on 10% of all images, comparing the amount of automatically detected pedestrians with those counted manually. Results shown in Figure 4 demonstrate how darkness and rain worsen overall accuracy, leading to a mean absolute difference with manual counts above 10%. Instead, shadow and sun conditions affect results slightly, leading to a mean absolute difference equal to roughly 7.5%.

Figure 4 Definition of systematic inaccuracies in outputs – how images are affected by lighting and weather condition

The second validity check aimed at defining the reliability of the dataset through a sensitivity analysis on data robustness and outliers. Data for the year 2019 was analyzed to ensure consistency in the analysis. First, a mean hourly number of detected pedestrians was computed. This should be the baseline value for comparisons. Then, hourly detections were grouped in random clusters. For each, the mean hourly value was computed to assess its deviation from the mean value. Figure 5 shows how splitting 2019 data into 5 random groups leads to a deviation from the mean lower than 5% for all moments of the day, with an exception for 7 and 8 am. This may be due to differences in footfall patterns during the weekday and weekends. Instead, splitting data into 5 random groups leads to a higher deviation from the mean, above the 5% threshold at multiple moments throughout the day.

Figure 5 Representativity study – minimum number of images to describe a moment within a fixed deviation from the mean value and outliers’ impact

The second phase of the study investigated specific patterns in the use of Corso Buenos Aires. The dataset was divided into five subsets, considering the Covid-19 pandemic restrictions. Then, hourly trends for workdays and weekends and for different day types were studied and compared among different restriction phases (see Figure 6). This analysis aimed at extending the one carried out in the first phase, with a focus on post lockdown restrictions. These can be classified as “necessity” restrictions (namely, movements were limited to necessity reasons, as work and grocery shopping), and “soft” restrictions (namely, leisure movements were allowed, such as shopping). The goal was to determine whether differences among the two phases could be recognized by means of pedestrian counting. Figure 6 gives an overview of the restriction phases and of the overall footfall trends.

Figure 6 Covid-19 monitoring.
Daily pedestrian counts through Covid-19 restriction phases

Figure 7 outlines detected pedestrian volume trends by day type and hour. The top graph shows how mean pedestrian counts never reached pre-covid levels during the study period, with slight differences registered between “necessity” and “soft” restrictions phases. The bottom graph depicts hourly patterns for each restriction phase. Lower volumes were registered at lunch time and in the evening in “necessity” phases, while higher volumes can be seen in the weekends during “soft” phases. Furthermore, the effects of the curfew implementation in the city at 10pm can be seen from “necessity 2” to “necessity 4” phases.

Figure 7 Covid-19 monitoring.
Daily pedestrian counts through Covid-19 restriction phases

The third step of the study consists in converting instantaneous pedestrian densities captured in images into pedestrian flow and related walking speed. To do so, pedestrian fundamental diagrams, specifically derived from an innovative application of dynamic microscopic model, were applied on the detections. The Social Force Model represents pedestrians as particles subject to forces (i.e., attraction and repulsion) in an analogy with fluid dynamics, quantifying pedestrian flows under business-as-usual conditions. The Social Distancing Model is a modification of the default parameters to simulate social distancing conditions (Espitia et al., 2022).

Figure 8 outlines the preliminary results on the application of these models to determine the overall influence of social distancing in the study area. The graph depicts Social Force Model and Social Distancing Model curves (red and blue curves), which quantify the hourly pedestrian flow in Corso Buenos Aires, given increasing pedestrian densities (x axis). These can be compared with the occurrences of the detected densities in the image dataset (black curve).

Figure 8 Transport modelling integration.
Definition of conditions that determine decrease in pedestrian flow

As can be seen, in 52% of the images in the dataset, the number of detected pedestrians would lead to a difference between business-as-usual and social distancing hourly flows greater than 8%. While images cannot be used to compute hourly flow, the graph gives insights on possible flows scenarios in different crowding conditions in Corso Buenos Aires, enabling a visual comparison with the occurrences of detected densities in images.

Lastly, a preliminary analysis on pedestrians’ distribution on sideways was carried out. As Corso Buenos Aires has a north-south orientation, its shading conditions change gradually during the day, allowing for a thorough pedestrian comfort study. In this phase, temperature and cloud conditions were researched by using an open-source meteorological dataset. These may influence the sidewalk choice. Figure 9 shows the shading conditions in Corso Buenos Aires on August 6th, 2021, between 12am and 5pm. The left sidewalk (east) is in the shade from 12am to 2pm, while the right one (west) is shaded from 3pm to 6pm.

Figure 9 Climate analysis.
Images showing shading conditions in Corso Buenos Aires on August 6th, 2021

In this phase, images were masked to detect pedestrians on the sidewalk of interest. Then, the share of pedestrians for each sidewalk was computed to obtain a comparable metric. Figure 10 outlines trends in sidewalk choice in 2019, depicting the average monthly share of pedestrians per sidewalk and the mean detected temperature. While a general preference for the left sidewalk can be observed, curves abruptly change between 2 and 3 pm in summer months with temperatures above 30° Celsius. These patterns are consistent with 2020 ones, proving potential for further analyses (see Figure 11).

Figure 10 Climate analysis.
Analysis of shading conditions in Corso Buenos Aires on August 6th, 2021
Figure 11 Climate analysis.
Sidewalk patterns, Pedestrians, May – September, 2019 – 2020

Conclusions and Future Research

This research aims at studying different methods to identify and count objects in images. The goal is to understand the potential of each method with available pre-trained weights. Then, the purpose is to analyse the street use of Corso Buenos Aires by means of ongoing studies.

The evolution in results obtained between the first steps and phase three highlight possibilities in development of new applications and studies involving image datasets. In fact, while models that were trained on broad scope datasets, i.e., COCO (Lin et al., 2014) would lead to modest results for small instances (i.e., pedestrians and cyclists), results showed a great improvement using a model specifically trained on the classes of interest (i.e., vehicles, pedestrians, cyclists) and on images with similar orientation. Further improvements could be obtained through data augmentation, limiting the effects of meteorological factors on images quality, and by fine-tuning the open-source models.

In general, the methodology proves to be time and cost-efficient with respect to the level of accuracy and details inferred while guaranteeing the privacy of individuals (Bernas et al., 2018). The use of an extensive dataset led to statistically significant results on aggregated temporal periods, overcoming the uncertainties of a single image. Furthermore, it gives the possibility to detect meaningful moments to be further analyzed by means of video analytics.

Future works include the growing integration of modelling outputs with real data, the replication and forecast of complex pedestrian patterns at both urban district and city levels, integrating multiple datasets and deepening the study on pedestrian comfort in different environmental conditions. An example is represented by the usage of this information and other data from different sources (wi-fi hot spots, mobile and telco data, etc.) to implement innovative predictive models able to replicate and forecast complex pedestrian flow patterns at both urban district and city levels, as implemented in the city of Melbourne (City of Melbourne, 2021) and in the city of Liverpool, Australia (Barthélemy et al., 2019).


References and Online Sources

Barthélemy, J., Verstaevel, N., Forehead, H., & Perez, P. (2019). Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City. Sensors, 19(9), 2048. https://doi.org/10.3390/s19092048

Bernas, M., Płaczek, B., Korski, W., Loska, P., Smyła, J., & Szymała, P. (2018). A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring. Sensors, 18(10), 3243. https://doi.org/10.3390/s18103243

City of Melbourne (2021). Pedestrian Counting System. Available at: https://www.melbourne.vic.gov.au/about-melbourne/research-and-statistics/city-population/Pages/pedestrian-counting-system.aspx

City of Montreal (2021). Annotated images taken from the video stream of traffic cameras. Information Technology Department. Available at: https://donnees.montreal.ca/ville-de-montreal/images-annotees-cameras-circulation

Espitia, E., Gorrini, A., Vacca, A., Deponte, D., Sarvi, M. (2022). How Covid-19 is Affecting Pedestrian Modelling and Simulation: The Case of Venice. Transportation Research Record. https://doi.org/10.1177/03611981221088224

Joint Research Centre (2019). The future of cities: opportunities, challenges and the way forward. European Commission Publications Office. https://data.europa.eu/doi/10.2760/375209

Geospatial Commission (2020). Unlocking the power of location. The UK’s Geospatial Strategy, 2020 to 2025. Cabinet Office. Available at: https://www.gov.uk/government/publications/unlocking-the-power-of-locationthe-uks-geospatial-strategy/unlocking-the-power-of-location-the-uks-geospatial-strategy-2020-to-2025

Gutta, S. (2021). Object Detection Algorithm — YOLO v5 Architecture. Medium. Available at: https://medium.com/analytics-vidhya/object-detection-algorithm-yolo-v5-architecture-89e0a35472ef

Jocher, G., Chaurasia, A., Stoken, A., et al. (2021). ultralytics/yolov5: v5.0 – YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations (v5.0). Zenodo. https://doi.org/10.5281/zenodo.4679653

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Transform Transport (2018). Re-imagining Corso Buenos Aires. Systematica Srl. Available at: https://research.systematica.net/research/re-imagining-corso-buenos-aires/

Zhao, Z., Zheng, P., Xu, S., & Wu, X. (2019). Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, 30, 3212-3232. https://doi.org/10.48550/arXiv.1807.05511

Shaping the Future of Society and Cities Worldwide: in Conversation with Andrea Gorrini

Dr. Andrea Gorrini is an environmental psychologist with over 10 years experience in the empirical investigation of pedestrian mobility, walkability, and crowd dynamics. His research work is aimed at supporting the design of architectural solutions for enhancing the comfort and safety of people navigating both outdoor urban scenarios and indoor facilities. He is particularly skilled in the design and execution of large data gathering campaigns, and he is expert in using different software for data analysis and statistics. Since 2019 he has collaborated with Systematica for the Horizon 2020 DIAMOND Project, and he currently acts as Head of Research at Transform Transport.

