The effect of different mobile uses on crash frequency among young drivers: application of statistical models and clustering analysis

References

• Al-Ghaith, W., Sanzogni, L., & Sandhu, K. (2010). Factors influencing the adoption and usage of online services in Saudi Arabia. The Electronic Journal of Information Systems in Developing Countries, 40(1), 1–32. https://doi.org/10.1002/j.1681-4835.2010.tb00283.x
   Google Scholar
• Alghnam, S., Towhari, J., Alkelya, M., Binahmad, A., & Bell, T. M. (2018). The effectiveness of introducing detection cameras on compliance with mobile phone and seatbelt laws: a before-after study among drivers in Riyadh, Saudi Arabia. Injury Epidemiology, 5(1), 1–8. https://doi.org/10.1186/s40621-018-0161-z
   PubMedGoogle Scholar
• Alhajyaseen, W. K., Almukdad, A., Hussain, Q., Almallah, M., Al Malki, M. A., Singaravelu, J., & Zammataro, S. (2021). Road safety status during COVID-19 pandemic: Exploring public and road safety expert’s opinions. International Journal of Injury Control and Safety Promotion, 29(2), 135–151. https://doi.org/10.1080/17457300.2021.1962915
   Google Scholar
• Archer, K. J., & Lemeshow, S. (2006). Goodness-of-fit test for a logistic regression model fitted using survey sample data. The Stata Journal: Promoting Communications on Statistics and Stata, 6(1), 97–105. https://doi.org/10.1177/1536867X0600600106
   Google Scholar
• Ataman, M. G., & Sarıyer, G. (2021). Predicting waiting and treatment times in emergency departments using ordinal logistic regression models. The American Journal of Emergency Medicine, 46, 45–50.
   PubMedGoogle Scholar
• Awadhalla, M. S., Asokan, G. V., Matooq, A., & Kirubakaran, R. (2016). Declining trends in injuries and ambulance calls for road traffic crashes in Bahrain post new traffic laws of 2015. Journal of Epidemiology and Global Health, 6(2), 59–65. https://doi.org/10.1016/j.jegh.2016.02.004
   PubMedGoogle Scholar
• Baig, M., Gazzaz, Z. J., Atta, H., Alyaseen, M. A., Albagshe, A. J., & Alattallah, H. G. (2018). Prevalence and attitude of university students towards mobile phone use while driving in Jeddah, Saudi Arabia. International Journal of Injury Control and Safety Promotion, 25(4), 372–377. https://doi.org/10.1080/17457300.2018.1431940
   PubMed Web of Science ®Google Scholar
• Čubranić-Dobrodolac, M., Čičević, S., Dobrodolac, M., & Nešić, M. (2013). The risks associated with using a mobile phone by young drivers. Transport, 28(4), 381–388. https://doi.org/10.3846/16484142.2013.865672
   Web of Science ®Google Scholar
• De Winter, J. C. (2013). Using the student’s t-test with extremely small sample sizes. Practical Assessment, Research & Evaluation, 18(10), 1–12. http://pareonline.net/getvn.asp?v=18&n=10.
   Google Scholar
• Dechter, R., & Pearl, J. (1989). Tree clustering for constraint networks. Artificial Intelligence, 38(3), 353–366. https://doi.org/10.1016/0004-3702(89)90037-4
   Web of Science ®Google Scholar
• Gazder, U., Al-Malki, V., & Alam, M. S. (2019). Risks of using smart devices for drivers [Paper presentation].2nd Smart Cities Symposium (SCS 2019),. https://doi.org/10.1049/cp.2019.0230
   Google Scholar
• Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7). Pearson.
   Google Scholar
• Harrell, F. E. (2015). Ordinal logistic regression. In Regression modeling strategies (pp. 311–325). Springer.
