Milano Digital Week – Exploring the Potentials of Tech Apps to Enhance Women’s Mobility

November 14th | 3:00 – 5:00pm (CET), Milano Digital Week – Online

Exploring the Potentials of Tech Apps to Enhance Women’s Mobility

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For the fifth edition of Milano Digital Week, Transform Transport teamed up with TeMA lab of Università degli Studi di Napoli Federico II, Sex and the City APS, and Walk21 Foundation for a webinar on November 14 from 3-5 pm CET about the role of technology and digital infrastructures in enabling safe urban experiences for women, in line with this year’s theme Lo sviluppo dei limiti – Progetti e visioni per una città e un pianeta condivisi (“The Development of Limits – Projects and Visions for a Shared City and Planet”). 

As experts in the field with a deep knowledge of the Milanese and Italian context, the speakers coming from academic to practical backgrounds will explore the potential and limits of ICT tools and user-based technology applications in promoting urban safety. They will also touch on the capacity of these tools to instigate corrective policy action and drive positive change in urban environments.


Andrea Gorrini, Head of Research
Fondazione Transform Transport ETS (Italy)

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.

Lamia Abdelfattah, Researcher
Fondazione Transform Transport ETS (Italy)

Lamia Abdelfattah is an urban planner and researcher working with Systematica and Transform Transport in the areas of mobility research and planning. She has a background in collaborative urban development and expertise in issues of spatial equity in cities with a particular focus on gender equality. Since joining the team in 2019, she has been involved in diverse projects and publications aimed at delivering sustainable, innovative and equitable tools in the field of urban mobility.

Florencia Andreola, Co-Founder
Sex & the City APS (Italy)

Florencia Andreola is an independent researcher with a Ph.D. in History of Architecture (University of Bologna). She is interested in sociology, politics, and the various disciplines that hybridize research on architecture and the city. She is co-founder of the association Sex & the City research project, which investigates the city from a gender perspective. She is the co-author, with Azzurra Muzzonigro, of “Milan Gender Atlas” / Milano Atlante di genere (LetteraVentidue, 2021). She curated Disagiotopia. Malessere, precarietà ed esclusione nell’era del tardo capitalismo (DEditore, 2020) and co-curated Milano. L’architettura dal 1945 a oggi (Hoepli, 2018), Backstage. L’architettura come lavoro concreto (Franco Angeli, 2016), and Guida all’architettura di Milano 1945-2015 (Hoepli, 2015).

Azzurra Muzzonigro, Co-Founder
Sex & the City APS (Italy)

Azzurra Muzzonigro is an architect, curator, and independent urban researcher with a Ph.D. in Urban Studies at the Roma Tre University. She teaches Urban Design in various universities, including Politecnico di Milano and Domus Academy. She holds a Masters in Building and Urban Design in Development from Bartlett UCL. She is co-founder of the association Sex & the City research project, which investigates the city from a gender perspective. In June 2015, she founded Waiting Posthuman Studio, a multidisciplinary research platform about art, architecture, urban planning and philosophy. She is the co-author of “Milan Gender Atlas” / Milano Atlante di genere (LetteraVentidue, 2021) with Florencia Andreola and Costruire Futuri, and Migrazioni, Città, Immaginazioni (Bompiani, 2018) with Leonardo Caffo.

Gerardo Carpentieri, Research Fellow
TeMA Lab, Università degli Studi di Napoli Federico II (Italy)

Gerardo Carpentieri is an engineer with a Ph.D. in Civil Systems Engineering at University of Naples Federico II. He teaches Land Use Planning at the Department of Civil, Architectural and Environmental Engineering of the University of Naples Federico II.

Carmen Guida, Postdoctoral Researcher
TeMA Lab, Università degli Studi di Napoli Federico II (Italy)

Carmen Guida is an engineer with a Ph.D. in Civil Systems Engineering at University of Naples Federico II and a Research Fellow of Land Use Planning at the Department of Civil, Architectural and Environmental Engineering of the University of Naples Federico II.

Jim Walker, Founder
Walk21 Foundation (UK)

For more than 20 years, Jim Walker has been a champion of research, policies and projects that enable people to enjoy walking in safe, attractive and accessible environments wherever they live. His focus is increasingly on supporting the needs of women, children, the elderly, people with disabilities and those with low incomes – especially in low and middle income countries – who rely on walking as their essential mobility.

Urban Landscapes in the Green Decade: in Conversation with Andrea Balestrini

Andrea Balestrini is a landscape planner with experience in nature-based solutions, public space design, and landscape governance within international R&I projects and consultancies. He studied at Politecnico di Milano and the University of Stuttgart. During his studies, he focused on the topic of landscape recovery and water-sensitive urban design in European cities as well as metropolis such as Seoul, Cairo, and Lima, where he worked and developed his diploma thesis with the Municipal Office for Green Areas and Parks (SERPAR) in collaboration with the research project Future Megacities, local universities, and NGOs. His professional background gathers both academic experiences and professional engagement in strategic plans, landscape masterplans, management of cultural landscapes, and climate adaptation. Since 2014, he works at LAND where he leads the LAND Research Lab®, a think tank for applied research and innovation in landscape and territorial transformation processes.

Q: How can we design inclusive and attractive urban landscapes in the age of uncertainty?

AB: In the slow and erratic post-pandemic recovery, cities are facing social and economic uncertainty sharpened by general political instability, cultural shift, and climate emergency. Most of these challenges find a testing ground in public open spaces. Established parks, streets, and squares need to be adapted to new uses and changed climate conditions; new open spaces are required to make cities more attractive and ecologically sound. On one hand, resources are lacking and citizens’ consensus is more and more crucial to ensure social and economic sustainability. On the other hand, traditional top-down design approaches and static masterplans failed in delivering adaptable solutions for quickly changing socio-economic trends; moreover, they were not able to involve communities and include a broader plethora of stakeholders willing to activate and take care of spaces. Nowadays, urban regeneration areas provide an unprecedented opportunity to rethink public spaces and test new collaborative and climate-friendly dynamics.  Meanwhile uses are a hot topic in urban regeneration practices and research while complex masterplans are being developed, as they allow, with their co-creation approach, all the key stakeholders to be involved in the city-making process from the early stages. From tactical urbanism initiatives – such as Piazze Aperte by the City of Milan – to experimental programs – such as the EU-funded project T-Factor – the temporary uses of urban regeneration areas leverage local relational ecosystems, thus unlocking the potential of people and places by activating cultural initiatives, educational programs, leisure events, and enhancing urban spaces. Urban regeneration areas anticipate changes and provide chances for successful and innovative Public-Private Partnerships (PPPs), helping public administrators, developers, and city makers in envisioning more resilient masterplans.

