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

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.

Graphical abstract

Keywords

traffic accident / crash / motorcycle / injury severity / crash size

References

[1] Abay K A, (2015). Investigating the nature and impact of reporting bias in road crash data. Transportation Research Part A, Policy and Practice, 71: 31–45

[2] Abdel-Aty M, Keller J, (2005). Exploring the overall and specific crash severity levels at signalized intersections. Accident Analysis and Prevention, 37: 417–425

[3] Abrari Vajari M, Aghabayk K, Sadeghian M, Shiwakoti N, (2020). A multinomial logit model of motorcycle crash severity at Australian intersections. Journal of Safety Research, 73: 17–24

[4] Ahmad N, Gayah V V, Donnell E T, (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

[5] Anstey K J, Horswill M S, Wood J M, Hatherly C, (2012). The role of cognitive and visual abilities as predictors in the multifactorial model of driving safety. Accident Analysis and Prevention, 45: 766–774

[6] Ayuso M, Bermúdez L, Santolino M, (2016). Copula-based regression modeling of bivariate severity of temporary disability and permanent motor injuries. Accident Analysis and Prevention, 89: 142–150

[7] Banz B C, Fell J C, Vaca F E, (2019). Complexities of young driver injury and fatal motor vehicle crashes. Yale Journal of Biology and Medicine, 92: 725–731

[8] Blackman R A, Haworth N L, (2013). Comparison of moped, scooter and motorcycle crash risk and crash severity. Accident; Analysis and Prevention, 57: 1–9

[9] Bonander C, Andersson R, Nilson F, (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

[10] Boulagouas W, García-Herrero S, Chaib R, Febres J D, Mariscal M Á Djebabra M, (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

[11] Broyles R W, Narine L, Clarke S R, Baker D R, (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

[12] Champahom T, Jomnonkwao S, Watthanaklang D, Karoonsoontawong A, Chatpattananan V, Ratanavaraha V, (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

[13] Chang F, Yasmin S, Huang H, Chan A H, Haque M M, (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

[14] Chiou Y C, Fu C, Ke C Y, (2020). Modelling two-vehicle crash severity by generalized estimating equations. Accident Analysis and Prevention, 148: 105841

[15] Clarke D D, Ward P, Bartle C, Truman W, (2010). Older drivers’ road traffic crashes in the UK. Accident Analysis and Prevention, 42: 1018–1024

[16] Curry A E, Metzger K B, Williams A F, Tefft B C, (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

[17] Dapilah F, Guba B Y, Owusu-Sekyere E, (2017). Motorcyclist characteristics and traffic behaviour in urban Northern Ghana: Implications for road traffic accidents. Journal of Transport & Health, 4: 237–245

[18] De Lapparent M, (2006). Empirical Bayesian analysis of accident severity for motorcyclists in large French urban areas. Accident Analysis and Prevention, 38( 2): 260–268

[19] De Lapparent M, (2008). Willingness to use safety belt and levels of injury in car accidents. Accident Analysis and Prevention, 40: 1023–1032

[20] Doan H T N, Hobday M B, (2019). Characteristics and severity of motorcycle crashes resulting in hospitalization in Ho Chi Minh City, Vietnam. Traffic Injury Prevention, 20: 732–737

[21] Elsemesmani H, Bachir R, El Sayed M J, (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

[22] Eluru N, Paleti R, Pendyala R M, Bhat C R, (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] Erhardt T, Rice T, Troszak L, Zhu M, (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

[24] Fountas G, Pantangi S S, Hulme K F, Anastasopoulos P Ch, (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

[25] Ghomi H., Hussein, M., (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] Gkritza K, (2009). Modeling motorcycle helmet use in Iowa: Evidence from six roadside observational surveys. Accident Analysis and Prevention, 41: 479–484

[27] Goodwin A H, Wang Y C, Foss R D, Kirley B, (2022). The role of inexperience in motorcycle crashes among novice and returning motorcycle riders. Journal of Safety Research, 82: 371–375

[28] Haque Md M, Chin H C, Huang H, (2009). Modeling fault among motorcyclists involved in crashes. Accident Analysis and Prevention, 41: 327–335

[29] Hassan H M, Al-Faleh H, (2013). Exploring the risk factors associated with the size and severity of roadway crashes in Riyadh. Journal of Safety Research, 47: 67–74

[30] Hassanzadeh K, Salarilak S, Sadeghi-Bazargani H, Golestani M, (2020). Motorcyclist risky riding behaviors and its predictors in an Iranian population. Journal of Injury & Violence Research, 12: 161–170

[31] Hernández-Alava M, Pudney S, (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

[32] Hu S R, Li C S, Lee C K, (2010). Investigation of key factors for accident severity at railroad grade crossings by using a logit model. Safety Science, 48( 2): 186–194

