基于混合Logit模型的出租车超速者运营因素分析

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (3): 229-236.

基于混合Logit模型的出租车超速者运营因素分析

周悦^a，江欣国^{a, b, c}，付川云^{* a, b, c}，刘海玥^a

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

收稿日期:2021-05-15 修回日期:2021-05-22 出版日期:2021-06-25 发布日期:2021-06-25
作者简介:周悦(1992- )，男，四川成都人，博士生。
基金资助:
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)。

Operational Factors Analysis for Taxi Speeders Using Mixed Logit Model

ZHOU Yue^a, JIANG Xin-guo ^{a, b, c}, FU Chuan-yun^{* a, b, c}, LIU Hai-yue^a

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

Received:2021-05-15 Revised:2021-05-22 Online:2021-06-25 Published:2021-06-25

摘要/Abstract

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

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.

Key words: traffic engineering, operational factor, mixed Logit model, taxi speeder, correlated random parameters

中图分类号:

U491

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.

ZHOU Yue,JIANG Xin-guo, FU Chuan-yun, LIU Hai-yue. Operational Factors Analysis for Taxi Speeders Using Mixed Logit Model[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 229-236.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract30265.shtml

http://www.tseit.org.cn/CN/Y2021/V21/I3/229

参考文献

[1] 江欣国, 周悦, 夏亮, 等. 出租车驾驶员交通违法行为演化博弈模型[J]. 西南交通大学学报, 2019, 54(6): 1121-1128. [JIANG X G, ZHOU Y, XIA L, et al. Evolutionary game model of traffic violations among taxi drivers[J]. Journal of Southwest Jiaotong University, 2019, 54(6): 1121-1128.]

[2] TSENG C-M. Operating styles, working time and daily driving distance in relation to a taxi driver's speeding offenses in Taiwan[J]. Accident Analysis & Prevention, 2013, 52: 1-8.

[3] WATSON B, SISKIND V, FLEITER J J, et al. Assessing specific deterrence effects of increased speeding penalties using four measures of recidivism[J]. Accident Analysis and Prevention, 2015, 84: 27-37.

[4] DING C, RIZZI M, STRANDROTH J, et al. Motorcyclist injury risk as a function of real-life crash speed and other contributing factors[J]. Accident Analysis & Prevention, 2019, 123: 374-386.

[5] KONG X, DAS S, JHA K, et al. Understanding speeding behavior from naturalistic driving data: Applying classification based association rule mining[J]. Accident Analysis & Prevention, 2020, 144: 105620.

[6] 周悦, 付川云, 江欣国, 等. 考虑空间效应的出租车超速行为道路因素分析[J]. 中国安全科学学报, 2021, 31 (3): 162-170. [ZHOU Y, FU C Y, JIANG X G, et al. Road factor analysis of taxi speeding behavior considering spatial effect[J]. China Safety Science Journal, 2021, 31(3): 162-170.]

[7] HUANG Y, SUN D, TANG J Y. Taxi driver speeding: who, when, where and how? A comparative study between Shanghai and New York City[J]. Traffic Injury Prevention, 2018, 19(3): 311-316.

[8] ZHOU Y, JIANG X, FU C, et al. Operational factor analysis of the aggressive taxi speeders using random parameters bayesian LASSO modeling approach[J]. Accident Analysis & Prevention, 2021, 157: 106183.

[9] WATSON B, WATSON A, SISKIND V, et al. Profiling high-range speeding offenders: Investigating criminal history, personal characteristics, traffic offences, and crash history[J]. Accident Analysis and Prevention, 2015, 74: 87-96.

[10] XU C, WANG X, YANG H, et al. Exploring the impacts of speed variances on safety performance of urban elevated expressways using GPS data[J]. Accident Analysis & Prevention. 2019, 123: 29-38.

[11] 黄钢, 瞿伟斌, 许卉莹. 基于改进密度聚类算法的交通事故地点聚类研究[J]. 交通运输系统工程与信息, 2020, 20(5): 169-176. [HUANG G, QU W B, XU H Y. Traffic accident location clustering based on improved DBSCAN algorithm[J]. Journal of Transportation System Engineering and Information Technology, 2020, 20(5): 169-176.]

