Jointly modeling the dependence of injury severity and crash size involved in motorcycle crashes in Cambodia using a copula-based approach

doi:10.1007/s42524-024-0302-8

, Vol.

Issue (): 0 https://doi.org/10.1007/s42524-024-0302-8

本期目录

Jointly modeling the dependence of injury severity and crash size involved in motorcycle crashes in Cambodia using a copula-based approach

Yaqiu LI¹, Lon VIRAKVICHETRA², Junyi ZHANG³(

), Haoran LI⁴, Yunpeng LU⁵

¹. School of Transportation, Southeast University, Nanjing, 211189, China; Graduate School of Advanced Science and Engineering, Hiroshima University, 1-5-1 Kagamiyama, Higashihiroshima 739-8529, Japan
². Graduate School of Advanced Science and Engineering, Hiroshima University, 1-5-1 Kagamiyama, Higashihiroshima 739-8529, Japan
³. School of Transportation, Southeast University, Nanjing 211189, China
⁴. School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China; Tsinghua University Suzhou Automotive Research Institute, Suzhou 215200, China
⁵. School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China

全文: PDF(952 KB) HTML

Abstract：

Escalating motorcycle crashes present a significant challenge due to the increase in motorcycle registrations and the corresponding increase in mortality rates. This issue is particularly acute in Cambodia, where motorcycles are the primary mode of transportation. In the analysis of motorcycle crashes, two key measures of severity are injury severity and crash size, notably the number of injuries. Typically, these indicators are analyzed independently to understand the impact and consequences of motorcycle accidents. Nevertheless, it is critical to recognize that both observed and unobserved factors may concurrently affect these crash indicators, indicating a possible interrelationship between injury severity and motorcycle crash size. Neglecting the joint occurrence of these variables can result in biased and incorrect parameter estimation. This research contributes to the existing body of knowledge by simultaneously analyzing the factors influencing both injury severity and motorcycle crash size. This approach further distinguishes itself by considering the interdependence between these two results utilizing a copula-based approach. Six models based on copulas were developed using the ordered logit model, which was designed to capture the ordinal nature of injury severity and crash size. By analyzing motorcycle crash data from 2016 in Cambodia, the Frank copula framework was identified as the most effective among the five approaches. The findings revealed that factors such as motorcycle-to-pedestrian collisions, head-on collisions, X junctions, and national roads significantly increase both motorcyclist injury severity and crash size. These insights are valuable for policymakers in formulating targeted strategies to improve motorcycle safety within transportation systems.

Key words： traffic accident crash motorcycle injury severity crash size

收稿日期: 2023-11-25

Corresponding Author(s): Junyi ZHANG

引用本文:

. [J]. Frontiers of Engineering Management, 10.1007/s42524-024-0302-8.
Yaqiu LI, Lon VIRAKVICHETRA, Junyi ZHANG, Haoran LI, Yunpeng LU. Jointly modeling the dependence of injury severity and crash size involved in motorcycle crashes in Cambodia using a copula-based approach. Front. Eng, , (): 0.

链接本文:

https://academic.hep.com.cn/fem/CN/10.1007/s42524-024-0302-8
https://academic.hep.com.cn/fem/CN/Y/V/I/0

Author	Study region and period	Bivariate dependent variables	Key findings
Yasmin et al. (2014)	Victoria, Australia (2006–2010)	Injury severity, collision type	Proposed and estimated a joint model for injury severity and collision type.
Ayuso et al. (2016)	Insurance data in Spain	Temporary disability, permanent motor injuries	Positive dependence is observed between the occurrence of temporary and permanent injuries.
Wali et al. (2018)	Virginia (2013)	Injury severity of at-fault, not-at-fault drivers	The driving errors committed by at-fault drivers have a more significant negative impact on injury severity than those of not-at-fault drivers.
Wang et al. (2019)	Connecticut (2016–2017)	Injury severity, crash type, vehicle damage, and driver error	The four dependent variables exhibit strong correlations.
Tamakloe et al. (2020)	Republic of Korea (2010–2016)	Bus involved crash severity, number of vehicles involved	Positive dependence is found between the two dependent variables.
Ghomi and Hussein (2021)	Hamilton city (2010–2017)	Frequency, severity of pedestrian in vehicle-pedestrian crashes	The frequency of collisions decreases at intersections along major transit routes when bus frequency increases. Conversely, intersections near schools see a higher incidence of collisions.
Ahmad et al. (2023)	District 3 of Pennsylvania (2015–2018)	Property-damage only injury and fatal, crash frequencies	Bivariate models with copulas outperform traditional bivariate and univariate models.
Huang et al. (2023)	Los Angeles County in California (2016–2019)	Primary and secondary crash severity	A significant dependency between primary crashes severity and secondary crashes severity, especially with high severity level
Phuksuksakul et al. (2023)	Queensland State (2012–2018)	Crash type, injury severity of pedestrian and bicyclist	The dependency between active traveler crash types and injury severity differs case by case; considering unobserved heterogeneity in external factors improves model fit.

