Are Taxi Riders Rational Cost Minimizers? A Case Study in Shenzhen, China

The choice between "hail a taxi" and "take public transit" for urban dwellers, seems to be determined by quality and quantity of mobility (how far, how long and how much) provided by two modes. The objective of this study is to analyze the travel performances between individual taxi trip and corresponding Alternative Optimal Transit Strategy (AOTS) to account for transit availability, further examine whether the decision to ride a taxi is indeed driven by cost minimization. The authors' research approach is to search for the AOTS by applying a hyperpath algorithm to each taxi trip occurred in a city. Their case study employs a multitude of data sources collected in Shenzhen, China in 2016, ranging from taxi global positioning system (GPS) trajectories of a weekday, detailed information of transit services, automated location data of buses, and planning data from the travel forecasting agency. The results show that Shenzhen transit has a high-level accessibility (over 92%) to cover wide-distributed taxi demand. In addition, the authors found even if all taxi riders in Shenzhen earn a high income, 10% of them would still be able to lower their general cost by switching to transit. Even such a rider further values the comfort provided by taxis at 10% of the total general cost, half of whom can still benefit from riding transit on average.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.
  • Corporate Authors:

    Transportation Research Board

    ,    
  • Authors:
    • Xu, Zhandong
    • Xie, Jun
    • Liu, Xiaobo
    • Nie, Yu (Marco)
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 7p

Subject/Index Terms

Filing Info

  • Accession Number: 01698267
  • Record Type: Publication
  • Report/Paper Numbers: 19-05386
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 1 2019 3:51PM