[12] 李颖, 赵莉, 赵祥模, 等. 基于大货车 GPS 数据的轨迹相似性度量有效性研究[J]. 中国公路学报, 2020, 33 (2): 146-157. [LI Y, ZHAO L, ZHAO X M, et al. Effectiveness of trajectory similarity measures based on truck GPS data[J]. China Journal of Highway Transportation, 2020, 33(2): 146-157.]

[13] PANGTANGI S S, AHMED S S, FOUNTAS G, et al. Do high visibility crosswalks improve pedestrian safety? A correlated grouped random parameters approach using naturalistic driving study data[J]. Analytic Methods in Accident Research, 2021, 30: 100155.

[14] ADRADE C. Categorizing continuous variables[J]. The Canada Journal of Psychiatry, 2002, 47(9): 886.

[1]	林建新, 刘博, 赵霞, 张蕾. 基于混合约束自编码器的机动车工况智能构建方法[J]. 交通运输系统工程与信息, 2022, 22(2): 109-116.
[2]	张小雨, 邵春福, 王博彬, 黄士琛. 新冠疫情影响下居民共享出行方式选择行为研究[J]. 交通运输系统工程与信息, 2022, 22(2): 186-196.
[3]	戢晓峰, 耿昭师. 山区双车道公路货车碰撞预测的双变量冲突极值模型[J]. 交通运输系统工程与信息, 2022, 22(2): 230-238.
[4]	张建波, 孙建平, 徐春玲, 郭镜霞, 温慧敏, 宋国华. 考虑交通运行条件影响的驾驶员特征聚类[J]. 交通运输系统工程与信息, 2022, 22(2): 330-336.
[5]	马景峰, 任刚, 李豪杰, 曹奇, 杜建玮. 电动自行车与机动车事故严重性影响因素分析[J]. 交通运输系统工程与信息, 2022, 22(2): 337-348.
[6]	宗芳, 王猛, 贺正冰. 考虑多车影响的分子动力学智能网联跟驰模型[J]. 交通运输系统工程与信息, 2022, 22(1): 37-48.
[7]	马小龙, 余强, 刘建蓓. 基于路侧毫米波雷达的车辆碰撞概率计算方法[J]. 交通运输系统工程与信息, 2022, 22(1): 57-66.
[8]	廖鹏, 唐铁桥. 基于车辆动力传动和转向系统的换道轨迹优化策略[J]. 交通运输系统工程与信息, 2022, 22(1): 98-105.
[9]	刘永红, 罗霞, 胡剑鹏. 考虑临时和预约需求的共享泊位动态分配方法[J]. 交通运输系统工程与信息, 2022, 22(1): 171-181.
[10]	袁振洲, 郭曼泽, 彭泳鑫, 杨洋. 基于梯度关联规则的老年行人交通事故风险识别[J]. 交通运输系统工程与信息, 2022, 22(1): 195-208.
[11]	张玉婷, 陈波佑, 张双焱, 闫学东, 李晓梦. 抵近信控交叉口分心驾驶识别模型[J]. 交通运输系统工程与信息, 2022, 22(1): 217-224.
[12]	宋浪, 王健, 杨滨毓, 安实. 连续流交叉口左转非机动车交通组织创新设计[J]. 交通运输系统工程与信息, 2022, 22(1): 225-233.
[13]	白婧荣, 唐伯明, 孙宗元, 毕辉云, 向恬君. 毗邻互通立交特长隧道驾驶负荷研究[J]. 交通运输系统工程与信息, 2022, 22(1): 301-310.
[14]	郭彦茹, 罗志雄, 王家川, 何方. 数据驱动的共享单车停放区规划方法研究[J]. 交通运输系统工程与信息, 2021, 21(6): 9-16.
[15]	李君羡, 沈宙彪, 童文聪, 吴志周. 基于分类车头时距的城市道路大型车影响分析[J]. 交通运输系统工程与信息, 2021, 21(6): 55-62.