Teen drivers remain one of the most long-standing traffic safety concerns, as they continue to be overrepresented in the fatal and injury crash statistics. No previous studies have explored significant variations in the effect of risk attributes contributing to teen driver collisions in multiple crash circumstances by degrees of crash severity. Therefore, substantial exploration of teen driver crashes is expected to facilitate the strategic employment of countermeasures effectively. This study aimed to analyze teen driver crashes to investigate the heterogeneous effect of contributing factors on crash severity outcomes. Three years (2017 to 2019) of police-investigated crash information was used for the state of Alabama. This research first applied latent class clustering to minimize the heterogeneity in the extracted dataset by dividing the data into meaningful clusters (subgroups of the whole data). Then, multinomial logit models were constructed to illustrate the significant risk factors influencing the severity outcomes in different crash scenarios. Marginal effects were computed to understand the impacts of variable categories better. The findings suggested that the significance and estimated impact of variables varied within clusters and between crash severity levels in the same cluster. The results of latent class segmented submodels represented real-world crash patterns demonstrating the cumulative effect of variable attributes. Such contextual understandings of underlying risk factors could help to strengthen existing teen driver educational interventions. In addition, the study outcomes could assist practitioners and policy makers in developing safety improvement strategies to reduce the causalities associated with teen driver crashes in distinct circumstances.
Centers for Disease Control and Prevention (CDC). National Center for Injury Prevention and Control (NCIPC). WISQARS (Web-Based Injury Statistics Query and Reporting System). https://www.cdc.gov/injury/wisqars/index.html.
RomerD.Adolescent Risk Taking, Impulsivity, and Brain Development: Implications for Prevention. Developmental Psychobiology, Vol. 52, No. 3, 2010, pp. 263–276. https://doi.org/10.1002/dev.20442.
4.
DasS.DuttaA.KongX.SunX.Hit and Run Crashes: Knowledge Extraction From Bicycle Involved Crashes Using First and Frugal Tree. International Journal of Transportation Science and Technology, Vol. 8, No. 2, 2019, pp. 146–160. https://doi.org/10.1016/j.ijtst.2018.11.001.
5.
MonroeK.IronsE.CrewM.NorrisJ.NicholsM.KingW. D.Trends in Alabama Teen Driving Death and Injury. Journal of Trauma and Acute Care Surgery, Vol. 77, No. 3 Supplement 1, 2014, pp. S51–S54. https://doi.org/10.1097/TA.0000000000000399.
6.
MayhewD. R.SimpsonH. M.New to the Road. Young Drivers and Novice Drivers: Similar Problems and Solutions? HS-041 039. Traffic Injury Research Foundation (TIRF), Ottowa, Ontario, Canada, 1990.
7.
McCarttA. T.MayhewD. R.BraitmanK. A.FergusonS. A.SimpsonH. M.Effects of Age and Experience on Young Driver Crashes: Review of Recent Literature. Traffic Injury Prevention, Vol. 10, No. 3, 2009, pp. 209–219. https://doi.org/10.1080/15389580802677807.
8.
MayhewD. R.SimpsonH. M.PakA.Changes in Collision Rates Among Novice Drivers During the First Months of Driving. Accident Analysis & Prevention, Vol. 35, No. 5, 2003, pp. 683–691. https://doi.org/10.1016/S0001-4575(02)00047-7.
9.
ByrnesJ. P.MillerD. C.SchaferW. D.Gender Differences in Risk Taking: A Meta-Analysis. Psychological Bulletin, Vol. 125, No. 3, 1999, pp. 367–383. https://doi.org/10.1037/0033-2909.125.3.367.
10.
ShopeJ. T.BinghamC. R.Teen Driving. Motor-Vehicle Crashes and Factors That Contribute. American Journal of Preventive Medicine, Vol. 35, No. 3 Supplement, 2008, pp. S261–S271. https://doi.org/10.1016/j.amepre.2008.06.022.
11.
AdanuE.PenmetsaP.JonesS.SmithR.Gendered Analysis of Fatal Crashes Among Young Drivers in Alabama, USA. Safety, Vol. 4, No. 3, 2018, p. 29. https://doi.org/10.3390/safety4030029.
12.
