Zhang, Xianghui; (2025) Mobile Phone Data-Driven Insights for Sustainable Mobility: From Individual Multimodal Behaviour to Policy Intervention. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Sustainable mobility becomes crucial in addressing the urgent challenges posed by climate change. Transitioning towards sustainable mobility involves promoting modal shifting, improving the efficiency of transport systems, and reducing unnecessary travel through policy interventions. Traditional data and methodologies are limited in capturing the complex dynamics of sustainable mobility, making it difficult to support key strategies for achieving sustainable mobility. Leveraging new forms of data, such as mobile phone data, provides comprehensive travel-related information with finer spatio-temporal granularity, enabling data-driven insights for sustainable mobility. This thesis develops a comprehensive data-driven framework using a bottom-up approach to generate insights into sustainable mobility. By overcoming the challenges of noise and sparsity in mobile phone application data, the research first transforms these data into detailed insights about individual travel behaviours across multiple modes (i.e., car, cycle, walk, bus, tube, train and stationery) and various groups (i.e., residents, non-residents with trip attractions, pass-through people). The framework then aggregates individual multimodal behaviours into multimodal transport networks to monitor and analyse systemic performance and emissions. Finally, it quantifies the effectiveness of sustainable mobility strategies at both individual and systemic levels by developing a generic methodology that incorporates causal inference. The methodological framework is implemented and demonstrated through three case studies in London, each representing a core strategy for sustainable mobility. The first case study employs the newly developed datasets to assess multimodal mobility during the COVID-19 pandemic, examining both individual behaviours and overall system performance. The second investigates road transport greenhouse gas emissions in London, focusing on emission estimation, contributor tracing and determinants identification. The final case study evaluates the impact of several Low Traffic Neighbourhoods in London on promoting sustainable mobility. Collectively, these case studies demonstrate how the proposed framework provides comprehensive and actionable insights, significantly enhancing our understanding and promotion of sustainable mobility.

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