Q: What prompted Systematica to launch Fondazione Transform Transport? 

AG: As a result of the success of its research unit Transform Transport, Systematica launched Fondazione Transform Transport ETS, a non-profit foundation that will oversee its current research initiatives and provide innovative, inclusive, and sustainable mobility solutions for shaping the future of society and cities worldwide. The foundation comes as part of a larger strategy aimed at augmenting the number and size of its self-financed and externally funded projects in order to increase the impact of the research findings on sustainable urban mobility and social inclusion in the coming years. The foundation, one of its kind in the field of transport planning and mobility engineering, grounds on 30 years of Systematica’s work and explores how disruptive technologies, increasingly and rapidly influencing urban mobility, can have a positive impact on cities, neighborhoods, and buildings, collaborating closely with municipalities and companies, and using Big data for greater insights.

Q: Which methodological approach are you following to collect and analyze data about urban mobility patterns?

AG: The investigation of urban mobility is a complex field of study that requires multidisciplinary knowledge considering the variety of relevant skills (e.g., urban planning, traffic engineering, computer science, social science, etc.). In this framework, the research of Transform Transport is based on the application of technology-enabled spatial analysis methods, commonly known as Urban Informatics. This discipline is an evidenced-based approach that, thanks to the recent development of advanced ICTs and the increasing availability of digitally widespread data sources, has led to innovative assessment tools and metrics that can provide valuable support to city decision-makers by unveiling hidden mobility patterns. Moreover, our research is based on a user-centric approach that focuses on the design and management of public transport services and infrastructure by considering the needs and expectations of the city users. As highlighted by the 2030 Agenda for Sustainable Development (UN’s SDG 11-Sustainable cities and communities), urban mobility should be designed to be inclusive for women, children, the elderly, and people with impaired mobility.

Q: As a foundation, what are Transform Transport’s plans to stay aligned with the strategy aimed at increasing the impact on sustainable urban mobility and social inclusion?

AG: This year, Transform Transport will be submitting over ten proposals to the European Union scientific research initiative ‘Horizon Europe’ and other European/ national institutions that would be funding the projects in the following years. It will also continue to actively participate in lectures, talks, and hackathons in partnership with Universities, mentoring and sharing methodologies with students. The foundation will keep promoting and developing research studies, disseminating them through books, publications, conferences, public talks, and events. One of the most recent books, Shifting Paradigm, is a structured collection of the most insightful investigations since the Covid-19 outbreak. Moreover, around 20 peer-reviewed scientific papers revolving around sustainable mobility topics in open access International Journals and Conference Proceedings were recently published, and the foundation will pursue these endeavors. With its team of experts, Transform Transport will continue to create new paradigms to tackle complex mobility challenges, placing sustainability and social inclusion at the core of its research as it always has.

Fondazione Transform Transport Launch

On May 11, Systematica celebrated the launch of its research foundation Fondazione Transform Transport with collaborators, friends, and professionals from the mobility engineering field, at the Fonderia Napoleonica Eugenia in Milan. 

Since its establishment in 1989 by Fabio Casiroli, Systematica has thrived in challenging the status quo of transport planning and mobility engineering and seeking new approaches to support sustainable growth through scientific research. 

During the event, we presented Transform Transport’s books, peer-reviewed open access papers, and its research approach through various projects aligned with Systematica’s vision, exploring how disruptive technologies, increasingly and rapidly influencing urban mobility, can have a positive impact on cities, neighborhoods, and buildings.

We also revealed some of the foundation’s plans for 2022. This year, Transform Transport will be submitting over ten proposals to the European Union scientific research initiative ‘Horizon Europe’ and other European/ national institutions that would be funding the projects in the following years. It will also keep promoting and developing research studies, disseminating them through books, publications, conferences, public talks, and events, and actively participating in lectures, talks, and hackathons in partnership with Universities, mentoring and sharing methodologies with students.

Walkability and the 15-minute City Model: An Integrated Approach for the City of Milan

The 15-minute city idea is founded on a radical approach to urban planning that revaluates the neighbourhood scale and prioritizes accessibility to daily services on the basis of walking and cycling. It is essentially a rights-based model promoting hyper-proximity as a vision for an equitable, viable and liveable urban system centralized around walking. In the following analysis, we utilize the chrono-centric ‘15-minute’ framework to evaluate the spatial distribution of walkable neighbourhoods across the city of Milan, Italy. Experimenting with a multi-level spatial analysis using heat maps, we propose a population-driven narrative of the walkable city.

Introduction

Across the world, lockdown measures introduced at the start of the pandemic aimed to drastically restrict people’s movements to the most essential shortest trips from one’s place of residence. In Milan, as in other cities, there were ordinances that prevented inhabitants from traveling beyond their domiciled neighbourhood and effectively reduced trip purposes to essential trips such as grocery shopping and pharmacy stops. Far from the stringent early lockdown urban experiences, cities today have returned nearly to their 100% potential and inhabitants are generally allowed to roam freely within the city provided that they follow certain health requirements as mandated at the state level.

Despite the major recovery of many urban mobility trends to pre-pandemic states in many parts of the world, the legacy of the lockdown state of near-immobility remains. For many reasons, this interim phase has provoked questions about our collective mobility behaviours and placed emphasis on the long-studied neighbourhood scale and its importance in quotidian urban life, particularly as a vehicle for promoting walkability and particularly in the European context. Albeit for entirely different motives, the 15-minute city (Moreno et al., 2021) concept gained traction during this period for its similarities with the lived experiences of the lockdown period(s).

As mobility experts, intrigued by the chrono-centric and walkability-based principles herein, we decided to test the concept’s practical application in a dense, mid-sized city such as Milan – incidentally, the first European epicentre of the pandemic in the early days of the crisis. Our initial models (discussed at length in the article, Mapping Milan Micro-centers) focused on supply-side metrics: the model looked at the variety and quantity of important daily services in neighbourhoods across Milan and analysed the population distribution across the city as a way to gauge the share of residents reaping the benefits of these successful micro-centers.

This study, in contrast, starts from the distribution of the population as a way to identify the areas accessible by walking by the largest number of people in Milan and comparing it to a static assessment of the distribution of daily services and walkable environmental conditions across the city. Pushing the envelope further, the demand-based model is dynamic in the sense that the ‘population density’ in a certain parcel/cell does not only represent people directly located in this unit, but also includes those able to access the unit within 15 minutes on foot.

This shift in the approach allowed us to achieve two things: (a) by looking at both resident and working populations, we enlarged the concept of 15-minute access to include foot access from place of work as well as place of residence, accounting for the fact that the walkable environs of the working population on a typical week day is more realistically tied to their place of work as opposed to their residential address; and (b) by mapping population using the ‘ cumulative population’ method (i.e.: people present in each cell as well as those able to reach this cell in a given timeframe), we draw attention to the areas of the city that are accessible to the largest share of the population as a basis for analysis, discussion and intervention.

The Complex Nature of Walkability

The path to achieve the 15-minute city is intricately tied to the potentials to deliver safe, comfortable and desirable walking environments. In other words, the idea is deeply intertwined with the concept of walkability. According to Speck’s (2013) General Theory of Walkability, the level of walkability of an area is driven not only by ‘usefulness’ (i.e. the utilitarian gain of a walking trip – to get from point A to point B), but is also a result of the safety, comfort and attractiveness of this walking environment. It is driven by a complex set of tangible and intangible characteristics including the physical infrastructure conditions, the level of network connectivity, high land-use mix, vitality and distinction of urban character. As shown in the article Sidewalks Map in Chapter 1, 45% of Milan’s sidewalks do not adhere to the minimum width required for two-person movement following the one-meter social distancing recommendation by local and global authorities (Regione Lombardia; 2020; World Health Organization, 2020) and consistent with the standard for comfortable sidewalk conditions as recommended in the street design guidelines by the Global Designing Cities Initiative & NACTO (2016).

However, in order to gain a holistic understanding of walkability as described herein, a walkability index, i.e. Walk Score will be used in this study to reflect the complex nature of walkability. Walk Score is a compound metric that considers various factors including both proximity and variety of services accessible by foot in a definite amount of time. Among the various indices used to measure walkability, the Walk Score index is chosen here for its consideration of travel time as a key variable, allowing us to tailor the analysis to reflect the 15-minute city approach.

