Urban Informatics to Understand Citizens’ Needs in Transport Planning
By Dr. Andrea Gorrini
The explosion of location-based data and data collection tools in recent decades has brought about a significant shift in urban and mobility planning. This shift has been driven by the ability to passively gather movement patterns on a large scale and synthesize them in real time, providing valuable insights into changes in mobility behavior. The ubiquity of geo-referenced data, made possible by digital miniaturization, has led to the decentralization of data collection, allowing for the collection of diverse and detailed movement data. This phenomenon, often referred to as “data in every pocket” (Choubassi & Abdelfattah, 2020), has had a transformative impact on how we document, collect, and understand mobility patterns (Batty, 2010).
The discipline of Urban Informatics has emerged as an evidence-based approach for urban planners to utilize technology-enabled spatial analysis methods. The development of advanced ICTs and the increasing availability of digitally widespread data sources have facilitated the creation of innovative assessment tools and metrics that provide valuable support to city decision-makers (Shi et al., 2021; Foth et al., 2011). The unprecedented volume, granularity, and diversity of data have overcome the limitations of traditional data collection methods, enabling a focus on specific communities, their behaviors, and their needs (Milne and Watling, 2019; Batty, 2013).
Conceptually speaking, Urban Informatics operates at the intersection of three domains: People, Places, and Technologies. In this framework, the non-profit research foundation Transform Transport provides innovative, inclusive, and sustainable mobility solutions for shaping the future of society and cities worldwide (see Figure 2). It 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. Transform Transport’s research is based on a multi-disciplinary approach to reimagine urban sustainable and inclusive mobility as a customized, simplified, and humanized daily experience (Ryghaug et al., 2023), in line with the UN’s 2030 Agenda for Sustainable Development (i.e., SDG 11.2-Sustainable Transport for All): “By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons.”
In that sense, the study “Covid-19 pandemic and activity patterns in Milan. Wi-Fi sensors and location-based data’’ (Gorrini et al., 2021) focuses on the utilization of location detection systems to monitor, understand, and predict activity patterns of city users. The research specifically analyzes a sample of aggregated traffic data that quantifies the number of mobile devices detected through a network of 55 Wi-Fi Access Points in Milan. The data collection period spans seven months, from January to July 2020, enabling an examination of the impact of the Covid-19 pandemic on activity patterns. The analysis incorporates two main approaches: (i) time series analysis, which explores trends, peak hours, and mobility profiles, and (ii) GIS-based spatial analysis, which examines landuse data, services availability, and public transport information. The findings demonstrate the effectiveness of Wi-Fi location data in monitoring and characterizing long-term trends in activity patterns within large-scale urban environments. Additionally, the study reveals a significant correlation between Wi-Fi data and the density distribution of various urban features, such as residential buildings, service and transportation facilities, entertainment venues, financial amenities, department stores, and bike-sharing docking stations. The study proposes a Suitability Analysis Index to enhance further data collection efforts by identifying areas in Milan that could benefit from additional Wi-Fi sensors. Future work aims to develop Wi-Fi sensing applications for real-time monitoring of mobility data.
The utilization of Wi-Fi sensors allows for the identification of macro-scale patterns, defining general profiles of mobility whilst not collecting information on site-specific behaviors. Another approach, focused on micro-scale data gathering is demonstrated in the study “Deep Learning Video Analytics for the Assessment of Street Experiments” (Ceccarelli et al., 2023). The study aimed to monitor pedestrian and vehicular flows in order to identify patterns of space utilization, with a specific focus on a public space for children in Bologna, as illustrated in Figure 4. The monitoring process involved the use of a camera and video analytics techniques to gather observations. The research generated a series of analyses pertaining to the observed flows in the area during the pre and post-intervention phases. The objective was to support an iterative design process based on the principles of tactical urbanism. By transitioning from a quantitative approach, which relied on the analysis of collected data, to a synthetic/design approach, the study aimed to facilitate data-driven design. The ultimate goal was to maximize the impact of positive interventions and address any critical issues identified.
Finally, another approach to the dissection of mobility patterns through the lens of Urban Informatics consists in the collection and analysis of IoT data, specifically vehicles. In this framework, the study “Free-flow Carsharing Systems in a Spatio-Temporal Urban Ecosystem” (Messa, 2021) focuses on the application of urban AI for predictive demand modeling, as depicted in Figure 5. In recent years, shared mobility has emerged as a prominent mode of transportation. However, the planning of shared mobility is often entrusted to private operators and is not considered a significant component of urban mobility. The objective of this research was to conduct a comprehensive analysis of the relationship between carsharing vehicle utilization and urban spatio-temporal patterns. The study specifically examined four weeks of free-flow carsharing data in Milan. Both temporal and spatial factors were taken into account, including time-invariant factors such as resident population density and time-variant factors such as public transport availability. These factors were analyzed in relation to the starting and ending points of car rentals, as well as the characteristics of origin-destination (OD) pairs. Statistical analyses were performed using average values for each hour of the day, separately for weekdays and weekends. The findings revealed daily utilization patterns that shed light on the connection between site-specific characteristics and carsharing demand.
About the Author
Dr. Andrea Gorrini is an environmental psychologist specializing in the study of human behavior in transport systems, crowd dynamics, and walkability. With a ten-year research background , his work is grounded in the Urban Informatics approach, which aims to promote inclusive and sustainable urban mobility. Dr. Gorrini is the Head of Research at Transform Transport, a non-profit research foundation established by Systematica in March 2022. In this role, he oversees research and development activities focused on innovation in mobility and transport planning. Additionally, since 2022, he has been appointed as an Ambassador of the Network for Diversity in Transport (European Commission, Directorate General for Mobility and Transport).
References
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Batty, M. (2013). Big data, smart cities and city planning. Dialogues in human geography, 3(3), 274–279. https://doi.org/10.1177/2043820613513390
Ceccarelli, G., Messa, F., Gorrini, A., Presicce, D., Choubassi, R. (2023, submitted). Deep Learning Video Analytics for the Assessment of Street Experiments: The Case of Bologna. Journal of Urban Mobility. Pre-print available at: https://transformtransport.org/research/urban-mobility-metrics/video-analytics-for-the-assessment-of-street-experiments-the-case-of-bologna/
Choubassi, R. and Abdelfattah, L. (2020). How Big Data is Transforming the Way We Plan Our Cities. FEEM Policy Brief, 20, 1–16. Available at: https://ssrn.com/abstract=3757431
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Gorrini, A., Messa, F., Ceccarelli, G., Choubassi, R. (2021). Covid-19 pandemic and activity patterns in Milan. Wi-Fi sensors and location-based data. TeMA — Journal of Land Use, Mobility and Environment, 14(2), 211–226. https://doi.org/10.6093/1970-9870/7886
Messa, F. (2021). Free-flow Carsharing Systems in a Spatio-temporal Urban Ecosystem: An Urban Informatics Approach. In: Proceedings of the 49th European Transport Conference 2021 (ETC 2021), 13–15 September 2021 — Online. https://doi.org/10.5281/zenodo.6493824
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