Redefining Variability: Evaluating Land Use and Transport Impacts on Urban Mobility Patterns

Projektinformationen

realTRIPS

ID Finanzhilfevereinbarung: 949670

DOI

10.3030/949670

EK-Unterschriftsdatum 18 Januar 2021

Startdatum 1 Februar 2021

Enddatum 31 Dezember 2026

Finanziert unter

• EXCELLENT SCIENCE - European Research Council (ERC)

Gesamtkosten

€ 1 468 363,00

EU-Beitrag

€ 1 468 363,00

Koordiniert durch

UNIVERSITY COLLEGE LONDON
United Kingdom

Periodic Reporting for period 3 - realTRIPS (Redefining Variability: Evaluating Land Use and Transport Impacts on Urban Mobility Patterns)

Berichtszeitraum: 2024-02-01 bis 2025-07-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Urban mobility concerns the movements of people within cities for their daily activities. It is closely related to every individual's lifestyle as well as the functioning of urban spaces, making it one of the most critical issues addressed in the Sustainable Development Goals (SDGs).

realTRIPS aims to pave a new path in urban mobility analysis by utilising emerging automatic data. This will be achieved through the development of an analytical and modelling framework, with a particular focus on addressing variability across spatial-temporal scales and among different population groups.

The term 'variability' has been interpreted from different perspectives and levels of abstraction. In summary, it is first addressed as data bias issues, which not only introduce uncertainty into scientific results but can also have implications for social justice if decisions are informed by biased data. Second, variability is interpreted as disparities among socioeconomic groups, measured by inequality in activity space in our research. Third, variability is discussed in terms of the transferability of knowledge and techniques developed in different urban contexts (e.g. whether models calibrated from London cases can be applied to predict city development in Nairobi).

The proposed research implements theoretical thinking by tackling urban mobility-related challenges with a data-driven analysis and modelling approach. The research team has developed a variety of applications in three case study cities: London, Shenzhen, and Nairobi. These applications are about daily movements and migrations, utilising digital footprint data, including travel card data, social media data (Twitter, Facebook, street view), and mobile data (aggregated signal data and, more recently, mobile app data).

The project's results are expected to make significant contributions to the development of inclusive and sustainable cities. This project is inherently interdisciplinary, and its outputs will be of interest to a wide range of research communities, including GIS engineering, urban analytics, AI, urban planning, transportation, and geography.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The proposed research framework incorporates key concepts from uncertainty in spatial analysis, time geography, land-use transport modelling, and urban planning. Within this theoretical framework, new measures of variability will be created and integrated as a function of space and time into operational urban models. Three case studies in three typical urban contexts (i.e. London, Shenzhen, and Nairobi) were conducted to demonstrate the transferability of technology and knowledge. The project has defined five research objectives, namely:
O1: To develop a new theoretical framework describing the variability of urban mobility patterns across spatiotemporal scales.
O2: To develop a set of variability measures for mobility pattern analysis.
O3: To integrate functions of variability into established computational urban models.
O4: To test the feasibility and generic applicability of the proposed framework, methods and models by applying them to case studies in typical urban contexts.
O4.1: To test the variability measure and analytical methods using real data in London, UK.
O4.2: To conduct a case study in Shenzhen, China.
O4.3: To conduct an experimental study in Nairobi, Kenya (and developing context)
O5: To disseminate the research output via academic, industrial and public channels and to maximise the impact of this research.

The first stage of the project (from the beginning until end-2023) has a focus on developing analytical models, which are to fulfil the defined objectives 1&2. The analytical methods and indices are demonstrated through case studies in three cities, pushed forward simultaneously, as reflected in the good number of relevant publications. Developing operational urban models is planned for the second stage of the project (from M25). Relevant work has been elaborated and scoped to deep gravity models for urban simulation and agent-based modelling for emergency planning applications.

In terms of dissemination, the team have presented in a number of conferences and workshops, including GISRUK 2022&2023, CPGIS 2022& 2023, ectqg 2021&2022, AUM2022 and Turing workshop. The PI has given invited talks to the Hongkong Polytechnic University, Tianjin University in China, The University of Tweete, the CPGIS educational webinar series and Turing workshops. Also, the team has hosted 1 visiting scholar and 1 visiting PhD student, with more visitors expecting to join us in the coming year.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

The project is expected to introduce innovations by developing a theoretical framework for urban mobility data-supported urban planning, an analytical framework for quantifying and interpreting variability from various perspectives, and applications that address specific concerns in three case study cities. Some of the expected progress is considered a significant achievement and is detailed below:

- Timely Research on COVID-19 Impact Using Automatic Location Data: We have produced three publications on tracking urban changes during the COVID-19 pandemic. One, published in PLOS ONE, focuses on tracking daily mobility changes using Twitter data. The second paper detects migration patterns using Twitter data, and the third paper measures the level of recovery using travel card data.

- New Research Agenda on Mobile App Data: Although not initially included in the project scope, mobile app data has become available only recently. Its usage and reliability are still in the early stages of commercial exploration. We conducted preliminary testing and analysis with sample datasets from various sources during realTRIPS. Despite some biases, we concluded that this type of data holds significant research and commercial potential compared to commonly used mobility data sources. More investigation are still ongoing, with publications in preparation.

- Addressing the Digital Injustice Problem in the Global South Context. A highly criticised barrier to the application of digital footprint data in Global South cities pertains to the representativeness of the dataset, which is another interpretation of variability in the context of our proposal. Ensuring equitable participation in data-enabled decision-making processes and increasing the visibility of these digitally invisible groups present significant challenges for the Global South. We expect to develop a methodology for identify the invisible groups and mitigating such injustice issues by technology and policy suggestions.

- A Comprehensive Report on Urban Mobility Data and Applications: This is an ongoing project to be completed in 2024. The team has conducted extensive literature reviews but has decided to produce a handbook-style open book instead of a traditional review paper, which is not yet available. The handbook is expected to become the first textbook for any urban mobility module, covering all fundamental concepts and theories, computational analytical methods, and urban simulation models, complete with reproducible Python coding for data science education.

modelling urban structure