Tab.1

Fig.1

Copula	Formulation	Dependence parameter	Kendal’s tau τ(θ)	Kendall’s tau
Gaussian Copula	$Φ 2 (Φ ? 1 (u), Φ ? 1 (v); ρ)$	$ρ ∈ [? 1, 1]$	$2 π arcsin θ$	$[? 1, 1]$
Student’s t Copula	$t C (t ? 1 (u), t ? 1 (v); ρ, k)$	$ρ ∈ [0, 1]$	$2 π arcsin θ$	$[? 1, 1]$
Clayton Copula	$(u ? θ + v ? θ ? 1) ? 1 θ$	$θ ∈ [? 1, ∞) ? 0$	$θ θ + 2$	$[0, 1)$
Gumbel Copula	$exp (? ((? ln ? (u)) θ + (? ln ? (v)) θ) 1 θ)$	$θ ∈ [1, ∞)$	$1 ? 1 θ$	$[0, 1)$
Frank Copula	$? 1 θ ln (1 + (exp ? (? θ u) ? 1) (exp ? (? θ v) ? 1) exp (θ) ? 1)$	$θ ∈ R {0}$	$1 ? 4 θ + 4 θ ∫ 0 θ x / θ exp ? (x) ? 1 d x$	$(? 1, 1)$
Joe Copula	$1 ? [(1 ? u) θ + (1 ? v) θ ? (1 ? u) θ (1 ? v) θ] 1 θ$	$θ ∈ (1, ∞)$	$1 + 4 θ 2 ∫ 01 x log ? (x) (1 ? x) 2 (1 ? θ) / θ d x$	$[0, 1)$

Tab.2

Tab.3

Fig.2

Tab.4

	Independent model				Frank copula model
	Injury severity		Crash size		Injury severity		Crash size
	Parameter estimate	Z score	Parameter estimate	Z score	Parameter estimate	Z score	Parameter estimate	Z score
Crash characteristics
Hit motorcycle (1 if the counterpart was a motorcycle, 0 otherwise)	?0.539	?5.663	2.534	32.438	?0.535	?5.389	1.771	37.227
Hit pedestrian (1 if the counterpart was a pedestrian, 0 otherwise)	0.655	6.653	0.261	3.475	0.243	6.560	0.332	2.053
Hit passenger vehicle (1 if the counterpart was a passenger vehicle, 0 otherwise)	1.153	10.342	?	?	1.051	8.552	?	?
Head on collision (1 if the collision type was head on, 0 otherwise)	0.474	5.962	0.312	3.185	0.395	3.643	0.455	3.626
Rear end collision (1 if the collision type was rear end collision, 0 otherwise)	?	?	0.341	3.266	?	?	0.289	5.679
Right angle collision (1 if the collision type was right angle collision, 0 otherwise)	?0.209	?2.577	0.338	3.362	?0.175	?1.910	0.259	6.128
Side swipe collision (1 if the collision type was side swipe collision, 0 otherwise)	?0.471	?4.247	0.435	3.500	?0.400	?3.491	0.273	2.742
Fall alone (1 if the motorcycle fell alone, 0 otherwise)	0.865	5.509	?	?	0.649	4.642	?	?
Casualty characteristics
Rider (1 if the casualty was rider, 0 otherwise)	0.748	12.509	?1.876	?30.531	0.732	10.691	?1.238	?28.127
20?29 years old (1 if the casualty’s age lay between 20 and 29 years old, 0 otherwise)	?	?	?0.365	?5.025	?	?	?0.309	?6.457
30?39 years old (1 if the casualty’s age lay between 30 and 39 years old, 0 otherwise)	0.250	3.542	?0.504	?5.874	0.314	2.459	?0.649	?5.034
40?49 years old (1 if the casualty’s age lay between 40 and 49 years old, 0 otherwise)	0.493	5.177	?0.855	?7.804	0.389	8.128	?0.729	?5.014
Above 50 years old (1 if the casualty’s age was above 50 years old, 0 otherwise)	0.599	6.655	?0.850	?8.096	0.521	3.460	?1.102	?6.847
Child (1 if the casualty was a child, 0 otherwise)	?	?	0.758	3.735	?	?	0.490	2.036
Farmer (1 if the casualty was a farmer, 0 otherwise)	?	?	0.149	2.569	?	?	0.118	2.585
Helmet (1 if the casualty was wearing a helmet, 0 otherwise)	?0.367	?5.666	?	?	?0.284	?6.289	?	?
Speeding (1 if the casualty was speeding, 0 otherwise)	0.132	2.061	?	?	0.176	3.131	?	?
Against flow (1 if the rider drove against flow of traffic, 0 otherwise)	0.199	2.110	?	?	0.201	2.621	?	?
Roadway characteristics
X junction (1 if the crash took place at a X junction road, 0 otherwise)	0.256	1.827	0.284	2.488	0.400	1.920	0.468	3.457
National road (1 if the crash took place at national road, 0 otherwise)	0.397	6.016	0.331	2.928	0.458	2.362	0.250	3.637
Provincial road (1 if the crash took place at provincial road, 0 otherwise)	?	?	0.310	2.463	?	?	0.329	6.383
Major road (1 if the crash took place at major road, 0 otherwise)	0.296	1.703	?	?	0.361	2.601	?	?
Minor road (1 if the crash took place at minor road, 0 otherwise)	?	?	0.343	2.223	?	?	0.362	2.460
Local road (1 if the crash took place at local road, 0 otherwise)	?	?	0.290	2.213	?	?	0.387	3.682
Urban (1 if the crash took place in urban, 0 otherwise)	?0.420	?7.381	?	?	?0.493	?8.364	?	?
Temporal characteristics
Weekend (1 if the crash took place on Weekend, 0 otherwise)	?	?	0.182	3.285	?	?	0.159	3.143
Festival (1 if the crash took place during national festival, 0 otherwise)	?	?	0.288	3.671	?	?	0.438	5.997
Thresholds 1	?0.183	?1.210	?1.239	?8.431	?0.094	?8.464	?1.127	?2.457
Thresholds 2	2.279	14.704	1.012	6.902	2.457	6.457	1.238	3.238
Thresholds 3	?	?	2.806	8.556	?	?	3.128	5.124
Kendall’s tau					0.142

