UX Mobility: Multi-User Walkability Route Planner

UX Mobility: Multi-User Walkability Route Planner

Recognising the diversity of urban characteristics, street design, possible routes and user types, the research focuses on assessing the level of walkability in the city of Milan (Italy) according to the unique experiences and needs of different users. This is based on: (i) the collection of comprehensive indicators and data; (ii) the construction of geospatial datasets; and (iii) the development of a pedestrian mobility routing system. The aim is to provide valuable information for urban planners and policy makers, and to promote the analysis of more inclusive and adaptive urban environments that improve the quality of life for multiple urban users.

Introduction

User Experience (UX) refers to the quality of interaction between a user and a system that enables the user to achieve a specific goal (Dickson-Deane & Chen, 2018). The concept of UX has received considerable attention in the field of Human-Computer Interaction (HCI) and interaction design over the past decades (Hassenzahl & Tractinsky, 2006) but it has also found its way into the field of urban design. More specifically, one of the pioneering examples of UX applied to urban transport planning is Massimo Vignelli’s widely admired New York subway map from the 1970s (see Figure 1). This highly readable subway map was an attempt to simplify an earlier three-color version that was geographically accurate but visually confusing.

Figure 1 Massimo Vignelli, Joan Charysyn, Bob Noorda, Unimark International Corporation – New York Subway Map (1970-1972). Freely taken from: MoMA Highlights: 375 Works from The Museum of Modern Art, New York

The interest in urban experience through participatory activities and processes introduced, under a different lens, the concept of a user-centered approach as a central tool for mapping and analyzing urban user experience. Subjective and interactive mapping goes beyond conventional urban diagnosis and analysis by introducing a ‘user layer’ that captures the needs, expectations, feelings and perceptions of urban users, with a focus on those in situations of vulnerability. The results of this approach are not limited to traditional maps, but collaboratively build new (qualitative) data layers that can uniquely inform and enrich the quantitative data traditionally used to measure urban performance (Al Maghraoui, 2019).

The challenge for the UX Mobility project is to develop a strong base of quantitative metrics that are available and open, on which subjective parameters can be built iteratively and interactively by user segments. This approach recognises that each road has unique quantitative and qualitative characteristics that can enhance or hinder the experience of different users (Gehl, 2010).

For pedestrians, factors such as architectural barriers, slope, road surface quality, volume of traffic, pavement width, thermal comfort and many other quantitative data are already recognised (Transport for London, 2020). On this basis, the research focuses on the need to recognise multiple and different typologies of users with different characteristics. For example, parents with pushchairs will prioritize wide, unobstructed pavements and safe crossings, while people with disabilities will seek smooth surfaces, gentle ramps and continuous footpaths. Older people may prefer streets with lots of benches, optimal thermal comfort, minimal gradients and easy access to public transport, while children walking to school benefit from urban environments with wide sidewalks, a minimum number of safe road junctions, passage through pedestrianized areas and proximity to other schools. 

Identifying specific features in the urban mobility environment that can meet or challenge the needs of different user groups forms the basis of the UX Mobility project efforts to create a more user-friendly and engaging environment. The user-centered approach to urban design not only influences the form of the city, but also its future development, being more inclusive and adaptable, fostering a community where everyone can navigate and thrive with ease.

Enabling Data and Methodology

This research is based on three key methodological steps:

1) Data collection: The first phase of the project (ongoing) involves the selection and collection of key indicators of walkability related to multiple users. The basic parameters for evaluating these indicators are based on guidelines developed by the Walk21 Foundation, a globally recognised organization dedicated to promoting walkability. These indicators provide a solid framework for evaluating different mobility scenarios and have been instrumental in defining the preliminary data collection process. This includes the collection of comprehensive data on various factors that influence the pedestrian experience, such as: pavement width, surface quality, slope, perceived safety, thermal comfort and other quantitative parameters that can describe urban pathways. To further explore these indicators, the proposed UX Mobility Survey has been launched to collect comprehensive data on UX factors that influence the pedestrian experience (see Figure 2).

