Severity analysis of freight car accidents considering time instability

doi:10.16265/j.cnki.issn1003-3033.2022.11.2245

Abstract

Abstract:

In order to improve truck traffic safety, a total of 29 213 accident data from 2014 to 2019 in the British STATS database were taken as the research object, the accident severity was taken as the dependent variable, and the crash characteristics related to people, vehicle, roadway and environment were considered as independent variables. The random parameter ordered logit model was constructed. Meanwhile, the log-likelihood ratio tests were used to test the time stability of the crash data within years period. The results show that there is serious time instability in the accident data, which should be divided into four groups:2014-2016, 2017, 2018 and 2019. this study finds 8 variables exhibit significant impacts on accident severity in all four crash models, including car drivers over 60 years old, truck turning, truck lane changing and so on. Other variables are only statistically significant in year-specific crash models. In addition, 9 variables exhibit heterogeneous effects on accident severity in a specific year accident models, including car drivers over 60 years old, truck lane changing, etc.

Key words: time instability, truck-car collision accident, accident severity, data heterogeneity, random parameters ordered Logit model

ZHOU Bei, SUN Qing, ZHANG Shengrui. Severity analysis of freight car accidents considering time instability[J]. China Safety Science Journal, 2022, 32(11): 160-167.

Figures/Tables 4

Tab.1

Tab.2

Likelihood ratio test results for adjacent years

年份分组	$L L (β t i, t i + 1)$	$L L (β t i)$	$L L (β t i + 1)$	似然比检验结果
2014— 2015	-2 880.86	-1 653.47	-1 213.97	26.84(20) [86.03%]
2015— 2016	-3 064.39	-1 213.97	-1 836.95	26.96(19) [89.54%]
2016— 2017	-3 276.95	-1 836.95	-1 425.08	29.85(17) [97.26%]
2017— 2018	-3 142.98	-1 425.08	-1 688.98	57.84(16) [99.99%]
2018— 2019	-3 351.50	-1 688.98	-1 648.34	28.35(16) [97.13%]

Tab.2

Tab.3

Tab.4

Parameters estimation and average marginal benefit value of random parameter ordered Logit models

自变量	2014—2016年模型				2017年模型				2018年模型				2019年模型
	参数估计	平均边际效益/%			参数估计	平均边际效益/%			参数估计	平均边际效益/%			参数估计	平均边际效益/%
	参数估计	轻微	严重	死亡	参数估计	轻微	严重	死亡	参数估计	轻微	严重	死亡	参数估计	轻微	严重	死亡
高速公路	-0.36	1.62	-1.45	-0.16	-0.77	2.61	-2.42	-0.19	—	—	—	—	-0.66	3.38			-3.18	-0.20
次干路	-0.21	0.98	-0.88	-0.10	—	—	—	—	—	—	—	—	—	—			—	—
双幅路	-0.48	2.21	-1.98	-0.23	—	—	—	—	-1.01	2.52	-2.43	-0.09	-0.42	2.47			-2.32	-0.15
高速出入口	-0.75	2.81	-2.52	-0.28	—	—	—	—	-2.25	2.87	-2.76	-0.10	-1.05	4.47			-4.22	-0.26
限速值小于等于48 km/h	-0.80	3.99	-3.58	-0.41	-0.60	2.55	-2.37	-0.19	-0.74	2.17	-2.09	-0.08	-0.77	4.81			-4.52	-0.29
限速值97 km/h以上	—	—	—	—	0.47	-1.81	1.68	0.13	—	—	—	—	—	—			—	—
T/十字/多路交叉口	-0.19	0.96	-0.86	-0.10	-0.47	1.97	-1.83	-0.14	—	—	—	—	—	—			—	—
环形交叉口	-0.56	2.37	-2.13	-0.24	-1.11	3.39	-3.15	-0.24	—	—	—	—	-1.16	5.14			-4.84	-0.30
路面结冰	-0.69	2.65	-2.38	-0.27	—	—	—	—	—	—	—	—	—	—			—	—
夜间有灯光	0.48	-2.88	2.58	0.30	1.07	-6.88	6.35	0.53	—	—	—	—	0.60	-4.67			4.37	0.29
夜间无灯光	0.21(1.73)	-1.18	1.06	0.12	1.08	-7.21	6.65	0.56	1.15	-5.65	5.43	0.21	0.81	-7.00			6.54	0.45
雨天/雪天	-0.26	1.23	-1.10	-0.13	—	—	—	—	—	—	—	—	—	—			—	—
城市	-0.51	2.61	-2.34	-0.27	-0.94	4.06	-3.76	-0.30	-0.70	2.07	-2.00	-0.08	-0.44	2.80			-2.63	-0.17
高峰期	-0.32	1.51	-1.35	-0.15	—	—	—	—	—	—	—	—	—	—			—	—
男性小汽车驾驶员	-0.18(1.27)	0.94	-0.84	-0.10	—	—	—	—	—	—	—	—	0.28(1.23)	-1.79			1.68	0.11
60岁以上小汽车驾驶员	0.30(1.31)	-1.71	1.53	0.18	0.73(0.87)	-4.07	3.76	0.31	1.08(1.00)	-4.67	4.50	0.17	0.72(1.09)	-5.71			5.35	0.36
小汽车车龄0~3年	—	—	—	—	—	—	—	—	—	—	—	—	-1.06(1.44)	5.53			-5.20	-0.33
小汽车车龄8~13年	—	—	—	—	—	—	—	—	0.44	-1.46	1.41	0.05	0.28	-1.90			1.78	0.12
小汽车车龄超过13年	—	—	—	—	—	—	—	—	0.44	-1.52	1.46	0.06	—	—			—	—
小汽车启动/停车	-1.04	4.28	-3.85	-0.43	-1.44	4.63	-4.29	-0.33	-1.03	2.46	-2.37	-0.09	-0.71	3.88			-3.65	-0.23
小汽车转弯	-0.72	3.00	-2.70	-0.30	-0.44	1.68	-1.56	-0.12	-0.89	2.06	-1.99	-0.07	-0.46	2.58			-2.42	-0.15
小汽车变道	-0.58	2.34	-2.10	-0.24	—	—	—	—	—	—	—	—	—	—			—	—
小汽车超车	—	—	—	—	0.82	-5.13	4.74	0.39	—	—	—	—	—	—			—	—
男性货车驾驶员	—	—	—	—	0.88(1.25)	-2.73	2.54	0.20	—	—	—	—	—	—			—	—
16~25岁货车驾驶员	—	—	—	—	—	—	—	—	-0.46	1.19	-1.14	-0.04	—	—			—	—
45~60岁货车驾驶员	0.31	-1.64	1.47	0.17	0.41(0.38)	-1.93	1.79	0.14	—	—	—	—	—	—			—	—
60岁以上货车驾驶员	0.43(0.58)	-2.60	2.32	0.27	—	—	—	—	—	—	—	—	—	—			—	—
货车启动/停车	-0.55	2.47	-2.22	-0.25	—	—	—	—	-1.30(1.81)	2.91	-2.80	-0.10	-1.14(1.09)	5.66			-5.32	-0.33
货车转弯	-0.92	3.58	-3.22	-0.36	-0.70	2.48	-2.30	-0.18	-0.58	1.46	-1.41	-0.05	-0.93	4.51			-4.25	-0.27
货车变道	-1.99(0.92)	5.15	-4.64	-0.51	-3.01(1.17)	5.16	-4.80	-0.37	-1.47	2.59	-2.50	-0.09	-2.45(1.73)	7.07			-6.67	-0.41
货车超车	—	—	—	—	—	—	—	—	—	—	—	—	-0.96	4.19			-3.95	-0.24