A demand-driven approach to the chronocentric city

This study is organized on 3 levels of analysis: (i) mapping resident population and the density of workers/employees at their place of work (workplace density) to give a first reading of the mobility character of its various districts (i.e. where trips are generated versus where they are attracted to most across the city); (ii) a static mapping of essential daily services to understand which areas have the highest variety and density of a predefined list of services which we identified as critical to the daily urban experience as based on a reinterpretation of the Parisian 15-minute city plan (Paris En Commun, 2020); and (iii) a compound walkability analysis using the patented Walk Score metric.

The first layer of analysis (i) aims to evaluate the density of the resident population and workplace population across Milan in relation to the pedestrian accessibility levels as allowed by the morphology of the urban fabric. The main result is therefore mapped as a “cumulative” value in the sense that it represents not only the resident or workplace population present in each cell, but also includes those who are able to reach this cell in a given timeframe (i.e. 5, 10 and 15 minutes).

The service proximity analysis (ii) is a pure compresence analysis calculated using simple distance buffers. It shows areas where residents can reach the majority of (at least 7 out of 9) service categories identified as crucial to support a balanced daily lifestyle of inhabitants. In total, the 9 service categories are: food/grocery stores, commercial stores (including clothes shops, electronics shops, etc.), cultural venues, educational facilities, parks and green spaces, restaurants, health facilities, sports facilities and other (post offices, banks, etc.). Each of these service groups includes a number of services based on open-source datasets available for the municipality of Milan. For example, educational facilities include nurseries, primary schools, high schools, etc. A GIS-based analysis of the static compresence of these services was carried out based on a pure isometric analysis with respect to 3 walking buffers – 300, 600 and 900 meters – which roughly correspond to the 5, 10 and 15-minute timeframes.

The Walk Score mapping (iii) measures the actual accessibility levels through isochronal analysis. It is based on the analysis of accessibility within 15 minutes to each group of services (there are 9 groups in total) calculated on a graph with the cost of the links based on travel time and inversely proportional to the slope of the road. The results of the 9 pedestrian accessibility analyses were then reported as indicators of accessibility to each service group on 150-meter grid. The final Walk Score value is a simple summation of these 9 individual analyses.

Results of the analysis: A mismatch between supply and demand

The resident and workplace pedestrian access maps offer an alternative approach to chrono-centric mapping based on population density as opposed to service density, highlighting distributional differences at different time scales, giving an indication of the urban character of each district. This conceptual analysis demonstrates how isochrones change shape and intensity depending on the given timeframe. In the resident pedestrian access map, micro-clusters of local centralities emerge from the 5-minute map. As the timescale is stretched, the donut shape of Milan’s second ring-road area in which the highest concentration of the resident population is located, begins to emerge (see Figure 1).

Figure 1 Pedestrian access of the Milanese population to different census sections of the city in 5/10/15 minutes

A similar interpretation can be drawn from the distribution of the workplace population. The worker pedestrian access maps show a more scattered pattern of highly accessible areas within 5 minutes of walking. At 15 minutes of walking, central districts of Milan emerge as the main cluster and a center-to-periphery gradation becomes clear. The centers with the highest pedestrian access to workplace populations at 15 minutes are the Duomo district followed by secondary concentrations in the north-east districts of Loreto, Centrale and Porta Garibaldi (see Figure 2). Areas scoring lowest in both clustering scenarios (resident population and workplace population) are peripheral neighborhoods and agricultural non-built areas, such as those on the southern end of the municipality map.

Figure 2 Pedestrian access of the working population to different census sections of the city in 5/10/15 minutes

In contrast to the demand-based models, the service compresence accessibility analysis offers a first look at supply side distribution of services across the city. The map highlights the distributional differences in service diversity and density in 5, 10 and 15-minute ranges. It is clear from the map that a large portion of the city by area does not guarantee the minimum access to 7 basic service groups within a 15-minute walk.

When compared to the demand-side maps, it is possible to observe that despite the high number of residents within 15 minutes reach of the districts of De Angeli, Lotto and Porta Genova, they are not adequately covered by all primary service groups. Central areas of Milan predominantly outnumber peripheral areas in terms of density and variety of their basic service offering.

The Walk Score analysis set at a 15-minute radius confirms the static findings of the service compresence map. In addition, it highlights further lower scoring areas in the north of Milan that, despite adequate service density, are potentially less walkable for the failure of the urban structure to support comfortable and efficient walking trips (see Figure 3).

Figure 3 Different analytical components of the 15-minute city model

Overlaying the Walk Score results with the NIL (Nuclei di Identità Locale) neighborhood classification allows us to study the results of the analysis in relation to typically recognized neighborhood units at the city level. The result of this comparison reveals a striking correlation between nearness of a neighborhood to the city center and high walkability levels as illustrated on the chart below (see Figure 4). This correlation does not translate, however, to population distribution trends.

Figure 4 Walk Score and 15-minute population accessibility by NIL neighborhoods

As shown in the same chart, apart from a few spikes in the number of people in close reach of well-performing and walkable districts such as Duomo and Porta Garibaldi-Porta Nuova, there is no clear trend in population distribution amongst the remaining neighborhoods. In fact, many neighborhoods with high population accessibility have low walkability scores such as Gorla, Cimiano and Gallaratese. Such neighborhoods have high attraction potential but low pedestrian access to services. These neighborhoods have high attraction potential but low pedestrian accessibility to services, signifying a need for intervention to promote equal access to services on foot across the city.

Intermodal support for the transitional phase

lleviate the burdens of a low walkable environment in the short run. Currently, only half of Milan benefits from the ease and convenience of a 15-minute city. A practical solution to support the model could be to boost first-and-last-mile mobility solutions to extend accessibility ranges while maintaining limited trip times and energy costs. Global trends show that micro-mobility devices have a significant role in reducing the first-and-last-mile gap and are often paired with public transport (Heineke et al., 2019; Zagorskas & Burinskienė, 2020; Boglietti et al, 2021). One of the main limitations of micro-mobility, however, is its demographic exclusion of higher age groups (ibid.). For older citizens and users with assisted mobility needs, on-demand transport may be a more suitable intermodal solution.

Final Discussion

The mapping analysis shows that Milan’s human-scale spatial structure has great potential to support walkability, should distributional differences and the mismatch between supply and demand in certain areas be addressed. From a demand perspective, the highest concentration of the resident population is within reasonable walking distance to the districts of the second ring road area, whereas the distribution of the working population cluster strongly in central districts.

On the other hand, if we look at the supply side of the equation, i.e. the walkability of the pedestrian network and the distribution of services, we find that there is missed potential in several pockets of high demand that are not adequately served by everyday destinations and are not sufficiently walkable from a multi-dimensional standpoint. By considering both sides of the equation, the combined series of maps captures not only the current state of the city, but the potential to extend its reach based on its intrinsic morphological capacity to attract daily users (i.e. residents and workers).

This research highlights the diversity of approaches to address walkability in cities and proposes a demand-driven approach to measure the 15-minute city. It progresses the view that 15-minute cities can only work if cities are not only efficient but are also designed to encourage more walking from a multi-angular perspective that includes both functional and experiential factors.

Walking infrastructure shortages could be overcome by broadening the perspective of the public realm as a whole and adopting a holistic approach of the walkable ‘urban living’ model. Short-term flexible interventions could focus on upgrading existing open spaces (through sidewalk redesign and integration of new micro-mobility modes) whereas long-term strategies should extend to policy design promoting egalitarian access opportunities across different districts in the city – and in particular in peripheral neighborhoods – while placing the walkability agenda at the center of neighborhood design. Ongoing local plans in the City of Milan reflect the shift to prioritize walkability as a sustainable growth principle. Both the Territorial Government Plan (PGT) 2030 and the Milan 2020 Adaptation Strategy developed in the wake of the pandemic crisis highlight the importance of pedestrian-focused interventions to improve urban connectivity and accessibility (Comune di Milano, 2019; Comune di Milano and AMAT Agenzia Mobilità Ambiente Territorio, 2020).

Moving Forward

A critical understanding of mobility under contemporary circumstances, which considers digital as well as physical infrastructure is essential for accurately representing the 15-minute city. There is potential in exploring the introduction of ‘zero-minute trips’ as part of a new hybrid conception of the 15-minute city that considers the contributions of digital services in replacing physical trips. Especially in the context of unprecedented growth of remote working since the start of the pandemic, urban mobility models stand to benefit from reductions in work commutes and thereby traffic congestion, which can both directly (hyper-proximity) and indirectly (road safety, reduced noise and environmental benefits) improve walkability prospects in cities. On the other hand, the marked drop in retail and replacement with online shopping has myriad effects on the urban mobility system. Whereas on the one hand it reduces user trips, it is replaced by increased logistics and delivery trips which has various implications on traffic and curbside utilization in urban areas. Both of these trends have been largely influenced by the pandemic situation.

A comprehensive view of mobility that considers the augmented role of digital infrastructure in fulfilling daily urban needs is essential for a critical analysis of mobility systems in the contemporary urban landscape. Notwithstanding the potentials offered by digital infrastructure substitutions, we also caution that an excessive reliance on these solutions could lead to the denial of what we believe is the key role of public space from a contemporary interpretation: fertile ground for triggering opportunities of relationship and aggregation, which is the basis of the concept of walkability.


