A non-linear and interaction effect analysis of various risk factors influencing mobile phone use while driving among long-haul truck drivers travelling across India

References

• Ahmed S, Uddin MS, Feroz SI, et al. (2023). Tendency of intra-city bus drivers to use cell phone while driving using ordered probit model. In American Institute of Physics Conference Series (January). doi: 10.1063/5.0120743.
   Google Scholar
• Choudhary P, Mahajan K, Velaga NR, et al. Modeling phone use prevalence and risk assessment among long-haul truck drivers in India. IATSS Res. 2021;46(1):112–121. doi: 10.1016/j.iatssr.2021.10.005.
   Web of Science ®Google Scholar
• Donkor I, Gyedu A, Edusei AK, et al. Mobile phone use among commercial drivers in Ghana : an important threat to road safety. Ghana Med J. 2018. 52(3):122–126. doi: 10.4314/gmj.v52i3.3.
   PubMedGoogle Scholar
• Baikejuli M, Shi J, Qian Q. Mobile phone use among truck drivers : the application and extension of the theory of planned behavior. Accid Anal Prev. 2023;179(October 2022):106894. doi: 10.1016/j.aap.2022.106894.
   PubMedGoogle Scholar
• Ji X, Huang H, Li Z, et al. Comparing interventions to reduce boredom in a low mental workload environment. Int J Occup Saf Ergon. 2022;28(3):1973–1979. doi: 10.1080/10803548.2021.1950374.
   PubMed Web of Science ®Google Scholar
• Ma C, Peng Y, Wu L, et al. Application of machine learning techniques to predict the occurrence of distraction-affected crashes with phone-use data. Transp Res Record. 2022;2676(2):692–705. doi: 10.1177/03611981211045371.
   Web of Science ®Google Scholar
• MoRTH. 2023. https://morth.nic.in/annual-report-2022-23.
   Google Scholar
• SaveLIFE Foundation. Distracted driving in India a study on mobile phone usage, pattern & behaviour; 2017. 1–68. http://savelifefoundation.org/wp-content/uploads/2017/04/Distracted-Driving-in-India_A-Study-on-Mobile-Phone-Usage-Pattern-and-Behaviour.pdf.
   Google Scholar
• Claveria JB, Hernandez S, Anderson JC, et al. Understanding truck driver behavior with respect to cell phone use and vehicle operation. Transp Res Part F Traffic Psychol Behav. 2019;65:389–401. doi: 10.1016/j.trf.2019.07.010.
   Web of Science ®Google Scholar
• Okati-Aliabad H, Habybabady RH, Sabouri M, et al. Different types of mobile phone use while driving and influencing factors on intention and behavior: insights from an expanded theory of planned behavior. PLoS One. 2024;19(3):e0300158. doi: 10.1371/journal.pone.0300158.
   PubMed Web of Science ®Google Scholar
• Li J, Liu J, Liu P, et al. Analysis of factors contributing to the severity of large truck crashes. Entropy. 2020;22(11):1191. doi: 10.3390/e22111191.
   PubMed Web of Science ®Google Scholar
• Chand A, Bhasi AB. 2019. Effect of driver distraction contributing factors on accident causations-A review. In AIP Conference Proceedings, 2134 (060004). doi: 10.1063/1.5120229.
   Google Scholar
• Nguyen-Phuoc DQ, Oviedo-Trespalacios O, Nguyen T, et al. The effects of unhealthy lifestyle behaviours on risky riding behaviours – a study on app-based motorcycle taxi riders in Vietnam. J Transp Health. 2020;16(October 2019):100666. doi: 10.1016/j.jth.2019.100666.
   Google Scholar
• Qian Q, He J, Shi J. Analysis of factors influencing aberrant riding behavior of food delivery riders: a perspective on safety attitude and risk perception. Transp Res Part F Traffic Psychol Behav. 2024;100(September 2023):273–288. doi: 10.1016/j.trf.2023.12.007.
   Google Scholar
• Ram T, Chand K. Effect of drivers’ risk perception and perception of driving tasks on road safety attitude. Transp Res Part F Traffic Psychol Behav. 2016;42:162–176. doi: 10.1016/j.trf.2016.07.012.
   Web of Science ®Google Scholar
• Toda M, Ezoe S. Multifactorial study of mobile phone dependence in medical students: relationship to health-related lifestyle, Type A behavior, and depressive state. OJPM. 2013;03(01):99–103. doi: 10.4236/ojpm.2013.31012.
   Google Scholar
• Islam M. Unraveling the differences in distracted driving injury severities in passenger car, sport utility vehicle, pickup truck, and minivan crashes. Accid Anal Prev. 2024;196(December 2023):107444. doi: 10.1016/j.aap.2023.107444.
   PubMedGoogle Scholar
• Ameksa M, Mousannif H, Al Moatassime H, et al. Application of machine learning techniques for driving errors analysis: systematic literature review. Int J Crashworthiness. 2024;27(5):1–9. doi: 10.1080/13588265.2023.2301146.
   Web of Science ®Google Scholar
• Fu X, Meng H, Yang H, et al. A hybrid deep learning method for distracted driving risk prediction based on spatio-temporal driving behavior data. Transportmetrica B. 2024;12(1):26–45. doi: 10.1080/21680566.2023.2297144.