Q: How can we design livable open spaces that comply with sustainability goals?

AB: We live in the ecology and energy transition, a global multi-level and inter-disciplinary effort to reduce emissions, restore nature, and boost a circular regenerative economy in compliance with the Sustainable Development Goals of the United Nations. Landscape design plays a crucial role in making cities more environmentally friendly by implementing green areas and resilient open spaces. However, sustainability must be measurable and visible to be accountable and communicated to the broader public. Ecosystem services can be quantified and contribute to sustainability certifications and decision-making processes. Several R&I projects have been financed by the European Commission – such as UNaLab – to define and assess how ecological and climate solutions are able to impact societal challenges. Combining this knowledge with data-driven planning procedures, LAND developed a methodology called LIM landscape information modelling®, a landscape approach to Building Information Modeling (BIM) capable of steering informed decisions for greener and healthier cities by combining the application of BIM, GIS, and visualization tools on a database specially developed for this purpose. LIM allows quantifying environmental parameters, simulating green growth and impact by providing spatial inputs, and build a sustainability pre-assessment to support design decisions, approval processes, and future maintenance.

Q: How can we effectively include nature in public space planning?

AB: Through their physiological processes, plants and animals provide a variety of ecosystem services and multiple benefits that humankind and the environment receive directly or indirectly. These include provisioning with 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). Urbanized areas are expanding, increasing the climate vulnerability of our environment, and urban green areas can help mitigate this phenomenon. However, they need to be planned correctly to effectively provide the expected services. The European Commission introduced Nature-based Solutions as “Solutions that are inspired and supported by nature,  cost-effective, and that simultaneously provide environmental, social and economic benefits,  and help build resilience. Such solutions bring more diverse and natural features and processes into cities, landscapes, and seascapes through locally adapted, resource-efficient, and systemic interventions.” Therefore, we must rethink urban planning and its definition of public spaces; we need to identify challenges, target ecosystem services, select solutions, and adapt them to the context through a co-creation process and a systemic approach. Cities should no longer belong only to humans; instead, they should embrace nature as a valuable ally to make our everyday urban spaces more livable.

Video Analytics for the Assessment of Street Experiments: The Case of Bologna

In March 2022, Fondazione Innovazione Urbana and the Municipality of Bologna built a new public space for children in Via Procaccini (Bologna) near a middle school (see Figure 1), using the approach of tactical urban planning and participatory design. The area chosen to build the new square was the subject of a mobility study conducted by Systematica and Transform Transport. The objective of the study was to monitor pedestrian and vehicular flows, with the aim of detailing specific patterns of space use. The monitoring was carried out through observations supported by a camera and video analytics techniques. The research produced a series of analyses related to observed flows in the area during the pre/ post-intervention phases. This was meant to support the iterative design process based on the tactical urbanism approach.

Figure 1 Aerial view of the new public space for children in Via Procaccini, Bologna (photo by Margherita Caprilli)


Planning infrastructures and services for sustainable urban mobility is one of the main challenges for European cities (Buhrmann, S., Wefering, F., Rupprecht, S., 2019), which are increasingly facing problems of traffic congestion, road safety, energy dependence, and air pollution (United Nations, 2016). In this context, planning efforts are focusing on walkability (Abley & Hill, 2005; Speck, 2013), namely how friendly the urban environment is for walking or spending time in public spaces. Improving the level of walkability of an urban area implies the presence of a comfortable, safe, and barrier-free pedestrian infrastructure, as well as people-friendly environments that allow opportunities for social interaction (Gehl, 2013).

The focus on walkability, which began with the principles highlighted in the European Charter of Pedestrian Rights issued by the European Parliament in 1981, has become even more evident given the unprecedented effects of the Covid-19 pandemic on urban mobility (Transform Transport, 2022). In July 2020, the European Commission (European Platform on Sustainable Urban Mobility Plans, 2020) provided guidelines for the implementation of pedestrian mobility planning interventions (e.g., extension of sidewalks, queue management in transportation infrastructure, creation of temporary public spaces, etc.) aimed at ensuring that citizens can access basic services on foot (i.e., 15 Minute Cities) (Moreno et al., 2021).

Although traditional approaches to the study of pedestrian mobility tend to focus on the spatial dimension (Annunziata & Garau, 2020; Steinfeld, 2011), the individual characteristics of pedestrians have a significant impact on the perceived level of walkability. As highlighted by the 2030 Agenda for Sustainable Development adopted by the United Nations Member States (United Nations, 2016) (i.e., SDG 11.2-Sustainable Transport for All), urban mobility should be designed to be more inclusive with respect to the needs of those in vulnerable situations, such as the elderly, children, and people with disabilities.

In this context, the research focuses on evaluating walkability for children aged 5-13 years, mainly middle school students. The study is part of recent efforts in designing safe, comfortable, and livable streets and public spaces for children (Peyton, 2019; Aerts, 2018; Danenberg, Doumpa & Karssenberg, 2018). Indeed, walkability for children includes the opportunity to play freely in the open air, walk independently and safely, and develop a sense of belonging to their neighborhood (Krysiak, 2020).

The study was conducted by Systematica and Transform Transport, in collaboration with the Fondazione Innovazione Urbana and the Department of Urban Planning, Housing and Environment of the Municipality of Bologna, with the aim of supporting the planning of sustainable and inclusive mobility strategies in the city of Bologna (see Figure 2). The first phase of the research (i.e., macroscopic scale) began in December 2020 with the objective of assessing the level of pedestrian accessibility of public services dedicated to children’s needs through GIS analysis of georeferenced data made available by the Geoportal of the city of Bologna. The results of this phase of the research were then presented on May 13, 2021, at the 6th Biennale dello Spazio Pubblico (Abdelfattah et al., 2021).

The second phase of the research (May to September 2021) focused on assessing the level of walkability for children in the city of Bologna, according to the following criteria: (i) accessibility of services; (ii) comfort of pedestrian infrastructure; (iii) road safety; and (iv) attractiveness of public spaces. The results of this GIS analysis (Gorrini et al., 2022) focused on the Navile neighborhood (i.e., mesoscale), selected by the Municipality of Bologna for the design of a new public space for children.

The third phase of the study (February to April 2022) involved Fondazione Innovazione Urbana and the Municipality of Bologna for the design of a new square (i.e., microscale), which was implemented in March 2022 through the approach of tactical urbanism and participatory design as part of the EN-UAC project “EX-TRA – EXperimenting with city streets to TRAnsform urban mobility” (No. 99950032). In particular, the new square was built in an area used for unregulated parking located in Via Procaccini (Navile), nearby a middle school.

