考虑空间效应的出租车超速行为道路因素分析

doi:10.16265/j.cnki.issn1003-3033.2021.03.023

中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (3): 162-170.doi: 10.16265/j.cnki.issn1003-3033.2021.03.023

考虑空间效应的出租车超速行为道路因素分析

周悦¹, 付川云^**1,2,3 讲师, 江欣国^1,2,3 教授, 毛程远⁴讲师, 刘海玥¹

1 西南交通大学交通运输与物流学院,四川成都 611756;
2 西南交通大学综合交通运输智能化国家地方联合工程实验室,四川成都 611756;
3 西南交通大学综合交通大数据应用技术国家工程实验室,四川成都 611756;
4 浙江师范大学工学院,浙江金华 321004

收稿日期:2020-12-02 修回日期:2021-02-01 出版日期:2021-03-28
作者简介:周悦 (1992—),男,四川成都人,博士研究生,主要研究方向为道路交通安全分析、驾驶行为时空特征分析和统计建模。E-mail: 415958144@qq.com。
基金资助:
国家自然科学基金资助(71801182,71771191);四川省科技创新人才基金资助(2019JDRC0023);浙江省自然科学基金资助(LY18G030021)。

Road factor analysis of taxi speeding behavior considering spatial effect

ZHOU Yue¹, FU Chuanyun^1,2,3, JIANG Xinguo^1,2,3, MAO Chengyuan⁴, LIU Haiyue¹

1 School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan 611756, China;
2 National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan 611756, China;
3 National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan 611756, China;
4 College of Engineering, Zhejiang Normal University, Jinhua Zhejiang 321004, China

Received:2020-12-02 Revised:2021-02-01 Published:2021-03-28

摘要/Abstract

摘要： 为充分利用道路特征干预出租车超速行为,搜集成都市区内出租车全球定位系统(GPS)轨迹数据,识别其超速行为,并采集道路特征数据,以各道路的出租车超速频数及平均超速严重度为超速特征,应用全局莫兰指数和4类空间回归模型,分别确定超速特征及道路因素的空间自相关性和显著影响出租车超速特征的道路因素。研究结果表明:出租车超速行为和道路特征均存在明显的空间自相关性;空间自相关模型(SAC)对超速频数的拟合效果最好,空间杜宾模型(SDM)对平均超速严重度的拟合效果最佳;路段的相接道路数、出入口数及车道数明显增加超速频数;道路长度和车道数显著增大平均超速严重度;施工区和单行道均与超速特征无关。

关键词: 出租车超速行为, 道路特征, 全球定位系统(GPS)轨迹数据, 超速频数, 超速严重度, 空间回归模型, 空间自相关性

Abstract: In order to prevent taxi speeding by utilizing road characteristics, GPS trajectory data of taxis in Chengdu city area were gathered to identify their speeding behavior, and road characteristics were extracted as well. Then, with speeding frequency and average speeding severity of each road as speeding characteristics, global Moran's I and four kinds of spatial regression models were adopted to analyze spatial autocorrelation of speeding characteristics and road factors and to explore significant influencing factors of the former. The results reveal that obvious spatial autocorrelation exists between taxi speeding and road characteristics. Spatial Autocorrelation Model (SAC) and Spatial Durbin Model (SDM) are the best for fitting of speeding frequency and average speeding severity estimation, respectively. Number of connected road, access number and lane number evidently increase taxi speeding frequency while road length and lane number significantly increase average speeding severity. Whereas, work zone and one-way roads are unrelated with speeding characteristics.

Key words: taxi speeding behavior, road characteristics, global position system (GPS) trajectory data, speeding frequency, speeding severity, spatial regression model, spatial autocorrelation

中图分类号:

X951

周悦, 付川云, 江欣国, 毛程远, 刘海玥. 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 Yue, FU Chuanyun, JIANG Xinguo, MAO Chengyuan, LIU Haiyue. Road factor analysis of taxi speeding behavior considering spatial effect[J]. China Safety Science Journal, 2021, 31(3): 162-170.

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考虑空间效应的出租车超速行为道路因素分析

Road factor analysis of taxi speeding behavior considering spatial effect

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