McDonaldC. C.CurryA. E.KandadaiV.SommersM. S.WinstonF. K.Comparison of Teen and Adult Driver Crash Scenarios in a Nationally Representative Sample of Serious Crashes. Accident Analysis & Prevention, Vol. 72, 2014, pp. 302–308. https://doi.org/10.1016/j.aap.2014.07.016.
13.
BinghamC. R.EhsaniJ. P.The Relative Odds of Involvement in Seven Crash Configurations by Driver Age and Sex. Journal of Adolescent Health, Vol. 51, No. 5, 2012, pp. 484–490. https://doi.org/10.1016/j.jadohealth.2012.02.012.
14.
HossainM. M.RahmanM. A.SunX.MitranE.Investigating Underage Alcohol-Intoxicated Driver Crash Patterns in Louisiana. Transportation Research Record: Journal of the Transportation Research Board, 2021. 2675: 769–782. https://doi.org/10.1177/03611981211019742.
15.
MayhewD. R.DonelsonA. C.BeirnessD. J.SimpsonH. M.Youth, Alcohol and Relative Risk of Crash Involvement. Accident Analysis & Prevention, Vol. 18, No. 4, 1986, pp. 273–287. https://doi.org/10.1016/0001-4575(86)90042-4.
16.
VoasR. B.TorresP.RomanoE.LaceyJ. H.Alcohol-Related Risk of Driver Fatalities: An Update Using 2007 Data. Journal of Studies on Alcohol and Drugs, Vol. 73, No. 3, 2012, pp. 341–350. https://doi.org/10.15288/jsad.2012.73.341.
17.
ShultsR. A.HaegerichT. M.BhatG.ZhangX.Teens and Seat Belt Use: What Makes Them Click?Journal of Safety Research, Vol. 57, 2016, pp. 19–25. https://doi.org/10.1016/j.jsr.2016.03.003.
18.
Peek-AsaC.BrittonC.YoungT.PawlovichM.FalbS.Teenage Driver Crash Incidence and Factors Influencing Crash Injury by Rurality. Journal of Safety Research, Vol. 41, No. 6, 2010, pp. 487–492. https://doi.org/10.1016/j.jsr.2010.10.002.
19.
WilliamsA. F.ShabanovaV. I.Situational Factors in Seat Belt Use by Teenage Drivers and Passengers. Traffic Injury Prevention, Vol. 3, No. 3, 2002, pp. 201–204. https://doi.org/10.1080/15389580213650.
20.
NeyensD. M.BoyleL. N.The Effect of Distractions on the Crash Types of Teenage Drivers. Accident Analysis & Prevention, Vol. 39, No. 1, 2007, pp. 206–212. https://doi.org/10.1016/j.aap.2006.07.004.
21.
HossainM. M.ZhouH.DasS.SunX.HossainA.Young Drivers and Cellphone Distraction: Pattern Recognition From Fatal Crashes. Journal of Transportation Safety & Security, 2022, pp. 1–26. https://doi.org/10.1080/19439962.2022.2048763.
22.
OuimetM. C.PradhanA. K.Brooks-RussellA.EhsaniJ. P.BerbicheD.Simons-MortonB. G.Young Drivers and their Passengers: A Systematic Review of Epidemiological Studies on Crash Risk. Journal of Adolescent Health, Vol. 57, No. 1, 2015, pp. S24–S35.e6. https://doi.org/10.1016/j.jadohealth.2015.03.010.
23.
WeissH. B.KaplanS.PratoC. G.Analysis of Factors Associated With Injury Severity in Crashes Involving Young New Zealand Drivers. Accident Analysis & Prevention, Vol. 65, 2014, pp. 142–155. https://doi.org/10.1016/j.aap.2013.12.020.
24.
RahmanM. A.HossainM. M.MitranE.SunX.Understanding the Contributing Factors to Young Driver Crashes: A Comparison of Crash Profiles of Three Age Groups. Transportation Engineering, Vol. 5, 2021, p. 100076. https://doi.org/10.1016/j.treng.2021.100076.
25.
ChapmanE. A.MastenS. V.BrowningK. K.Crash and Traffic Violation Rates Before and After Licensure for Novice California Drivers Subject to Different Driver Licensing Requirements. Journal of Safety Research, Vol. 50, 2014, pp. 125–138. https://doi.org/10.1016/j.jsr.2014.05.005.
26.
McCarttA. T.ShabanovaV. I.LeafW. A.Driving Experience, Crashes and Traffic Citations of Teenage Beginning Drivers. Accident Analysis & Prevention, Vol. 35, No. 3, 2003, pp. 311–320. https://doi.org/10.1016/S0001-4575(02)00006-4.
