Bus Accident Severity Analysis Based on Comprehensive
Accident Intensity

doi:10.16097/j.cnki.1009-6744.2022.06.016

Abstract

Abstract: To classify the severity of bus accidents more accurately and identify the influence factors for the bus accidents, this paper proposed a classification method based on comprehensive accident intensity and K-means algorithm. An analysis model of the influence factors of bus accident severity was also developed based on the results of the classification method. Comparing to the traditional qualitative classifications of accident severity, the comprehensive accident intensity method was introduced to calculate the bus accident severity, and the accident severity was classified by the K-means clustering algorithm. Then, 17 factors from environment, drivers, roads/vehicles and accident characteristics were selected as independent variables. The results of comprehensive accident intensity & K- means classification method and traditional four classification method were used as the dependent variables. The ordered Logit model was used to analyze the bus accident severity. In addition, the average marginal effect was used to quantify the impact of each significant factor and the bus accident data of Foshan City in 2021 was analyzed as an example. The results show that the ordered Logit model based on the classification method of comprehensive accident intensity and K-means algorithm has superior statistical performance. Peak periods, lane changes, speeding, excessive acceleration distracted driving, and pulling in and out of stations will increase the probability of serious bus accidents by 11.57%, 29.06%, 23.98%, 17.13%, 30.97% and 12.27%, respectively. Daytime and sunny days respectively reduce the probability of a serious bus accident by 22.31% and 12.34%.

Key words: urban traffic, influence factors of bus accidents, ordered Logit model, bus accident severity, comprehensive accident intensity

摘要： 为更加精确地对公交事故严重程度进行分类以探究其影响因素，本文提出一种基于事故综合强度+K-means的公交事故严重程度分类方法，并基于此分类方法建立公交事故严重程度影响因素分析模型。首先，针对传统事故严重程度分类中的定性分类方法，引入事故综合强度法定量计算公交事故严重程度，并运用K-means聚类算法对事故严重程度进行聚类。其次，选取环境、驾驶员、道路车辆和事故特征这 4 方面的 17 个因素作为自变量，分别将事故综合强度+K-means分类法和传统分类法的结果作为因变量，运用有序Logit模型分析公交车事故严重程度，同时利用平均边际效应量化各显著因素的影响程度，以佛山市2021年156起公交车事故数据为例进行分析。结果表明，基于事故综合强度+K-means分类法的有序Logit模型具有更好的拟合优度。高峰期、换道、超速、加速度过大、注意力分散和进出站会增大发生极严重公交车事故的概率，增大的概率分别为11.57%、29.06%、23.98%、17.13%、30.97%和12.27%；白天和晴天会减小发生极严重公交车事故的概率，减少的概率分别为22.31%和12.34%。

关键词: 城市交通, 公交事故影响因素, 有序Logit模型, 公交事故严重程度, 事故综合强度

CLC Number:

U491.3

LIU Qiang, YAN Xiu, XIE Qian, XIE Xiao-min. Bus Accident Severity Analysis Based on Comprehensive Accident Intensity[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 152-159.

刘强, 严修, 谢谦, 解孝民. 基于事故综合强度的公交事故严重程度分析[J]. 交通运输系统工程与信息, 2022, 22(6): 152-159.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.06.016

http://www.tseit.org.cn/EN/Y2022/V22/I6/152

References

[1] WANG L Z, WANG Y P, SHI L Y, et al. Analysis of risky driving behaviors among bus drivers in China: The role of enterprise management, external environment and attitudes towards traffic safety[J]. Accident Analysis & Prevention, 2022, 168: 106589.

[2] 朱彤, 秦丹, 董傲然, 等. 公交驾驶员违规类型同交通事故间的关联分析[J]. 交通运输系统工程与信息, 2022, 22(2): 322-329. [ZHU T, QIN D, DONG A R, et al. Relational analysis between bus drivers' violation type and traffic accident[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 322-329.]