Tab.5

	Independent model							Frank copula model
	Crash severity			Injury number				Crash severity			Injury number
	Minor	Severe	Fatal	1	2	3	≥4	Minor	Severe	Fatal	1	2	3	≥4
Crash characteristics
Hit motorcycle (1 if the counterpart was a motorcycle, 0 otherwise)	0.0864	0.0105	?0.0969	?0.1577	0.0409	0.0647	0.0521	0.0629	0.0135	?0.0764	?0.1330	?0.0413	0.0728	0.1015
Hit pedestrian (1 if the counterpart was a pedestrian, 0 otherwise)	?0.1233	0.0266	0.0967	?0.0372	?0.0030	0.0167	0.0234	?0.1107	0.0144	0.0963	?0.0243	?0.0133	0.0157	0.0219
Hit passenger vehicle (1 if the counterpart was a passenger vehicle, 0 otherwise)	?0.2142	0.0394	0.1748	?	?	?	?	?0.1052	0.0091	0.0962	?	?	?	?
Head on collision (1 if the collision type was head on, 0 otherwise)	?0.0872	0.0158	0.0714	?0.0452	?0.0035	0.0213	0.0274	?0.0623	0.0083	0.0541	?0.0372	?0.0154	0.0167	0.0360
Rear end collision (1 if the collision type was rear end collision, 0 otherwise)	?	?	?	?0.0485	?0.0047	0.0224	0.0308	?	?	?	?0.0294	?0.0125	0.0107	0.0312
Right angle collision (1 if the collision type was right angle collision, 0 otherwise)	0.0356	?0.0010	?0.0346	?0.0487	?0.0040	0.0228	0.0299	0.1229	?0.0269	?0.0960	?0.0320	?0.0129	0.0110	0.0339
Side swipe collision (1 if the collision type was side swipe collision, 0 otherwise)	0.0809	?0.0035	?0.0774	?0.0618	?0.0066	0.0289	0.0396	0.1169	?0.0122	?0.1046	?0.0312	?0.0091	0.0127	0.0276
Fall alone (1 if the motorcycle fell alone, 0 otherwise)	?0.1527	0.0150	0.1377	?	?	?	?	?0.1460	0.0121	0.1339	?	?	?	?
Human characteristics
Rider (1 if the casualty was rider, 0 otherwise)	?0.1514	0.0562	0.0951	0.1538	0.1777	?0.0482	?0.2833	?0.1134	0.0093	0.1041	0.1544	0.1774	?0.0472	?0.2845
20?29 years old (1 if the casualty’s age lay between 20 and 29 years old, 0 otherwise)	?	?	?	0.0497	0.0088	?0.0224	?0.0360	?	?	?	0.0836	0.0166	?0.0351	?0.0651
30?39 years old (1 if the casualty’s age lay between 30 and 39 years old, 0 otherwise)	?0.0441	0.0045	0.0396	0.0694	0.0110	?0.0317	?0.0488	?0.0623	0.0090	0.0533	0.0959	0.0157	?0.0315	?0.0802
40?49 years old (1 if the casualty’s age lay between 40 and 49 years old, 0 otherwise)	?0.0870	0.0088	0.0782	0.1191	0.0176	?0.0549	?0.0818	?0.0504	0.0016	0.0487	0.0721	0.0022	?0.0310	?0.0433
Above 50 years old (1 if the casualty’s age was above 50 years old, 0 otherwise)	?0.1063	0.0118	0.0945	0.1178	0.0183	?0.0544	?0.0816	?0.0777	0.0065	0.0713	0.1045	0.0148	?0.0537	?0.0656
Child (1 if the casualty was a child, 0 otherwise)	?	?	?	?0.1074	?0.0127	0.0503	0.0698	?	?	?	?0.0935	?0.0017	0.0171	0.0781
Farmer (1 if the casualty was a vendor, 0 otherwise)	?	?	?	?0.0213	?0.0021	0.0099	0.0134	?	?	?	?0.0176	?0.0102	?0.0119	0.0397
Helmet (1 if the casualty was wearing a helmet, 0 otherwise)	0.0615	?0.0003	?0.0612	?	?	?	?	0.0372	?0.0079	?0.0293	?	?	?	?
Speeding (1 if the casualty was speeding, 0 otherwise)	?0.0232	?0.0023	0.0210	?	?	?	?	?0.0058	?0.0025	0.0083	?	?	?	?
Against flow (1 if the rider drove against flow of traffic, 0 otherwise)	?0.0348	0.0029	0.0319	?	?	?	?	?0.0355	0.0034	0.0321	?	?	?	?
Roadway characteristics
X junction (1 if the crash took place at a X junction road, 0 otherwise)	?0.0447	0.0036	0.0410	?0.0403	?0.0045	0.0187	0.0261	?0.0445	0.0034	0.0411	?0.0455	?0.0083	0.0210	0.0328
National road (1 if the crash took place at national road, 0 otherwise)	?0.0746	0.0166	0.0580	?0.0481	?0.0023	0.0222	0.0281	?0.0745	0.0084	0.0661	?0.0412	?0.0070	0.0208	0.0275
Provincial road (1 if the crash took place at provincial road, 0 otherwise)	?	?	?	?0.0441	?0.0046	0.0207	0.0280	?	?	?	?0.0335	?0.0027	0.0251	0.0111
Major road (1 if the crash took place at major road, 0 otherwise)	?0.0516	0.0039	0.0477	?	?	?	?	?0.0679	0.0063	0.0616	?	?	?	?
Minor road (1 if the crash took place at minor road, 0 otherwise)	?	?	?	?0.0485	?0.0055	0.0225	0.0315	?	?	?	?0.0382	?0.0031	0.0179	0.0234
Local road (1 if the crash took place at local road, 0 otherwise)	?	?	?	?0.0411	?0.0044	0.0192	0.0264	?	?	?	?0.0260	?0.0022	0.0106	0.0176
Urban (1 if the crash took place in urban, 0 otherwise)	0.0685	0.0043	?0.0728	?	?	?	?	0.0386	0.0039	?0.0424	?	?	?	?
Temporal characteristics
Weekend (1 if the crash took place on Weekend, 0 otherwise)	?	?	?	?0.0259	?0.0025	0.0121	0.0163	?	?	?	?0.0275	?0.0034	0.0109	0.0200
Festival (1 if the crash took place during national festival, 0 otherwise)	?	?	?	?0.0411	?0.0044	0.0193	0.0262	?	?	?	?0.0321	?0.0054	0.0148	0.0227