27.
PaletiR.EluruN.BhatC. R.Examining the Influence of Aggressive Driving Behavior on Driver Injury Severity in Traffic Crashes. Accident Analysis & Prevention, Vol. 42, No. 6, 2010, pp. 1839–1854. https://doi.org/10.1016/j.aap.2010.05.005.
28.
DudduV. R.KukkapalliV. M.PulugurthaS. S.Crash Risk Factors Associated With Injury Severity of Teen Drivers. IATSS Research, Vol. 43, No. 1, 2019, pp. 37–43. https://doi.org/10.1016/j.iatssr.2018.08.003.
29.
AdanuE. K.HainenA.JonesS.Latent Class Analysis of Factors That Influence Weekday and Weekend Single-Vehicle Crash Severities. Accident Analysis & Prevention, Vol. 113, 2018, pp. 187–192. https://doi.org/10.1016/j.aap.2018.01.035.
BinghamC. R.ShopeJ. T.ParowJ. E.RaghunathanT. E.Crash Types: Markers of Increased Risk of Alcohol-Involved Crashes Among Teen Drivers. Journal of Studies on Alcohol and Drugs, Vol. 70, No. 4, 2009, pp. 528–535. https://doi.org/10.15288/jsad.2009.70.528.
32.
WangJ.ZhangG.Modeling and Examining the Teenage and Adult Freeway Crash Risks and Injury Severities in Washington State. Journal of Transportation Safety and Security, Vol. 3, No. 3, 2011, pp. 207–221. https://doi.org/10.1080/19439962.2011.591518.
33.
DissanayakeS.AmarasinghaN.Safety Experience of Young Drivers in Comparison to Experienced Drivers. In Proc., 2nd Transportation and Development Institute Congress, T&DI Congress 2014: Planes, Trains, and Automobiles (VarmaA.GoslingG. D., eds.), Orlando, FL, American Society of Civil Engineers, June 8–11, 2014, pp. 344–355. https://doi.org/10.1061/9780784413586.033.
34.
MuellerA. S.TrickL. M.Driving in Fog: The Effects of Driving Experience and Visibility on Speed Compensation and Hazard Avoidance. Accident Analysis & Prevention, Vol. 48, 2012, pp. 472–479. https://doi.org/10.1016/j.aap.2012.03.003.
35.
MuellerA. S.CicchinoJ. B.Teen Driver Crashes Potentially Preventable by Crash Avoidance Features and Teen-Driver-Specific Safety Technologies. Journal of Safety Research, Vol. 81, 2022, pp. 305–312. https://doi.org/10.1016/j.jsr.2022.03.007.
36.
VachalK.FacultyR.MalchoseD.What can we Learn About North Dakota’s Youngest Drivers From their Crashes?Accident Analysis & Prevention, Vol. 41, No. 3, 2009, pp. 617–623. https://doi.org/10.1016/j.aap.2009.02.014.
37.
HamannC.PriceM.Peek-AsaC.Characteristics of Crashes and Injuries Among 14 and 15 Year Old Drivers, by Rurality. Journal of Safety Research, Vol. 73, 2020, pp. 111–118. https://doi.org/10.1016/j.jsr.2020.02.019.
38.
LinC.WuD.LiuH.XiaX.BhattaraiN.Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study. Applied Science, Vol. 10, No. 5, 2020, p. 1675. https://doi.org/10.3390/app10051675.
39.
KimW.SvancaraA. M.Kelley-BakerT.Understanding the Impact of Road Design Characteristic on Teen Driver’s Fatality. Traffic Injury Prevention, Vol. 21, No. 5, 2020, pp. 313–318. https://doi.org/10.1080/15389588.2020.1753038.
40.
WuQ.ZhangG.CiY.WuL.TarefderR. A.“Dely” AlcántaraA.Exploratory Multinomial Logit Model–Based Driver Injury Severity Analyses for Teenage and Adult Drivers in Intersection-Related Crashes. Traffic Injury Prevention, Vol. 17, No. 4, 2016, pp. 413–422. https://doi.org/10.1080/15389588.2015.1100722.
41.
