Ride-sourcing compared to its public-transit alternative using big trip data
Preprint, 2021

Because ride-sourcing risks increasing GHG emissions by replacing public transit (PT) for some trips, understanding the relation of ride-sourcing to PT in urban mobility is crucial. This study explores the conflict relationship (i.e., competition) between ride-sourcing and PT through the lens of big data analysis. We apply 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 (from 6 am to 11 pm) and there is a PT alternative such that the walking distance associated with it is less than 800 m 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. We find that competition is more likely to arise if: 1) the travel time by ride-sourcing < 15 min 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.

public transit

built environment

urban mobility

travel time

glass-box model



Yuan Liao

Chalmers, Space, Earth and Environment, Physical Resource Theory, Physical Resource Theory 2

Sustainable cities: the use of large amounts of data to understand and handle movement patterns and congestion

Formas (2016-01326), 2017-01-01 -- 2019-12-31.

Areas of Advance

Information and Communication Technology



Driving Forces

Sustainable development

Subject Categories

Transport Systems and Logistics

Environmental Sciences

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