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

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  • Liao, Yuan

Abstract

Ride-sourcing risks increasing GHG emissions by 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 800m 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 <15min 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) land use is 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.

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  • Liao, Yuan, 2021. "Ride-sourcing compared to its public-transit alternative using big trip data," Journal of Transport Geography, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:jotrge:v:95:y:2021:i:c:s0966692321001885
    DOI: 10.1016/j.jtrangeo.2021.103135
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    2. Steven R. Gehrke & Michael P. Huff, 2024. "Spatial equity implications and neighborhood indicators of ridehailing trip frequency and vehicle miles traveled in the phoenix metro region," Transportation, Springer, vol. 51(1), pages 271-295, February.
    3. Li, Qiumeng & Fuerst, Franz & Luca, Davide, 2023. "Do shared E-bikes reduce urban carbon emissions?," LSE Research Online Documents on Economics 120310, London School of Economics and Political Science, LSE Library.

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