   Google Scholar
• Heckmann, T., Gegg, K., Gegg, A., & Becht, M. (2014). Sample size matters: Investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Natural Hazards and Earth System Sciences, 14(2), 259–278. https://doi.org/10.5194/nhess-14-259-2014
   Web of Science ®Google Scholar
• Hill, T., Sullman, M. J., & Stephens, A. N. (2019). Mobile phone involvement, beliefs, and texting while driving in Ukraine. Accident; Analysis and Prevention, 125, 124–131. https://doi.org/10.1016/j.aap.2019.01.035
   PubMed Web of Science ®Google Scholar
• Ismaeel, R., Hibberd, D., Carsten, O., & Jamson, S. (2020). Do drivers self-regulate their engagement in secondary tasks at intersections? An examination based on naturalistic driving data. Accident; Analysis and Prevention, 137, 105464. https://doi.org/10.1016/j.aap.2020.105464
   PubMed Web of Science ®Google Scholar
• Jamal, A., Rahman, M. T., Al-Ahmadi, H. M., & Mansoor, U. (2019). The dilemma of road safety in the eastern province of Saudi Arabia: Consequences and prevention strategies. International Journal of Environmental Research and Public Health, 17(1), 157. https://doi.org/10.3390/ijerph17010157
   PubMed Web of Science ®Google Scholar
• Kim, T. K. (2015). T-test as a parametric statistic. Korean Journal of Anesthesiology, 68(6), 540–546. https://doi.org/10.4097/kjae.2015.68.6.540
   PubMed Web of Science ®Google Scholar
• Lachin, J. M. (1981). Introduction to sample size determination and power analysis for clinical trials. Controlled Clinical Trials, 2(2), 93–113. https://doi.org/10.1016/0197-2456(81)90001-5
   PubMedGoogle Scholar
• Li, X., Yan, X., Wu, J., Radwan, E., & Zhang, Y. (2016). A rear-end collision risk assessment model based on drivers’ collision avoidance process under influences of cell phone use and gender—a driving simulator based study. Accident; Analysis and Prevention, 97, 1–18. https://doi.org/10.1016/j.aap.2016.08.021
   PubMed Web of Science ®Google Scholar
• Liberti, L., Lavor, C., Maculan, N., & Mucherino, A. (2014). Euclidean distance geometry and applications. SIAM Review, 56(1), 3–69. https://doi.org/10.1137/120875909
   Web of Science ®Google Scholar
• Lu, P., Wang, H., & Tolliver, D. (2019). Prediction of bridge component ratings using ordinal logistic regression model. Mathematical Problems in Engineering, 2019:1–11. https://doi.org/10.1155/2019/9797584
   Google Scholar
• Márquez, L., Cantillo, V., & Arellana, J. (2015). Mobile phone use while driving: A hybrid modeling approach. Accident; Analysis and Prevention, 78, 73–80. https://doi.org/10.1016/j.aap.2015.02.016
   PubMed Web of Science ®Google Scholar
• McNeish, D. M., & Stapleton, L. M. (2016). The effect of small sample size on two-level model estimates: A review and illustration. Educational Psychology Review, 28(2), 295–314. https://doi.org/10.1007/s10648-014-9287-x
   Web of Science ®Google Scholar
• Nasr Esfahani, H., Arvin, R., Song, Z., & Sze, N. N. (2019). Prevalence of cell phone use while driving and its impact on driving performance, focusing on near-crash risk: A survey study in Tehran. Journal of Transportation Safety & Security, 17, 1–21.
   Google Scholar
• Opoku, R. A. (2017). Attitudes towards mobile phone usage in public places among young Saudi adults. International Journal of Mobile Communications, 15(3), 235–251. https://doi.org/10.1504/IJMC.2017.083458
   Google Scholar
• Oviedo-Trespalacios, O., Nandavar, S., Newton, J. D., Demant, D., & Phillips, J. G. (2019). Problematic use of mobile phones in Australia… is it getting worse? Frontiers in Psychiatry, 10(105), 1–15. https://doi.org/10.3389/fpsyt.2019.00105
   PubMedGoogle Scholar
• Papadimitriou, E., Argyropoulou, A., Tselentis, D. I., & Yannis, G. (2019). Analysis of driver behaviour through smartphone data: The case of mobile phone use while driving. Safety Science, 119, 91–97. https://doi.org/10.1016/j.ssci.2019.05.059
   Web of Science ®Google Scholar
• Przepiorka, A. M., Błachnio, A. P., & Sullman, M. J. (2018). Factors influencing intentions to text while driving among Polish drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 55, 306–313. https://doi.org/10.1016/j.trf.2018.03.015
   Web of Science ®Google Scholar
• Saifuzzaman, M., Haque, M. M., Zheng, Z., & Washington, S. (2015). Impact of mobile phone use on car-following behavior of young drivers. Accident; Analysis and Prevention, 82, 10–19. https://doi.org/10.1016/j.aap.2015.05.001
   PubMed Web of Science ®Google Scholar
• Scheneider, M. J. (2018). What is cognitive distraction and why is it so dangerous to drivers? https://www.aktriallaw.com/Articles/What-is-cognitive-distraction-and-why-is-it-so-dangerous-to-drivers.shtml.