Tab.6

1	K A, Abay (2015). Investigating the nature and impact of reporting bias in road crash data. Transportation Research Part A, Policy and Practice, 71: 31–45 https://doi.org/10.1016/j.tra.2014.11.002
2	M, Abdel-Aty J, Keller (2005). Exploring the overall and specific crash severity levels at signalized intersections. Accident Analysis and Prevention, 37: 417–425 https://doi.org/10.1016/j.aap.2004.11.002
3	M, Abrari Vajari K, Aghabayk M, Sadeghian N, Shiwakoti (2020). A multinomial logit model of motorcycle crash severity at Australian intersections. Journal of Safety Research, 73: 17–24 https://doi.org/10.1016/j.jsr.2020.02.008
4	N, Ahmad V V, Gayah E T, Donnell (2023). Copula-based bivariate count data regression models for simultaneous estimation of crash counts based on severity and number of vehicles. Accident Analysis and Prevention, 181: 106928 https://doi.org/10.1016/j.aap.2022.106928
5	K J, Anstey M S, Horswill J M, Wood C, Hatherly (2012). The role of cognitive and visual abilities as predictors in the multifactorial model of driving safety. Accident Analysis and Prevention, 45: 766–774 https://doi.org/10.1016/j.aap.2011.10.006
6	M, Ayuso L, Bermúdez M, Santolino (2016). Copula-based regression modeling of bivariate severity of temporary disability and permanent motor injuries. Accident Analysis and Prevention, 89: 142–150 https://doi.org/10.1016/j.aap.2016.01.008
7	B C, Banz J C, Fell F E, Vaca (2019). Complexities of young driver injury and fatal motor vehicle crashes. Yale Journal of Biology and Medicine, 92: 725–731
8	R A, Blackman N L, Haworth (2013). Comparison of moped, scooter and motorcycle crash risk and crash severity. Accident; Analysis and Prevention, 57: 1–9 https://doi.org/10.1016/j.aap.2013.03.026
9	C, Bonander R, Andersson F, Nilson (2015). The effect of stricter licensing on road traffic injury events involving 15 to 17-year-old moped drivers in Sweden: A time series intervention study. Accident Analysis and Prevention, 83: 154–161 https://doi.org/10.1016/j.aap.2015.07.022
10	W, Boulagouas S, García-Herrero R, Chaib J D, Febres M Á, Mariscal M, Djebabra (2020). An investigation into unsafe behaviors and traffic accidents involving unlicensed drivers: A perspective for alignment measurement. International Journal of Environmental Research and Public Health, 17: 6743 https://doi.org/10.3390/ijerph17186743
11	R W, Broyles L, Narine S R, Clarke D R, Baker (2003). Factors associated with the likelihood of injury resulting from collisions between four-wheel drive vehicles and passenger cars. Accident Analysis and Prevention, 35: 677–681 https://doi.org/10.1016/S0001-4575(02)00046-5
12	T, Champahom S, Jomnonkwao D, Watthanaklang A, Karoonsoontawong V, Chatpattananan V, Ratanavaraha (2020). Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes. Accident Analysis and Prevention, 141: 105537 https://doi.org/10.1016/j.aap.2020.105537
13	F, Chang S, Yasmin H, Huang A H, Chan M M, Haque (2021). Injury severity analysis of motorcycle crashes: A comparison of latent class clustering and latent segmentation based models with unobserved heterogeneity. Analytic Methods in Accident Research, 32: 100188 https://doi.org/10.1016/j.amar.2021.100188
14	Y C, Chiou C, Fu C Y, Ke (2020). Modelling two-vehicle crash severity by generalized estimating equations. Accident Analysis and Prevention, 148: 105841 https://doi.org/10.1016/j.aap.2020.105841
15	D D, Clarke P, Ward C, Bartle W, Truman (2010). Older drivers’ road traffic crashes in the UK. Accident Analysis and Prevention, 42: 1018–1024 https://doi.org/10.1016/j.aap.2009.12.005
16	A E, Curry K B, Metzger A F, Williams B C, Tefft (2017). Comparison of older and younger novice driver crash rates: Informing the need for extended Graduated Driver Licensing restrictions. Accident Analysis and Prevention, 108: 66–73 https://doi.org/10.1016/j.aap.2017.08.015
17	F, Dapilah B Y, Guba E, Owusu-Sekyere (2017). Motorcyclist characteristics and traffic behaviour in urban Northern Ghana: Implications for road traffic accidents. Journal of Transport & Health, 4: 237–245
18	M, De Lapparent (2006). Empirical Bayesian analysis of accident severity for motorcyclists in large French urban areas. Accident Analysis and Prevention, 38( 2): 260–268 https://doi.org/10.1016/j.aap.2005.09.001
19	M, De Lapparent (2008). Willingness to use safety belt and levels of injury in car accidents. Accident Analysis and Prevention, 40: 1023–1032 https://doi.org/10.1016/j.aap.2007.11.005
