A remarkable trend has been observed in the state of Washington in which the number of fatalities per billion vehicle miles traveled during warm months highly exceeds those in colder ones. Moreover, the outbreak of the COVID-19 pandemic exacerbated this trend. Therefore, the current study aimed to address how the weather conditions and the COVID-19 pandemic affected motorcyclists’ injury severities. A random parameter multinomial logit model with heterogeneity in means and variances was estimated as a methodological approach. Further, to investigate the temporal stability between cold and warm weather conditions and the pandemic, a holistic model and six separate models were developed. Several factors were found to drastically affect motorcyclist injuries depending on the weather conditions. For instance, during cold months, dry roads and lighting conditions played a significant role, whereas during warm months, distracted driving and lane changing were key contributors. The estimation results also highlighted the impact of the COVID-19 pandemic in altering these factors and their impacts on injury severities sustained by motorcyclists. In particular, the use of drugs had a more significant impact in 2020 compared with previous years in both cold and warm weather models. Additionally, the indicator of vehicles running off the road after the crash was only significant during the pandemic for both cold and warm months. The study findings highlight the urgent need to improve motorcycle safety by implementing policies and countermeasures and intensifying efforts to examine the impacts of weather conditions and pandemics on the injury severity of motorcyclists.
AgarwalM.MazeT. H.SouleyretteR. R.Impacts of Weather on Urban Freeway Traffic Flow Characteristics and Facility Capacity. Proceedings of the 2005 Mid-Continent Transportation Research Symposium, 2005, p. 14.
2.
KelarestaghiK. B.ZhangW.WangY.XiaoL.HancockK.HeaslipK. P.Impacts to Crash Severity Outcome Due to Adverse Weather and Other Causation Factors. Advances in Transportation Studies, Vol. 43, No. September, 2017, pp. 31–42. https://doi.org/10.4399/97888255077373.
3.
PisanoP. A.GoodwinL. C.RossettiM. A.U.S Highway Crashes in Adverse Road Weather Conditions. 24th Conference on Institutional Information Processing System, 2008, pp. 1–15.
4.
LinM. R.KrausJ. F.A Review of Risk Factors and Patterns of Motorcycle Injuries. Accident Analysis and Prevention, Vol. 41, No. 4, 2009, pp. 710–722. https://doi.org/10.1016/j.aap.2009.03.010.
5.
VajariM. A.AghabaykK.SadeghianM.ShiwakotiN.A Multinomial Logit Model of Motorcycle Crash Severity at Australian Intersections. Journal of Safety Research, Vol. 73, 2020, pp. 17–24. https://doi.org/10.1016/j.jsr.2020.02.008
6.
AlnawmasiN.ManneringF.A Statistical Assessment of Temporal Instability in the Factors Determining Motorcyclist Injury Severities. Analytic Methods in Accident Research, Vol. 22, 2019, pp. 1–20. https://doi.org/10.1016/j.amar.2019.100090.
7.
SchneiderW. H.SavolainenP. T.Comparison of Severity of Motorcyclist Injury by Crash Types. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2265: 70–80.
8.
WaseemM.AhmedA.SaeedT. U.Factors Affecting Motorcyclists’ Injury Severities: An Empirical Assessment Using Random Parameters Logit Model with Heterogeneity in Means and Variances. Accident Analysis and Prevention, Vol. 123, 2019, pp. 12–19. https://doi.org/10.1016/j.aap.2018.10.022.
9.
ZubaidiH.ObaidI.AlnedawiA.DasS.HaqueM. M.Temporal Instability Assessment of Injury Severities of Motor Vehicle Drivers at Give-Way Controlled Unsignalized Intersections: A Random Parameters Approach with Heterogeneity in Means and Variances. Accident Analysis and Prevention, Vol. 156, No. April, 2021, p. 106151. https://doi.org/10.1016/j.aap.2021.106151.
10.
BehnoodA.ManneringF.Time-of-Day Variations and Temporal Instability of Factors Affecting Injury Severities in Large-Truck Crashes. Analytic Methods in Accident Research, Vol. 23, 2019, p. 100102. https://doi.org/10.1016/j.amar.2019.100102.
11.
Al-BdairiN. S. S.BehnoodA.HernandezS.Temporal Stability of Driver Injury Severities in Animal-Vehicle Collisions: A Random Parameters with Heterogeneity in Means (and Variances) Approach. Analytic Methods in Accident Research, Vol. 26, 2020. https://doi.org/10.1016/j.amar.2020.100120.