HossainM. M.SunX.MitranE.RahmanM. A.Investigating Fatal and Injury Crash Patterns of Teen Drivers With Unsupervised Learning Algorithms. IATSS Research, Vol. 45, No. 4, 2021, pp. 561–573. https://doi.org/10.1016/j.iatssr.2021.07.002.
42.
ShultsR. A.BergenG.SmithT. J.CookL.KindelbergerJ.WestB.Characteristics of Single Vehicle Crashes With a Teen Driver in South Carolina, 2005–2008. Accident Analysis & Prevention, Vol. 122, 2019, pp. 325–331. https://doi.org/10.1016/j.aap.2017.08.002.
43.
CarneyC.McgeheeD.HarlandK.WeissM.RabyM.Using Naturalistic Driving Data to Assess the Prevalence of Environmental Factors and Driver Behaviors in Teen Driver Crashes. Report. AAA Foundation for Traffic Safety, Washington DC, 2015, pp. 3–4.
44.
CarneyC.HarlandK. K.McGeheeD. V.Examining Teen Driver Crashes and the Prevalence of Distraction: Recent Trends, 2007–2015. Journal of Safety Research, Vol. 64, 2018, pp. 21–27. https://doi.org/10.1016/j.jsr.2017.12.014.
45.
LinZ.(David) FanW.Exploring Bicyclist Injury Severity in Bicycle-Vehicle Crashes Using Latent Class Clustering Analysis and Partial Proportional Odds Models. Journal of Safety Research, Vol. 76, 2021, pp. 101–117. https://doi.org/10.1016/j.jsr.2020.11.012.
46.
ManneringF. L.BhatC. R.Analytic Methods in Accident Research: Methodological Frontier and Future Directions. Analytic Methods in Accident Research, Vol. 1, 2014, pp. 1–22. https://doi.org/10.1016/j.amar.2013.09.001.
47.
DepaireB.WetsG.VanhoofK.Traffic Accident Segmentation by Means of Latent Class Clustering. Accident Analysis & Prevention, Vol. 40, No. 4, 2008, pp. 1257–1266. https://doi.org/10.1016/j.aap.2008.01.007.
48.
SunM.SunX.ShanD.Pedestrian Crash Analysis With Latent Class Clustering Method. Accident Analysis & Prevention, Vol. 124, 2019, pp. 50–57.
49.
ChenZ.(David) FanW.A Multinomial Logit Model of Pedestrian-Vehicle Crash Severity in North Carolina. International Journal of Transportation Science and Technology, Vol. 8, No. 1, 2019, pp. 43–52. https://doi.org/10.1016/j.ijtst.2018.10.001.
50.
Abrari VajariM.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.
51.
IranitalabA.KhattakA.Comparison of Four Statistical and Machine Learning Methods for Crash Severity Prediction. Accident Analysis & Prevention, Vol. 108, 2017, pp. 27–36. https://doi.org/10.1016/j.aap.2017.08.008.
52.
DiStefanoC.Cluster Analysis and Latent Class Clustering Techniques. In Handbook of Developmental Research Methods (LaursenB.LittleT. D.CardN. A., eds.), The Guilford Press, New York, NY, 2012, pp. 645–666.
53.
SunZ.XingY.WangJ.GuX.LuH.ChenY.Exploring Injury Severity of Bicycle-Motor Vehicle Crashes: A Two-Stage Approach Integrating Latent Class Analysis and Random Parameter Logit Model. Journal of Transportation Safety and Security, Vol. 14, No. 11, 2022, pp. 1838–1864. https://doi.org/10.1080/19439962.2021.1971814.
54.
SongL.FanW.Combined Latent Class and Partial Proportional Odds Model Approach to Exploring the Heterogeneities in Truck-Involved Severities at Cross and T-Intersections. Accident Analysis & Prevention, Vol. 144, 2020, p. 105638. https://doi.org/10.1016/j.aap.2020.105638.
55.
HennigC.LiaoT. F.Comparing Latent Class and Dissimilarity Based Clustering for Mixed Type Variables With Application to Social Stratification. Journal of the Royal Statistical Society, Vol. 62, No. 3, 2013, pp. 309–369.
56.
VermuntJ. K.MagidsonJ.Latent Class Cluster Analysis. In Applied Latent Class Analysis (HagenaarsJ. A.McCutcheonA. L., eds.), Cambridge University Press, New York, NY, 2002, pp. 89–106.
57.
