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基于混合Logit模型的出租车超速者运营因素分析
引用本文:周悦,江欣国,付川云,刘海玥.基于混合Logit模型的出租车超速者运营因素分析[J].交通运输系统工程与信息,2021,21(3):229-236.
作者姓名:周悦  江欣国  付川云  刘海玥
作者单位:西南交通大学,a. 交通运输与物流学院;b. 综合交通运输智能化国家地方联合工程实验室; c. 综合交通大数据应用技术国家工程实验室,成都 611756
基金项目:国家自然科学基金/National Natural Science Foundation of China(71801182,71771191);四川省教育厅科研项目/ Sichuan Provincial Department of Education Research Fund Project(17SB0564)。
摘    要:根据超速频次、平均超速严重度和平均超速持续时间等3类超速特征,判断出租车超速者类型,明确运营因素对超速者类型的影响。本文利用成都市出租车GPS轨迹数据提取此3类超速特征,并筛选出若干运营因素。而后,应用模糊C-均值聚类确定4类出租车超速群体(超速者I、II、 III和IV型)。运用相关随机参数混合Logit模型,估计单个超速者的某运营因素在超过超速群体平均水平时,对该超速者类型的影响。结果显示:超速者I和II型的每小时超速频数、平均超速严重度低,但平均超速持续时间高;超速者III和IV型则相反。当超速者的日均行程距离、日均收入、低限速道路行程比例、夜间行程比例和全时段速度标准差超过群体均值时,其成为超速者III 和IV型的概率提高了15.39%~77.09%和42.98%~302.38%;成为超速者 I 型和 II 型的概率降低了-0.09%~26.57%和38.74%~68.34%。此外,日均行程距离、低限速道路上行程比例和夜间速度标准差等3类因素在估计中表现出异质性。

关 键 词:交通工程  运营因素  混合Logit模型  出租车超速者  相关随机系数  
收稿时间:2021-05-15

Operational Factors Analysis for Taxi Speeders Using Mixed Logit Model
ZHOU Yue,JIANG Xin-guo,FU Chuan-yun,LIU Hai-yue.Operational Factors Analysis for Taxi Speeders Using Mixed Logit Model[J].Transportation Systems Engineering and Information,2021,21(3):229-236.
Authors:ZHOU Yue  JIANG Xin-guo  FU Chuan-yun  LIU Hai-yue
Institution:a. School of Transportation and Logistics; b. National United Engineering Laboratory of Integrated and Intelligent Transportation; c. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
Abstract:To classify the types of taxi speeders and estimate the effect of operational factors according to speeding frequency, average speeding severity, and average speeding duration, this study collected taxi GPS trajectories in Chengdu, China. The speeding characteristics and operational factors are extracted from the GPS data. Four types of taxi speeders (Speeder I, II, III, and IV) were identified by the Fuzzy C- means algorithm. Operational factors were processed to be binary indicators by comparing the average values of the whole speeders. Furthermore, a correlated random parameters mixed Logit model was introduced to investigate the influence of operational factor indicators. The results show that Speeder I and II have higher hourly speeding frequency, higher speeding severity, and shorter speeding duration than Speeder III and IV. For the operational factors, if the indicators of daily driving distance, daily income, distance ratio of driving on low-speed limits road, distance ratio of driving at night, as well as speed variances of daytime, nighttime, and peak hours equal to 1 (greater than average values), the possibilities of being Speeder III and IV are increased by 15.39%~77.09% and 42.98%~302.38%, respectively. However, the possibilities of being Speeder I and II are reduced by -0.09%~26.57% and 38.74%~68.34%, respectively. Indicators of daily driving distance, distance ratio of driving on low-speed limits road, and nighttime speed variance are associated with unobserved heterogeneity among speeders.
Keywords:traffic engineering  operational factor  mixed Logit model  taxi speeder  correlated random parameters  
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