In this study, empirical road crashes data (2011–2020) of total accidents, persons killed, and injured from Ministry of Road Transport and Highways (MORTH), India with risk factors is analyzed and evaluated with Pareto charts, Pearson correlation matrix, and ANOVA General Linear model (GLM) using Minitab 18.0 software. Our results show that road environment (weather conditions, traffic control type, junction type, and road type) and human behavior related factors are the dominant causes for accident occurrences and its severity. Two wheeler, vehicle age of <5 and 5–10 years are contributed for more number of accidents. Also, 25–35 years age group of male and female causes more fatal accidents than other age group. Finally, 77 unique factors are filtered from 55 recent studies, and 146 risk factors are observed from MORTH database. In total, 223 factors are identified and grouped into 23 categories to enhance road safety and accident database management system for reducing the crash injury severity. These findings contribute to the advancement of automotive safety technologies, intelligent transportation systems, and data-driven accident prevention strategies. This research also underscores the urgent need for integrating AI-powered safety features, improving vehicle structural resilience, and optimizing in-vehicle monitoring systems to mitigate road crash severity and enhance automobile safety innovations.
Road Transport Research Wing-MORTH-India. Ministry of Road Transport and Highways, Government of India. Road accidents data (2011–2020), http://morth.nic.in (2020, accessed 22 December 2023).
2.
Roni YairGMahalelD.Who by accident? The social morphology of car accidents. Risk Anal2010; 30(9): 1411–1423.
3.
HordofaGGAssegidSGirmaA, et al. Prevalence of fatality and associated factors of road traffic accidents among victims reported to Burayu town police stations, between 2010 and 2015, Ethiopia. J Transp Health2018; 10: 186–193.
4.
OmidzadehDSajadiSMBozorgi-AmiriA, et al. A sustainability approach to vehicle modular platform design: a mathematical model. Proc Inst Mech Eng E J Process Mech Eng2022; 236(6): 2296–2310.
5.
MohammedAAAmbakKMosaAM, et al. A review of the traffic accidents and related practices worldwide. Open Transp J2019; 13(1): 65–83.
6.
Backer-GrøndahlAFyhriAUllebergP, et al. Accidents and unpleasant incidents: worry in transport and prediction of travel behavior. Risk Anal2009; 29(9): 1217–1226.
7.
KukićDLipovacKPešićD, et al. The differences of road safety performance of countries based on outcome indicators. Saf Sci2016; 89: 279–287.
8.
QureshiAIHuangWKhanS, et al. Mandated societal lockdown and road traffic accidents. Accid Anal Prev2020; 146: 105747.
9.
AlhajyaseenWKMAlmukdadAHussainQ, et al. Road safety status during COVID-19 pandemic: exploring public and road safety expert’s opinions. Int J Inj Contr Saf Promot2022; 29(2): 135–151.
10.
Diaz-VarelaERVazquez-GonzalezIMarey-PérezMF, et al. Assessing methods of mitigating wildlife-vehicle collisions by accident characterization and spatial analysis. Transp Res D Transp Environ2011; 16(4): 281–287.
11.
MohamedD.Road safety determinants in low and middle income countries. Int J Inj Contr Saf Promot2018; 26(1): 99–107.
12.
HuangHLiuTYangR.Do Confucian values and working experience matter? The impact of provincial governors’ characteristics on the management level of major road accidents in China. Risk Anal2023; 43(11): 2169–2185.
13.
DarmaYKarimMRAbdullahS.An analysis of Malaysia road traffic death distribution by road environment. Sadhana Acad Proc Eng Sci2017; 42(9): 1605–1615.
14.
YangCRemenyte-PrescottRAndrewsJ.Application of network traffic flow model to road maintenance. Proc Inst Civ Eng Transp2015; 168(3): 256–266.
15.
AminMSRZareieAAmador-JiménezLE.Climate change modeling and the weather-related road accidents in Canada. Transp Res D Transp Environ2014; 32: 171–183.
16.
LanZCaiM.Dynamic traffic noise maps based on noise monitoring and traffic speed data. Transp Res D Transp Environ2021; 94: 102796.
17.
