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浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (4): 720-726    DOI: 10.3785/j.issn.1008-973X.2021.04.014
土木工程     
道路开口对临近交叉口交通安全的影响
张琦1(),陈红1,*(),周继彪2,张敏1,郭璘2,杨仁法2
1. 长安大学 运输工程学院，陕西 西安 710064
2. 宁波工程学院 建筑与交通工程学院，浙江 宁波 315211
Effect of roadway access on traffic safety at adjacent intersection
Qi ZHANG1(),Hong CHEN1,*(),Ji-biao ZHOU2,Min ZHANG1,Lin GUO2,Ren-fa YANG2
1. College of Transportation Engineering, Chang’an University, Xi’an 710064, China
2. School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China
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摘要：

为了弥补道路开口对临近交叉口交通安全定量影响研究的不足，补充目前有关道路开口影响邻近交叉口的事故修正系数（CMF）评估的缺失，基于宁波市鄞州区事故坐标点云数据对道路开口影响邻近交叉口的CMF值进行有效评估. 提出高效的CMF值评估方法，包括用于建立事故、交叉口及临近交叉口的道路开口三者相关性、适用于从规模事故点云数据中准确识别事故黑点的两阶段聚类算法（TSCA）和针对影响因素分析的零膨胀负二项分布回归（ZINB）模型. 结果显示，TSCA算法能够根据60、70及80三种事故频数阈值，准确识别出24、16及10个交叉口事故黑点；利用ZINB模型对识别出的24、16及10个交叉口事故黑点的主要影响因素建模，计算出道路开口的CMF值分别达到1.17、1.19和1.20. 研究表明，道路开口对临近交叉口安全的影响较明显，对高事故率黑点的影响更显著.
关键词： 交通安全;  道路开口;  城市交叉口;  事故修正系数;  两阶段聚类算法（TSCA）;  零膨胀负二项回归（ZINB）    
Abstract:

Roadway access CMF-values were effectively estimated based on the point cloud data of the accident coordinates in Yinzhou District, Ningbo in order to fill the gap in the quantitative assessment of the effect of the roadway access on the traffic safety at the adjacent intersection and complement the crash modification factor (CMF) of roadway access impacted on the adjacent intersection. An efficient method was presented, including a two-stage clustering approach (TSCA) for developing relationship among accidents, intersections, and roadway access close to the intersections, which is applicable for accurate identification of accident hotspots from large-scale point cloud data and the zero-inflated negative binomial model (ZINB) for influencing factor analysis. Results showed that TSCA accurately identified 24, 16 and 10 intersection accident hotspots according to 60, 70 and 80 accident frequency threshold, respectively. ZINB was applied to model the main influencing factors of the 24, 16 and 10 black factors identified by TSCA, respectively. CMF-values were estimated by 1.17, 1.19, and 1.20, respectively. Roadway access has a significant impact on the safety of the adjacent intersection and a more significant effect on the intersection with a higher accident rate.
Key words: traffic safety    roadway access    urban intersection    crash modification factor    two-stage clustering approach (TSCA)    zero-inflated negative binomial model (ZINB)
收稿日期: 2020-03-28 出版日期: 2021-05-07
CLC:  U 491  
基金资助: 国家重点研发计划资助项目（2017YFC0803906）
通讯作者: 陈红     E-mail: 2017021077@chd.edu.cn;glch@chd.edu.cn
作者简介: 张琦（1992—），男，博士生，从事交通安全的研究. orcid.org/0000-0002-7943-3989. E-mail： 2017021077@chd.edu.cn
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引用本文:

张琦,陈红,周继彪,张敏,郭璘,杨仁法. 道路开口对临近交叉口交通安全的影响[J]. 浙江大学学报(工学版), 2021, 55(4): 720-726.