The area was the subject of the study conducted by Systematica and Transform Transport, aimed at monitoring pedestrian and vehicular flows during the pre and post-intervention phases. The monitoring was carried out through camera-supported observations and automated image analysis techniques (Ibrahim, Haworth and Cheng, 2020; Barthélemy et al., 2019; Zhao et al., 2019). The study produced a series of temporal and spatial analyses related to the observed flows in the area (e.g., density maps, space use, average speeds, road crossing behaviors, etc.), with the aim of supporting the iterative design process based on the tactical urbanism approach.

Figure 2 Research activities schedule

Enabling Data and Methodology

Data Collection Planning

As part of the redevelopment of the area in Via Procaccini, a video data collection campaign was carried out with the aim of conducting a quantitative analysis of the use of the space during the pre- and post-intervention phases. Specifically, the data collection campaign involved monitoring the area for about two months, as follows:

  • Pre-intervention: from 07/02/2022 to 04/03/2022;
  • Post-intervention: from 02/04/2022 to 31/04/2022.

Video data collection was focused on three daily time slots, with the aim of analyzing space use and activation characteristics in relation to:

  • 7 am – 9 am: school entrance;
  • 1 pm – 3 pm: school exit;
  • 5 pm – 7 pm: spontaneous use of the area.

Data Collection Tools and Placement

Video data were collected using an EXIR camera with a wide-angle lens, an image quality equal to 1280×720 pixels, and a frame rate equal to 15 FPS (Frames Per Second).

The placement of video data collection equipment was guided by principles of flexibility and reproducibility of the study related to the possibility of relocating the equipment to new areas of intervention. In addition, it was determined by the need to obtain an overall shot of the space to determine its uses (see Figure 3).

Following these principles, the camera was installed on a pole at the corner of Via Procaccini and Via di Vincenzo (north side), at a height of about 3m and with a mean axis of framing parallel to Via Procaccini facing the area of intervention.

Figure 3 Screenshot example related to global space framing

Data Analysis Process

The data collected by the camera were processed in four stages:

  1. Recognize and track objects in the videos using deep learning algorithms;
  2. Transpose the recognized objects from perspective view to 2D view through a process of geo-referencing the frames;
  3. Process the geo-referenced data and develop summary metrics for characterizing and quantifying the recognized object events in the area;
  4. Visualize and map the developed metrics using GIS.

Object Recognition and Tracking

The first phase of the analysis was carried out using deep learning algorithms for object recognition and tracking to quantify pedestrians and vehicles in the area. In particular, two open source algorithms were used: YOLO v5 and Deep Sort.

YOLO-You Only Look Once (Jocher et al., 2021; Gutta, 2021) is an object recognition algorithm based on convolutional neural networks (i.e., CNN). In the context of this study, an open source model of Yolo v5 was used, trained on images taken from CCTV cameras in the city of Montreal.

Deep Sort is an object tracking method based on a recursive application of the Kalman filter, which allows a unique ID to be assigned to the same object recognized in multiple images. More specifically, the Kalman filter uses estimated information about an object’s position and velocity to make predictions about its future location.

The result of the object recognition and tracking process is the creation of a text document in which information about the objects is made explicit in an anonymous form. Each line of the document describes the video frame, object ID (expressed in numeric characters), object class (i.e., pedestrian, vehicle), pixel X, and pixel Y.


The second phase of the analysis focused on translating the image coordinates related to the objects identified in the videos (i.e., pixel X, pixel Y) into geographic coordinates (i.e., latitude, longitude). The objective of this phase is to eliminate perspective distortion of the images by locating pedestrians in the target area.

The methodology uses the QGIS implementation of the Thin Plate Spline algorithm as a transformation technique, with Ground Control Points selected to anchor points in the perspective image. The procedure is repeated through an automatic matching algorithm in each frame of the video.

Data Preprocessing and Metrics Development

The third phase of analysis included data validation/ preprocessing and the development of summary metrics designed to quantify and characterize the observed area. In this phase, data from individual videos were merged and spatially discretized on a 1m x 1m grid.

The merged data were then filtered, with the aim of mitigating the influence of factors that systematically prevent object recognition in certain areas of space (i.e., under-sampling) or that promote repeated object recognition (i.e., over-sampling). These may be related to geometric framing configurations or weather and lighting conditions.

To this end, cells that had a cumulative value of recognized objects below the first percentile or above the ninety-ninth percentile of the data distribution were filtered out of the analysis at each time level.

In addition, for the analyses on average pedestrian speeds observed in the area, an additional data validation process was carried out by filtering out objects with a speed greater than the ninety-eighth percentile from the distribution of values. 

GIS Analysis and Mapping

The fourth phase of the analysis focused on the production of maps and graphics obtained through the open-source software QGIS v3.26. Maps describing the average use of the area observed in the phase before and after the tactical urbanism intervention were elaborated (see Figure 4).

Data Analysis Metrics

Data metrics were then implemented, describing the use of the new public space in Via Procaccini. These are discretized spatially and temporally as follows:

  • Spatial discretization aims at aggregating pedestrian patterns in the study area on a 1m x 1m grid with the following classification of cells:
    • Public Space:
      • Games;
      • Seating;
      • Benches.
  • Temporal discretization aims at identifying cumulative and granular use patterns with a logic of progressive disaggregation such as:
    • Average pre-/ post-intervention days;
    • Weekdays/holidays;
    • Time slots (7:00-9:00 am, 1:00-3:00 pm, 5:00-7:00 pm);
    • 15-minute intervals.


Cumulative Permanence Time

Cumulative permanence time, expressed in equivalent minutes, quantifies the total number of minutes spent in the study area, discretized on a 1m x 1m grid. This metric made it possible to identify the most heavily trampled areas, hierarchizing the intervention area in relation to its uses. Cumulative dwell time is calculated as the sum of detected pedestrians in a cell, cumulated at different temporal disaggregation.

Average Speed

Analyses of average speeds have made it possible to describe how pedestrians and vehicles use urban space, providing spatial and behavioral classification into:

  • Places of rest and play;
  • Crosswalk areas;
  • Areas of vehicular circulation.

Average speed is a cell-based metric, calculated as the mean velocity in a cell at different temporal disaggregation. Velocities were computed through the Python library MovingPandas which enabled the extraction of trajectories’ attributes for the detected pedestrians.