PapadimitriouEFiltnessATheofilatosA, et al. Review and ranking of crash risk factors related to the road infrastructure. Accid Anal Prev2019; 125: 85–97.
18.
KitaliAEAlluriPSandoT, et al. Identification of secondary crash risk factors using penalized logistic regression model. Transp Res Rec2019; 2673(17): 036119811984905.
19.
LuTBuehlerRMondscheinA, et al. Designing a bicycle and pedestrian traffic monitoring program to estimate annual average daily traffic in a small rural college town. Transp Res D Transp Environ2017; 53: 193–204.
20.
ChangTBSheuJJHuangJW.Vehicle air leakage ventilation and its effect on cabin indoor air quality. Proc Inst Mech Eng E J Process Mech Eng2017; 231(6): 1226–1234.
21.
MukherjeeDMitraS.Comprehensive study of risk factors for fatal pedestrian crashes in urban setup in a developing country. Transp Res Rec2020; 2674(8): 100–118.
22.
AldredR.Inequalities in self-report road injury risk in Britain: a new analysis of National Travel Survey data, focusing on pedestrian injuries. J Transp Health2018; 9: 96–104.
23.
ChenLGaoYLiMJ, et al. A new inverse data envelopment analysis approach to achieve China’s road transportation safety objectives. Saf Sci2021; 142: 105362.
24.
DemİrİzAOBayrakOÜBayataHF.Corridor capacity analysis with mesoscopic simulation: Erzincan province sample. Sadhana Acad Proc Eng Sci2021; 46(1): 10.
25.
ElvikRKatharina HøyeA.The use of low-level theory to guide the interpretation of road safety evaluation studies. Saf Sci2023; 158: 105963.
26.
HollóPEkslerVZukowskaJ.Road safety performance indicators and their explanatory value: a critical view based on the experience of Central European countries. Saf Sci2010; 48(9): 1142–1150.
27.
MohapatraSSDeyPPChandraS.Conflicting volume for U-turns at uncontrolled median openings. Proc Inst Civ Eng Transp2016; 169(4): 195–204.
28.
VangiDGulinoMSFiorentinoA, et al. Crash momentum index and closing velocity as crash severity index. Proc Inst Mech Eng D J Automob Eng2019; 233(13): 3318–3326.
29.
SilvaPBAndradeMFerreiraS.Machine learning applied to road safety modeling: a systematic literature review. J Traffic Transp Eng Engl Ed2020; 7(6): 775–790.
30.
DapilahFGubaBYOwusu-SekyereE.Motorcyclist characteristics and traffic behaviour in urban Northern Ghana: implications for road traffic accidents. J Transp Health2017; 4: 237–245.
31.
MunirSLuoZDixonT, et al. The impact of smart traffic interventions on roadside air quality employing machine learning approaches. Transp Res D Transp Environ2022; 110: 103408.
32.
SaranyadeviSMurugeswariRBathrinathS.Road risk assessment using fuzzy context-free grammar based association rule miner. Sadhana Acad Proc Eng Sci2019; 44(6): 1–14.
33.
SchlöglMStützRLaahaG, et al. A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset. Accid Anal Prev2019; 127: 134–149.
34.
AdanuEKAgyemangWIslamR, et al. A comprehensive analysis of factors that influence interstate highway crash severity in Alabama. J Transp Saf Secur2022; 14(9): 1552–1576.
35.
ArasanVTArkatkarSS.Microsimulation study of effect of volume and road width on PCU of vehicles under heterogeneous traffic. J Transp Eng2010; 136(12): 1110–1119.
36.
AsgarianFSNamdariMSooriH.Worldwide prevalence of alcohol in fatally injured motorcyclists: a meta-analysis. Traffic Inj Prev2019; 20(7): 685–689.
37.
AshrafIHurSShafiqM, et al. Catastrophic factors involved in road accidents: underlying causes and descriptive analysis. PLoS One2019; 14(10): 1–29.
38.
AtnafuBKaurG.Analysis and predict the nature of road traffic accident using data mining techniques in Maharashtra, India. Int J Eng Technol Sci Res2017; 4(1): 2394–3386.
39.