LiuP.FanW.Exploring Injury Severity in Head-on Crashes Using Latent Class Clustering Analysis and Mixed Logit Model: A Case Study of North Carolina. Accident Analysis & Prevention, Vol. 135, 2020, p. 105388. https://doi.org/10.1016/j.aap.2019.105388.
58.
DziakJ. J.CoffmanD. L.LanzaS. T.LiR.JermiinL. S.Sensitivity and Specificity of Information Criteria. Briefings in Bioinformatics, Vol. 21, No. 2, 2020, pp. 553–565. https://doi.org/10.1093/bib/bbz016.
59.
ChenY.WuK.ChenX.TangC.ZhuQ.An Entropy-Based Uncertainty Measurement Approach in Neighborhood Systems. Information Sciences, Vol. 279, 2014, pp. 239–250. https://doi.org/10.1016/j.ins.2014.03.117.
60.
SivasankaranS. K.BalasubramanianV.Exploring the Severity of Bicycle – Vehicle Crashes Using Latent Class Clustering Approach in India. Journal of Safety Research, Vol. 72, 2020, pp. 127–138. https://doi.org/10.1016/j.jsr.2019.12.012.
61.
WahabL.JiangH.A Multinomial Logit Analysis of Factors Associated With Severity of Motorcycle Crashes in Ghana. Traffic Injury Prevention, Vol. 20, No. 5, 2019, pp. 521–527. https://doi.org/10.1080/15389588.2019.1616699.
62.
YeF.LordD.Investigation of Effects of Underreporting Crash Data on Three Commonly Used Traffic Crash Severity Models. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2241: 51–58.
63.
IslamM.HosseiniP.JalayerM.An Analysis of Single-Vehicle Truck Crashes on Rural Curved Segments Accounting for Unobserved Heterogeneity. Journal of Safety Research, Vol. 80, 2022, pp. 148–159. https://doi.org/10.1016/j.jsr.2021.11.011.
64.
BogardM.Marginal Effects vs Odds Ratios. Econometrics Sense, March11, 2016, p. 1.
65.
WashingtonS.KarlaftisM. G.ManneringF.AnastasopoulosP.Statistical and Econometric Methods for Transportation Data Analysis. Chapman and Hall/CRC, New York, NY, 2020.
66.
StewartT.Overview of Motor Vehicle Crashes in 2020. DOT HS 813 266. National Highway Traffic Safety Administration, Washington, D.C., 2022.
67.
IslamS.JonesS. L.DyeD.Comprehensive Analysis of Single- and Multi-Vehicle Large Truck at-Fault Crashes on Rural and Urban Roadways in Alabama. Accident Analysis & Prevention, Vol. 67, 2014, pp. 148–158. https://doi.org/10.1016/j.aap.2014.02.014.
68.
AdanuE. K.AgyemangW.IslamR.JonesS.A Comprehensive Analysis of Factors That Influence Interstate Highway Crash Severity in Alabama. Journal of Transportation Safety & Security, Vol. 14, No. 9, 2022, pp. 1552–1576. https://doi.org/10.1080/19439962.2021.1949414.
69.
SivakM.Evaluation of Changes in Teenage Driver Exposure. HLDI, 2013.
70.
YadavA. K.VelagaN. R.Modelling the Relationship Between Different Blood Alcohol Concentrations and Reaction Time of Young and Mature Drivers. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 64, 2019, pp. 227–245. https://doi.org/10.1016/j.trf.2019.05.011.
71.
VianoD. C.ParenteauC. S.Ejection and Severe Injury Risks by Crash Type and Belt Use With a Focus on Rear Impacts. Traffic Injury Prevention, Vol. 11, No. 1, 2010, pp. 79–86. https://doi.org/10.1080/15389580903479186.
72.
Simons-MortonB. G.OuimetM. C.ChenR.KlauerS. G.LeeS. E.WangJ.DingusT. A.Peer Influence Predicts Speeding Prevalence Among Teenage Drivers. Journal of Safety Research, Vol. 43, No. 5–6, 2012, pp. 397–403.
73.
GongL.(David) FanW.Modeling Single-Vehicle Run-Off-Road Crash Severity in Rural Areas: Accounting for Unobserved Heterogeneity and Age Difference. Accident Analysis & Prevention, Vol. 101, 2017, pp. 124–134. https://doi.org/10.1016/j.aap.2017.02.014.
74.
