TY - JOUR
T1 - Mining Factors Affecting Taxi Detour Behavior From GPS Traces at Directional Road Segment Level
AU - Wu, Zhouhao
AU - Li, Yaxiang
AU - Wang, Xin
AU - Su, Juan
AU - Yang, Liu
AU - Nie, Yu
AU - Wang, Yuanqing
N1 - Funding Information:
This work was supported in part by the Chinese National Nature Science Foundation under Grant 51878062 and Grant 51908462 and in part by the National Key Research and Development Program of China under Grant 2018YFB1601000
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - Taxi detour behavior
KW - map matching
KW - multi-layer road intex systems
KW - network complexity
KW - spatio-temporal distribution features
KW - taxi data mining
KW - urban computing
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U2 - 10.1109/TITS.2021.3074976
DO - 10.1109/TITS.2021.3074976
M3 - Article
AN - SCOPUS:85105864124
SN - 1524-9050
VL - 23
SP - 8013
EP - 8023
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 7
ER -