[33] Huang H, Ding X, Yuan C, Liu X, Tang J, (2023). Jointly analyzing freeway primary and secondary crash severity using a copula-based approach. Accident Analysis and Prevention, 180: 106911

[34] Islam M, (2021). The effect of motorcyclists’ age on injury severities in single-motorcycle crashes with unobserved heterogeneity. Journal of Safety Research, 77: 125–138

[35] Kashani A T, Jafari M, Bondarabadi M A, Dabirinejad S, (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] Khan U R, Razzak J A, Jooma R, Wärnberg M G, (2022). Association of age and severe injury in young motorcycle riders: A cross-sectional study from Karachi, Pakistan. Injury, 53: 3019–3024

[37] Kole E, Koedijk K, Verbeek M, (2007). Selecting copulas for risk management. Journal of Banking & Finance, 31( 8): 2405–2423

[38] Lao M, Lukusa T M, (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

[39] Lee C, Abdel-Aty M, (2008). Presence of passengers: Does it increase or reduce driver’s crash potential?. Accident Analysis and Prevention, 40: 1703–1712

[40] Lee J Y, Chung J H, Son B, (2008). Analysis of traffic accident size for Korean highway using structural equation models. Accident Analysis and Prevention, 40: 1955–1963

[41] Lin M R, Kraus J F, (2009). A review of risk factors and patterns of motorcycle injuries. Accident Analysis and Prevention, 41: 710–722

[42] Ma J, Kockelman K M, Damien P, (2008). A multivariate poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. Accident Analysis and Prevention, 40: 964–975

[43] Mannering F L, Bhat C R, (2014). Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research, 1: 1–22

[44] Martey E N, Attoh-Okine N, (2019). Analysis of train derailment severity using vine copula quantile regression modeling. Transportation Research Part C, Emerging Technologies, 105: 485–503

[45] Martin E, Courtright T, Nkurunziza A, Lah O, (2023). Motorcycle taxis in transition? Review of digitalization and electrification trends in selected East African capital cities. Case Studies on Transport Policy, 13: 101057

[46] McLellan B A, Rizoli S B, Brenneman F D, Boulanger B R, Sharkey P W, Szalai J P, (1996). Injury pattern and severity in lateral motor vehicle collisions: A Canadian experience. Journal of Trauma and Acute Care Surgery, 41( 4): 708–713

[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] Mohamad I, Jomnonkwao S, Ratanavaraha V, (2022). Using a decision tree to compare rural versus highway motorcycle fatalities in Thailand. Case Studies on Transport Policy, 10: 2165–2174

[49] Moudon A V, Lin L, Jiao J, Hurvitz P, Reeves P, (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

[50] NelsenR B (2006). An introduction to copulas. Springer

[51] Ngoc A M, Minh C C, Nhu N T, Nishiuchi H, Huynh N, (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

[52] Nguyen-Phuoc D Q, De Gruyter C, Nguyen H A, Nguyen T, Ngoc Su D, (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

[53] Nguyen-Phuoc D Q, Nguyen H A, De Gruyter C, Su D N, Nguyen V H, (2019). Exploring the prevalence and factors associated with self-reported traffic crashes among app-based motorcycle taxis in Vietnam. Transport Policy, 81: 68–74

[54] Pai C W, Lin H Y, Tsai S H, Chen P L, (2018). Comparison of traffic-injury related hospitalisation between bicyclists and motorcyclists in Taiwan. PLoS One, 13( 1): e0191221

[55] Park E S, Lord D, (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

[56] Park G J, Shin J, Kim S C, Na D S, Lee H J, Kim H, Lee S W, In Y N, (2019). Protective effect of helmet use on cervical injury in motorcycle crashes: A case–control study. Injury, 50: 657–662

[57] Peek-Asa C, McArthur D L, Kraus J F, (1999). The prevalence of non-standard helmet use and head injuries among motorcycle riders. Accident; Analysis and Prevention, 31: 229–233

[58] Pervez A, Lee J, Huang H, (2021). Identifying factors contributing to the motorcycle crash severity in Pakistan. Journal of Advanced Transportation, 2021: 1–10

[59] Phuksuksakul N, Yasmin S, Haque Md M, (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

[60] Piyapromdee U, Adulyanukosol V, Lewsirirat S, (2015). Increasing road traffic injuries in underage motorcyclists. Journal of Southeast Asian Orthopaedics, 39: 3–7

[61] Quddus M A, Noland R B, Chin H C, (2002). An analysis of motorcycle injury and vehicle damage severity using ordered probit models. Journal of Safety Research, 33( 4): 445–462

[62] Rice T M, Troszak L, Erhardt T, Trent R B, Zhu M, (2017). Novelty helmet use and motorcycle rider fatality. Accident Analysis and Prevention, 103: 123–128

