Journal of South China University of Technology(Natural Science) >

2023 , Vol. 51 >Issue 1: 114 - 122

DOI: https://doi.org/10.12141/j.issn.1000-565X.210758

Traffic & Transportation Engineering

Analysis of Freeway Crash Severity Based on Spatial Generalized Ordered Probit Model

Expand
  • School of Civil Engineering and Transportation，South China University of Technology，Guangzhou 510640，Guangdong，China
胡郁葱（1970-），女，博士，副教授，主要从事交通规划与设计研究。E-mail:ychu@scut.edu.cn.

Received date: 2021-12-01

  Online published: 2022-07-20

Supported by

the International Science & Technology Cooperation Program of China(2017YFE0134500)

Fold

Abstract

To provide a deep insight into the significant factors that affect the severity of freeway crashes, this study took the crash data from the Dongguan section of the Guang-Shen Yanjiang Freeway in China from 2014 to 2019 as the research object. Crash severity levels were divided into three categories (i.e., no injury crash, minor injury crash, severe injury or fatality crash). Accounting for spatial correlation among adjacent crashes via conditional autoregressive priors, spatial generalized ordered Probit models with different correlation distance thresholds were developed, where the crash severity was used as the dependent variable and 13 potential influencing factors were used as independent variables. The research results show that there is significant spatial correlation among crashes; the spatial generalized ordered Probit models outperform the generalized ordered Probit model and multinomial Logit model; and the spatial model with 250-meters correlation distance threshold achieves the best performance. The results of model parameter estimation reveal that the type and registered province of vehicles, the time of crash occurrence, curvature of crash location, bridge section, and crash type have significant effects on freeway crash severity. The marginal effects of these factors indicate that: as compared with crashes with cars involved only, the involvement of bus, truck and other type vehicles will increase the probability of severe injury or fatality by 3.27%, 1.53%, and 4.11%, respectively; the involvement of vehicles from other provinces will increase the probability of severe injury or fatality by 1.02%; as compared with those occurring on weekend, spring, and bridge, crashes occurring on weekdays, summer, and non-bridge sections would increase the probability of severe injury or fatality by 0.87%, 2.38%, and 0.08%, respectively; the probability of heavy casualties caused by bicycle accidents is 1.64% lower than that of multi-vehicle accidents; the probability of severe injury or fatality will decrease by 1.54% for per 1 km-1 increase in horizontal curvature of crash location.

Key words： traffic engineering; freeway safety; spatial generalized ordered Probit model; crash severity; spatial correlation distance

Cite this article

HU Yucong, WEI Hu, ZENG Qiang . Analysis of Freeway Crash Severity Based on Spatial Generalized Ordered Probit Model[J]. Journal of South China University of Technology(Natural Science), 2023 , 51(1) : 114 -122 . DOI: 10.12141/j.issn.1000-565X.210758

References

1 ZHAO J， DENG W ．Traffic accidents on expressways：new threat to China［J］．Traffic Injury Prevention，2012，13（3）：230-238．
2 YU R， ABDEL-ATY M ．Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data［J］．Safety Science，2014，63：50-56．
3 HAQ M T， ZLATKOVIC M， KSAIBATI K ．Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway：a hierarchical Bayesian random intercept approach［J］．Accident Analysis & Prevention，2020，144：105654．
4 HAQ M T， ZLATKOVIC M， KSAIBATI K ．Assessment of tire failure related crashes and injury severity on a mountainous freeway：Bayesian binary logit approach［J］．Accident Analysis & Prevention，2020，145：105693．
5 SAVOLAINEN P T， MANNERING F L， LORD D，et al ．The statistical analysis of highway crash-injury severities：a review and assessment of methodological alternatives［J］．Accident Analysis & Prevention，2011，43（5）：1666-1676．
6 王磊，吕璞，林永杰 ．高速公路交通事故影响因素分析及伤害估计［J］．中国安全科学学报，2016，26（3）：86-90．
6 WANG Lei， Pu LYU， LIN Yongjie ．Traffic accidents on freeways：influencing factors analysis and injury severity evaluation［J］．China Safety Science Journal，2016，26（3）：86-90．
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．
7 CHEN Zhaoming， XU Wenyuan， QU Youyang，et al ．Severity of traffic crashes on freeways based on mixed logit model［J］．Journal of Transport Information and Safety，2019，37（3）：42-50．
8 HOU Q， HUO X， LENG J，et al ．Examination of driver injury severity in freeway single-vehicle crashes using a mixed logit model with heterogeneity-in-means［J］．Physica A：Statistical Mechanics and its Applications，2019，531：121760．
9 ZENG Q， GU W， ZHANG X，et al ．Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors［J］．Accident Analysis & Prevention，2019，127：87-95．
10 ZHANG X， WEN H， YAMAMOTO T，et al ．Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data：using a Bayesian multinomial logit model with conditional autoregressive priors［J］．Journal of Safety Research，2021，76：248-255．
11 HUANG H， PENG Y， WANG J，et al ．Interactive risk analysis on crash injury severity at a mountainous freeway with tunnel groups in China［J］．Accident Analysis & Prevention，2018，111：56-62．
12 吕璞，柏强，陈琳 ．融合深度反残差与注意力机制的山区高速公路事故严重程度预测模型［J］．中国公路学报，2021，34（6）：205-213．
12 Pu LYU， BAI Qiang， CHEN Lin ．A model for predicting the severity of accidents on mountainous expressways based on deep inverted residuals and attention mechanisms［J］．China Journal of Highway and Transport，2021，34（6）：205-213．
13 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．
14 ZENG Q， WEN H， HUANG H，et al ．A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments［J］．Accident Analysis & Prevention，2017，100：37-43．
15 赵跃峰，张生瑞，马壮林 ．基于部分优势比的公路隧道交通事故严重程度分析模型［J］．中国公路学报，2018，31（9）：159-166．
15 ZHAO Yuefeng， ZHANG Shengrui， MA Zhuanglin ．Analysis of traffic accident severity on highway tunnels using the partial proportion odds model［J］．China Journal of Highway and Transport，2018，31（9）：159-166．
16 ELURU N， BHAT C R， HENSHER D A ．A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes［J］．Accident Analysis & Prevention，2008，40（3）：1033-1054．
17 SPIEGELHALTER D， THOMAS A， BEST N，et al ．WinBUGS user manual［M］．Cambridge：MRC Biostatistics Unit，2003．
18 SPIEGELHALTER D J， BEST N G， CARLIN B P，et al ．Bayesian measures of model complexity and fit［J］．Journal of the Royal Statistical Society：Series B，2002，64（4）：583-639．
19 WEN H， XUE G ．Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways［J］．Accident Analysis & Prevention，2020，144：105667．
20 HUANG H， SIDDIQUI C， ABDEL-ATY M ．Indexing crash worthiness and crash aggressivity by vehicle type［J］．Accident Analysis & Prevention，2011，43（4）：1364-1370．
21 CHRISTOFOROU Z， COHEN S， KARLAFTIS M G ．Vehicle occupant injury severity on highways：an empirical investigation［J］．Accident Analysis & Prevention，2010，42（6）：1606-1620．
Options
Outlines

/

〈 〉