20	H T N, Doan M B, Hobday (2019). Characteristics and severity of motorcycle crashes resulting in hospitalization in Ho Chi Minh City, Vietnam. Traffic Injury Prevention, 20: 732–737 https://doi.org/10.1080/15389588.2019.1643460
21	H, Elsemesmani R, Bachir M J, El Sayed (2020). Association between trauma center level and outcomes of adult patients with motorcycle crash–related injuries in the United States. Journal of Emergency Medicine, 59: 499–507 https://doi.org/10.1016/j.jemermed.2020.06.018
22	N, Eluru R, Paleti R M, Pendyala C R, Bhat (2010). Modeling injury severity of multiple occupants of vehicles: Copula-based multivariate approach. Transportation Research Record: Journal of the Transportation Research Board, ( 2165): 1–11
23	T, Erhardt T, Rice L, Troszak M, Zhu (2016). Motorcycle helmet type and the risk of head injury and neck injury during motorcycle collisions in California. Accident Analysis and Prevention, 86: 23–28 https://doi.org/10.1016/j.aap.2015.10.004
24	G, Fountas S S, Pantangi K F, Hulme P Ch, Anastasopoulos (2019). The effects of driver fatigue, gender, and distracted driving on perceived and observed aggressive driving behavior: A correlated grouped random parameters bivariate probit approach. Analytic Methods in Accident Research, 22: 100091 https://doi.org/10.1016/j.amar.2019.100091
25	H., Hussein, M., Ghomi (2021). An integrated clustering and copula-based model to assess the impact of intersection characteristics on violation-related collisions. Accident Analysis & Prevention, 159: 106283
26	K, Gkritza (2009). Modeling motorcycle helmet use in Iowa: Evidence from six roadside observational surveys. Accident Analysis and Prevention, 41: 479–484 https://doi.org/10.1016/j.aap.2009.01.009
27	A H, Goodwin Y C, Wang R D, Foss B, Kirley (2022). The role of inexperience in motorcycle crashes among novice and returning motorcycle riders. Journal of Safety Research, 82: 371–375 https://doi.org/10.1016/j.jsr.2022.07.003
28	M, Haque Md H C, Chin H, Huang (2009). Modeling fault among motorcyclists involved in crashes. Accident Analysis and Prevention, 41: 327–335 https://doi.org/10.1016/j.aap.2008.12.010
29	H M, Hassan H, Al-Faleh (2013). Exploring the risk factors associated with the size and severity of roadway crashes in Riyadh. Journal of Safety Research, 47: 67–74 https://doi.org/10.1016/j.jsr.2013.09.002
30	K, Hassanzadeh S, Salarilak H, Sadeghi-Bazargani M, Golestani (2020). Motorcyclist risky riding behaviors and its predictors in an Iranian population. Journal of Injury & Violence Research, 12: 161–170
31	M, Hernández-Alava S, Pudney (2016). bicop: A command for fitting bivariate ordinal regressions with residual dependence characterized by a copula function and normal mixture marginals. Stata Journal, 16( 1): 159–184 https://doi.org/10.1177/1536867X1601600114
32	S R, Hu C S, Li C K, Lee (2010). Investigation of key factors for accident severity at railroad grade crossings by using a logit model. Safety Science, 48( 2): 186–194 https://doi.org/10.1016/j.ssci.2009.07.010
33	H, Huang X, Ding C, Yuan X, Liu J, Tang (2023). Jointly analyzing freeway primary and secondary crash severity using a copula-based approach. Accident Analysis and Prevention, 180: 106911 https://doi.org/10.1016/j.aap.2022.106911
34	M, Islam (2021). The effect of motorcyclists’ age on injury severities in single-motorcycle crashes with unobserved heterogeneity. Journal of Safety Research, 77: 125–138 https://doi.org/10.1016/j.jsr.2021.02.010
35	A T, Kashani M, Jafari M A, Bondarabadi S, Dabirinejad (2020). Factors affecting the accident size of motorcycle-involved crashes: A structural equation modeling approach. International Journal of Injury Control and Safety Promotion, 28: 16–21
36	U R, Khan J A, Razzak R, Jooma M G, Wärnberg (2022). Association of age and severe injury in young motorcycle riders: A cross-sectional study from Karachi, Pakistan. Injury, 53: 3019–3024 https://doi.org/10.1016/j.injury.2022.04.017
37	E, Kole K, Koedijk M, Verbeek (2007). Selecting copulas for risk management. Journal of Banking & Finance, 31( 8): 2405–2423
38	M, Lao T M, Lukusa (2023). Statistical approaches for assessing the effectiveness of safety devices use in preventing head injuries from motorcycle crashes. Case Studies on Transport Policy, 11: 100935 https://doi.org/10.1016/j.cstp.2022.100935
39	C, Lee M, Abdel-Aty (2008). Presence of passengers: Does it increase or reduce driver’s crash potential?. Accident Analysis and Prevention, 40: 1703–1712