The results of this research activity have been published in the following scientific Journals:

Abdelfattah L., Deponte D., Fossa G. (2022). The 15-minute city as a hybrid model for Milan. TeMA – Journal of Land Use, Mobility and Environment, 71-86. https://doi.org/10.6093/1970-9870/8653

Abdelfattah, L., Deponte, D., Fossa, G. (2022). 15-Minute City: Interpreting the Model to Bring Out Urban Resiliencies. Transportation Research Procedia, 60, 330-337. https://doi.org/10.1016/j.trpro.2021.12.043

References

Boglietti, S., Barabino, B., Maternini, G. (2021). Survey on e-Powered Micro Personal Mobility Vehicles: Exploring Current Issues towards Future Developments. Sustainability, 13(7), 3692. https://doi.org/10.3390/su13073692

Comune di Milano (2019). Milano 2030 Visione, Costruzione, Strategie, Spazi. Piano di Governo del Territorio (PGT). Available at: https://www.comune.milano.it/documents/20126/2053134/01DP_Relazione_Generale.pdf/baba55b0-c49a-ce8a-d9d2-68380cce21d2?t=15547220560977

Comune di Milano and AMAT Agenzia Mobilità Ambiente Territorio (2020). Milan 2020. Adaptation Strategy. Open Streets: Strategies, actions and tools for cycling and walking, ensuring distancing measures within the urban travel and towards a sustainable mobility. Available at: https://www.comune.milano.it/documents/20126/7117896/Open+streets.pdf/d9be0547-1eb0-5abf-410b-a8ca97945136?t=1589195741171

Global Designing Cities Initiative and National Association of City Transportation Officials (2016). Global street design guide. Island Press.

Heineke, K., Kloss, B., Scurtu, D. and Weig, F., 2019. Micromobility’s 15,000-mile checkup. Retrieved from McKinsey & Company Automative & Assembly: https://www. mckinsey. com/industries/automotive-andassembly/our-insights/micromobilitys-15000-mile-checkup.

Moreno, C., Allam, Z., Chabaud, D., Gall, C., & Pratlong, F. (2021). Introducing the “15-Minute City”: Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities. Smart Cities, 4(1), 93–111. https://doi.org/10.3390/smartcities40100066

Paris En Commun (2020). Ville du 1/4h. Le Programme d’Anne Hidalgo. Available at: https://annehidalgo2020.com/le-programme/

Regione Lombardia (2020). Ordinanza n. 604 del 10/09/2020. Ulteriori misure per la prevenzione e gestione dell’emergenza epidemiologica da Covid-19. Available at: https://www.regione.lombardia.it/wps/wcm/connect/209b7430-4bbf-4203-b9ab-89c97bb599a2/ORDINANZA+604+del+10+settembre+2020.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-209b7430-4bbf-4203-b9ab-89c97bb599a2-nhUNJzC

Speck, J. (2013). Walkable city: How downtown can save America, one step at a time. Macmillan.

World Health Organization (2020). Corona-virus disease (COVID-19) advice for the public. World Health Organization. Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public

Zagorskas, J. and Burinskienė, M. (2020). Challenges Caused by Increased Use of E-Powered Personal Mobility Vehicles in European Cities. Sustainability, 12(1), 273. https://doi.org/10.3390/su12010273

Assessing the Level of Walkability for Women Using GIS and Location-Based Open Data: The Case of New York City

Utilizing GIS and location-based open data, this study examined the level of walkability for women in New York City. As highlighted through a thematic literature review, women experience the city differently than men, since they are more concerned with security issues related to aggression and harassment, as a major inhibitor of mobility in public spaces especially at nighttime. The analysis focused on the level of usefulness, comfort, safety, security, and attractiveness of the city. The results of the proposed Walkability for Women Index (WWI) helped to identify and characterize a short list of suitable urban areas where to prioritize interventions. Moreover, the results of the proposed WWI have been compared to the National Walkability Index (NWI), in order to highlight the impact of individual characteristics of pedestrians on the perceived level of walkability.

User-centered approach for walkability assessment 

Although traditional approaches related to walkability tend to focus on the spatial dimension (Wang and Yang, 2019), individual characteristics of city users have a significant impact on the perceived level of pedestrian friendliness of streets and public spaces (e.g., demographics and socioeconomics characteristics, travel purposes, mobility preferences, etc.). In particular, the measures currently in place do not sufficiently consider vulnerable population groups (i.e., SDG 11.2-Sustainable Transport for All) (United Nations, 2016), including people with impaired mobility, elderly, children, and women.

In this framework, this study focuses on the needs and expectations of women while walking (Pollard and Wagnild, 2017). As highlighted by Golan et al. (2019), Andersdotter Fabre et al. (2021), and Sethi and Velez-Duque (2021), women experience the city differently than men, since they are more concerned with security issues related to aggression and harassment. These constraints take the form of precautionary or avoidance behaviors due to fear of violence, perception of risk, and sense of vulnerability (e.g., suppressed, re-routed or delayed walking trips, etc.), as a major inhibitor of mobility for women in public spaces especially at nighttime (Koskela, 1999; Loukaitou-Sideris, 2014; Vera-Grey, 2018). 

In recognition of these challenges, this study aims to assess the level of walkability for women focusing on a case study inNew York City (NYC, US). First, a thematic literature review was conducted on some of the most recent and relevant scientific contributions about this topic, considering both their conceptual and methodological approach. This led to identification of the following four criteria: (i) accessibility to essential services within a comfortable walking distance or usefulness; (ii) comfort of the road infrastructure in terms of sidewalks, public space, and green areas; (iii) perceived level of safety and security while walking; (iii) vitality of the social context or attractiveness. Then, using Geographic Information Systems (GIS), as well as a series of open data specifically focused on the needs of female pedestrians, the current study proposed an enriched Walkability for Women Index, aiming at identifying the census block groups and neighborhoods of NYC characterized by the lowest level of walkability in relation to the needs of women.

Enabling data and methodology 

The results of the literature review were exploited to select a series of relevant geolocated datasets, which were retrieved, sorted, and filtered from open-data repositories, geoportals and census databases. The select indicators were analyzed through Geographic Information Systems (GIS) to design a multi-layer map of NYC, focusing on each walkability criterion. The indicators were then included in the calculation of the Level of Usefulness Index (LUI), Level Comfort Index (LCI), Level Safety and Security Index (LSSI), and Level of Attractiveness Index (LAI). This was essentially based on weighted summations of the Z-scores of the variables proposed in this study. 

The weights definition was based on: (i) assigning a reference magnitude based on the results of the proposed literature review to strengthen the impact of the indicators and criteria which were defined by previous research works as more relevant considering the needs of women while walking; and (ii) a preliminary sensitivity analysis for calibrating values to balance and optimize the calculation of the proposed multidimensional data analysis.

The calculation of the Walkability for Women Index (WWI) represents a synthesis of four proposed indexes. According to the proposed methodology, the constant parameters KLUI (corresponding to 0.2), KLCI (corresponding to 0.2), KLSSI (corresponding to 0.4), and KAI (corresponding to 0.2) were weighted to accentuate the impact of the level of safety and security on WWI (∑ constant parameters = 1).

List of retrieved data that were analyzed and merged for assessing the level of walkability for women in New York City.

Walkability for Women Index

Results helped to identify and characterize a short list of suitable neighborhoods where to prioritize interventions to enhance the level of walkability for women by focusing, for example, on guaranteeing the presence of relevant public services within a walkable distance of 15 minutes from place of residence, and on the installation of surveillance and lighting systems to convey a sense of security. Interventions could be focused on differentiated urban planning strategies specifically tailored to women’s needs, aimed at guaranteeing the presence of relevant public services within a walkable distance, and on the installation of surveillance and lighting systems to convey a sense of security to the city users.

Maps of the LUI-Level of Usefulness Index (a), LCI-Level of Comfort Index (b), LSSI-Level of Safety and Security Index (c), and LAI-Level of Attractiveness (d) of NYC.

The results of the analysis achieved through the proposed Walkability for Women Index (WWI) can be compared to available standard walkability measures of walkability in New York City, with particular reference to the National Walkability Index (Thomas and Rourk Reyes, 2021), in order to highlight the impact of individual characteristics of pedestrians, namely their gender, on the perceived level of walkability.

Results of the proposed Walkability for Women Index (WWI) and National Walkability Index (NWI) of the census block groups and Neighborhood Tabulation Areas of NYC (interactive map).

The Neighborhood Tabulation Areas identified through the proposed Walkability for Women Index (WWI) could be further investigated through the observations of pedestrian dynamics supported by GPS data, video cameras, and Wi-Fi sensors to collect quantitative information about activity patterns in the city, but also through qualitative data collection methods (e.g., focus groups, audit tools, survey questionnaires, collaborative mapping platforms, co-design laboratories, etc.) focused on the subjective evaluations of women about the level of walkability of a specific area. Moreover, the collected disaggregated data will be used to support the definition of guidelines and policies for the inclusion of women’s needs in the design of future transport services.


