Mahabadi, Zahra; (2025) Multi-Scale Spatial and Temporal Analysis of Travel Behaviour: A Multi-Model Comparative Approach. Doctoral thesis (Ph.D), UCL (University College London).

Text Mahabadi_Thesis.pdf - Accepted Version Access restricted to UCL open access staff until 1 November 2026. Download (25MB)

Abstract

Understanding the relationship between the built environment and travel behaviour is fundamental to shaping sustainable urban futures, optimising transport networks, and informing data-driven policymaking. Traditional studies are often constrained by coarse spatial resolutions and static temporal frameworks, making them incapable of capturing the nuances and dynamics of mobility patterns. Conventional approaches, reliant on census data, travel surveys, and traffic counts, offer generalised insights that obscure the fine-scale interactions between urban morphology and travel dynamics. Recent advancements in mobile phone and GPS-based tracking have expanded the scale of mobility analysis, yet these methods struggle with spatial and temporal inconsistencies, challenges in trip segmentation, and difficulties in distinguishing transport modes. This research adopts a multi scale spatial and temporal analysis to examine the relationship between urban form and travel behaviour, leveraging a multi-model comparative approach. Here, travel behaviour is analysed within high-resolution spatial units across multiple spatial scales and assessed through vehicle movements with a focus on their temporal distribution. To achieve this, the study integrates three key datasets: 1- Road network data, 2- Building attributes, 3- Trip chain data derived from 91 traffic cameras across Cambridge, UK, ensuring a systematic and continuous observation of vehicular flows. The introduction of functional role mapping further refines the analysis by assigning movement-based roles to different locations, providing a structured framework for understanding trip chains and how the built environment influences mobility patterns. Additionally, three levels of spatial aggregation enable the evaluation of immediate and broader built environment influences, while temporal normalisation techniques refine the dataset, ensuring stability and consistency across varying timeframes. By integrating spatial granularity, temporal insights, and advanced modelling, this research establishes a scalable analytical framework for understanding urban travel dynamics. The findings provide valuable insights, supporting smarter infrastructure planning, optimised traffic flow, and sustainable transport solutions aligned with real-world mobility patterns.

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