AwadallaDMAlbuquerqueFDB. Impact of roadside design compliance and hazard offset on the risk of single-vehicle, run-off-road crash fatalities. Int J Inj Contr Saf Promot2021; 28(4): 468–478.
40.
AzimiGRahimiAAsgariH, et al. Severity analysis for large truck rollover crashes using a random parameter ordered logit model. Accid Anal Prev2020; 135: 105355.
41.
BeheshtiMHHajizadehRFarhang DehghanS, et al. Investigation of the accidents recorded at an emergency management center using the Pareto chart: a cross-sectional study in Gonabad, Iran, during 2014-2016. Heal Emerg Disast Q2018; 3(3): 143–150.
42.
BenlaghaNCharfeddineL.Risk factors of road accident severity and the development of a new system for prevention: new insights from China. Accid Anal Prev2020; 136: 105411.
43.
BucsuházyKMatuchováEZůvalaR, et al. Human factors contributing to the road traffic accident occurrence. Transp Res Proc2020; 45(2019): 555–561.
44.
DalaiBLandgeVS.Crash risk factor identification using association rules in Nagpur city, Maharashtra, India. Curr Sci2022; 123(6): 781–790.
45.
DasSMousaviSMShirinzadM.Pattern recognition in speeding related motorcycle crashes. J Transp Saf Secur2021; 14: 1–18.
46.
DashDPSethiNDashAK.Education, human error, and road crash risk: an empirical investigation in the Indian states. J Public Aff2021; 21(1): 1–10.
47.
DashDPSethiNDashAK.Identifying the causes of road traffic accidents in India: an empirical investigation. J Public Aff2020; 20(2): 1–15.
48.
de OliveiraRPAchcarJA. Victims of road accidents with serious injuries and dependence on some individual, climatic and infrastructure factors on federal highways in Brazil. Int J Inj Contr Saf Promot2020; 27(3): 355–361.
49.
EboliLForcinitiCMazzullaG.Factors influencing accident severity: an analysis by road accident type. Transp Res Proc2020; 47: 449–456.
50.
ElvikRVadebyAHelsT, et al. Updated estimates of the relationship between speed and road safety at the aggregate and individual levels. Accid Anal Prev2019; 123: 114–122.
51.
Hidalgo-FuentesSSospedra-BaezaMJ.Gender and age distribution of motorcycle crashes in Spain. Int J Inj Contr Saf Promot2019; 26(1): 108–114.
52.
HøyeA.Bicycle helmets – to wear or not to wear? A meta-analyses of the effects of bicycle helmets on injuries. Accid Anal Prev2018; 117: 85–97.
53.
JalayerMShabanpourRPour-RouholaminM, et al. Wrong-way driving crashes: a random-parameters ordered probit analysis of injury severity. Accid Anal Prev2018; 117: 128–135.
54.
JamalAZahidMRahmanMT, et al. Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study. Int J Inj Contr Saf Promot2021; 28(4): 1–20.
55.
LeeJNamBHAbdel-AtyM.Effects of pavement surface conditions on traffic crash severity. J Transp Eng2015; 141(10): 1–11.
56.
LeeJChaeJYoonT, et al. Traffic accident severity analysis with rain-related factors using structural equation modeling – a case study of Seoul City. Accid Anal Prev2018; 112: 1–10.
57.
LilhareSSwarnkarM.A study of injury characteristics in road traffic accidents by different road user category. Int Surg J2016; 4(1): 395.
58.
LiuZHeJZhangC, et al. The impact of road alignment characteristics on different types of traffic accidents. J Transp Saf Secur2020; 12(5): 697–726.
59.
MalinFNorrosIInnamaaS.Accident risk of road and weather conditions on different road types. Accid Anal Prev2019; 122: 181–188.
60.
Mehrara MolanARezapourMKsaibatiK. Investigating the relationship between crash severity, traffic barrier type, and vehicle type in crashes involving traffic barrier. J Traffic Transp Eng Engl Ed2020; 7(1): 125–136.
61.
MohamadiMMehryarHRLakSS.Effective factors in the occurrence of road accidents in west Azerbaijan, Iran (2010–2016). J Surg Trauma2019; 7(4): 161–169.
62.
