Due to convenience and flexibility, online ride-hailing has become increasingly more prevalent across the world. However, many violations and road crashes involving online ride-hailing were related to the unhealthy working pace of drivers, especially inadequate rest. This paper enriches the literature by providing a first look into the latent break patterns of online ride-hailing drivers based on a one-month order record dataset. A data mining and knowledge discovery process is presented for extracting and analyzing characteristics of online ride-hailing drivers’ work and rest based on GPS trajectory data, as follows: 1) logical judging to identify non-work order-gaps; 2) dynamic topic modeling to discover latent break patterns; and 3) integrating the topic modeling results with feature analysis results of order-gaps to summarize the time-dependent characteristics of online ride-hailing drivers’ special working pace. The case study results show that the latent break patterns extracted from two cities’ online ride-hailing order records are significantly different in the strength and cycles of the topics, which is greatly related to the travel supply-demand conditions and urban characters. Furthermore, the proposed analytical framework can help mobile transportation platform companies to better understand online ride-hailing markets from the perspective of drivers and to adjust their marketing strategies in real time.
Discovering Implicit Working Pace of Online Ride-Hailing Drivers: An Exploratory Study
IEEE Transactions on Intelligent Transportation Systems ; 23 , 8 ; 10504-10513
2022-08-01
3637170 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Driving safety assessment for ride-hailing drivers
Elsevier | 2020
|Ride-matching for the ride-hailing platform with heterogeneous drivers
Elsevier | 2023
|Europäisches Patentamt | 2023
|Europäisches Patentamt | 2022
|