What Makes a Good Cabman? Behavioral Patterns Correlated with High-Earning and Low-Earning Taxi Driving

Author:

Jin ShuxinORCID,Su Juan,Wu Zhouhao,Wang DiORCID,Cai Ming

Abstract

The average hourly income of taxi drivers could be improved by understanding the realized income of taxi drivers and investigating the variables that determine their income. Based on 4.85 million taxi-trajectory GPS records in Shenzhen, China, this study built a multi-layer road index system in order to reveal the behavioral patterns of drivers with varying income levels. On this basis, late-shift drivers were further selected and classified into two categories, namely high-earning and low-earning groups. Each driver within these groups was further classified into three income levels and four categories of factors were defined (i.e., occupied trips and duration, operational region, search speed, and taxi service strategies). The sample-based multinomial logit model was used to reveal the significance of these income-influencing factors. The results indicate significant differences in the drivers’ behavioral habits and experience. For instance, high-earning drivers focused more on improving efficiency using mobility intelligence, while low-earning drivers were more likely to invest in working hours to boost their revenue.

Funder

Special Scientific Research Program of Education Department of Shaanxi Province of china

Fundamental Research Funds for the Central Universities, Sun Yat-sen University

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference38 articles.

1. Understanding taxi service strategies from taxi GPS traces;IEEE Trans. Intell. Transp. Syst.,2015

2. From taxi GPS traces to social and community dynamics: A survey;ACM Comput. Surv. CSUR,2013

3. Tu, J., and Duan, Y. (2017, January 26–29). Detecting Congestion and Detour of Taxi Trip via GPS Data. Proceedings of the 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), Shenzhen, China.

4. Modeling Location Choice of Taxi Drivers for Passenger Pick-Up Using GPS Data;IEEE Intell. Transp. Syst. Mag.,2020

5. Work-related factors, fatigue, risky behaviours and traffic accidents among taxi drivers: A comparative analysis among age groups;Int. J. Inj. Control Saf. Promot.,2020

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3