Times of Activation

The first phase of analysis focused on the study of activation times of Via Procaccini in the phases before and after the tactical urbanism intervention. This analysis focused on weekdays and holidays, divided into three time slots identified as moments characterized by entry to school (7:00 am – 9:00 am), exit from school (1:00 pm – 3:00 pm), and spontaneous use of the area (5:00 pm – 7:00 pm). The three time slots were then divided into 15-minute intervals with the aim of characterizing at a granular level the patterns of use and frequentation of the area.

The level of use was quantified as cumulative dwell time, expressed in equivalent minutes  with the aim of estimating the number of minutes spent in the study area. More specifically, equivalent minutes correspond to the sum of minutes spent on each cell of the 1m x 1m grid onto which the area is discretized at each time interval.

Charts 1 and 2 summarize the results of the analysis for weekdays and holidays, depicting the cumulative occupancy time in the period before and after the redevelopment intervention, and the percentage difference in the time intervals. The cumulative occupancy time increases in most of the time intervals following the intervention, confirming the transition from a crossing area to a rest and play area.

Chart 1 shows the trends for weekdays. Significant percentage use level differences are shown between 7:30 am – 7:45 am (+87%) and between 2:15 pm – 2:30 pm (+88%). These results denote a tendency to stop more in the area during the times before and after school hours. In addition, there is a strong percentage increase in the 5:00 pm – 7:00 pm time slot (+77%), in which the area, following the redevelopment, is activated in relation to dynamics no longer related to school functions.

Chart 1 Cumulative dwell time per 15 minutes, pre/ post-intervention, weekdays

Chart 2 summarizes the results of the analysis for holidays, highlighting patterns of use not related to school functions. Growth peaks in cumulative dwell time emerge, reaching +270% in the 1 pm – 3 pm range. There is also an increase in dwell times in the afternoon slot, with an average increase of +33%. In contrast, the percentage differences related to the morning describe an increase in use related to minimal dwell times and may be related to the seasonality of data collection.

Chart 2 Cumulative dwell time every 15 minutes, pre/ post-intervention, holidays

Space Utilization

The space utilization of the new public space in Via Procaccini was quantified through metrics describing cumulative dwell time and average speeds. The cumulative dwell time, expressed in equivalent minutes, made it possible to quantify the number of minutes spent in the study area discretized on a 1m x 1m grid and identify the most trampled areas, thus hierarchizing the intervention area in relation to its uses and describing the overall change in space use by its users.

In particular, the analysis of pedestrian flows was aimed at quantifying the presence of pedestrians and the new spatial configurations defined because of the redevelopment. The results show 43% growth in the cumulative dwell time recorded in the area following the redevelopment intervention. Strong changes in the spatial distribution of pedestrians in the study area are also highlighted.

Figure 5A shows the pedestrian use patterns of Via Procaccini in the phase before the redevelopment of the area. Two main passage corridors are highlighted at the two sidewalks on Via Procaccini and a parking area on the sidewalk between Via di Vincenzo and Via Procaccini. Three crossing corridors complementary to the crosswalks between Via Procaccini and Via di Vincenzo are also noted, corresponding to the extension of the sidewalks (1, 2) and the diagonal visual axis of the area (3).

Figure 5B represents the new pedestrian dynamics that originated because of the redevelopment. There is a general increase in dwell time (+43%) and a shift in the preferred place for stopping and playing, which is now identified in the new public space.

Natural crossing corridors (1, 2 in Figure 5A), integrated into the redevelopment design, are activated again; new ones also emerge (1, 2, 3 in Figure 5B). 1, 2 are related to the placement of benches in public space, which blocks the diagonal axis of the area, lengthening the diagonal crossing (1) and promoting passing phenomena adjacent to the square on the side of the roadway (2). Finally, the crossing area at the crosswalk between Via Procaccini and Via di Vincenzo (3) widens, including diagonal flows connecting the square and the school.

In addition, the analysis of average speeds aims to characterize the new modes of use of public space. Figure 6 (A and B) presents the average pedestrian speeds recorded in the study area during the pre- and post-intervention phases. In general, there is a decrease in speeds throughout the area, with a sharp decrease in the new public space. There are also higher speeds in the areas corresponding to the roadway in both phases of the study.

Finally, an increase in average speed at the crosswalk between Via Procaccini and Via da Faenza is shown in Figure 6B, followed by a decrease at the sidewalk on Via Procaccini.

Public Space

Figure 7 shows the spatial configurations of the new public space originated from the tactical urbanism intervention. The cumulative dwell time of the area, transformed from a crossing area to a rest and play area, increases by 216%. It is also noticeable (see Figure 7B) that the crossing corridors in the area, which were used before the intervention, remain active. However, new use characteristics emerge related to the present furniture (1, 2 games, 3, 4 seats, 5, 6 benches), which have the most significant cumulative dwell times in the area.

More specifically, Table 1 lists summary metrics describing the use of the three types of furniture in relation to the overall use of the new public space during the post-intervention phase. Seating has the largest increase in average permanence time compared to the new public space (+100%), followed by benches (+82%) and games (+79%).

Table 1 Public Space/ furniture, post-intervention

Chart 3 shows the delta of use of the furniture with respect to the overall space of the new public space in the three time slots of analysis. Strong percentage growth in the use of the three objects can be seen on weekdays, where benches and seating show the largest changes, particularly in the afternoon (benches +203%, seating +195%, games +163%). On holidays, on the other hand, an inverse trend emerges in the morning, where the use of the area related to crossing needs prevails. In the daytime and afternoon, on the other hand, strong positive deltas emerge for benches (13-15 +176%, 17-19 +64%) and games (13-15 +116%, 17-19 +37%).

Chart 3 Delta [%] time of stay between furniture and new public space, post-intervention

Figure 8 presents the spatial characteristics related to average speeds in the new public space. This presents a major lowering of average speeds (-42%), related to the new play and parking functions within it. Four crossing corridors in the area, complementary to the play areas identified in the furniture analysis, are highlighted, where the average speeds present values above 0.82 m/s.

Final Remarks and Future Work

Results made it possible to quantify through a critical reading the effectiveness of the urban regeneration intervention implemented in Via Procaccini. The proposed data-driven design approach is presented in this case through the monitoring of the pre/ post-intervention phases.

Figure 9 Analysis of successes (in green) and critical issues (in red) related to pedestrian use of the intervention

As shown in Figure 9, space use is in direct relation to the form and functions present. Previous analyses, in fact, have highlighted the delta between pre/ post-intervention phases and the role that furniture plays in influencing dwell time. By analyzing the maps with a critical view, it is possible to identify the successful and critical elements of this intervention and use these observations to inform subsequent design phases.