[3] SAM E F, DANIELS S, BRIJS K, et al. Modelling public bus/minibus transport accident severity in Ghana[J]. Accident Analysis & Prevention, 2018, 119: 114-121.

[4] REUBEN T, SUNGHO L, ENOCH F S, et al. Investigating factors affecting bus/minibus accident severity in a developing country for different subgroup datasets characterised by time, pavement, and light conditions[J]. Accident Analysis & Prevention, 2021, 159: 106286.

[5] 顾宏艳, 沈金星, 于淼, 等. 公交车单车事故严重程度影响因素分析 [J]. 科学技术与工程, 2021, 21(33): 14401-14407. [GU H Y, SHEN J X, YU M, et al. Analysis of influencing factors of single-vehicle bus accident severity[J]. Science Technology and Engineering, 2021, 21(33): 14401-14407.]

[6] 林庆丰, 邓院昌. 基于Logistic的城市公交事故严重程度影响因素分析: 以广东省为例[J]. 中山大学学报(自然科学版), 2020, 59(4): 120-127. [LIN Q F, DENG Y C. Logistic analysis of influencing factors of urban bus accident severity: a case study of Guangdong province[J]. Journal of Sun Yat-sen University (Natural Science), 2020, 59(4): 120-127.]

[7] 陈昭明, 徐文远, 曲悠扬, 等. 基于混合Logit模型的高速公路交通事故严重程度分析[J]. 交通信息与安全, 2019, 37(3): 42-50. [CHEN Z M, XU W Y, QU Y Y, et al. Analysis of highway traffic accident severity based on mixed Logit model[J]. Journal of Transport Information and Safety, 2019, 37(3): 42-50.]

[8] 何杨. 基于驾驶员角度的交通事故心理致因研究[D]. 南京: 南京师范大学, 2021. [HE Y. Study on psychological causes of traffic accidents from the perspective of drivers[D]. Nanjing: Nanjing Normal University, 2021.]

[9] 金雪纯子. 特大交通事故影响因素建模与分析[D]. 北京: 北京交通大学, 2021. [JIN X C Z. Influencing factors modeling and analysis of extraordinarily severe traffic crashes[D]. Beijing: Beijing Jiaotong University, 2021.]

[10] 杭福兵. 城市公交事故数据分析与影响因素挖掘[D]. 南京: 东南大学, 2020. [HANG F B. Bus accident analysis and risk factors mining for urban bus transportation[D]. Nanjing: Southeast University, 2020.]

[11] 王炜, 过秀成. 交通工程学[M]. 南京: 东南大学出版社, 2011. [WANG W, GUO X C. Traffic engineering[M]. Nanjing: Southeast University Press, 2011.]

[12] 郑宣传, 魏运, 秦勇, 等. 一种改进K-means模型的城市轨道交通突发事件分级方法[J]. 交通运输系统工程与信息, 2019, 19(3): 134-140. [ZHENG X C, WEI Y, QIN Y, et al. Classification method of urban rail transit emergencies based on improved K-means algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3): 134-140.]

[13] 周悦, 江欣国, 付川云, 等. 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 Y, JIANG X G, FU C Y, et al. Operational factors analysis for taxi speeders using mixed Logit model[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 229-236.]

[14] 刘文歌, 杨晶. 基于混合Logit模型的轨道交通安全统计生命价值[J]. 中国安全科学学报, 2020, 30(4): 186- 191. [LIU W G, YANG J. Value of statistical life in rail traffic safety based on mixed Logit model[J]. China Safety Science Journal, 2020, 30(4): 186-191.]

[15] 董傲然, 王长帅, 秦丹, 等. 机动车-行人事故中行人伤害严重程度分析[J]. 中国安全科学学报, 2020, 30 (11): 141-147. [DONG A R, WANG C S, QIN D, et al. Analysis on injury severity of pedestrian in motor vehiclepedestrian accidents[J]. China Safety Science Journal, 2020, 30(11): 141-147.

Bus Accident Severity Analysis Based on Comprehensive Accident Intensity

基于事故综合强度的公交事故严重程度分析

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