40	J Y, Lee J H, Chung B, Son (2008). Analysis of traffic accident size for Korean highway using structural equation models. Accident Analysis and Prevention, 40: 1955–1963 https://doi.org/10.1016/j.aap.2008.08.006
41	M R, Lin J F, Kraus (2009). A review of risk factors and patterns of motorcycle injuries. Accident Analysis and Prevention, 41: 710–722 https://doi.org/10.1016/j.aap.2009.03.010
42	J, Ma K M, Kockelman P, Damien (2008). A multivariate poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. Accident Analysis and Prevention, 40: 964–975 https://doi.org/10.1016/j.aap.2007.11.002
43	F L, Mannering C R, Bhat (2014). Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research, 1: 1–22 https://doi.org/10.1016/j.amar.2013.09.001
44	E N, Martey N, Attoh-Okine (2019). Analysis of train derailment severity using vine copula quantile regression modeling. Transportation Research Part C, Emerging Technologies, 105: 485–503 https://doi.org/10.1016/j.trc.2019.06.015
45	E, Martin T, Courtright A, Nkurunziza O, Lah (2023). Motorcycle taxis in transition? Review of digitalization and electrification trends in selected East African capital cities. Case Studies on Transport Policy, 13: 101057 https://doi.org/10.1016/j.cstp.2023.101057
46	B A, McLellan S B, Rizoli F D, Brenneman B R, Boulanger P W, Sharkey J P, Szalai (1996). Injury pattern and severity in lateral motor vehicle collisions: A Canadian experience. Journal of Trauma and Acute Care Surgery, 41( 4): 708–713 https://doi.org/10.1097/00005373-199610000-00019
47	Ministry of Public Works and Transport (MPWT), General Department of Land Transport (GDLT), Land Transport Department (LTD) (2022). Traffic Safety in Cambodia. The 13th Public and Private Joint Forum in Asian Region, 2022–11
48	I, Mohamad S, Jomnonkwao V, Ratanavaraha (2022). Using a decision tree to compare rural versus highway motorcycle fatalities in Thailand. Case Studies on Transport Policy, 10: 2165–2174 https://doi.org/10.1016/j.cstp.2022.09.016
49	A V, Moudon L, Lin J, Jiao P, Hurvitz P, Reeves (2011). The risk of pedestrian injury and fatality in collisions with motor vehicles, a social ecological study of state routes and city streets in King County, Washington. Accident Analysis and Prevention, 43: 11–24 https://doi.org/10.1016/j.aap.2009.12.008
50	R B Nelsen (2006). An introduction to copulas. Springer
51	A M, Ngoc C C, Minh N T, Nhu H, Nishiuchi N, Huynh (2023). Influence of the human development index, motorcycle growth and policy intervention on road traffic fatalities–A case study of Vietnam. International journal of transportation science and technology, 12( 4): 925–936 https://doi.org/10.1016/j.ijtst.2022.09.004
52	D Q, Nguyen-Phuoc C, De Gruyter H A, Nguyen T, Nguyen D, Ngoc Su (2020). Risky behaviours associated with traffic crashes among app-based motorcycle taxi drivers in Vietnam. Transportation Research Part F: Traffic Psychology and Behaviour, 70: 249–259 https://doi.org/10.1016/j.trf.2020.03.010
53	D Q, Nguyen-Phuoc H A, Nguyen C, De Gruyter D N, Su V H, Nguyen (2019). Exploring the prevalence and factors associated with self-reported traffic crashes among app-based motorcycle taxis in Vietnam. Transport Policy, 81: 68–74 https://doi.org/10.1016/j.tranpol.2019.06.006
54	C W, Pai H Y, Lin S H, Tsai P L, Chen (2018). Comparison of traffic-injury related hospitalisation between bicyclists and motorcyclists in Taiwan. PLoS One, 13( 1): e0191221 https://doi.org/10.1371/journal.pone.0191221
55	E S, Park D, Lord (2007). Multivariate poisson-lognormal models for jointly modeling crash frequency by severity. Transportation Research Record: Journal of the Transportation Research Board, 2019( 1): 1–6 https://doi.org/10.3141/2019-01
56	G J, Park J, Shin S C, Kim D S, Na H J, Lee H, Kim S W, Lee Y N, In (2019). Protective effect of helmet use on cervical injury in motorcycle crashes: A case–control study. Injury, 50: 657–662 https://doi.org/10.1016/j.injury.2019.01.030
57	C, Peek-Asa D L, McArthur J F, Kraus (1999). The prevalence of non-standard helmet use and head injuries among motorcycle riders. Accident; Analysis and Prevention, 31: 229–233 https://doi.org/10.1016/S0001-4575(98)00071-2
58	A, Pervez J, Lee H, Huang (2021). Identifying factors contributing to the motorcycle crash severity in Pakistan. Journal of Advanced Transportation, 2021: 1–10
59	N, Phuksuksakul S, Yasmin M, Haque Md (2023). A random parameters copula-based binary logit-generalized ordered logit model with parameterized dependency: Application to active traveler injury severity analysis. Analytic Methods in Accident Research, 38: 100266 https://doi.org/10.1016/j.amar.2023.100266