12.
EisenbergD.WarnerK. E.Effects of Snowfalls on Motor Vehicle Collisions, Injuries, and Fatalities. American Journal of Public Health, Vol. 95, No. 1, 2005, pp. 120–124. https://doi.org/10.2105/AJPH.2004.048926.
13.
BrodskyH.HakkertA. S.Risk of a Road Accident in Rainy Weather. Accident Analysis and Prevention, Vol. 20, No. 3, 1988, pp. 161–176. https://doi.org/10.1016/0001-4575(88)90001-2.
14.
EisenbergD.The Mixed Effects of Precipitation on Traffic Crashes. Accident Analysis and Prevention, Vol. 36, No. 4, 2004, pp. 637–647. https://doi.org/10.1016/S0001-4575(03)00085-X.
15.
AnwariN.BhowmikT.Abdel-AtyM.EluruN.ParkJ.Analyzing the Time to Death of Pedestrian Fatalities: A Copula Approach. Journal of Safety Research, Vol. 92, 2025, pp. 55–67. https://doi.org/10.1016/j.jsr.2024.11.007.
16.
SherretzL. A.FarharB. C.An Analysis of the Relationship Between Rainfall and the Occurrence Of Traffic Accidents. Journal of Applied Meteorology and Climatology, 1978.
17.
WisutwattanasakP.SeC.ChampahomT.KasemsriR.JomnonkwaoS.RatanavarahaV.Factors Affecting Single and Multivehicle Motorcycle Crashes: Insights from Day and Night Analysis Using XGBoost-SHAP Algorithm. Big Data and Cognitive Computing, Vol. 8, No. 10, 2024. https://doi.org/10.3390/bdcc8100128.
18.
WangC.Abdel-AtyM.EasaS. MChenF.ChengJ.JamalA.Evaluating Helmet-Wearing of Single-Vehicle Overspeeding Motorcycle Crashes: Insights from Temporal Instability in Parsimonious Pooled Framework. Traffic Injury Prevention, Vol. 25, No. 4, 2024, pp. 623–630. https://doi.org/10.1080/15389588.2024.2331644.
19.
DasS.VierkantV.GonzalezJ. C.KutelaB.SheykhfardA.Bayesian Network for Motorcycle Crash Severity Analysis. Transportation Research Record: Journal of the Transportation Research Board, 2023. 2677: 51–63.
20.
ZahidM.HabibM. F.IjazM.AmeerI.UllahI.AhmedT.HeZ.Factors Affecting Injury Severity in Motorcycle Crashes: Different Age Groups Analysis Using Catboost and SHAP Techniques. Traffic Injury Prevention, Vol. 25, No. 3, 2024, pp. 472–481. https://doi.org/10.1080/15389588.2023.2297168.
21.
SeC.ChampahomT.JomnonkwaoS.WisutwattanasakP.LaphromW.RatanavarahaV.Temporal Instability and Transferability Analysis of Daytime and Nighttime Motorcyclist-Injury Severities Considering Unobserved Heterogeneity of Data. Sustainability, Vol. 15, No. 5, 2023, Article no. 4486. https://doi.org/10.3390/su15054486.
22.
ZubaidiH.TamakloeR.Al-BdairiN. S. S.AlnedawiA.ObaidI.Exploring Senior Motorcyclist Injury Severity Crashes: Random Parameter Model with Heterogeneity in Mean and Variance. IATSS Research, Vol. 47, No. 1, 2023, pp. 1–13. https://doi.org/10.1016/j.iatssr.2022.12.001.
23.
ChampahomT.SeC.JomnonkwaoS.BoonyooT.LeelamanothumA.RatanavarahaV.Temporal Instability of Motorcycle Crash Fatalities on Local Roadways: A Random Parameters Approach with Heterogeneity in Means and Variances. International Journal of Environmental Research and Public Health, Vol. 20, No. 5, 2023, pp. Article no. 3845. https://doi.org/10.3390/ijerph20053845.
24.
PervezA.LeeJ.HuangH.Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan. Journal of Advanced Transportation, Vol. 2021, 2021, Article no. 6636130. https://doi.org/10.1155/2021/6636130.
25.
RezapourM.Mehrara MolanA.KsaibatiK.Analyzing Injury Severity of Motorcycle At-Fault Crashes Using Machine Learning Techniques, Decision Tree and Logistic Regression Models. International Journal of Transportation Science and Technology, Vol. 9, No. 2, 2020, pp. 89–99. https://doi.org/10.1016/j.ijtst.2019.10.002.