The results of this research activity have been published in the scientific Journal Findings: Gorrini, A., Presicce, D., Choubassi, R., Sener, I.N. (2021). Assessing the Level of Walkability for Women Using GIS and Location-Based Open Data: The Case of New York City. Findings. https://doi.org/10.32866/001c.30794

Acknowledgments

We thank Dr. Ipek Nese Sener (Texas A&M Transportation Institute, Austin, Texas, USA) for her fruitful contribution as co-author of the paper. The analyzed data were treated according to the GDPR-General Data Protection Regulation (EU, 2016/679). This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

Andersdotter Fabre, E., Julin, T., Lahoud, C., and Martinuzzi, C. (2021). Her City. A guide for cities to sustainable and inclusive urban planning and design together with girls. United Nations Human Settlements Programme (UN-Habitat), Kenya. Available online: https://unhabitat.org/sites/default/files/2021/03/02032021_her_city_publication_low.pdf 

Golan, Y., Wilkinson, N., Henderson, J.M., and Weverka, A. (2019). Gendered walkability: Building a daytime walkability index for women. Journal of Transport and Land Use, 12(1). https://doi.org/10.5198/jtlu.2019.1472

Koskela, H. (1999). ‘Gendered exclusions’: women’s fear of violence and changing relations to space. Geografiska Annaler: Series B, Human Geography, 81(2), 111-124. https://doi.org/10.1111/j.0435-3684.1999.00052.x

Loukaitou-Sideris A. (2014). Fear and safety in transit environments from the women’s perspective. Security Journal, 27(2), 242–256. https://doi.org/10.1057/sj.2014.9

Pollard, T.M. and Wagnild, J.M. (2017). Gender differences in walking (for leisure, transport and in total) across adult life: a systematic review. BMC Public Health, 17, 341. https://doi.org/10.1186/s12889-017-4253-4

Sethi, S. and Velez-Duque, J. (2021). Walk with women: Gendered perceptions of safety in urban spaces. Leading Cities, Boston.

Thomas, J. and Rourk Reyes (2021). National Walkability Index. Methodology and User Guide. United States Environmental Protection Agency (EPA). Available online: https://www.epa.gov/sites/default/files/2021-06/documents/national_walkability_index_methodology_and_user_guide_june2021.pdf

United Nations (2016). Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations: New York, NY, USA. Available online: https://sdgs.un.org/2030agenda

Vera-Grey, F. (2018). The Right Amount of Panic: How Women Trade Safety for Freedom. Policy Press, Bristol.

Wang, H., & Yang, Y. (2019). Neighbourhood walkability: A review and bibliometric analysis. Cities, 93, 43-61. https://doi.org/10.1016/j.cities.2019.04.015

Sensors Data for Unlocking Hidden City Metrics

The recent developments of ICT tools for collecting traffic data in the urban environment have allowed a tremendous increase in the quality and amount of easily accessible data. This enables transport planners and decision makers to analyze, explain, and estimate complex mobility patterns. Thanks to the collaboration with Blimp.ai, the research is based on the analysis of proprietary data on pedestrian counts collected in Milan from the beginning of November 2020 to the end of July 2021 through a network of sensors. Data is used to assess the impact of the Covid-19 restrictions on pedestrian dynamics with a time series analysis. This is further analysed through an extended GIS-based analysis of potential attractors of city users (e.g., land mixed-use, POIs, Public Transport, etc.), to assess the interplay between pedestrian traffic, city characteristics and restrictions.

Urban Informatics for Transport Planning

The growth of urban data produced by sensors scattered across our cities allows better management and optimization of transport services in real time (Li et al., 2020). Within the Urban Informatics approach (Foth et al., 2011), the aim is to assess the operational conditions of mobility services, detecting anomalies, and avoiding service disruptions. The more powerful ICT solutions for analysis of travel patterns can be enabled by several different types of location detection systems. This study uses sensors-based analysis-acquisition systems (Buch et al., 2011), in which data is processed by an Artificial Intelligence to count pedestrians.

In this context, the Covid-19 pandemic has revealed a crucial application of traffic data monitoring: to examine the effects of the pandemic on urban mobility and to measure the effectiveness of the cities’ responses to the crisis (European Platform on Sustainable Urban Mobility Plans, 2020; Comune di Milano and AMAT, 2020).

This research proposes a combined spatial and temporal analysis of pedestrian count data in the City of Milan to assess the impact of Covid-19 lockdown restriction measures on pedestrian traffic and the interplay with city attractors. By analogy with a methodology presented by Transport Transport for the analysis of Wi-Fi data, pedestrian counts data is used to estimate hidden mobility patterns in the City of Milan, focusing on the latter waves of the pandemic (from the beginning of November 2020 to the end of July 2021).

Enabling Data and Methodology 

Blimp.ai developed a sensor (aided by an A.I.) which collects disaggregated traffic data (e.g., vehicular traffic, pedestrian and bicycle flows). Thanks to the collaboration with them, Transform Transport analyzed pedestrian activity data collected through a network of sensors installed in advertising billboards. Sensors are located next to newspaper stands, typically on larger sidewalks or plazas, and they count passersby in a cone shape.

A few values were missing throughout the data collection period, likely due to malfunctioning of sensors. Moreover, some values were inexplicably high or low relative to others, therefore, a data validation process was necessary to find and remove outliers. Those values were removed and replaced by null values. However, some other strange values were scattered over the study period highlighting the need for a more systematic way to detect outliers. Being a robust and easy technique (Schwertman et. al., 2004), the interquartile range method was chosen. Values detected as being outliers were then replaced by null values. Moreover, sensors that had too high a frequency of removed or missing values were excluded from the study, as they would likely be unreliable. This process led to a final selection of 47 sensors.

Average pedestrian counts of each sensor over the 9-month study period.

Results of Temporal analysis

Figure 1 Trend analysis (count and moving average)

The chart (see Figure 1) presents the result of the time series analysis of the average values per sensor from November 1st, 2020, to July 29th, 2021. The types of Covid-19 restrictions enacted under each scenario are highlighted in the chart, and described in Table 1 by ascending order of strictness:

Table 1 Types of Covid-19 restrictions

A 7-day moving average smoothes out short-term variations, showing longer-term trends. However, the rapid evolution of covid restrictions limits the potential to interpret the precise effect they had. For this purpose, a series of t-tests were conducted, resulting in a significant (p < 0.05) difference between the number of pedestrians detected on a daily average during all scenarios. Therefore, it can be deduced that the Covid-19 restrictions had a sizable effect on pedestrian activity.

Figure 2 shows the average count of pedestrians per day of the week, for the various phases. Values are more homogeneous the stricter the Covid-19 measures get, but Wednesday remains the least active day of the week for all phases.

Figure 2 Correlation analysis, circadian

Results of Spatial Analysis

A series of geospatial data about land use, urban amenities and services was collected thanks to open data from local authorities (i.e. Geoportal of the City of Milan, Geoportal of Lombardy Region, Italian National Institute of Statistics), and from OpenStreetMap. These are listed in Table 2.


Table 2 Correlation analysis, explanatory factors selected

The count or area of these factors were measured in a 400m radius around each sensor (namely 5 minutes walking distance).

A series of correlation analyses (Pearson’s Correlation Coefficient) revealed a positive correlation between pedestrian counts and attractors, summarized in the table below. Moreover, results show that public transport services are often the most important factors for pedestrian flows, with particular reference to tram stops and bike-sharing docking stations.

Table 3 Results of correlation analyses

To assess and forecast potential pedestrian activity patterns a Suitability Analysis Index was constructed from the r values of Scenario 1, the one that likely resembles the medium-term future of Covid-19 restrictions.

The Z values are obtained by evaluating the amount of selected amenities (i.e., tram stops, bikesharing docking stations and administrative buildings), normalized on a 0-1 scale using a normal cumulative probability distribution function. The formula aboved is then used with i standing for every cell, resulting in the map below of the potentialities for relevant pedestrian activities around newspaper stands.

Results of Suitability Analysis Index (interactive map)

Conclusions and Future Work

Sensors-based techniques have been shown to be very effective for collecting traffic data of different users in urban areas, with decreasing costs and great progress being made on the A.I. powering those tools (Buch et al., 2011). However, their compliance with the General Data Protection Regulation (National Centre IOT and Privacy, 2020) is crucial for privacy and acceptability of this technology.

This research work was conducted with a large proprietary dataset of pedestrian counts, spanning a 9-month period (from November 2020 to the end of July 2021), collected by Blimp.ai through 47 sensors across Milan. Statistical analysis showed the sizable impact of the Covid-19 restriction measures on pedestrian activity. This method could be applied in real time to assess the effectiveness of those kinds of policies in the future.

A spatial statistical analysis showed that sensors close to tram stops, bikesharing stations and administrative buildings had higher pedestrian activities. Given that this spatial data is easily collectable for major cities, this study can be replicated in different contexts across the world. Future research could make use of more sensors and advanced analytical tools to accurately estimate the expected pedestrian activity in a city.