60	U, Piyapromdee V, Adulyanukosol S, Lewsirirat (2015). Increasing road traffic injuries in underage motorcyclists. Journal of Southeast Asian Orthopaedics, 39: 3–7
61	M A, Quddus R B, Noland H C, Chin (2002). An analysis of motorcycle injury and vehicle damage severity using ordered probit models. Journal of Safety Research, 33( 4): 445–462 https://doi.org/10.1016/S0022-4375(02)00051-8
62	T M, Rice L, Troszak T, Erhardt R B, Trent M, Zhu (2017). Novelty helmet use and motorcycle rider fatality. Accident Analysis and Prevention, 103: 123–128 https://doi.org/10.1016/j.aap.2017.04.002
63	S M, Rifaat R, Tay A, De Barros (2012). Severity of motorcycle crashes in Calgary. Accident Analysis and Prevention, 49: 44–49 https://doi.org/10.1016/j.aap.2011.02.025
64	D R, Roehler C, Ear E M, Parker P, Sem M F, Ballesteros (2015). Fatal motorcycle crashes: A growing public health problem in Cambodia. International Journal of Injury Control and Safety Promotion, 22: 165–171 https://doi.org/10.1080/17457300.2013.876050
65	B J, Russo P T, Savolainen W H IV, Schneider P C, Anastasopoulos (2014). Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model. Analytic Methods in Accident Research, 2: 21–29 https://doi.org/10.1016/j.amar.2014.03.001
66	P, Savolainen F, Mannering (2007). Probabilistic models of motorcyclists’ injury severities in single- and multi-vehicle crashes. Accident Analysis and Prevention, 39: 955–963 https://doi.org/10.1016/j.aap.2006.12.016
67	C, Se T, Champahom S, Jomnonkwao P, Chaimuang V, Ratanavaraha (2021). Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: A correlated random parameters ordered probit approach with heterogeneity in means. Accident Analysis and Prevention, 161: 106352 https://doi.org/10.1016/j.aap.2021.106352
68	C, Se T, Champahom S, Jomnonkwao N, Kronprasert V, Ratanavaraha (2022). The impact of weekday, weekend, and holiday crashes on motorcyclist injury severities: Accounting for temporal influence with unobserved effect and insights from out-of-sample prediction. Analytic Methods in Accident Research, 36: 100240 https://doi.org/10.1016/j.amar.2022.100240
69	M S B, Shaheed K, Gkritza W, Zhang Z, Hans (2013). A mixed logit analysis of two-vehicle crash severities involving a motorcycle. Accident Analysis and Prevention, 61: 119–128 https://doi.org/10.1016/j.aap.2013.05.028
70	A O, Sirajudeen T H, Law S V, Wong C P, Ng (2022). The motorcycle deaths to passenger car deaths ratio and economic performance: A panel data analysis. Accident Analysis and Prevention, 165: 106533 https://doi.org/10.1016/j.aap.2021.106533
71	J, Stutts R, Foss C, Svoboda (2004). Characteristics of older motorcyclist crashes. Annual Proceedings—Association for the Advancement of Automotive Medicine, 48: 197–211
72	N N, Sze S C, Wong (2007). Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accident Analysis and Prevention, 39: 1267–1278 https://doi.org/10.1016/j.aap.2007.03.017
73	P, Talving P G R, Teixeira G, Barmparas J, DuBose C, Preston K, Inaba D, Demetriades (2010). Motorcycle-related injuries: Effect of age on type and severity of injuries and mortality. Journal of Trauma and Acute Care Surgery, 68: 441–446 https://doi.org/10.1097/TA.0b013e3181cbf303
74	R, Tamakloe J, Hong D, Park (2020). A copula-based approach for jointly modeling crash severity and number of vehicles involved in express bus crashes on expressways considering temporal stability of data. Accident Analysis and Prevention, 146: 105736 https://doi.org/10.1016/j.aap.2020.105736
75	L T, Truong H T T, Nguyen C D, Gruyter (2019). Mobile phone use while riding a motorcycle and crashes among university students. Traffic Injury Prevention, 20: 204–210 https://doi.org/10.1080/15389588.2018.1546048
76	L T, Truong H T T, Nguyen R, Tay (2020). A random parameter logistic model of fatigue-related motorcycle crash involvement in Hanoi, Vietnam. Accident Analysis and Prevention, 144: 105627 https://doi.org/10.1016/j.aap.2020.105627
77	G F, Ulfarsson F L, Mannering (2004). Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents. Accident Analysis and Prevention, 36( 2): 135–147 https://doi.org/10.1016/S0001-4575(02)00135-5
78	United Nations Development Programme (UNDP) (2020). Road Traffic Accidents in Cambodia