   Google Scholar
• Scott-Parker, B., & Oviedo-Trespalacios, O. (2017). Young driver risky behaviour and predictors of crash risk in Australia, New Zealand and Colombia: Same but different? Accident; Analysis and Prevention, 99(Pt A), 30–38. https://doi.org/10.1016/j.aap.2016.11.001
   PubMedGoogle Scholar
• Shaaban, K., Gaweesh, S., & Ahmed, M. M. (2018). Characteristics and mitigation strategies for cell phone use while driving among young drivers in Qatar. Journal of Transport & Health, 8, 6–14. https://doi.org/10.1016/j.jth.2018.02.001
   Web of Science ®Google Scholar
• Shaaban, K., Gaweesh, S., & Ahmed, M. M. (2020). Investigating in-vehicle distracting activities and crash risks for young drivers using structural equation modeling. PloS One, 15(7), e0235325.
   PubMed Web of Science ®Google Scholar
• Suzuki, K., Tang, K., Alhajyaseen, W., Suzuki, K., & Nakamura, H. (2022). An international comparative study on driving attitudes and behaviors based on questionnaire surveys. IATSS Research, 46(1), 26–35. https://doi.org/10.1016/j.iatssr.2021.10.002
   Web of Science ®Google Scholar
• Timmermans, C., Alhajyaseen, W., Al Mamun, A., Wakjira, T., Qasem, M., Almallah, M., & Younis, H. (2019). Analysis of road traffic crashes in the State of Qatar. International Journal of Injury Control and Safety Promotion, 26(3), 242–250.
   PubMed Web of Science ®Google Scholar
• Timmermans, C., Alhajyaseen, W., Reinolsmann, N., Nakamura, H., & Suzuki, K. (2019). Traffic safety culture of professional drivers in the State of Qatar. IATSS Research, 43(4), 286–296. https://doi.org/10.1016/j.iatssr.2019.03.004
   Web of Science ®Google Scholar
• Timmermans, C., Shawky, M., Alhajyaseen, W., & Nakamura, H. (2022). Investigating the attitudes of Egyptian drivers toward traffic safety. IATSS Research, 46(1), 73–81. https://doi.org/10.1016/j.iatssr.2021.11.007
   Web of Science ®Google Scholar
• Uzondu, C., Jamson, S., & Lai, F. (2019). Investigating unsafe behaviours in traffic conflict situations: An observational study in Nigeria. Journal of Traffic and Transportation Engineering (English Edition), 6(5), 482–492. https://doi.org/10.1016/j.jtte.2018.06.002
   Google Scholar
• Wheeler, T. J. (2009, September). Large-scale neighbor-joining with NINJA. In International Workshop on Algorithms in Bioinformatics., 375–389. Springer.
   Google Scholar
• Yannis, G., Papadimitriou, E., Karekla, X., & Kontodima, E. (2010). Mobile phone use by young drivers: effects on traffic speed and headways. Transportation Planning and Technology, 33(4), 385–394. https://doi.org/10.1080/03081060.2010.494030
   Web of Science ®Google Scholar
• Yannis, G., Theofilatos, A., & Marinou, P. (2015). Attitudes of Greek drivers with focus on mobile phone use while driving. Traffic Injury Prevention, 16(8), 831–834. https://doi.org/10.1080/15389588.2015.1030737
   PubMed Web of Science ®Google Scholar
• Young, R. (2014). An unbiased estimate of the relative crash risk of cell phone conversation while driving an automobile. SAE International Journal of Transportation Safety, 2(1), 46–66. https://doi.org/10.4271/2014-01-0446
   Google Scholar
• Young, K., Regan, M., & Hamm, M. (2007). Driver distraction: A review of the literature. In I. J. Faulks, M. Regan, M. Stevenson, J. Brown, A. Porter & J. D. Irwin (Eds.). Distracted driving, pp. 379–405. Australasian College of Road Safety.
   Google Scholar
• Young, K. L., Rudin-Brown, C. M., & Lenné, M. G. (2010). Look who’s talking! A roadside survey of drivers’ cell phone use. Traffic Injury Prevention, 11(6), 555–560. https://doi.org/10.1080/15389588.2010.499442
   PubMed Web of Science ®Google Scholar