Comprehensive Identification of Taxi Driving State Based on Kinematic Feature Pattern

doi:10.11908/j.issn.0253-374x.23044

Home > Archive>Volume 52, Issue 11, 2024 >1742-1749. DOI:10.11908/j.issn.0253-374x.23044

Comprehensive Identification of Taxi Driving State Based on Kinematic Feature Pattern
DOI:
                        10.11908/j.issn.0253-374x.23044
                    
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                        DONG Chunjiao1,2DONG Chunjiao
Laboratory of Transport Pollution Control and Monitoring Technology， Transport Planning and Research Institute， Ministry of Transport， Beijing 100028， China;Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport， Ministry of Transport， Beijing Jiaotong University， Beijing 100044， China
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WU Rui1WU Rui
Laboratory of Transport Pollution Control and Monitoring Technology， Transport Planning and Research Institute， Ministry of Transport， Beijing 100028， China
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YANG Daoyuan1YANG Daoyuan
Laboratory of Transport Pollution Control and Monitoring Technology， Transport Planning and Research Institute， Ministry of Transport， Beijing 100028， China
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ZHAO Dan3ZHAO Dan
School of Traffic Management， People’s Public Security University of China， Beijing 100038， China
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LI Ning4LI Ning
The Architectural Design & Research Institute of Zhejiang University Co.， Ltd.， Hangzhou 310028， China
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Affiliation:1.Laboratory of Transport Pollution Control and Monitoring Technology， Transport Planning and Research Institute， Ministry of Transport， Beijing 100028， China;2.Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport， Ministry of Transport， Beijing Jiaotong University， Beijing 100044， China;3.School of Traffic Management， People’s Public Security University of China， Beijing 100038， China;4.The Architectural Design & Research Institute of Zhejiang University Co.， Ltd.， Hangzhou 310028， China
Clc Number:U491.1
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Abstract:

Based on GNSS data， the rule and method of extracting taxi kinematic segments were proposed. According to the principal component analysis （PCA） and cumulative contribution rate， 8 key indicators were determined to represent kinematic segments. Combined with K-mean clustering algorithm， the taxi kinematic feature patterns were obtained. In order to ensure the objective rationality of the weight of key indicators， the CRITIC method considering the correlation of indicators and the entropy weight method considering the dispersion degree of indicators were adopted. The VIKOR evaluation model based on Nash equilibrium combination weight was established to evaluate the driving state of the taxi under multi spatio-temporal scenarios. The results show that the combined weighting method based on the Nash equilibrium can effectively integrate the merits of the CIRTIC method and entropy weight method and obtain a more reasonable weight coefficient. In terms of overall safety， efficiency and comfort， the driving condition of the main road and secondary road is better than that of the branch road. The safety of taxi driving is the best in the morning peak， and relatively ordinary in the evening peak and flat peak.

Key words:kinematic segment;taxi kinematic feature pattern;combined weighting VIKOR evaluation model;driving state comprehensive identification

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DONG Chunjiao, WU Rui, YANG Daoyuan, ZHAO Dan, LI Ning. Comprehensive Identification of Taxi Driving State Based on Kinematic Feature Pattern[J].同济大学学报(自然科学版),2024,52(11):1742~1749

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Received:February 13,2023
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Online: December 03,2024
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