In the case of cumulative dwell time, which measures the use of the different areas of the new public space, the flows and infrastructure do not always coincide. The crosswalks across Via Procaccini are not perceived by the flows as the only possible place to cross the street. In particular, the crosswalks at the height of Via di Vincenzo are only partially used by pedestrians crossing the street; most pedestrians use the space in direct connection with the centerline of the street, cutting diagonally across the roadway. In parallel, the crosswalks at the height of Via da Faenza are not particularly used. In contrast, the crossings parallel to Via Procaccini are used by most users, as is the sidewalk opposite the public space, which manages to effectively channel flows.

In addition to the crossings outside the crosswalks, it is also possible to identify a flow parallel to the new public space, outside the planters, which follows the prevailing direction of travel in the widening. This alternative route occurs within the roadway and often in the opposite direction of travel, remaining outside the impassable furnishings of the pedestrianized space.

The elements of success and criticality highlighted in Figure 9 contain information that is extremely useful to a possible subsequent design phase. The shift from a quantitative approach, based on the analysis of collected data, and a synthetic/ design approach is the basis of data-driven design: the goal is to maximize the effect of positive interventions and resolve the critical issues found. The ideal approach to this type of design is the construction of an iterative process, which quantifies each intervention separately and whose design part is repeated after each monitoring phase, creating a continuous circle of hypotheses and solutions.

The results of this research have been presented at the 22nd International Walk21 Conference on Walking and Liveable Communities, 19-23 September 2022, Dublin (Ireland).


We thank Fondazione Innovazione Urbana and the Municipality of Bologna for their fruitful collaboration. An extended Italian version of the study report is available at this link. The analyzed data were treated according to the GDPR-General Data Protection Regulation (EU, 2016/679). This research didn’t receive any specific grant from any funding agency in the public, commercial, or not-for-profit sectors.


Abdelfattah, L., Boni, G., Carnevalini, G., Choubassi, R., Gorrini, A., Messa, F., Presicce, D. (2021). A user-centric approach to the 15-minute city. Examining children’s walkability in Bologna. In: Proceedings of the 57th ISOCARP World Planning Congress 2021, 8-11 November 2021, Doha (Qatar), pp. 582–592. Available at:

Abley, S., Hill, E. (2005). Designing living streets-a guide to creating lively, walkable neighbourhoods. Living Streets.

Aerts, J. (2018). Shaping urbanization for children: A handbook on child responsive urban planning. UNICEF. Available at:

Annunziata, A., Garau, C. (2020). A literature review on walkability and its theoretical framework. emerging perspectives for research developments. In: International Conference on Computational Science and Its Applications, Springer. pp. 422–437.

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.

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:

City of Montreal (2021). Annotated images taken from the video stream of traffic cameras. Information Technology Department. Available at:

Danenberg, R., Doumpa, V., Karssenberg, H. (2018). The City at Eye Level. STIPO Publishing. Available at:

European Platform on Sustainable Urban Mobility Plans (2020). COVID-19 SUMP Practitioner Briefing. CIVITAS SATELLITE CSA. Available at:

Gehl, J. (2013). Cities for people. Island press.

Gorrini, A., Presicce, D., Messa, F., Choubassi, R. (2022, submitted). Walkability for Children in Bologna: Beyond the 15-minute City Framework. Journal of Urban Mobility.

Gutta, S. (2021). Object Detection Algorithm — YOLO v5 Architecture. Medium. Available at:                                                                                                                            

Ibrahim, M. R., Haworth, J., & Cheng, T. (2020). Understanding cities with machine eyes: A review of deep computer vision in urban analytics. Cities, 96, 102481. https://doi:10.1016/j.cities.2019.102481

Jocher, G., Chaurasia, A., Stoken, A., et al. (2021). ultralytics/yolov5: v5.0 – YOLOv5-P6 1280 models, AWS, and YouTube integrations (v5.0). Zenodo.

Krysiak, N. (2020). Designing Child-Friendly High Density Neighbourhoods. Transforming our cities for the health, wellbeing and happiness of children. Cities For Play. Available at:

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, 93–111.

Peyton, A. (2019). Designing Streets for Kids. NACTO-Global Designing Cities Initiative. Available at:

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

Steinfeld, E. (2011). Universal Design Handbook – Second Edition. McGraw-Hill Publishers.

Transform Transport (2022). Shifting Paradigm: the impact of Covid-19 on transport planning (second edition). Fondazione Transform Transport ETS. Available at:

United Nations (2016). Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations. Available at:

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.

Deep Learning Video Analytics to Assess VGA Measures in Public Spaces

Since the introduction of the Social Logic of Space (Hillier and Hanson, 1984) and the further developments of Space Syntax theories during the following decades (Hillier et al., 1996; Hillier, 2007), the proposed methodologies have been proven effective to analyze the space in his physical configuration. In this regard, the study proposes the application of Visibility Graph Analysis (VGA) measures to Piazza Duomo, one of the busiest public squares in Milan, Italy. These metrics were tested in relation to the pedestrian movements measured through remote sensing technologies. Deep-learning video-analytics methods were used to detect the pedestrian occupancy in five different moments of the day (see Figure 1). Detections were then reprojected on the plane to obtain the spatial utilization, discretized on the grid used to calculate the VGA metrics. Then, a series of correlation analyses tested the grid-based analyses against the cumulated footfall and a movement metric. The results show how the correlation between the VGA measures and the pedestrian movements varies greatly at different radii, considered through the variation of the restricted visibility parameter, ranging from negative weak correlations to positive moderate correlations for the cumulative density, and from positive moderate correlations to weak correlations for the turnover metric. The results, in line with other recent research works, shows how pure VGA measures can only partially describe an open public space, posing an interesting step in the direction of future works, aimed at the development of a more complex model based on the Space Syntax theories.

Figure 1 Analyzed footfall data (08:00) reprojected on the grid and superimposed on the footage frame

Literature Review

Space Syntax theories have been tested since their introduction in the effort to correlate the syntactic measures with empirical evidence, such as the recorded flows of pedestrians or vehicles. In the case of the Grid-based analysis (i.e., VGA), the metrics were tested in various settings. In one of the most notable example, an urban environment case study is represented by the experiments made on the Barnsbury area in London (Turner, 2003), based on 116 gate counted pedestrian movements. More recently, a building-based case study is used as setting for an occupancy analysis in an educational building (Tomé et al., 2015). In this case, video analytics is used to identify moving and standing individuals, to analyze the utilization patterns of the space.

Empirical pedestrian gathering techniques can be divided in two main categories: automatic and manual systems (Conroy, 2001). Both can rely on sensing technologies to quantify space utilization, as outlined in Table 1 (van Nes et al., 2021). Among them, videos represent the most versatile mean of gathering, as they can be mounted at a fixed location (i.e., snapshots, general movement traces, gate counts, mean occupancy), or mobile (i.e., pedestrian following).

