Ride-sourcing compared to its public-transit alternative using big trip data

Ride-sourcing risks increasing\ua0GHG emissions\ua0by replacing public transit (PT) for some trips therefore, understanding the relation of ride-sourcing to PT in urban mobility is crucial. This study explores the competition between ride-sourcing and PT through the lens of big data analysis. This research uses 4.3 million ride-sourcing trip records collected from Chengdu, China over a month, dividing these into two categories, transit-competing (48.2%) and non-transit-competing (51.8%). Here, a ride-sourcing trip is labelled transit-competing if and only if it occurs during the day and there is a PT alternative such that the walking distance associated with it is less than 800\ua0m for access and egress alike. We construct a glass-box model to characterise the two ride-sourcing trip categories based on trip attributes and the built environment from the enriched trip data. This study provides a good overview of not only the main factors affecting the relationship between ride-sourcing and PT, but also the interactions between those factors. The built environment, as characterised by points of interest (POIs) and transit-stop density, is the most important aspect followed by travel time, number of transfers, weather, and a series of interactions between them. Competition is more likely to arise if: (1) the travel time by ride-sourcing <15\ua0min or the travel time by PT is disproportionately longer than ride-sourcing; (2) the PT alternative requires multiple transfers, especially for the trips happening within the transition area between the central city and the outskirts; (3) the weather is good; (4)\ua0land use\ua0is high-density and high-diversity; (5) transit access is good, especially for the areas featuring a large number of business and much real estate. Based on the main findings, we discuss a few recommendations for transport planning and policymaking

Understanding Human Mobility with Emerging Data Sources: Validation, spatiotemporal patterns, and transport modal disparity

Human mobility refers to the geographic displacement of human beings, seen as individuals or groups, in space and time. The understanding of mobility has broad relevance, e.g., how fast epidemics spread globally. After 2030, transport is likely to become the sector with the highest emissions in the 2\ub0C\ua0scenario. Better informed policy-making requires up-to-date empirical mobility data with good quality. However, the conventional methods are limited when dealing with new challenges. The prevalence of digital technologies enables a large-scale collection of human mobility traces, through social media data and GPS-enabled devices etc, which contribute significantly to the understanding of human mobility. However, their potentials for the further application are not fully exploited.This thesis uses emerging data sources, particularly Twitter data, to enhance the understanding of mobility and apply the obtained knowledge in the field of transport. The thesis answers three questions: Is Twitter a feasible data source to represent individual and population mobility? How are Twitter data used to reveal the spatiotemporal dynamics of mobility? How do Twitter data contribute to depicting the modal disparity of travel time by car vs public transit? In answering these questions, the methodological contribution of this thesis lies in the applied side of data science.Using geotagged Twitter data, mobility is firstly described by abstract metrics and physical models; in Paper A to reveal the population heterogeneity of mobility patterns using data mining techniques; and in Paper B to estimate travel demand with a novel approach to address the sparsity issue of Twitter data. In Paper C, GIS techniques are applied to combine the travel demand as revealed by Twitter data and the transportation network to give a more realistic picture of the modal disparity in travel time between car and public transit in four cities in different countries at a high spatial and temporal granularity. The validation of using Twitter data in mobility study contributes to better utilisation of this low-cost mobility data source. Compared with a static picture obtained by conventional data sources, the dynamics introduced by social media data among others contribute to better-informed policymaking and transport planning