Acknowledgement 

The analyzed data were treated according to the General Data Protection Regulation (EU, 2016/679). Systematica thanked the team of Blimp.ai (an e-Novia company) for their fruitful collaboration and for sharing data. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References and Online Sources

Buch, N., Velastin, S.A. and Orwell, J. (2011). A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions on Intelligent Transportation Systems, 12 (3), 920–939. https://doi.org/10.1109/TITS.2011.2119372

Comune di Milano and Agenzia Mobilità Ambiente Territorio (2020). Milan 2020. Adaptation Strategy. Comune di Milano, Milan (Italy). Available at: https://www.comune.milano.it/documents/20126/7117896/Open+streets.pdf/d9be0547-1eb0-5abf-410b-a8ca97945136?t=1589195741171

European Platform on Sustainable Urban Mobility Plans (2020). COVID-19 SUMP Practitioner Briefing. CIVITAS SATELLITE CSA. Available at: https://www.polisnetwork.eu/wp-content/uploads/2020/07/COVID-19-SUMPPractitionersBriefing_Final.pdf

Gorrini, A., Messa, F., Ceccarelli, G. & Choubassi, R. (2021). Covid-19 pandemic and activity patterns in Milan. Wi-Fi sensors and location-based data. TeMA – Journal of Land Use, Mobility and Environment, 14 (2), 211-226. https://doi.org/10.6093/1970-9870/7886

Foth, M., Choi, J.H.j., Satchell, C. (2011). Urban informatics. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, pp. 1–8. https://doi.org/10.1145/1958824.1958826

Li, W., Batty, M. and Goodchild, M.F. (2020). Real-time gis for smart cities. International Journal of Geographical Information Science, 34 (2), 311-324. https://doi.org/10.1080/13658816.2019.1673397

National Centre IOT and Privacy (2020). Physical Audience Measuring Technologies and Privacy Concerns. White Paper, v.01/2020. Available at: https://iotprivacy.it/wp-content/uploads/2020/06/Whitepaper-Physical-Audience-Measuring-Technologies-and-Privacy-Concerns-ENG.pdf

Schwertman, N. C., Owens, M. A., & Adnan, R. (2004). A simple more general boxplot method for identifying outliers. Computational statistics & data analysis, 47(1), 165-174. https://doi.org/10.1016/j.csda.2003.10.012

Systematica (2021). Shifting Paradigm: the impact of Covid-19 on transport planning. Systematica Srl. Available at :https://issuu.com/systematica/docs/covid19_sys_issuu

Tactical Urbanism for Urban Design Interventions: in Conversation with Fabrizio Prati

Fabrizio Prati is an urban designer with 10 years of international experience in safe and sustainable urban design, mobility, street and public spaces design. He joined NACTO in 2015 and as the Associate Director of Design he helps oversee projects and activities under the Bloomberg’s Initiative for Global Road Safety (BIGRS) program and the development of new publications. He is one of the authors of the Global Street Design Guide. He graduated from the post-graduate Master of Urban Design at UC Berkeley and previously obtained a Master in City and Regional Planning from the Paris School of Planning. Fabrizio is a city enthusiast and avid urban explorer.

Q: What are the main pros and cons of both tactical urbanism interventions and traditional urban design interventions?

FP: Tactical urbanism allows the opportunity to demonstrate bold ideas and helps communities experience change quickly. These low-cost, short-term interventions are meant to inspire and enable more permanent changes. They also create opportunities to test multiple designs prior to capital investment, thereby reducing the risk of the project’s failure. Using easily accessible, low-cost materials makes transformations not only cost-effective and implementation quicker but also more scalable and replicable, so many more projects can be implemented in a faster time frame.  A significant advantage of tactical urbanism is the opportunity to engage the community and refine the design based on their insights and how the new space is used.

Not everything can be (nor should be) tested using interim materials, some physical changes can only be done in capital construction, and in some locations, it may be more appropriate to use more permanent materials, especially when there is already buy-in. Another challenge is the maintenance of these spaces, whose quality can deteriorate faster than traditional projects. For this reason, it’s important to create a long-term plan and to think about all process steps from the beginning: incorporating maintenance, upkeep, and the replacement of paint, materials, and elements, as well as planning for the capital construction phase.

Q: How to monitor the effectiveness of the proposed interim interventions (e.g., how to collect data – methodology, and how to integrate quantitative and qualitative data, etc.)?

FP: Monitoring is a fundamental part of the success of any project. For decades, streets have been evaluated based on the efficiency and capacity of moving motor vehicles. Still, proper mobility and road safety improvements should focus on a combination of street users and data types. In addition to motorists, understanding the impacts on pedestrians, cyclists, people doing business, and freight and service operators will give a greater understanding of the value of the project. But streets are public spaces, the most commonly available type.

Cities should collect quantitative and qualitative data looking at existing data and research, technical drawings, measurements, counts, surveys, observational data, and interviews or focus groups. The same data should be collected before and after the project is completed to analyze the impact and strategically use the data to improve the final project checking against initial assumptions and expected challenges.

Communicating this information visually and concisely is key in advocating for the project to become permanent and to learn valuable lessons from the process.

Q: What are the differences and analogies in tactical urbanism interventions among EU, US, and other countries?

FP: Great question. In our experience, tactical urbanism is very contextual, and almost every country or even every city uses slightly different solutions, materials, or elements in their interventions. This makes complete sense because the idea is to find easily available, accessible, and affordable elements and materials and find site-specific solutions that adapt to local culture, needs, and challenges.

There are differences in raw materials and labor costs that make some solutions more affordable in certain contexts and almost prohibitive in others. For instance, a modular element that can be affordable in certain parts of the world might be very expensive somewhere else because it’s not produced locally. 

There are a lot of similarities too, definitely in the approach and sometimes even in the material used or the elements. Cities get inspired by each other and try to find creative solutions to transform their streets and public spaces.


Upon the Plurality and Subjectivity of Meaning in Data: in Conversation with Andrea Galli

With a strong background in design (Politecnico di Torino) and innovation management (Scuola Superiore Sant’Anna), Andrea Galli is the Director of Strategy and Development at Accurat and teaches Computational Design as Adjunct Professor at Politecnico di Milano. Formerly project leader at Carlo Ratti Associati, over the last ten years he has been testing the impact of a data-driven approach to projects bridging between the urban environment and the digital one.

Q: Your working philosophy at Accurat pivots on the concept of ‘data humanism’. Could you speak a little bit more about what that means to you and how you think it affects the work you produce at the company?

AG: The concept of data humanism was gradually shaped in the last 10 years through the work we conducted on more than 400 projects. In particular, on each of them we always tried to inject personality into data viz to engage users, and specificity to remind them of the real-world issues they’re solving for. 

Approaching data from a human perspective today in Accurat means to empathize with them by not just looking at numbers, but focusing on actions and interactions implied by those numbers. Therefore, to understand what data can tell us we believe we need to ask them the right questions, but at the same time we are aware that what humans define as the “right” question is always very personal and subjective.

For this reason, our continuous research around data visualization relies on a wide range of diversified expertises and points of view that require a multidisciplinary environment, not only to excel but also to be successfully orchestrated. That’s why, in Accurat today we are information designers, ux and ui designers, data scientists, front-end developers, architects, sociologists, musicians, biologists and many others. 

I ultimately consider the result of such an alchemy what on a daily basis allows us to foster a humanistic philosophy by developing data-driven solutions.

Q: During the SYS Talks: The data shall not be standardized you presented earlier this year, you touched upon the plurality and subjectivity of meaning in data, stating that ‘data is not an oracle telling us the truth’. Could you tell us about the process of constructing data stories tailored to the clients’ needs and the role of narrative building in the process and outcomes?

AG: I’d like to start answering by giving you a general idea of data growth in recent years. In 2018 the total amount of data created, captured, copied and consumed in the world was 33 zettabytes, this grew to 59ZB in 2020 and is about to be tripled by 2025 (in case you are wondering how much 1ZB is). So we can definitely say that, unlike a few years ago, information availability is not an issue anymore. But more information also means more noise and complexity. At the same time, more data experts are flooding into the market. We are talking about 7.6 million professionals in 2019 all over Europe, that will reach 11.3 million by 2025.

Scientists and analysts can certainly offer a relevant contribution to make big quantities of data more digestible by using various techniques. However, in our experience, this is often not enough to provide meaningful results for the end-users in search of intuitive and usable solutions.

This is the reason why we always try to work closely with our clients and their data experts to understand the context in which data are generated and interrogated. We can thus feed an iterative creative process made of cycles that are so composed: hypothesis formulations – solutions test – feedback collection. The results are digital/physical – or even hybrid – experiences that, by following a consistent overarching narrative, are totally oriented to the resolution of very practical issues through the scientifically founded combination of visual design and digital technologies.

Q:In your opinion, how can data visualization (irrespective of the medium) strengthen urban planning processes and impact project outcomes?

AG: Being an architectural and urban designer by background, over the last ten years, I’ve been continuously pushed to explore the impact of a data-driven approach to large scale projects. In my opinion, today data represents a strong common thread among various disciplines that before were considered as very far one from the other, and this represents a precious opportunity to explore new approaches to a practise in a dramatic need of innovation, such as urban design.