26.
ChengW.GillG. S.SakraniT.DasuM.ZhouJ.Predicting Motorcycle Crash Injury Severity Using Weather Data and Alternative Bayesian Multivariate Crash Frequency Models. Accident Analysis and Prevention, 2017, pp. 172–180. https://doi.org/10.1016/j.aap.2017.08.032.
27.
HsuC. K.Heat-Induced Risks of Road Crashes among Older Motorcyclists: Evidence from Three Motorcycle-Dominant Cities in Taiwan. Journal of Transport and Health, Vol. 35, No. 101754, 2024. https://doi.org/10.1016/j.jth.2023.101754.
28.
AlnawmasiN.ManneringF.A Temporal Assessment of Distracted Driving Injury Severities Using Alternate Unobserved-Heterogeneity Modeling Approaches. Analytic Methods in Accident Research, Vol. 34, 2022, p. 100216. https://doi.org/10.1016/j.amar.2022.100216.
29.
Da SilvaA. A.StröherG. R.TeixeiraH. M.CordeiroM. V. G.OlandoskiM.Von-BahtenL. C.Impact of the COVID-19 Pandemic on the Epidemiology of Traffic Accidents: A Cross-Sectional Study. Revista do Colegio Brasileiro de Cirurgioes, Vol. 49, 2022, pp. 1–10. https://doi.org/10.1590/0100-6991e-20223364-en.
30.
ScorranoM.DanielisR.Active Mobility in an Italian City: Mode Choice Determinants and Attitudes before and During the Covid-19 Emergency. Research in Transportation Economics, Vol. 86, No. June2020, 2021, p. 101031. https://doi.org/10.1016/j.retrec.2021.101031.
31.
WadaY.BizenY.InabaM.Exploring the Effects of COVID-19 on Motorcycle Riding Patterns and Its Importance. Frontiers in Psychology, Vol. 14, No. January, 2023. https://doi.org/10.3389/fpsyg.2023.994128.
32.
ZafriN. M.KhanA.JamalS.AlamB. M.Impacts of the Covid-19 Pandemic on Active Travel Mode Choice in Bangladesh: A Study from the Perspective of Sustainability and New Normal Situation. Sustainability, Vol. 13, No. 12: 6975, 2021. https://doi.org/10.3390/su13126975.
33.
WashingtonS.KarlaftisM.ManneringF. P. Anastasopoulos. Statistical and Econometric Methods for Transportation Data Analysis. CRC Press Taylor & Francis Group, 2020.
34.
BehnoodA.ManneringF.Determinants of Bicyclist Injury Severities in Bicycle-Vehicle Crashes: A Random Parameters Approach with Heterogeneity in Means and Variances. Analytic Methods in Accident Research, Vol. 16, 2017, pp. 35–47. https://doi.org/10.1016/j.amar.2017.08.001.
35.
ManneringF. L.ShankarV.BhatC. R.Unobserved Heterogeneity and the Statistical Analysis of Highway Accident Data. Analytic Methods in Accident Research, Vol. 11, 2016, pp. 1–16. https://doi.org/10.1016/j.amar.2016.04.001.
36.
IslamM.PandeA.Analysis of Single-Vehicle Roadway Departure Crashes on Rural Curved Segments Accounting for Unobserved Heterogeneity. Transportation Research Record: Journal of the Transportation Research Board, 2020. 2674: 146–157.
37.
YuM.ShenJ.MaC.Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters. Transportation Research Record: Journal of the Transportation Research Board, 2021. 2675: 1720–1729.
38.
McFaddenD.Econometric Models of Probabilistic Choice. In Structural Analysis of Discrete Data and Econometric Applications (ManksiC. F.McFaddenD., eds.), MIT Press, Cambridge, England, pp. 198–272.
39.
PetrinA.TrainK.Omitted Product Attributes in Discrete Choice Models. NBER Working Paper No. 9452, National Bureau of Economic Research, Cambridge, MA, 2003. https://www.nber.org/papers/w9452
40.
McFaddenD.Econometric Models of Probabilistic Choice. In Structural Analysis of Discrete Data with Econometric Applications, ManskiC. F.McFaddenD., Eds., MIT Press, Cambridge, MA, 1981, pp. 198–272.
41.
