An empirical investigation of taxi driver response behavior to ride-hailing requests: A spatio-temporal perspective

被引:24
作者
Xu, Ke [1 ]
Sun, Luping [2 ]
Liu, Jingchen [1 ]
Wang, Hansheng [1 ]
机构
[1] Peking Univ, Guanghua Sch Management, Beijing, Peoples R China
[2] Cent Univ Finance & Econ, Business Sch, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
ONLINE; MODEL;
D O I
10.1371/journal.pone.0198605
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Using data provided by a ride-hailing platform, this paper examines the factors that affect taxi driver response behavior to ride-hailing requests. The empirical investigation from a driver's perspective is of great importance for ride-hailing service providers, given that approximately 40% of the hailing requests receive no response from any driver. To comprehensively understand taxi driver response behavior, we use a rich dataset to generate variables related to the spatio-temporal supply-demand intensities, the economic incentives, the requests' and the drivers' characteristics. The results show that drivers are more likely to respond to requests with economic incentives (especially a firm subsidy), and those with a lower spatio-temporal demand intensity or a higher spatio-temporal supply intensity. In addition, drivers are more likely to respond to requests involving rides covering a greater geographical distance and to those with a smaller number of repeated submissions. The drivers' characteristics, namely, the number of requests received and the number of requests responded, however, have relatively little impacts on their response probability to the current request. Our findings contribute to the related literature and provide managerial implications for ride-hailing service providers.
引用
收藏
页数:17
相关论文
共 20 条
[1]  
American Master Cabbie, 2016, RIS OF THE APP
[2]  
[Anonymous], 2017, EC INFORM DAILY
[3]   Effectiveness of rapid rail transit system in Beijing [J].
Cheng, Hui-Min ;
Ning, Yi-Zi ;
Ma, Xiaoke ;
Liu, Xin ;
Zhang, Zhong-Yuan .
PLOS ONE, 2017, 12 (07)
[4]   Quantifying Cross and Direct Network Effects in Online Consumer-to-Consumer Platforms [J].
Chu, Junhong ;
Manchanda, Puneet .
MARKETING SCIENCE, 2016, 35 (06) :870-893
[5]  
Dou Y, 2016, DYNAMIC PLATFORM COM, P17
[6]  
Horwitz J., 2017, ONE YEAR UBER DIDI M
[7]  
iResearch Consulting Group, 2017, 2017 CHIN LOC LIF SE
[8]   An Optimization Framework For Online Ride-sharing Markets [J].
Jia, Yongzheng ;
Xu, Wei ;
Liu, Xue .
2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017), 2017, :826-835
[9]   Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach [J].
Ke, Jintao ;
Zheng, Hongyu ;
Yang, Hai ;
Chen, Xiqun .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2017, 85 :591-608
[10]  
Lee C., 2017, Economics Working Paper