MoradiANazariSSHRahmaniK.Sleepiness and the risk of road traffic accidents: a systematic review and meta-analysis of previous studies. Transp Res F Traffic Psychol Behav2019; 65: 620–629.
63.
MukherjeeDMitraS.Pedestrian safety analysis of urban intersections in Kolkata, India using a combined proactive and reactive approach. J Transp Saf Secur2022; 14(5): 754–795.
64.
NatarajanPSivasankaranSKBalasubramanianV.Identification of contributing factors in vehicle pedestrian crashes in Chennai using multiple correspondence analysis. Transp Res Proc2020; 48: 3486–3495.
65.
PonnaluriRV.Road traffic crashes and risk groups in India: analysis, interpretations, and prevention strategies. IATSS Res2012; 35(2): 104–110.
66.
PotoglouDCarlucciFCiràA, et al. Factors associated with urban non-fatal road-accident severity. Int J Inj Contr Saf Promot2018; 25(3): 303–310.
67.
RolisonJJRegevSMoutariS, et al. What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records. Accid Anal Prev2018; 115: 11–24.
68.
SivasankaranSKBalasubramanianV.Exploring the severity of bicycle – vehicle crashes using latent class clustering approach in India. J Safety Res2020; 72: 127–138.
69.
SivasankaranSKNatarajanPBalasubramanianV.Identifying patterns of pedestrian crashes in urban metropolitan roads in India using association rule mining. Transp Res Proc2020; 48: 3496–3507.
70.
StanojevićDStanojevićPJovanovićD, et al. Impact of riders’ lifestyle on their risky behavior and road traffic accident risk. J Transp Saf Secur2020; 12(3): 400–418.
71.
SunLLLiuDChenT, et al. Analysis on the accident casualties influenced by several economic factors based on the traffic-related data in China from 2004 to 2016. Chin J Traumatol Engl Ed2019; 22(2): 75–79.
72.
TimmermansCAlhajyaseenWAl MamunA, et al. Analysis of road traffic crashes in the state of Qatar. Int J Inj Contr Saf Promot2019; 26(3): 242–250.
73.
UsecheSAMontoroLSanmartinJ, et al. Healthy but risky: a descriptive study on cyclists’ encouraging and discouraging factors for using bicycles, habits and safety outcomes. Transp Res F Traffic Psychol Behav2019; 62: 587–598.
74.
Van PetegemJWHWegmanF. Analyzing road design risk factors for run-off-road crashes in the Netherlands with crash prediction models. J Safety Res2014; 49: 121.e1–127.e1.
75.
WangKBhowmikTYasminS, et al. Multivariate copula temporal modeling of intersection crash consequence metrics: a joint estimation of injury severity, crash type, vehicle damage and driver error. Accid Anal Prev2019; 125: 188–197.
76.
WangXYuenKFShiW, et al. The determinants of passengers’ safety behaviour on public transport. J Transp Health2020; 18: 100905.
77.
WaseemMAhmedASaeedTU.Factors affecting motorcyclists’ injury severities: an empirical assessment using random parameters logit model with heterogeneity in means and variances. Accid Anal Prev2019; 123: 12–19.
78.
WengJGanXChenJ.A separate analysis of crash frequency for the highways involving traffic hazards and involving no traffic hazards. J Transp Saf Secur2021; 13(8): 822–841.
79.
YuRHanLZhangH.Trajectory data based freeway high-risk events prediction and its influencing factors analyses. Accid Anal Prev2021; 154: 106085.
80.
ZhouHYuanCDongN, et al. Severity of passenger injuries on public buses: a comparative analysis of collision injuries and non-collision injuries. J Safety Res2020; 74: 55–69.
81.
ZouYTarkoAPChenE, et al. Effectiveness of cable barriers, guardrails, and concrete barrier walls in reducing the risk of injury. Accid Anal Prev2014; 72: 55–65.
82.
RodJEKingMOviedo-TrespalaciosO.The impact of perceived injury risk and psychosocial factors on walking equity. Transp Res D Transp Environ2023; 116: 103590.
83.
WangCQuddusMAIsonSG.The effect of traffic and road characteristics on road safety: a review and future research direction. Saf Sci2013; 57: 264–275.
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