Data visualization at urban scale is directly linked to the way we, as designers, interpret places: their meaning, their heritage, their potential. It offers  an  extraordinary opportunity to improve our understanding of people’s behavioural dynamics, interactions and of evolution of the natural and man-made environment. For example, a growing application of data visualization to the urban planning process is represented by the real time comparison of multiple scenarios that can be then corrected and validated by human decision-makers to meet stakeholders’ interests and needs.

But, if the analysis of data acts so powerfully at cognitive level, it’s important to be aware of the fact that in a data-driven approach to urban design, the selection and the representation of data is intrinsically a subjective (human) creative process, a real design operation directly influencing the future of the cities we live in. 

Therefore, if they really want to improve the quality of projects outcomes, I strongly believe that more than ever designers must be conscious of their editorial role, and provide an expert, competent, but primarily transparent angle on data.


Stories: a platform built for place–based storytelling

Almost 70 years after Jane Jacobs’ arguments for smaller, mixed-use, connected neighbourhoods were published, the world is beginning to fully grasp the importance of neighbourhood-scale urban planning and actively supported service accessibility. If not already prompted by global sustainability agendas, such as the UN Sustainable Development Goals, the mobility standstill brought about by the coronavirus pandemic in early 2020 has provided a real-life simulation of what it would mean to live entirely within a few short blocks from home. Emphasis on the sustainability of the micro-neighbourhood grew as slogans such as the ’15-minute city’ and ‘walkable neighbourhoods’ gained the attention of mainstream media. In this context, the digital platform “Stories” acts as an open source and decentralized tool designed to engage citizens on urban initiatives/policies transforming the way they live and move around the urban context. Based on data-driven stories, the digital platform allows a two-way framework of communication between people and local entities, who can mutually collect and share -on a voluntary base – geo-referenced information. Stories is built on open source technologies – such as OpenStreetMap – allowing private and public stakeholders to freely fork, personalize and deploy their own place-based stories. In this regard Stories doesn’t rely on centralized data owned by private actors, but it is backed by aggregated and anonymous data. Offline capabilities allow the platform usage on areas affected by poor network connectivity.

Using Urban Informatics to understand and respond to citizens’ needs

The explosion of location-based data and data collection tools in recent decades has led to a paradigm shift for urban and mobility planning. For the first time, movement patterns happening all around the city could be gathered passively at a large scale and synthesized in ways that give us insight about changes in mobility behavior in real-time. Not only that, but the ubiquity of geo-referenced data (in large part owing to digital miniaturisation) has led to the decentralization of data collection to billions of data sharers, significantly broadening the type and depth of movement data we can collect; a phenomenon we like to call ‘data in every pocket’ (Choubassi & Abdelfattah, 2020). Simply put, Big Data (and in particular location-based data) is transforming how we document, collect and understand mobility patterns. In the words of Batty, “This revolution in tracking human and other motion in digital form enables the collection of multiple attributes at the finest of scales of urban observation” (Batty, 2010, p. 576).

The discipline through which urban planners employ these technology-enabled spatial analysis methods has come to be known as Urban Informatics. Urban Informatics is an evidenced-based approach that, thanks to the recent development of advanced Information and Communication Technologies (ICTs) and the increasing availability of digitally widespread data sources, has led to innovative assessment tools and metrics that can provide valuable support to city decision makers (Foth et al., 2011). Due to unprecedented data volumes, granularity and diversity, problems of sampling bias associated with traditional data collection methods can be sufficiently overcome, allowing the data to be manipulated to focus on specific communities, their behaviors and needs (Batty, 2013; Milne and Watling, 2019).

It is within this framework that this project is conceived. Conceptually speaking, Urban Informatics operates at the intersection of three domains: People, Places, and Technologies. The Stories App project is the brainchild and execution of two entities: Systematica, an expert mobility consultancy that has long occupied itself with the domain of Place using both a human-centered approach and a data-driven approach, and Accurat, a leader in the field of Data Science and data visualization, with a deep-rooted philosophy of ‘Data Humanism’ (Lupi, 2017), which is an approach that attempts to deliver data to people in tangible and approachable ways. In that sense, the Stories project is one that combines the expertise of two industry experts into a multi-disciplinary approach to reimagine urban mobility as a customized, simplified and humanized daily experience.

Urban Informatics relational chart
(adapted from: https://research.qut.edu.au/designlab/groups/urban-informatics/)

Despite recent efforts towards sustainable and inclusive mobility in urban contexts, the needs of vulnerable city users are still largely overlooked and/or not explicitly addressed through mainstream planning instruments. As highlighted by the 2030 Agenda for Sustainable Development adopted by all United Nations Member States (i.e., SDG 11.2-Sustainable Transport for All), public transport should be designed to be inclusive of diverse types of users: “By 2030, [transport systems should] provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons” (United Nations, 2016, p. 24). A user-centered approach to mobility planning is central to the Stories App vision. Customized transit planning is etched into the DNA of the project with the aim to ensure transport inclusivity and promote equal access to opportunity in the city.

How Covid-19 impacted urban mobility and everyday life

The unprecedented disruption caused by the COVID-19 pandemic has given rise to a large cultural and technical debate on upcoming forms of urban development and mobility, in the attempt to envision potential trajectories of new paradigms and keep up with the pace of our changing cities (Transform Transport, 2021). City administrations around the world are recognizing walkability, universal accessibility, public realm and living locally as crucial planning dimensions. In particular, the recent focus on the ’15-minute city’ model represents a new perspective for evaluating sustainable, inclusive, and resilient urban organisms, meeting the human desire for community and place identity. The model envisions access to the urban life experience, e.g., essential facilities and services, in a timeframe of 15 minutes from home, by walking or cycling, as defined by Moreno et al. (2021). The recent pandemic clearly showed this potential as soft mobility modes were allowed for social and recreational life even during lockdown periods.

Having said that, encouraging the shift towards a sustainable urban mobility with focus on public transport, shared-micro mobility, and active modes such as walking and cycling is one of the main challenges European cities nowadays have to face (Buhrmann et al., 2019). Among all possible urban challenges, a recent focus on walkability, which contributes to the quality of life of its citizens by enhancing physical activity, well-being, and social inclusion (Speck, 2013) is shaking all advanced urban planning activities. Taking up this challenge, cities worldwide are improving pedestrian mobility, including barrier-free and safe sidewalks, but also human-scale environments which allow people to enjoy walking and gather in comfort (Gehl, 2013).

One of the main goals of European cities is to promote active modes of travel providing Sustainable Urban Mobility Plans or SUMPs for short (Buhrmann et al., 2019). Within a short time frame and following the impact of the COVID-19 pandemic, city administrations have adopted and implemented short and long-term plans to redistribute vehicular road space towards cyclist and pedestrian infrastructure. Milan, a city that was hit hard since the outbreak of the virus becoming one of the early epicentres of the pandemic is nowadays one of the forefront cities embracing this approach, adding 35 kilometres of cycle lanes in the city center under its Open Streets plan (Comune di Milano and AMAT Agenzia Mobilità Ambiente Territorio, 2020; Laker, 2020). In Milan as around the world, initiatives showing clear aims to favour pedestrian movement and active travel options quickly rose to the top of local mobility agendas.

The Stories App

Stories is a platform built for place–based storytelling, comprising four components or “layers” of interactivity: Placement, Guidance, Analysis, and Engagement.

Developed by Accurat & Systematica

When users log in, they are greeted with a map of a locale marked by visual indicators. These icons show quality of life measures: proximity to green spaces, transportation, areas of culture and recreation, and demographics. The placement of these data visualizations shows the quality of amenities in the corresponding area across the four aforementioned categories. Users can customize Stories to reflect specific considerations.

Developed by Accurat & Systematica
Developed by Accurat & Systematica

For example: the mobility category can be adjusted to only display bus lines, or bike racks. This visual system gives users an at–a–glance view of how well–suited an area is to meet their needs, whether they’re exploring as tourists or evaluating places to live.

Developed by Accurat & Systematica
Developed by Accurat & Systematica

After gathering their bearings, users are invited to take a tour by choosing a “Story” from a menu. In this example, the “15-minute-city” is the theme. When activated, the guided overview plays, taking users through the municipalities’ neighborhoods and highlighting places relevant to the Story via audio narration. The experience unfolds through a series of cards, which will feel familiar to users of Instagram Stories. Those who wish to deepen their exploration can do so with granular analysis capabilities, replete with data visualizations. Lastly, as a send–off, users are invited to participate through contributing tips to maintain the accuracy of data, or by simply signing up to receive notifications for new features and Stories.

Developed by Accurat & Systematica
Developed by Accurat & Systematica

Stories’ initial prototype was developed for Milan. Jacobs’ 15 Minute City served as a source of inspiration and a framework for ideating location–based narratives within the city (incidentally, the concept is also available as one of four options to activate the storytelling functionality). The app was designed as an alternative to tech giants’ map–based, navigational offerings, which are supported by (and thus, favor) local business interests.

