Spatial variation of ridesplitting adoption rate in Chicago

Mingyang Du, Lin Cheng, Xuefeng Li, Qiyang Liu and Jingzong Yang

Transportation Research Part A: Policy and Practice, 2022, vol. 164, issue C, 13-37

Abstract: Ridesplitting, a form of ride-hailing service where passengers with similar travel routes are matched to the same driver, can reduce the negative effects of solo ride-hailing trips and bring various environmental and social benefits. However, limited efforts were made to examine the spatial variation of ridesplitting trips, which was not conducive to the formulation of ridesplitting policies. To fill the gap, this work investigates the spatial variation of ridesplitting adoption rate (the proportion of ride-hailing trips with shared trip authorized, RAR) and its association with built environment and socio-economic factors at the census tract level, using the ride-hailing trip data in Chicago. To addressing the spatial heterogeneity, geographically weighted regression models are established to detect the factors influencing the RAR during different time periods, such as weekday, weekend, weekday morning peak and evening peak. Modeling results show that GWR models outperform the traditional global models in terms of model fit. The census tract level factors including subway station density, frequency of transit, land use mix, homicide density, percent female, the share of nonwhite, and percent zero-vehicle households have impacts on RAR, and the coefficient estimates of each explanatory variable vary across regions. The research results can help urban planners and transportation network companies develop refined policies to promote shared ride-hailing trips.

Keywords: Ridesplitting adoption rate; Spatial variation; Built environment; Socio-economic factor; Geographically weighted regression (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.tra.2022.07.018

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