   Google Scholar
• Islam M, Patel D, Hasan AS, et al. An exploratory analysis of two-vehicle crashes for distracted driving with a mixed approach: machine learning algorithm with unobserved heterogeneity. J Transp Saf Secur. 2023:1–37. doi: 10.1080/19439962.2023.2248035.
   Web of Science ®Google Scholar
• Qu F, Dang N, Furht B, et al. Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques. J Big Data. 2024;11(1):1–44. doi: 10.1186/s40537-024-00890-0.
   Google Scholar
• Lin C, Zhang H, Gong B, et al. Factors identification and prediction for mind wandering driving using machine learning. J Adv Transp. 2021;2021:1–13. doi: 10.1155/2021/4216215.
   Web of Science ®Google Scholar
• Ziakopoulos A, Kontaxi A, Yannis G. Analysis of mobile phone use engagement during naturalistic driving through explainable imbalanced machine learning. Accid Anal Prev. 2023;181(July 2022):106936. doi: 10.1016/j.aap.2022.106936.
   PubMedGoogle Scholar
• Mcdonald AD, Ferris TK, Wiener TA. Classification of driver distraction : a comprehensive analysis of feature generation, machine learning, and input measures. Hum Factors. 2019;62(6):1019–1035. doi: 10.1177/0018720819856454.
   PubMed Web of Science ®Google Scholar
• Niu Y, Li Z, Fan Y. Analysis of truck drivers’ unsafe driving behaviors using four machine learning methods. Int J Ind Ergon. 2021;86:103192. doi: 10.1016/j.ergon.2021.103192.
   Web of Science ®Google Scholar
• Alsagri AS, Alrobaian AA, Nejlaoui M. Techno-economic evaluation of an off-grid health clinic considering the current and future energy challenges: a rural case study. Renew Energy. 2021;169:34–52. doi: 10.1016/j.renene.2021.01.017.
   Web of Science ®Google Scholar
• Nejlaoui M, Alghafis A, Sadig H. Design optimization and experimental validation of low-cost flat plate collector under central qasssim climate. J Appl Comput Mech. 2021;7(2):811–819. doi: 10.22055/jacm.2020.34932.2515.
   Web of Science ®Google Scholar
• Nejlaoui M, Alghafis A, Sadig H. Six sigma robust multi-objective design optimization of flat plate collector system under uncertain design parameters. Energy. 2022;239:121883. doi: 10.1016/j.energy.2021.121883.
   Web of Science ®Google Scholar
• National Aids Control Organization. Conducting a mapping study of long distance truck driver halt points and identifying transport sector stakeholders who can carry out HIV prevention interventions among the long distance truck drivers; 2006. https://naco.gov.in/sites/default/files/TRUCKERS%20MAPPING%20REPORT%202007.pdf.
   Google Scholar
• Santos K, Dias JP, Amado C. A literature review of machine learning algorithms for crash injury severity prediction. J Safety Res. 2022;80:254–269. doi: 10.1016/j.jsr.2021.12.007.
   PubMed Web of Science ®Google Scholar
• Hoddinott J, Yohasnes Y. Classification and regression trees an introduction; 1999. https://www.ifpri.org/publication/classification-and-regression-trees-introduction.
   Google Scholar
• Breiman L. Arcing the edge. 1997. 4:1–14. https://statistics.berkeley.edu/sites/default/files/tech-reports/486.pdf.
   Google Scholar
• Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci. 1997;55(1):119–139. doi: 10.1006/jcss.1997.1504.
   Web of Science ®Google Scholar
• Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August, 2016:785–794. doi: 10.1145/2939672.2939785.
   Google Scholar
• Lundberg SM, Lee S. A unified approach to interpreting model predictions. In 31st Conference on Neural Information Processing Systems, 2017. Section 2, 1–10. doi: 10.48550/arXiv.1705.07874.
   Google Scholar
• Kong X, Zhang A, Zhang Y, et al. Investigating relationships between phone use while driving behavior and drivers’ socio-demographic characteristics – an interpretable machine learning approach. SSRN J. 2021;1–28 doi: 10.2139/ssrn.3981311.
   Google Scholar
• Mcleod FN, Cherrett TJ, Bektas T, et al. Quantifying environmental and fi nancial bene fi ts of using porters and cycle couriers for last-mile parcel delivery. Transp Res Part D. 2020;82(June 2019):102311. doi: 10.1016/j.trd.2020.102311.
   Google Scholar
• Huang Y, Zohar D, Robertson MM, et al. Development and validation of safety climate scales for lone workers using truck drivers as exemplar. Transp Res Part F Psychol Behav. 2013;17:5–19. doi: 10.1016/j.trf.2012.08.011.
   Web of Science ®Google Scholar
• Montuori P, Sarnacchiaro P, Nubi R, et al. The use of mobile phone while driving : behavior and determinant analysis in one of the largest metropolitan area of Italy. Accid Anal Prev. 2021;157(September 2020):106161. doi: 10.1016/j.aap.2021.106161.
   PubMedGoogle Scholar