Stories’ ethos—to put users’ needs first—is embedded into the prototype’s architecture. Open source, location–based data made available through the Commune di Milano’s geoportal powers this version, and represents a compelling use case. The prototype demonstrates how municipal data can be put to use as the foundation for a handy mobile app, equally valuable to locals and visitors.

At a higher level, the Milan prototype shows how Stories can be leveraged by local governing bodies to create a positive feedback loop for information–sharing. We anticipate that users who find the app valuable will, in turn, be more likely to contribute tips, ultimately improving the accuracy of city data. The end–product of this scenario is a strong, sticky offering, unique in its ability to serve sponsoring entities and users, both as a practical navigational tool and an ethical way to solicit data.

Open Data for Unlocking Hidden City Metrics 

The methodological approach which sets the current research is based on the application of Geographic Information Systems (GIS) for the analysis of a series of relevant location-based data which were retrieved, sorted and filtered from the Geoportal of the City of Milan (2020).

According to the Territorial Government Plan of the Municipality of Milan (2019), the territory of the City of Milan  is divided into 88 Neighborhoods or Nuclei of Local Identity (NIL): a system of areas connected by means of mobility infrastructures and services, and characterized by urban vitality, distinctive features, historical heritage, but also by ongoing renovation projects. Thus, NIL have been considered as the most appropriate spatial units for the proposed GIS analysis, in terms of historical peculiarity, granularity, average area.

The retrieved open data were used to design a series of multi-layered maps of the City of Milan, considering the territorial boundaries of the above described NIL. This was aimed at estimating the spatial distribution of each data set, and to identify and characterize the NIL where to prioritize interventions focusing on the accessibility of relevant services within a comfortable walking distance.

In particular, the selected datasets were analyzed considering the spatial distribution of punctual and areal vectors on NIL. For comparing the various indicators among them, each one has been normalized on a 0-100 scale, creating Z-scores that follow the normal distribution of the values. Data analysis started from the definition of four indexes, as follows: Demographics; Proximity services; Green areas; and Mobility.

The calculation of the Demographics Index was based on the density distribution (inhab/sqm) of the number of inhabitants living in the census sections of each NIL. The Proximity Services Index was based instead on the spatial distribution of social, religious, educational, security, health, administrative, and cultural services on each NIL. The Green Areas Index was based on estimating the aerial extension (sqm) of public parks and gardens on each NIL.

Finally, the considered dataset for the calculation of the Mobility Index spans several domains: (i) subway stations, railway stations, and local public transport stops (TP); (ii) cycling infrastructures (CI); (iii) sidewalk infrastructures (SI). This was based on estimating the number of inhabitants and workers served by transport services and infrastructures, and on the spatial distribution of cycling (m) and sidewalk infrastructure (m). Then, the index was defined by the weighted summation of the Z-scores of each data:

Through a preliminary sensitivity analysis on the considered data, the constant parameters KTP (corresponding to 0.8), KCI (corresponding to 0.1), and KSIS (corresponding to 0.1) were weighted to accentuate the impact of subway, railway and local public transport service on the proposed index (Σ constant parameters = 1).

The results of the analysis are then visualized considering both the proposed indexes and the possibility to explore the localization of each layer through an isometric buffer of 1 km, which approximately corresponds to 15 minutes by walking.

Users of Stories encounter the fruits of these calculations as objective scores, visualized within iconography that calls to mind navigational tools, such as a compass or sonar radar. The data visualizations—circular discs divided into four, color–coded sections—display an area’s performance across the four indexes. The consolidated visualization also allows users to easily weigh a location’s performance in one index versus another.

Scalability and integration of the app in future research projects

The research represents a preliminary step aimed at identifying the areas of the City of Milan where it is suitable to execute a series of data collection activities. In particular, neighborhoods characterized by a lack of accessibility of proximity services, green areas and public services and infrastructures could be further investigated through audit tools and survey questionnaires focused on the subjective evaluations of the city users about the level of walkability of a specific area.

One of the next steps will be aimed at transforming the results of the proposed in-depth data collection and analysis activities into an interactive communication tool, which could raise public awareness of the research findings. Thus, results will be exploited by using open-source platforms for data visualization to produce web-based, fully accessible and interactive thematic maps. Moreover, the app will include new functionalities related crowd source collaborative mapping through the direct contribution of the users, based on the possibility to incorporate feedback from the users and to collect data about their mobility patterns (Bolognesi and Galli, 2017).

Stories is built to scale to accommodate environments big and small, spread–out and dense. Its application is equally relevant to college campuses and metropolitan areas even bigger than Milan. Additionally, beyond opportunities in the public sector, Stories can be sponsored by private entities. The 2022 Olympics committee, for instance, could leverage Stories for the 2022 games in Milan and Cortina. Other types of companies that rely on users to self–navigate might find value in Stories’ potential for customization. Sponsors are invited to contribute expertise as material for audio narration. A museum seeking to highlight particular collection items, or a real estate firm looking to give users an overview of a neighborhood, are prime examples.

In conclusion, the study represents a valuable example of the possibility to analyze digitally widespread open data sources to support decision-makers. According to the 2030 UN Agenda for Sustainable Development (i.e. SDG 11.2-Sustainable Transport for All), the presented results could be of notable interest to public institutions involved in the design of sustainable and inclusive mobility strategies.


Acknowledgments

The analyzed data were treated according to the General Data Protection Regulation (EU, 2016/679). The team thanks Comune di Milano and Agenzia Mobilità Ambiente Territorio for their support. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

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Technology is the new democracy for the urban planning: in Conversation with Tiffanie Yamashita

Tiffanie Yamashita is an urban systems engineer and collaborate with Systematica from 8 consecutive years. She started as an intern for her final-year program at the Université Technologique de Compiègne, gradually gained confidence and today she is one of the five Project Managers guiding Systematica’s most complex assignments worldwide. The multidisciplinary approach of her degree allowed her to keep a holistic vision to tackle urban issues within a specific consultancy field such as the transport planning and mobility engineering.

The advisory activities of Systematica have the aim to develop planning and design strategies to promote sustainable way of moving while caring about the urban and architectural quality of the projects. The role with Tiffanie’s team is to develop analytical studies that support arguments to defend strategic design solutions with evidence-based in a communicative way, in order to help their clients in their decision-making process.

Q: What are the main mobility challenges in your area of interest?

TY: The richness of my work comes from the great variety of projects I work on, depending on their nature, scale and the socio-geographical contexts; a variety that comes along shades of mobility challenges we need to deal with in our everyday work. The type of client is the first discriminant: the public authorities have (or should have) as their main target the acceptance of the project by the locals and the functionality of the new development while for the private developer, the approval from the public authorities is a must and aspires for a successful project, also investment-wise. The consultancy activity should keep in mind those variables and propose solutions that could be implemented realistically. The main challenge in mobility planning in new developments is how much to push for the shift in mobility habits – find the right balance to support the transition towards sustainable way of moving.

Q: What is in your opinion the role of technology in planning the cities and neighbourhoods of tomorrow? How much we should depend on them and how can we future proof our cities against continuous changes?

TY: Technology is the new democracy for the urban planning. The use of Big Data makes accessible valuable information on people’s movements in a scale and frequency that would be too expensive to conduct with traditional surveys, especially for small-medium cities. The speed with which Big Data could deliver the information makes it easier to assess the efficiency / impact of pilot projects and to adjust them to respond better to the population’s needs. When technologies regard infrastructures and devices, they are not only a tool for observation but they actually influence the way people move. In this context, the key changers are the size of the devices (smaller), the energy they consume (more & more electric), the service type (shared, free-floating, etc.) and the level of autonomy (AV).  As highlighted in the other industries, when it comes to technologies, the evolution has an exponential trend and the prediction is highly variable. To accommodates for the future technologies and make them accessible and integrated in the neighbourhoods of tomorrow, cities should plan for flexibility which means more space for all modes of transport: better balance between soft modes and space for vehicles and better use of current provisions by sharing the existing assets.

Q: Considering the growing concerns over climate change, how important is the role of transport planners to ensure a sustainable and low impact design? what would be the main solutions to encourage the shift towards the non-motorised and sustainable modes of transport in developments and cities?

TY: Literature and global estimates point out that transport is one of the sectors that has the greatest impact on the carbon footprint, thus transport planners have definitely a role to play to help the shift toward sustainable modes of transport at every scale. Encouraging the shift from the individual and private modes to collective and shared modes of moving should be the base principle for new developments and planning cities. The transport planners can influence the paradigm at different levels:

  • Provide alternative mobility solutions competitive to traditional transport modes by implementing new services or new connections. The effort is put on the soft mobility and MAAS network to make the connections among different modes the smoothest possible;

  • Make the trips by private and motorised modes less convenient. This can be achieved with a set of strategies such as reducing the parking availability, introducing slow-speed zones, shared streets and pedestrian areas.

  • Reduce the need to travel (far). The essence of the concept of the 15-min cities held in the fact that cities are planned to have all necessary services at walking /cycling distance creating dynamic neighbourhoods, well served by soft mode infrastructure.