Figure 2 The survey questionnaire launched by the UX Mobility project

2) Geospatial dataset and index integration: The second phase of the UX Mobility project will involve the construction of the geospatial dataset based on the street graph of Milan (Italy) and will include each of the indicators described above. This street map will be constructed using the open source data of OpenStreetMap and then updated and refined using Geographic Information Systems (GIS). The different indices will be integrated into the graph according to specific methodologies based on the index typology. This integration will ensure that the map reflects the various indicators by providing a solid basis for the development of the routing system;

3) Routing System Development: The third phase of the project will involve the development of the routing system based on a computational framework developed in Python. The tool will process shapefiles, elaborate the network, normalize the data, interactively select the start and end point on the map, assign different weights from 0 to 1 according to the user’s preference, and finally generate routes that reflect the combination of weights selected by the user. The current code uses the flexibility and power of Python to manipulate geospatial data, providing a personalized first approach to route planning. The ability to explore different weights, record the preferences of different user segments in the route calculation and receive feedback on the results allows us to identify the value of each parameter and potential navigation solutions tailored to the specific needs and priorities of each user (Bast et al., 2016; Novack et al., 2018). Comparing the general shortest distance routes with the choice-based paths and due to the fine granularity of the dataset used in the proposed methodology, the routing system has the potential to show specific pedestrian behaviors. This allows for insights such as why pedestrians might choose one sidewalk over another (see Figure 3).

Figure 3 Mock-up example of the routing system proposed by the UX Mobility project

Expected Results and Future Work

The expected outcomes of the UX Mobility project concern the achievement of both practical and theoretical advances in mobility and urban design. Using a user-centered approach, this study aims to advance the current analytical model of the city by providing a preliminary and advanced insight into the different needs and preferences of different user groups (see Figure 4).

This includes a wide range of target groups, taking into account different criteria such as age (e.g., children, elderly), gender, multi-sensory needs (e.g., people with disabilities), and lifestyle preferences (e.g., tourists, general commuters).

The project aims to support the development of more inclusive and adaptable urban environments and provide valuable information for urban planners and policy makers in developing solutions to improve urban mobility infrastructure. By identifying and addressing the specific needs of new urban users, we aim to bridge the gap between quantitative data and subjective user experiences, promoting a more holistic approach to urban mobility. The development phase will explore future areas of research and development, including potential improvements to the routing system, further refinement of the indicators and wider application of the methodology to other urban areas.

Figure 4 User-specific paths based on urban mobility features

Acknowledgments

We are grateful to the Walk21 Foundation for supporting this research and sharing information on key indicators of walkability. The data analyzed was treated in accordance with the General Data Protection Regulation (GDPR) (EU, 2016/679). This research did not receive any specific grant from any funding body in the public, commercial or non-profit sectors.

References

Al Maghraoui, O. (2019). Designing for Urban Mobility-Modeling the traveler experience (Doctoral dissertation, Université Paris Saclay, COmUE). Available at: http://www.theses.fr/2019SACLC006/document

Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., … & Werneck, R. F. (2016). Route planning in transportation networks. Algorithm engineering: Selected results and surveys, 19-80. https://doi.org/10.1007/978-3-319-49487-6_2

Dickson-Deane, C. and Chen, H.L.O. (2018). Understanding User Experience. In: Encyclopedia of Information Science and Technology, Fourth Edition (pp. 7599-7608). IGI Global. https://doi.org/10.4018/978-1-5225-7598-6.ch117

Gehl, J. (2010). Cities for People. Washington, DC: Island Press. Available at: https://archive.org/details/cities-for-people-jan-gehl/page/n3/mode/2up

Hassenzahl, M. and Tractinsky, N. (2006). User Experience – A Research Agenda. Behaviour and Information Technology, 25(2), 91-97. https://doi.org/10.1080/01449290500330331

Novack, T., Wang, Z., Zipf, A. (2018). A System for Generating Customized Pleasant Pedestrian Routes Based on OpenStreetMap Data. Sensors. 2018; 18(11):3794. https://doi.org/10.3390/s18113794Transport for London (2020). The Planning for Walking Toolkit. Tools to Support the Development of Public Realm Design in London. Available at: https://content.tfl.gov.uk/the-planning-for-walking-toolkit.pdf

Transport for London (2020). The Planning for Walking Toolkit. Tools to Support the Development of Public Realm Design in London. Available at: https://content.tfl.gov.uk/the-planning-for-walking-toolkit.pdf