79	J R N, Vissoci E, Krebs B, Meier I F, Vieira L, de Andrade F, Byiringiro S, Rulisa C, Staton (2020). Road traffic crash experience among commercial motorcyclists in Kigali, Rwanda. International Journal of Injury Control and Safety Promotion, 27: 181–187 https://doi.org/10.1080/17457300.2020.1724158
80	B, Wali A J, Khattak N, Ahmad (2019). Examining correlations between motorcyclist’s conspicuity, apparel related factors and injury severity score: Evidence from new motorcycle crash causation study. Accident Analysis and Prevention, 131: 45–62 https://doi.org/10.1016/j.aap.2019.04.009
81	B, Wali A J, Khattak J, Xu (2018). Contributory fault and level of personal injury to drivers involved in head-on collisions: Application of copula-based bivariate ordinal models. Accident; Analysis and Prevention, 110: 101–114 https://doi.org/10.1016/j.aap.2017.10.018
82	E A, Walshe C, Ward McIntosh D, Romer F K, Winston (2017). Executive function capacities, negative driving behavior and crashes in young drivers. International Journal of Environmental Research and Public Health, 14: 1314 https://doi.org/10.3390/ijerph14111314
83	K, Wang T, Bhowmik S, Yasmin S, Zhao N, Eluru E, Jackson (2019). Multivariate copula temporal modeling of intersection crash consequence metrics: A joint estimation of injury severity, crash type, vehicle damage and driver error. Accident Analysis and Prevention, 125: 188–197 https://doi.org/10.1016/j.aap.2019.01.036
84	M, Waseem A, Ahmed T U, Saeed (2019). Factors affecting motorcyclists’ injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances. Accident Analysis and Prevention, 123: 12–19 https://doi.org/10.1016/j.aap.2018.10.022
85	World Health Organization (2017). Accelerating action for implementation of the decade of action for road safety. Technical Report
86	World Health Organization (2018). Global status report on road safety
87	C Y, Wu B P, Loo (2016). Motorcycle safety among motorcycle taxi drivers and nonoccupational motorcyclists in developing countries: A case study of Maoming, South China. Traffic Injury Prevention, 17( 2): 170–175 https://doi.org/10.1080/15389588.2015.1048336
88	D, Xiao Q, Yuan S, Kang X, Xu (2021). Insights on crash injury severity control from novice and experienced drivers: A bivariate random-effects probit analysis. Discrete Dynamics in Nature and Society, 2021: 1–13
89	C, Xin R, Guo Z, Wang Q, Lu P S, Lin (2017). The effects of neighborhood characteristics and the built environment on pedestrian injury severity: A random parameters generalized ordered probability model with heterogeneity in means and variances. Analytic Methods in Accident Research, 16: 117–132 https://doi.org/10.1016/j.amar.2017.10.001
90	T, Yamamoto J, Hashiji V N, Shankar (2008). Underreporting in traffic accident data, bias in parameters and the structure of injury severity models. Accident Analysis and Prevention, 40( 4): 1320–1329 https://doi.org/10.1016/j.aap.2007.10.016
91	T, Yamamoto V N, Shankar (2004). Bivariate ordered-response probit model of driver’s and passenger’s injury severities in collisions with fixed objects. Accident Analysis and Prevention, 36: 869–876 https://doi.org/10.1016/j.aap.2003.09.002
92	X, Yan J, He C, Zhang Z, Liu C, Wang B, Qiao (2021). Temporal analysis of crash severities involving male and female drivers: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 30: 100161 https://doi.org/10.1016/j.amar.2021.100161
93	S, Yasmin N, Eluru (2013). Evaluating alternate discrete outcome frameworks for modeling crash injury severity. Accident Analysis and Prevention, 59: 506–521 https://doi.org/10.1016/j.aap.2013.06.040
94	S, Yasmin N, Eluru A R, Pinjari R, Tay (2014). Examining driver injury severity in two vehicle crashes – A copula based approach. Accident Analysis and Prevention, 66: 120–135 https://doi.org/10.1016/j.aap.2014.01.018
95	Y, Yuan M, Yang Z, Gan J, Wu C, Xu D, Lei (2019). Analysis of the risk factors affecting the size of fatal accidents involving trucks based on the structural equation model. Transportation Research Record: Journal of the Transportation Research Board, ( 2673): 112–124
96	F, Zambon M, Hasselberg (2006). Factors affecting the severity of injuries among young motorcyclists—A Swedish nationwide cohort study. Traffic Injury Prevention, 7: 143–149 https://doi.org/10.1080/15389580600555759
97	H, Zubaidi R, Tamakloe N S S, Al-Bdairi A, Alnedawi I, Obaid (2022). Exploring senior motorcyclist injury severity crashes: Random parameter model with heterogeneity in mean and variance. IATSS Research, 47( 1): 1–13

Viewed

Full text

Abstract

Cited

Shared

Discussed