Analysis of ride-sourcing drivers' working Pattern(s) via spatiotemporal work slices: A case study in Hangzhou

Yining Di, Meng Xu, Zheng Zhu, Hai Yang and Xiqun Chen

Transport Policy, 2022, vol. 125, issue C, 336-351

Abstract: Ride-sourcing services have become an integral part of urban transportation systems. In a cooperative relationship with the platform, ride-sourcing drivers have the flexibility to work according to their habits or preferences, which could constitute heterogeneous working pattern(s) among drivers. Based on real-world ride-sourcing data, we analyze drivers' working pattern(s) via their work slices (WSs). A WS is defined as a time window during which a driver is continuously online to take ride-sourcing orders. We use the Gaussian mixture model to classify the WSs according to the start time/start place attributes and obtain 9 clusters with significant spatiotemporal differences. It is found that WSs starting from rural areas that are closed to urban areas show no significant spatiotemporal grouping and mediocre performance in terms of fare income and fare-cost ratio, while WSs starting from distant rural areas show the worst performance; WSs starting from urban/suburban areas reflect more diverse spatiotemporal groupings and significant differences in attributes of delivery time and fares earned. We also analyze drivers' working pattern(s) by summarizing their habitual WS clusters. Findings include that drivers with suburban-dominant/rural-dominant patterns have a disadvantage in working efficiency in terms of the shorter order distance and longer cruising time. If geometrical distance allows, they can improve their income by switching to urban WS clusters occasionally. However, frequently switching between WS clusters can lead to a decrease in fares income. According to our results, the platform can introduce differentiated policies, including subsidies and order-matching algorithms, to better handle the spatiotemporal inequality, ultimately improving the service performance and overall benefits.

Keywords: ride-sourcing markets; Drivers' patterns; Gaussian mixture model; Clustering (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.tranpol.2022.06.014

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