LombardiD. A.HorreyW. J.CourtneyT. K.Age-Related Differences in Fatal Intersection Crashes in the United States. Accident Analysis & Prevention, Vol. 99, 2017, pp. 20–29. https://doi.org/10.1016/j.aap.2016.10.030.
75.
AnarkooliA. J.HosseinlouM. H.Analysis of the Injury Severity of Crashes by Considering Different Lighting Conditions on Two-Lane Rural Roads. Journal of Safety Research, Vol. 56, 2016, pp. 57–65. https://doi.org/10.1016/j.jsr.2015.12.003.
76.
EluruN.BhatC. R.A Joint Econometric Analysis of Seat Belt Use and Crash-Related Injury Severity. Accident Analysis & Prevention, Vol. 39, No. 5, 2007, pp. 1037–1049. https://doi.org/10.1016/j.aap.2007.02.001.
77.
BhamG. H.ManepalliU. R. R.; Mack-Blackwell Rural Transportation Center. Identification and Analysis of High Crash Segments on Interstate, US, and State Highway Systems of Arkansas. Final Project Report. Arkansas Highway and Transportation Department, Fayettefille, 2009, p. 68.
78.
DhunganaP.QuM.The Risks of Driving on Roadways With 50 Miles per Hour Posted Speed Limit. Journal of Safety Research, Vol. 36, No. 5, 2005, pp. 501–504. https://doi.org/10.1016/j.jsr.2005.10.013.
79.
YuR.Abdel-AtyM.Investigating the Different Characteristics of Weekday and Weekend Crashes. Journal of Safety Research, Vol. 46, 2013, pp. 91–97. https://doi.org/10.1016/j.jsr.2013.05.002.
80.
ChoiJ.KimS.HeoT. Y.LeeJ.Safety Effects of Highway Terrain Types in Vehicle Crash Model of Major Rural Roads. KSCE Journal of Civil Engineering, Vol. 15, No. 2, 2011, pp. 405–412. https://doi.org/10.1007/s12205-011-1124-x.
81.
TangZ.ChenS.ChengJ.GhahariS. A.LabiS.Highway Design and Safety Consequences: A Case Study of Interstate Highway Vertical Grades. Journal of Advanced Transportation, Vol. 2018, 2018. https://doi.org/10.1155/2018/1492614.
82.
KulanthayanS.PhangW. K.HayatiK. S.Traffic Light Violation Among Motorists in Malaysia. IATSS Research, Vol. 31, No. 2, 2007, pp. 67–73. https://doi.org/10.1016/s0386-1112(14)60224-7.
83.
ZhangG.TanY.JouR. C.Factors Influencing Traffic Signal Violations by Car Drivers, Cyclists, and Pedestrians: A Case Study From Guangdong, China. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 42, 2016, pp. 205–216. https://doi.org/10.1016/j.trf.2016.08.001.
84.
SharmaA.RilettL.WuZ. M.WangS.Speed Limit Recommendation in Vicinity of Signalized High-Speed Intersection. Nebraska Department of Transportation Research Report. Nebraska LTAP at DigitalCommons@University of Nebraska – Lincoln, 2012, p. 37.
85.
FarmerC. M.Reliability of Police-Reported Information for Determining Crash and Injury Severity. Traffic Injury Prevention, Vol. 4, No. 1, 2003, pp. 38–44. https://doi.org/10.1080/15389580309855.
86.
ĀbeleL.HausteinS.MøllerM.MartinussenL. M.Consistency Between Subjectively and Objectively Measured Hazard Perception Skills Among Young Male Drivers. Accident Analysis & Prevention, Vol. 118, 2018, pp. 214–220. https://doi.org/10.1016/j.aap.2018.02.022.
87.
GibbonsR.CheungJ.LutkevichP.The Future of Roadway Lighting. Federal Highway Administration, Washington, D.C., 2015.
88.
JermakianJ. S.Crash Avoidance Potential of Four Passenger Vehicle Technologies. Accident Analysis & Prevention, Vol. 43, No. 3, 2011, pp. 732–740. https://doi.org/10.1016/j.aap.2010.10.020.
89.
EiksundS.A Geographical Perspective on Driving Attitudes and Behaviour Among Young Adults in Urban and Rural Norway. Safety Science, Vol. 47, No. 4, 2009, pp. 529–536. https://doi.org/10.1016/j.ssci.2008.07.034.
