Young novice drivers’ road crash injuries and contributing factors: A crash data investigation

References

• Association for the Advancement of Automotive Medicine [AAAM]. 2008. Abbreviated Injury Scale 2005, Update 2008. Barrington, IL: AAAM.
   Google Scholar
• Choudhary P, Velaga NR. 2019. Effects of phone use on driving performance: a comparative analysis of young and professional drivers. Saf Sci. 111:179–187. doi:10.1016/j.ssci.2018.07.009.
   Web of Science ®Google Scholar
• Duddu VR, Penmetsa P, Pulugurtha SS. 2018. Modeling and comparing injury severity of at-fault and not at-fault drivers in crashes. Accid Anal Prev. 120:55–63. doi:10.1016/j.aap.2018.07.036.
   PubMed Web of Science ®Google Scholar
• Eboli L, Forciniti C. 2020. The severity of traffic crashes in Italy: an explorative analysis among different driving circumstances. Sustainability. 12(3):856. doi:10.3390/su12030856.
   Web of Science ®Google Scholar
• Freire MR, Gauld C, McKerral A, Pammer K. 2021. Identifying interactive factors that may increase crash risk between young drivers and trucks: a narrative review. Int J Environ Res Public Health. 18(12):6506. doi:10.3390/ijerph18126506.
   PubMed Web of Science ®Google Scholar
• Gicquel L, Ordonneau P, Blot E, Toillon C, Ingrand P, Romo L. 2017. Description of various factors contributing to traffic accidents in youth and measures proposed to alleviate recurrence. Front Psychiatry. 8:94. doi:10.3389/fpsyt.2017.00094.
   PubMed Web of Science ®Google Scholar
• Haleem K. 2016. Investigating risk factors of traffic casualties at private highway-railroad grade crossings in the United States. Accid Anal Prev. 95(Pt A):274–283. doi:10.1016/j.aap.2016.07.024.
   PubMedGoogle Scholar
• Islam S, Hossain AB, Shaban M. 2023. Older driver at-fault crashes at unsignalized intersections in Alabama: injury severity analysis with supporting evidence from a deep learning based approach. J Safety Res. 85:419–428. doi:10.1016/j.jsr.2023.04.009.
   PubMed Web of Science ®Google Scholar
• Islam SM, Washington S, Kim J, Haque MM. 2023. A hierarchical multinomial logit model to examine the effects of signal strategies on right-turn crash injury severity at signalised intersections. Accid Anal Prev. 188:107091. doi:10.1016/j.aap.2023.107091.
   PubMed Web of Science ®Google Scholar
• Lucidi F, Giannini AM, Sgalla R, Mallia L, Devoto A, Reichmann S. 2010. Young novice driver subtypes: relationship to driving violations, errors and lapses. Accid Anal Prev. 42(6):1689–1696. doi:10.1016/j.aap.2010.04.008.
   PubMed Web of Science ®Google Scholar
• Miqdady T, de Oña J. 2020. Identifying the factors that increase the probability of an injury or fatal traffic crash in an urban context in Jordan. Sustainability. 12(18):7464. doi:10.3390/su12187464.
   Web of Science ®Google Scholar
• Naik B, Tung LW, Zhao S, Khattak AJ. 2016. Weather impacts on single-vehicle truck crash injury severity. J Safety Res. 58:57–65. doi:10.1016/j.jsr.2016.06.005.
   PubMed Web of Science ®Google Scholar
• Penmetsa P, Pulugurtha SS, Duddu VR. 2017. Examining injury severity of not-at-fault drivers in two-vehicle crashes. Transportation Research Record. 2659(1):164–173. doi:10.3141/2659-18.
   Google Scholar
• Roman GD, Poulter D, Barker E, McKenna FP, Rowe R. 2015. Novice drivers’ individual trajectories of driver behavior over the first three years of driving. Accid Anal Prev. 82:61–69. doi:10.1016/j.aap.2015.05.012.
   PubMed Web of Science ®Google Scholar
• Seacrist T, Belwadi A, Prabahar A, Chamberlain S, Megariotis J, Loeb H. 2016. Comparison of crash rates and rear-end striking crashes among novice teens and experienced adults using the SHRP2 Naturalistic Driving Study. Traffic Inj Prev. 17 Suppl 1(sup1):48–52. doi:10.1080/15389588.2016.1188384.
   PubMedGoogle Scholar
• Sharafeldin M, Farid A, Ksaibati K. 2022. Injury severity analysis of rear-end crashes at signalized intersections. Sustainability. 14(21):13858. doi:10.3390/su142113858.
   Web of Science ®Google Scholar
• Stipancic J, Zangenehpour S, Miranda-Moreno L, Saunier N, Granié MA. 2016. Investigating the gender differences on bicycle-vehicle conflicts at urban intersections using an ordered logit methodology. Accid Anal Prev. 97:19–27. doi:10.1016/j.aap.2016.07.033.
   PubMed Web of Science ®Google Scholar
• Sun Z, Wang D, Gu X, Abdel-Aty M, Xing Y, Wang J, Lu H, Chen Y. 2023. A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes. Accid Anal Prev. 192:107235. doi:10.1016/j.aap.2023.107235.
   PubMed Web of Science ®Google Scholar
• Wang K, Shirani-Bidabadi N, Razaur Rahman Shaon M, Zhao S, Jackson E. 2021. Correlated mixed logit modeling with heterogeneity in means for crash severity and surrogate measure with temporal instability. Accid Anal Prev. 160:106332. doi:10.1016/j.aap.2021.106332.
   PubMed Web of Science ®Google Scholar
• Wang X, Xu L. 2021. Factors influencing young drivers’ willingness to engage in risky driving behavior: continuous lane-changing. Sustainability. 13(11):6459. doi:10.3390/su13116459.
   Web of Science ®Google Scholar
• Wang Z, Yue Y, Li Q, Nie K, Tu W, Liang S. 2017. Analyzing risk factors for fatality in urban traffic crashes: A case study of Wuhan, China. Sustainability. 9(6):897. doi:10.3390/su9060897.
   Web of Science ®Google Scholar
• Wen X, Xie Y, Wu L, Jiang L. 2021. Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accid Anal Prev. 159:106261. doi:10.1016/j.aap.2021.106261.
   PubMed Web of Science ®Google Scholar
• Xu C, Wang C, Liu P. 2018. Evaluating the combined effects of weather and real-time traffic conditions on freeway crash risks. Wea. Climate Soc. 10(4):837–850. doi:10.1175/WCAS-D-17-0124.1.
   Web of Science ®Google Scholar
• Yan M, Shen Y. 2022. Traffic accident severity prediction based on random forest. Sustainability. 14(3):1729. doi:10.3390/su14031729.
   Web of Science ®Google Scholar