Tab.4

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变量类型	分类描述	编码
事故严重程度y	—	1:轻微受伤,2:严重受伤,3:死亡
货车驾驶员特征	性别x₁	0:女性^*,1:男性
货车驾驶员特征	年龄x₂/岁	1:16~25,2:25~45^*,3:45~60,4:60以上
小汽车驾驶员特征	性别x₃	0:女性^*,1:男性
小汽车驾驶员特征	年龄x₄/岁	1:16~25,2:25~45^*,3:45~60,4:60以上
货车车辆特征	车龄x₅/年	1:0~3,2:3~8^*,3:8~13,4:13以上
货车车辆特征	行驶意图x₆	1:启动/停车,2:转弯,3:变道,4:超车,5:直行^*
小汽车车辆特征	车龄x₇/年	1:0~3,2:3~8^*,3:8~13,4:13以上
小汽车车辆特征	行驶意图x₈	1:启动/停车,2:转弯,3:变道,4:超车,5:直行^*
道路特征	道路等级x₉	1:高速公路,2:干线公路^*,3:次干路,4:地方公路
	道路类型x₁₀	1:单幅路(无中央分隔带)^*,2:双幅路(中央分隔带),3:高速出入口
	限速值x₁₁/(km·h^-1)	1:小于等于48,2: 64~97^*,3:97以上
	交叉口类型x₁₂	1:路段^*,2:环形交叉口,3:T/十字/多路交叉口
	信号控制类型x₁₃	1:无控制^*,2:信号灯控制,3:停车让行控制,4:人工控制
	过街人为管控x₁₄	1:无控制^*,0:有控制
	行人过街设施x₁₅	1:过街天桥/人行横道等设施,2:行人信号灯,3:无^*
	道路表面环境x₁₆	1:干燥^*,2:潮湿,3:结冰,4:其他
环境特征	季节x₁₇	1:春天,2:夏天^*,3:秋天,4:冬天
	光照x₁₈	1:白天^*,2:夜间有灯光,3:夜间无灯光
	天气x₁₉	1:晴天^*,2:雨天/雪天,3:有雾
时空特征	事故发生区域x₂₀	1:城市,0:乡村^*
	事故发生时间x₂₁	1:周内,0:周末^*
	事故发生时段x₂₂	1:高峰期,0:平峰期^*

年份	2014—2016	2017	2018	2019
2014— 2016	—	124.72(16) [>99.99%]	87.51(14) [>99.99%]	91.03(18) [>99.99%]
2017	49.13(23) [99.88%]	—	52.12(14) [>99.99%]	39.09(18) [99.72%]
2018	47.86(23) [99.83%]	39.89(16) [99.92%]	—	34.10(18) [98.78%]
2019	45.11(23) [99.62%]	43.07(16) [99.97%]	36.29(14) [99.91%]	—