Qi ZHANG,Hong CHEN,Ji-biao ZHOU,Min ZHANG,Lin GUO,Ren-fa YANG. Effect of roadway access on traffic safety at adjacent intersection. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 720-726.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.04.014    或    http://www.zjujournals.com/eng/CN/Y2021/V55/I4/720

图 1  两阶段聚类算法流程
图 2  有效事故数据分布图
图 3  嵩江路与钱湖路区域事故黑点识别示例
变量 变量解释 T=60 T=70 T=80
均值 方差 最大 最小 均值 方差 最大 最小 均值 方差 最大 最小
事故计数 交叉口影响范围内每日事故发生数 1.32 2.02 9 0 1.39 1.97 9 0 1.51 1.91 9 0
道路开口数量 交叉口影响范围内开口数量 1.96 1.43 4 0 2.38 1.30 4 0 2.80 1.07 4 1
主要道路流量 取常用对数，无量纲 4.28 0.04 4.48 4.01 4.30 0.02 4.48 4.02 4.34 0.01 4.48 4.07
次要道路流量 取常用对数，无量纲 4.10 0.03 4.43 3.62 4.15 0.02 4.43 3.68 4.19 0.02 4.43 3.73
主要道路车道数 ? 9.04 4.92 14 6 9.25 4.88 14 6 9.40 4.84 14 6
次要道路车道数 ? 7.11 3.55 12 3 7.13 3.46 10 3 7.20 3.36 10 3
天气 二元分类变量，正常（0）降雨（1） ? ? ? ? ? ? ? ? ? ? ? ?
星期 7个哑变量表示 ? ? ? ? ? ? ? ? ? ? ? ?
交叉口上方是否有高架通过 二元分类变量，无（0）有（1） ? ? ? ? ? ? ? ? ? ? ? ?
交叉口区域是否有商业中心 二元分类变量，无（0）有（1） ? ? ? ? ? ? ? ? ? ? ? ?
表 1  变量选取及统计描述
变量 T = 60 T = 70 T = 80
系数 CMF 95%置信区间 系数 CMF 95%置信区间 系数 CMF 95%置信区间
道路开口数量 0.16 1.17 1.07~1.28 0.18 1.19 1.11~1.29 0.18 1.20 1.14~1.25
主要道路流量 0.77 2.16 1.36~3.42 0.64 1.90 1.26~2.86 0.61 1.84 1.24~2.73
次要道路流量 0.46 1.58 1.14~2.21 0.51 1.66 1.24~2.24 0.52 1.67 1.29~2.19
主要道路车道数 ?0.20 0.79 0.76~0.89 ?0.21 0.81 0.76~0.87 ?0.21 0.81 0.75~0.87
次要道路车道数 ?0.32 0.73 0.68~0.77 ?0.28 0.76 0.71~0.80 ?0.26 0.77 0.73~0.82
交叉口上方是否有高架通过 0.15 1.16 1.08~1.25 0.19 1.21 1.13~1.29 0.21 1.23 1.15~1.32
交叉口区域是否有商业中心 0.10 1.11 1.05~1.16 0.08 1.08 1.05~1.13 0.08 1.08 1.04~1.13
表 2  主要解释变量事故修正系数表
1 郭璘, 周继彪, 董升, 等 基于改进K-means算法的城市道路交通事故分析[J]. 中国公路学报, 2018, 31 (4): 241- 249
GUO Lin, ZHOU Ji-biao, DONG Sheng, et al Analysis of urban road traffic based on improved K-means algorithm[J]. China Journal of Highway and Transport, 2018, 31 (4): 241- 249
doi: 10.3969/j.issn.1001-7372.2018.04.028
2 卢佩莹, 姚申君, 吴建平, 等 基于GIS的道路热区鉴别方法[J]. 交通运输工程学报, 2011, 11 (4): 97- 102
LU Pei-ying, YAO Shen-jun, WU Jian-ping, et al Identification method of road hot zone based on GIS[J]. Journal of Traffic and Transportation Engineering, 2011, 11 (4): 97- 102
3 KOWTANAPANICH W, TANABORIBOON Y, CHADBUNCHACHAI W Applying public participation approach to black spot identification process[J]. IATSS Research, 2006, 30 (1): 73- 85
doi: 10.1016/S0386-1112(14)60158-8
4 RAIHAN M A, ALLURI P, WU W, et al Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models[J]. Accident Analysis and Prevention, 2019, 123: 303- 313
doi: 10.1016/j.aap.2018.12.009
5 DONG C J, CLARKE D B, YAN X D, et al Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections[J]. Accident Analysis and Prevention, 2014, (70): 320- 329
6 孟祥海, 郑来, 秦观明 基于模糊逻辑的交通事故预测及影响因素分析[J]. 交通运输系统工程与信息, 2009, 9 (2): 87- 92
MENG Xiang-hai, ZHENG Lai, QIN Guan-ming Traffic accidents prediction and prominent influence factors analysis based on fuzzy logic[J]. Journal of Transportation Systems Engineering and Information Technology, 2009, 9 (2): 87- 92
doi: 10.3969/j.issn.1009-6744.2009.02.014
7 龙雪琴, 关宏志 基于仿真的城市道路交叉口最小安全间距研究[J]. 武汉理工大学学报: 交通科学与工程版, 2012, 36 (3): 510- 514
LONG Xue-qin, GUAN Hong-zhi Study on the minimal safety intersection spacing of city road based on simulation[J]. Journal of Wuhan University of Technology: Transportation Science and Engineering, 2012, 36 (3): 510- 514
