Mining Factors Affecting Taxi Detour Behavior From GPS Traces at Directional Road Segment Level

Zhouhao Wu, Yaxiang Li, Xin Wang, Juan Su, Liu Yang, Yu Nie, Yuanqing Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


In the urban traffic research field, taxi detour behavior analysis can be regarded as one of the most crucial and challenging topics accounting for real-world routing network dynamics with complicated external inducement such as 'avoiding congestion sections', 'unfamiliarity with road maps' or just 'earning more fee under a longer travel path'. We carried out an interdisciplinary research framework to build a more holistic and profound view of the spatio-temporal distribution of the taxi detour behavior at directional road segment (DRS) level. First, a map matching based detour clustering method was proposed to deal with one week of taxi GPS tracing (divided into 3.4 million occupied trips). Then we employed an established multi-layer road index system in Shenzhen, China, to illustrate the spatio-temporal distribution variation of taxi detour features and statistics. Furthermore, three categories of DRS factors related to road structural attributes, traffic dynamics and point-of-interests (POIs) were defined to fit a selected-sample-based binary logit model. Some remarkable findings include: (i) in Shenzhen on average, 23.5 percent of taxi trips made a detour larger than 2.1 kilometers, which could be astonishingly high considering that only a very few trips yielded formal complaints for fraudulent detouring; (ii) both the level of detour intensity and ratio are affected by road features and dynamics in different spatio-temporal interaction patterns.

Original languageEnglish (US)
Pages (from-to)8013-8023
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number7
StatePublished - Jul 1 2022


  • Taxi detour behavior
  • map matching
  • multi-layer road intex systems
  • network complexity
  • spatio-temporal distribution features
  • taxi data mining
  • urban computing

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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