Table 1 The table show the classical data gathering methods and the available data outputs.

Table 2 classifies the technologies presented in Table 1 based on automatic data counting methodologies, purposes and limitations and Space Syntax analyses typologies. Data counting methodologies can be divided in two categories:

  • Algorithm-based counting methods, namely those that rely on additional analyses to retrieve data (i.e., images, videos);
  • Sensor-based counting methods, which depend on the technology embedded in the sensor to retrieve data (i.e., Wi-Fi, Bluetooth, GPS/ LBS technologies).

The analysis purposes categorization, see Table 2, is structured in three groups, enabling the study of:

  • Spatial patterns, pedestrian behaviors, and movements in an area (e.g., grouping, sitting, etc.);
  • Densities and the quantification of the footfall around hotspots;
  • Routes and the analysis of routing preferences (e.g., most walked paths, etc.).

Limitations of sensors can be classified as:

  • The analysis is restricted to a limited area, as for images and videos (i.e. extension);
  • The analysis is restricted to a limited number of people, as for Wi-Fi, Bluetooth, GPS technologies (i.e., users).

Lastly, the sensors are classified based on which Space Syntax analyses can be linked to them:

  • Line-based analyses relate to route preferences and traces (e.g., videos, GPS/LBS);
  • Grid-based analyses mainly relate to densities and patterns (e.g., images, videos, Wi-Fi, Bluetooth).
Table 2: The table shows data gathering techniques and automatic counting methods.


Case study definition and VGA metrics selection

Piazza Duomo (Milan, Italy), is chosen as a case study as it is structured as a pedestrian space, interrupted by a limited number of elements, as the subway entrances. In the analyses, the square is studied together with the area surrounding it, with a buffer of 250 meters from the center of the square, to create an uninterrupted linear system. The drawing used in Depthmap is obtained from a collection of open geodata, which have been enriched to include pedestrian spaces not visible from satellite. Figure 2 shows the extent of the pedestrian are considered for the analysis.

Figure 2 Extent of the area considered for VGA (left) and drawing used on Depthmap (right), with the 2x2m grid superimposed on the middle part of the square.

The analyzed space is configured as an open-geometry area, that is expected to be used in diverse manners, as follows: (i) users crossing the central area to get to the other side of the square; (ii) users walking slowly and stopping to appreciate the architecture of the square; and (iii) users walking in relation to urban functions (e.g., subway accesses, shopping venues, etc.). VGA metrics chosen to describe the area are listed in Table 3, these are calculated with diverse restricted visibility distances (i.e., NR – Not Restricted, 150m, 100m and 50m).

Table 3 Selected VGA metrics, with respective properties and short description, for an extensive explanation see Koutsolampros et al. 2019.

Video description and analytics techniques

Five videos were analyzed to obtain pedestrian footfall data. The footage was recorded on July 15, 2021, in five different moments of the day, specifically at 08:00, 11:00, 12:45, 15:00 and 18:00, each of them depicted a 30-minute time interval, with a size of 1920×1080 pixels and a frame rate of 15 frame per second (FPS).

Yolov5 (Jocher et al., 2021) with DeepSORT integration was used to detect pedestrians in the square, as visible in Figure 3. An open-source model was used, which was trained on webcam images in Montreal, Canada (City of Montreal, 2021), obtaining a mean Average Precision (mAP) equal to 0.809 in pedestrians’ recognition on the original training set.

Figure 3 Sample frame of the used footage, without detection (right) and with detected pedestrians (left).

Video georeferencing

A georeferencing technique was implemented, to estimate the geographical position of the distribution of pedestrians in the square. The methodology uses the QGIS implementation of the Thin Plate Spline algorithm as a transformation technique, with 240 Ground Control Points selected to anchor points in the perspective image. The procedure is repeated through an automatic matching algorithm in each frame of the videos.

Detections’ measurements

The georeferenced pedestrian coordinates (x, y) in each frame were then associated to the corresponding cell of the VGA grid, in Figure 2. Then, a mean occupancy value, expressed as people/sec, was computed, averaging the number of detections that fell into the same grid cell within fifteen frames. Finally, two metrics were computed:

  • Cumulated footfall measure, defined as the cumulative sum of the mean occupancy value in thirty seconds bins;
  • Turnover variation measure, computed as the sum of the difference in occupancy in a cell every five seconds and the activation measure (i.e., measuring the frequency of activation of the cell), normalized on the mean occupancy of the cell.


Measured pedestrian footfall

The video analytics process allowed for the calculation of the occupancy of the space on the 2x2m grid. Since the footage was taken at different hours of the day, the footfall measured in the videos differs in magnitude and patterns, reflecting the varying nature of the public space. Figure 1 shows the detection collected during the 08:00 video, and Table 4 shows the results of the video analytics, including descriptive statistics to highlight the diversity of the results.

Table 4 Summary of the measured pedestrian footfall. The number of cells considered in the video is n = 2.060, meanwhile the number of cells included in the Footfall Data (FD) and Movement Data (MD) datasets is n = 1,921.

Then, outliers were removed from the distributions, considering the 1st and the 99th percentile of each video sequence and two metrics were defined:

  • Footfall Data (FD) is defined as the sum of the cumulated mean occupancy for the five videos. It represents the utilization pattern of the square, describing an average situation of different moments of the day (see Figure 4);
  • Movement Data (MD) is defined as the mean value of the measure of variation (Var) for the five videos. It represents the cells with a high turnover, namely the areas where the pedestrians don’t stop but generally keep moving (see Figure 5).
Figure 4 Map showing the Footfall Data (FD) on the grid, with the outliers highlighted in black.
Figure 5 Map showing the Movement Data (MD) on the grid, with the outliers highlighted in black.

VGA metrics

The VGA analyses were calculated for the area shown in Figure 2, for several maximum visibility distances (i.e., NR – Not Restricted, 150m, 100m and 50m), to compare the results and understand which distance can describe effectively an open public space. In Figure 6, it is possible to see how out of the ten selected metrics, three of them (i.e., I_Cm – Isovist Compactness, I_DM – Isovist Drift Magnitude, and I_O – Isovist Occlusivity) are not related to the maximum visibility distance parameters, since those are based on the un-restricted isovist’s shape. The other metrics show a consistent difference among the four distances, namely the transition from the large-scale topology to the open public space topology.

Figure 6 VGA metrics with the variation of the restricted visibility parameters. All the values are shown as regular intervals from the minimum (lighter colour) to the maximum value (stronger colour).