doi: 10.3963/j.issn.2095-3844.2012.03.017
8 卓曦, 施文荣, 施群 城市干道信控交叉口最小间距计算[J]. 交通运输系统工程与信息, 2014, 14 (5): 81- 86
ZHUO Xi, SHI Wen-rong, SHI Qun Minimum spacing calculation for signalized intersections on the urban arterial[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14 (5): 81- 86
doi: 10.3969/j.issn.1009-6744.2014.05.012
9 GATTIS J L, CHIMKA J R, EVANS A Access spacing based on turning-vehicle acceleration[J]. Transportation Research Record: Journal of the Transportation Research Board, 2017, 2618: 1- 7
doi: 10.3141/2618-01
10 HALEEM K, GAN A, LU J Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areas[J]. Accident Analysis and Prevention, 2013, 55: 12- 21
doi: 10.1016/j.aap.2013.02.018
11 LI H, GRAHAM D J, MAJUMDAR A The impacts of speed cameras on road accidents: an application of propensity score matching methods[J]. Accident Analysis and Prevention, 2013, (60): 148- 157
12 XIE K, WANG X, HUANG H, et al Corridor-level signalized intersection safety analysis in Shanghai, China using Bayesian hierarchical models[J]. Accident Analysis and Prevention, 2013, 50: 25- 33
doi: 10.1016/j.aap.2012.10.003
13 KWIGIZILE V, BOATENG R A, OH J, et al. Evaluating effectiveness of pedestrian countdown signals on safety of older drivers [C]// The 95th Annual Meeting of the Transportation Research Board. Washington D. C. : Transportation Research Board, 2016.
14 FENG S, XIAO W Research and application of DBSCAN clustering algorithm based on density[J]. Computer Engineering and Applications, 2007, 43 (20): 216- 221
15 HARTIGAN J A, WONG M A Algorithm AS 136: a k-means clustering algorithm[J]. Journal of the Royal Statistical Society, 1979, 28 (1): 100- 108
16 CARSON J, MANNERING F The effect of ice warning signs on ice-accident frequencies and severities[J]. Accident Analysis and Prevention, 2001, 33 (1): 99- 109
doi: 10.1016/S0001-4575(00)00020-8
17 LEE J, MANNERING F Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis[J]. Accident Analysis and Prevention, 2002, 34 (2): 149- 161
doi: 10.1016/S0001-4575(01)00009-4
18 WENG J, YING E G, HAN H Evaluation of shipping accident casualties using zero-inflated negative binomial regression technique[J]. Journal of Navigation, 2016, 69 (2): 433- 448
doi: 10.1017/S0373463315000788
19 KIM D H, RAMJAN L M, MAK K K Prediction of vehicle crashes by drivers' characteristics and past traffic violations in Korea using a zero-inflated negative binomial model[J]. Traffic Injury Prevention, 2016, 17 (1): 86- 90
doi: 10.1080/15389588.2015.1033689
20 MANNERING F L, BHAT C R Analytic methods in accident research: methodological frontier and future directions[J]. Analytic Methods in Accident Research, 2014, 1: 1- 22
doi: 10.1016/j.amar.2013.09.001
21 LORD D, MANNERING F The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives[J]. Transportation Research Part A, 2010, 44 (5): 291- 305
22 CARTER D, SRINIVASAN R, GROSS F. Recommended protocols for developing crash modification factors NCHRP 20-7 (314) final report [R]. Washington D.C.: AASHTO, 2012.
23 LORD D, BONNESON J Development of accident modification factors for rural frontage road segments in Texas[J]. Transportation Research Record: Journal of the Transportation Research, 2007, 2023 (1): 20- 24
doi: 10.3141/2023-03
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