BhatC. R.Simulation Estimation of Mixed Discrete Choice Models Using Randomized and Scrambled Halton Sequences. Transportation Research Part B: Methodological, Vol. 37, 2003, pp. 837–855. https://doi.org/10.1016/S0191-2615(02)00090-5.
42.
HaltonJ. H.On the Efficiency of Certain Quasi-Random Sequences of Points in Evaluating Multi-Dimensional Integrals. Numerische Mathematik, Vol. 2, 1960, pp. 84–90. https://doi.org/10.1007/BF01386213.
43.
TrainK.Halton Sequences for Mixed Logit. eScholarship.org, Vol. 15, No. 4, 1999, pp. 250–260.
44.
FagnantD. J.KockelmanK. M.Motorcycle Use in the United States: Crash Experiences, Safety Perspectives, and Countermeasures. Journal of Transportation Safety and Security, Vol. 7, No. 1, 2015, pp. 20–39. https://doi.org/10.1080/19439962.2014.894164.
45.
HurtH.Jr.OuelletJ.ThomD.Motorcycle Accident Cause Factors and Identification of Countermeasures: Volume 1 Technical Report. 1981.
46.
ZambonF.HasselbergM.Factors Affecting the Severity of Injuries among Young Motorcyclists - A Swedish Nationwide Cohort Study. Traffic Injury Prevention, Vol. 7, No. 2, 2006, pp. 143–149. https://doi.org/10.1080/15389580600555759.
47.
BolinskiR. S.WaltersS.Salisbury-AfsharE.OuelletL. J.JenkinsW. D.AlmirolE.Van HamB.FletcherS.JohnsonC.SchneiderJ. A.OmpadD.PhoM. T.The Impact of the COVID-19 Pandemic on Drug Use Behaviors, Fentanyl Exposure, and Harm Reduction Service Support among People Who Use Drugs in Rural Settings. International Journal of Environmental Research and Public Health, Vol. 19, No. 4, 2022, Article no. 2230. https://doi.org/10.3390/ijerph19042230.
48.
CzeislerM. É.LaneR. I.PetroskyE.WileyJ. F.ChristensenA.NjaiR.WeaverM. D.RobbinsR.Facer-ChildsE. R.BargerL. K.CzeislerC. A.HowardM. E.RajaratnaS. M. W.Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic — United States, June 24–30, 2020. Drug and Alcohol Review, Vol. 39, No. 4, 2020, pp. 297–300. https://doi.org/10.15585/mmwr.mm6932a1.
49.
DietzeP. M.PeacockA.Illicit Drug Use and Harms in Australia in the Context of COVID-19 and Associated Restrictions: Anticipated Consequences and Initial Responses. Drug and Alcohol Review, Vol. 39, No. 4, 2020, pp. 297–300. https://doi.org/10.1111/dar.13079.
50.
RobertsA.RogersJ.MasonR.SiriwardenaA. N.HogueT.WhitleyG. A.LawG. R.Alcohol and Other Substance Use during the COVID-19 Pandemic: A Systematic Review. Drug and Alcohol Dependence, Vol. 229, 2021, p. 109150. https://doi.org/10.1016/j.drugalcdep.2021.109150.
51.
ChoudharyP.PawarN. M.VelagaN. R.PawarD. S.Overall Performance Impairment and Crash Risk Due to Distracted Driving: A Comprehensive Analysis Using Structural Equation Modelling. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 74, 2020, pp. 120–138. https://doi.org/10.1016/j.trf.2020.08.018.
52.
LiZ.ChenC.CiY.ZhangG.WuQ.LiuC.(Sean) QianZ.Examining Driver Injury Severity in Intersection-Related Crashes Using Cluster Analysis and Hierarchical Bayesian Models. Accident Analysis and Prevention, Vol. 120, No. June, 2018, pp. 139–151. https://doi.org/10.1016/j.aap.2018.08.009.
53.
ElliottM. A.BaughanC. J.SextonB. F.Errors and Violations in Relation to Motorcyclists’ Crash Risk. Accident Analysis and Prevention, Vol. 39, No. 3, 2007, pp. 491–499. https://doi.org/10.1016/j.aap.2006.08.012.
54.
SalumJ. H.KitaliA. E.BwireH.SandoT.AlluriP.Severity of Motorcycle Crashes in Dar Es Salaam, Tanzania. Traffic Injury Prevention, Vol. 20, No. 2, 2019, pp. 189–195. https://doi.org/10.1080/15389588.2018.1544706.