Correlation analyses

In this phase, the VGA metrics, with the variation of the restricted visibility distance parameter, and the Footfall Data (FD) were normalized, while the Movement Data (MD) was linearized. Then, using Pearson’s Correlation Coefficient, the datasets were analyzed with a series of correlations analyses. The results for the Footfall Data (FD) and the Movement Data (MD) are shown, respectively in Figure 7 and Figure 8.

Figure 7 Linear trend lines showing (when significant) the correlation results between the normalized Footfall Data (Z_FD) and the normalized values of the VGA metrics.
Figure 8 Linear trend lines showing (when significant) the correlation results between the linearized Movement Data (Z_MD) and the normalized values of the VGA metrics.


The correlation results for the Footfall Data (FD), visible in Figure 7, do not show strong correlations between the analyzed data. The values range from negative to positive correlations, but the absolute values are never higher than .376, in the case of the Isovist Drift Magnitude. The restricted visibility parameter influences results for (i) Connectivity, (ii) Through Vision, (iii) Visual Control, and (iv) Visual Integration (HH).

The correlation analysis results for the Movement Data (MD), shown in Figure 8 express different outcomes. The correlation coefficient is still moderate at best, such as the cases of Connectivity (.373 – NR and .411 – 150m), Through Vision (.373 – NR), Visual Clustering Coefficient (-.326 – NR and -.391 – 150m), Visual Control (.402 – NR and .468 – 150m), and Visual Integration (HH) (.396 – NR and .303 – 150m)

These results can be related to a case-specific utilization of the space for the Duomo case study. The touristic nature of the square and the presence of the subway accesses lead to a higher cumulative density in the middle part of the space, but, at the same time, the area utilized to move around draw some “desire lines”, especially near the gallery, that are also well represented in the VGA analyses. The Movement Data (MD) metric allow to clean the recorded movement data from the noise of people standing still in the space, narrowing the analysis to the actual flows.

In support of this statement, it is possible to see how the short-radius Visual Integration (50m), which shows a positive correlation of .326 with the Footfall Data (FD), is not linked to the topological shape of the larger environment, but it is very similar to results obtainable in a perfect square shape: higher values in the middle, with lower values toward the edges, also the Connectivity follow a very similar pattern. Meanwhile, the Movement Data (MD) is better correlated with the large-radius and, thus, showing how the environment shapes the presence of pedestrian flows. Finally, in the case of Through Vision, which can be regarded as an alternative to EVA agents for 360° field of view, the comparison between the Footfall Data (FD) and Movement Data (MD) shows much more consistent results in the latter case.

The outcomes of these analyses are in line with other recent research studies (Ericson et al., 2020; Koutsolampros et al., 2019), and show limitations in the application of VGA metrics in relation to different typologies of spaces. However, the proposed case study is a particularly complex environment chosen to stress the reliability of the VGA metrics in a space characterized by an inherent asymmetry of functions and the presence of strong attractors and generators, namely the subway accesses. These factors weight heavily in the pedestrian movements, greatly influencing the cumulated density as expressed in the FD dataset. On the other hand, the utilization of a “movement-based” metric, expressed in the MD dataset, allow to partially re-focus the analysis on the movements.

Conclusions and Future Work

The proposed case study is utilized to test the VGA metrics values, obtained by varying the restrict visibility parameter, in the central part of Piazza Duomo (Milan, Italy). The central portion of the public space was recorded through a webcam, and the footage was studied through video analytics techniques, measuring the pedestrian activity of the space discretized in a 2x2m grid.

The metrics and the pedestrian movements were tested in correlation analyses, showing weak to moderate correlation values. In this framework, the proposed Movement Data (MD) shows positive moderate correlation with VGA metrics usually associated to movements, namely Connectivity, Through Vision and Visual Integration (HH). As future improvement, this dataset can be refined by introducing tracking technologies in the video analytics techniques, associating a unique ID to each pedestrian, and recording its location and movement speed.

Future works include the application of the Agent Analysis (Turner, 2003), based on the VGA metrics, considering a series of access gates, acting as starting points of space exploration and the implementation of anchors-based metrics, specifically a metric value representing the distance from the main anchors. The weighting of the VGA metric, especially the Visual Integration (HH) with the distance from selected points could lead to a more reliable representation of the public space, in the effort to build a calibrated VGA model.

Lastly, future work will be focused on the possibility to further characterized pedestrian profiles by using proxemics, speeds and trajectories data collected through tracking video analytics techniques, in line with the conceptualization of different walking behaviors of single pedestrians and groups in (i) time driven pedestrians, (ii) space driven pedestrians and (iii) social driven pedestrians.

The results of this research work have been presented at the 13th International Space Syntax Symposium: Messa, F., Ceccarelli, G., Gorrini, A., Presicce, D., Choubassi, R. (2022). Deep Learning Video Analytics to Assess VGA Measures and Proxemic Behaviour in Public Spaces. In: Proceedings of the 13th International Space Syntax Symposium (13SSS), 22-24 June 2022, Bergen (Norway). Available at:


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.


City of Montreal (2021). Annotated images taken from the video stream of traffic cameras. Information Technology Department. Available at:

Conroy, R. (2001). Spatial navigation in immersive virtual environments. University of London, University College London (United Kingdom).

Ericson, J. D., Chrastil, E. R., & Warren, W. H. (2020). Space syntax visibility graph analysis is not robust to changes in spatial and temporal resolution. Environment and Planning B: Urban Analytics and City Science, 48(6), 1478-1494.

Hillier, B. (2007). Space is the machine: a configurational theory of architecture. Space Syntax.

Hillier, B., & Hanson, J. (1984). The Social Logic of Space. Cambridge, New York: Cambridge University Press.

Hillier, B., Major, M.D., Desyllas, J. Karimi, K. Campos, B., Stonor, T. (1996). Tate Gallery, Millbank: a study of the existing layout and new masterplan proposal. UCL (University College London): London, UK. Available at:

Jocher G., Stoken A., Chaurasia A., Borovec J., NanoCode012, TaoXie, Kwon Y., Michael K., Changyu L., Fang J., Abhiram V., Laughin, tkianai, xyNONG, Skalski P., Hogan A., Nadar J., imyhxy, Mammana L., … & wanghaoyang0106 (2021). ultralytics/yolov5: v6.0 – YOLOv5n ‘Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support (v6.0). Zenodo

Koutsolampros, P., Sailer, K., Varoudis, T., & Haslem, R. (2019). Dissecting Visibility Graph Analysis: The metrics and their role in understanding workplace human behaviour. In: Proceedings of the 12th International Space Syntax Symposium (Vol. 12). Available at:

Tomé, A., Kuipers, M., Pinheiro, T., Nunes, M., & Heitor, T. (2015). Space–use analysis through computer vision. Automation in Construction, 57, 80-97.