[63] Rifaat S M, Tay R, De Barros A, (2012). Severity of motorcycle crashes in Calgary. Accident Analysis and Prevention, 49: 44–49

[64] Roehler D R, Ear C, Parker E M, Sem P, Ballesteros M F, (2015). Fatal motorcycle crashes: A growing public health problem in Cambodia. International Journal of Injury Control and Safety Promotion, 22: 165–171

[65] Russo B J, Savolainen P T, Schneider W H IV, Anastasopoulos P C, (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

[66] Savolainen P, Mannering F, (2007). Probabilistic models of motorcyclists’ injury severities in single- and multi-vehicle crashes. Accident Analysis and Prevention, 39: 955–963

[67] Se C, Champahom T, Jomnonkwao S, Chaimuang P, Ratanavaraha V, (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

[68] Se C, Champahom T, Jomnonkwao S, Kronprasert N, Ratanavaraha V, (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

[69] Shaheed M S B, Gkritza K, Zhang W, Hans Z, (2013). A mixed logit analysis of two-vehicle crash severities involving a motorcycle. Accident Analysis and Prevention, 61: 119–128

[70] Sirajudeen A O, Law T H, Wong S V, Ng C P, (2022). The motorcycle deaths to passenger car deaths ratio and economic performance: A panel data analysis. Accident Analysis and Prevention, 165: 106533

[71] Stutts J, Foss R, Svoboda C, (2004). Characteristics of older motorcyclist crashes. Annual Proceedings—Association for the Advancement of Automotive Medicine, 48: 197–211

[72] Sze N N, Wong S C, (2007). Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accident Analysis and Prevention, 39: 1267–1278

[73] Talving P, Teixeira P G R, Barmparas G, DuBose J, Preston C, Inaba K, Demetriades D, (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

[74] Tamakloe R, Hong J, Park D, (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

[75] Truong L T, Nguyen H T T, Gruyter C D, (2019). Mobile phone use while riding a motorcycle and crashes among university students. Traffic Injury Prevention, 20: 204–210

[76] Truong L T, Nguyen H T T, Tay R, (2020). A random parameter logistic model of fatigue-related motorcycle crash involvement in Hanoi, Vietnam. Accident Analysis and Prevention, 144: 105627

[77] Ulfarsson G F, Mannering F L, (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

[78] United Nations Development Programme (UNDP) (2020). Road Traffic Accidents in Cambodia

[79] Vissoci J R N, Krebs E, Meier B, Vieira I F, de Andrade L, Byiringiro F, Rulisa S, Staton C, (2020). Road traffic crash experience among commercial motorcyclists in Kigali, Rwanda. International Journal of Injury Control and Safety Promotion, 27: 181–187

[80] Wali B, Khattak A J, Ahmad N, (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

[81] Wali B, Khattak A J, Xu J, (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

[82] Walshe E A, Ward McIntosh C, Romer D, Winston F K, (2017). Executive function capacities, negative driving behavior and crashes in young drivers. International Journal of Environmental Research and Public Health, 14: 1314

[83] Wang K, Bhowmik T, Yasmin S, Zhao S, Eluru N, Jackson E, (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

[84] Waseem M, Ahmed A, Saeed T U, (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

[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] Wu C Y, Loo B P, (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

[88] Xiao D, Yuan Q, Kang S, Xu X, (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] Xin C, Guo R, Wang Z, Lu Q, Lin P S, (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

[90] Yamamoto T, Hashiji J, Shankar V N, (2008). Underreporting in traffic accident data, bias in parameters and the structure of injury severity models. Accident Analysis and Prevention, 40( 4): 1320–1329

[91] Yamamoto T, Shankar V N, (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

[92] Yan X, He J, Zhang C, Liu Z, Wang C, Qiao B, (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

[93] Yasmin S, Eluru N, (2013). Evaluating alternate discrete outcome frameworks for modeling crash injury severity. Accident Analysis and Prevention, 59: 506–521

[94] Yasmin S, Eluru N, Pinjari A R, Tay R, (2014). Examining driver injury severity in two vehicle crashes – A copula based approach. Accident Analysis and Prevention, 66: 120–135

[95] Yuan Y, Yang M, Gan Z, Wu J, Xu C, Lei D, (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] Zambon F, Hasselberg M, (2006). Factors affecting the severity of injuries among young motorcyclists—A Swedish nationwide cohort study. Traffic Injury Prevention, 7: 143–149

[97] Zubaidi H, Tamakloe R, Al-Bdairi N S S, Alnedawi A, Obaid I, (2022). Exploring senior motorcyclist injury severity crashes: Random parameter model with heterogeneity in mean and variance. IATSS Research, 47( 1): 1–13

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