Driver factors are increasingly recognized as important factors contributing to traffic-related morbidity and mortality. This study aimed to investigate how driver factors, including driver-related demographics, driving-related experience, and trip-related behaviors, influenced the risk of crashes and near-crashes (CNC). Using the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset, we employed a mixed-effects logistic regression model to examine the effects. Results showed that male drivers, young adults, and drivers with abnormal psychological symptoms had significantly higher probabilities of undergoingCNC.Driverswithcrashorviolationexperience, andfeweryearsofdrivingwerehighlysusceptible to CNC. Furthermore, driving impairment, alcohol consumption, distracted driving, less than one hand on the wheel, and traffic law violations significantly increased the probability of having CNC. Our findings can help raise the safety awareness of major stakeholders and inform the development of countermeasures to reduce crashes.
Get full access to this article
View all access options for this article.
References
1.
AbdelrahmanA. (2019). Driver Behavior Modelling and Risk Profiling Using Large-Scale Naturalistic Driving Data. PhD thesis, Queen’s University (Canada).
2.
AhmedM. M.GhasemzadehA. (2018). The impacts of heavy rain on speed and headway behaviors: an investigation using the shrp2 naturalistic driving study data. Transportation research part C: emerging technologies, 91:371–384.
3.
BoseD.Segui-GomezM.ScDCrandallJ. R. (2011). Vulnerability of female drivers involved in motor vehicle crashes: an analysis of us population at risk. American journal of public health, 101(12):2368–2373.
4.
CaoH.ZhangZ.SongX.WangH.LiM.ZhaoS.WangJ. (2020). An investigation on the link between driver demographic characteristics and distracted driving by using the shrp 2 naturalistic driving data. Journal of intelligent and connected vehicles.
5.
ChandA.JayeshS.BhasiA. (2021). Road traffic accidents: An overview of data sources, analysis techniques and contributing factors. Materials Today: Proceedings, 47:5135–5141.
6.
Espinoza-MolinaF. E.Ojeda-RomeroC. F.Zumba-PaucarH. D.Pillajo-QuijiaG.Arenas-RamírezB.Aparicio-IzquierdoF. (2021). Road safety as a public health problem: Case of ecuador in the period 2000–2019. Sustainability, 13(14):8033.
7.
FergusonS. A. (2012). Alcohol-impaired driving in the united states: contributors to the problem and effective countermeasures. Traffic injury prevention, 13(5):427–441.
8.
FringsD.RoseA.RidleyA. M. (2012). Bicyclist fatalities involving heavy goods vehicles: Gender differences in risk perception, behavioral choices, and training. Traffic injury prevention, 13(5):493–498.
9.
HoskingS. (2007). The effects of text messaging on young novice driver performance. Distracted driving, pages155–187.
10.
LegreeP. J.HeffnerT. S.PsotkaJ.MartinD. E.MedskerG. J. (2003). Traffic crash involvement: Experiential driving knowledge and stressful contextual antecedents. Journal of applied psychology, 88(1):15.
11.
MousaS. R.BakhitP. R.IshakS. (2019). An extreme gradient boosting method for identifying the factors contributing to crash/near-crash events: a naturalistic driving study. Canadian Journal of Civil Engineering, 46(8):712–721.
12.
Nasim KhanM.DasA.AhmedM. M. (2020). Non-parametric association rules mining and parametric ordinal logistic regression for an in-depth investigation of driver speed selection behavior in adverse weather using shrp2 naturalistic driving study data. Transportation research record, 2674(11):101–119.
13.
NCSA (2019). Traffic safety facts, research note, 2018 fatal motor vehicle crashes: Overview. United States. National Highway Traffic Safety Administration.
14.
OsmanO. A.RakhaH. (2020). Application of deep learning for characterization of drivers’ engagement in secondary tasks in in-vehicle systems. Transportation research record, 2674(8):429–440.
15.
PrechtL.KeinathA.KremsJ. F. (2017). Effects of driving anger on driver behavior–results from naturalistic driving data. Transportation research part F: traffic psychology and behaviour, 45:75–92.
16.
RomanoE.FellJ.LiK.Simons-MortonB. G.VacaF. E. (2021). Alcohol-related deaths among young passengers: an analysis of national alcohol-related fatal crashes. Journal of safety research, 79:376–382.
17.
RoserM.MoosmannF. (2008). Classification of weather situations on single color images. In 2008 IEEE Intelligent Vehicles Symposium, pages 798–803. IEEE.
18.
SeacristT.DouglasE. C.HuangE.MegariotisJ.PrabaharA.KashemA.ElzarkaA.HaberL.MacKinneyT.LoebH. (2018). Analysis of near crashes among teen, young adult, and experienced adult drivers using the shrp2 naturalistic driving study. Traffic injury prevention, 19(sup1):S89–S96.
19.
SeshadriK.Juefei-XuF.PalD. K.SavvidesM.ThorC. P. (2015). Driver cell phone usage detection on strategic highway research program (shrp2) face view videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 35–43.
20.
VictorT.DozzaM.BärgmanJ.BodaC.-N.EngströmJ.FlannaganC.LeeJ. D.MarkkulaG. (2015). Analysis of naturalistic driving study data: Safer glances, driver inattention, and crash risk. Technical report.
21.
ZangiN.Srour-ZreikR.RidelD.ChasidimH.BorowskyA. (2022). Driver distraction and its effects on partially automated driving performance: A driving simulator study among young-experienced drivers. Accident Analysis & Prevention, 166:106565.