Turner, A. (2003). ‘Analysing the visual dynamics of spatial morphology.’ Environment and Planning B: Planning and Design, 30(5), 657-676.

van Nes, A., & Yamu, C. (2021). Empirical Data Collection and Analysis, and Connecting Data with Space Syntax. In A. van Nes, & C. Yamu (Eds.), Introduction to Space Syntax in Urban Studies (pp. 133-170). Springer.

Spatial Data for Low Carbon Cities: in Conversation with Iacopo Testi

Iacopo Testi is a research affiliate at the Massachusetts Institute of Technology (Senseable City Laboratory) where he conducts research at the intersection of urban sustainability, data science and artificial intelligence. In 2021, Iacopo was nominated “Spatial Data Scientist of the Year” by Carto, one of the global leaders in location intelligence. In October 2020, he was sponsored by the European Space Agency within its Network of Resources (NoR). He is currently an advisor at URBAN AI, an associate at The Smart City Association Italy (TSCAI), and an earth observation data scientist at RHEA Group. 

Q: There seems to be a growing interest in data-driven city planning. Could you briefly explain what it refers to and why it is important from your perspective?

IT: Data-driven city planning usually refers to the concept of taking urban decisions based on meaningful information. As human species, we are producing and collecting an unthinkable and unprecedented amount of data, that, translated into valuable insights, might assist city planners to adopt more informed decisions. Nevertheless, using data to study and design cities is nothing new, as shown by the “Teoria general de la urbanització”, published in 1867 by the pioneer of urbanism Ildefonso Cerdà.

From my perspective, a substantial difference today is made by the type of data we are capable of collecting and one of the biggest challenges that humanity is facing: climate change. As everyone has probably already heard and read, 55 percent of the world’s population lives in urban areas, a proportion expected to increase to 68 percent by 2050. Cities are major contributors to climate change, producing more than 60 percent of greenhouse emissions, but accounting for less than 3 percent of the Earth’s surface.

Therefore, collecting and using hyper-local information in cities is necessary to combat climate change. Furthermore, quantifying and measuring emissions is fundamental to promoting mitigation strategies and unfolding hyper-local solutions to global challenges.

Q: What do you think was the cultural impact of data trends during the Covid-19 pandemic?

IT: One of the first times in history that data was utilized to better comprehend a pandemic’s dynamic was around 1850 in London, when John Snow identified households experiencing death from cholera, discovering the source of contaminated water. Nevertheless, for several reasons, cultural impact did not gain momentum at that time.

During the Covid-19 pandemic, also due to technological advances, data played a fundamental role to shape policies and restrictions in many different countries. Many people all around the world realized the importance of data trends, as their daily habits would depend on those. Most importantly, data collection during the pandemic increased our behavioral awareness, as many individuals would shape their routine according to the number of infections.

Moreover, especially during the lockdown measures, data trends globally displayed a drastic decrease in business activities as well as consistent drops in greenhouse emissions and air pollution. Thanks also to satellite missions, that are constantly monitoring the atmosphere, data trends clearly revealed that it is necessary to find a balance between mobility patterns and emissions to consistently combat climate change, particularly in cities. The Covid-19 pandemic reminded us all that we need to change our daily habits if we want to have a tangible positive environmental impact on our planet.

Q: In your opinion, what is the importance of data visualization and mapping tools for cities and which potential developments do you see in the near future?

IT: Communication tools, visualizing urban data processes or results, constitute a necessary interface between the digital and physical world. Unfolding with visual clarity methodologies and outputs is a fundamental practice not only to convey meaningful messages that need to be comprehended by a non-expert audience but also to allow effective interactions with decision makers.

First of all, an important point to ensure the reliability of communication between the digital and physical world is the implementation of statistically robust validation processes that need to be applied to the data before they are considered for decision-making purposes. Moreover, visualizations need to reflect the intrinsic complexity of cities, considering both quantitative and qualitative information to involve the numerous urban stakeholders, such as communities, policy-makers, city departments, research institutions, subject matter experts, etc.

I tend to believe that a factor that will play a crucial role in the incoming years is the seamless interconnection between policies and data visualizations. This requires an omni-disciplinary approach that speaks the language of social scientists, policy-makers, engineers, designers, planners, etc. to turn interactive cartographies and visualizations into political (from ancient Greek – technique and art to rule cities) instruments to render our cities more environmentally, socially and economically sustainable.

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 London 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.


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.


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:

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:

Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., & Newth, D. (2015). Understanding human mobility from Twitter. PloS one, 10 (7), e0131469.

Kapadia, S. (2019). Topic Modeling in Python: Latent Dirichlet Allocation (LDA). Available at: 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.

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.

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


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).


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.


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.

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:

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:

European Environment Agency (2019). Air Pollutant Emission Inventory Guidebook. Technical guidance to prepare national emission inventories. Available at:

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:

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:

European Platform on Sustainable Urban Mobility Plans (2020). COVID-19 SUMP Practitioner Briefing. CIVITAS SATELLITE CSA. Available at:

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:

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.

Regione Lombardia (2014). Linee guida per la redazione di studi di Fattibilità. Available at:à.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.

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

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.

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:

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.


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.


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.


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.

Helbing, D. and P. Molnar (1995). Social force model for pedestrian dynamics. Phys. Rev. E, 51(5), 4282-4286.

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.

Kretz, T. (2015). On oscillations in the social force model. Physica A: Statistical Mechanics and its Applications, 438, 272-285.

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.

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:

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


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).


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 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.

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.

City of Melbourne (2021). Pedestrian Counting System. Available at:

City of Montreal (2021). Annotated images taken from the video stream of traffic cameras. Information Technology Department. Available at:

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.

Joint Research Centre (2019). The future of cities: opportunities, challenges and the way forward. European Commission Publications Office.

Geospatial Commission (2020). Unlocking the power of location. The UK’s Geospatial Strategy, 2020 to 2025. Cabinet Office. Available at:

Gutta, S. (2021). Object Detection Algorithm — YOLO v5 Architecture. Medium. Available at:

Jocher, G., Chaurasia, A., Stoken, A., et al. (2021). ultralytics/yolov5: v5.0 – YOLOv5-P6 1280 models, AWS, and YouTube integrations (v5.0). Zenodo.

Lin, T., Maire, M., Belongie, S.J., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context. ECCV.

Transform Transport (2018). Re-imagining Corso Buenos Aires. Systematica Srl. Available at:

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.

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.