Prediction of high-risk bus drivers characterized by aggressive driving behavior

References

• Bagdadi, O., & Várhelyi, A. (2011). Jerky driving—An indicator of accident proneness? Accident; Analysis and Prevention, 43(4), 1359–1363. doi:10.1016/j.aap.2011.02.009
   PubMed Web of Science ®Google Scholar
• Brochu, E., Cora, V. M., & De Freitas, N. (2010). A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599.
   Google Scholar
• Burgel, B. J., Gillen, M., & White, M. C. (2012). Health and safety strategies of urban taxi drivers. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 89(4), 717–722. doi:10.1007/s11524-012-9685-722669644
   PubMed Web of Science ®Google Scholar
• Chen, H., Chou, H. W., Su, J. W., & Wen, F. H. (2019). Structural interrelationships of safety climate, stress, inattention and aberrant driving behavior for bus drivers in Taiwan. Transportation Research Part A: Policy and Practice, 130, 118–133. doi:10.1016/j.tra.2019.09.007
   Web of Science ®Google Scholar
• Crizzle, A. M., Bigelow, P., Adams, D., Gooderham, S., Myers, A. M., & Thiffault, P. (2017). Health and wellness of long-haul truck and bus drivers: A systematic literature review and directions for future research. Journal of Transport & Health, 7, 90–109. doi:10.1016/j.jth.2017.05.359
   Web of Science ®Google Scholar
• Dingus, T. A., Guo, F., Lee, S., Antin, J. F., Perez, M., Buchanan-King, M., & Hankey, J. (2016). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences of the United States of America, 113(10), 2636–2641. doi:10.1073/pnas.1513271113
   PubMed Web of Science ®Google Scholar
• Dougherty, M. (1995). A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies, 3(4), 247–260. doi:10.1016/0968-090X(95)00009-8
   Web of Science ®Google Scholar
• Erdogan, Z., & Kurcer, M. A. (2019). The effect of energy drink consumption on fatigue and sleep disorders in bus drivers. Journal of Transportation Safety & Security, 11(2), 117–128. doi:10.1080/19439962.2018.1458765
   Web of Science ®Google Scholar
• Feng, F., Bao, S., Sayer, J. R., Flannagan, C., Manser, M., & Wunderlich, R. (2017). Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data. Accident; Analysis and Prevention, 104, 125–136. doi:10.1016/j.aap.2017.04.012
   PubMed Web of Science ®Google Scholar
• John,. M., Flin, R., & Mearns, K. (2006). Bus driver well-being review: 50 years of research. Transportation Research Part F: Traffic Psychology and Behaviour, 9(2), 89–114. doi:10.1016/j.trf.2005.10.002
   Google Scholar
• Global Spa Summit. (2010). Spas and the global wellness market: Synergies and opportunities.
   Google Scholar
• Gómez‐Ortiz, V., Cendales, B., Useche, S., & Bocarejo, J. P. (2018). Relationships of working conditions, health problems and vehicle accidents in bus rapid transit (BRT) drivers. American Journal of Industrial Medicine, 61(4), 336–343. doi:10.1002/ajim.22821
   PubMed Web of Science ®Google Scholar
• Han, Z., Zhao, J., Leung, H., Ma, K. F., & Wang, W. (2019). Review of deep learning models for time series prediction. IEEE Sensors Journal, 21(6), 7833–7848. doi: 10.1109/JSEN.2019.2923982
   Web of Science ®Google Scholar
• Han, T. H., Min, K. P., & Son, J. G. (2014). A case study on device for wellness services (Vol. 1639, pp. 14–26). Weekly Technology Trends of National IT Industry Promotion Agency.
   Google Scholar
• Häusser, J. A., Mojzisch, A., Niesel, M., & Schulz-Hardt, S. (2010). Ten years on: A review of recent research on the Job Demand–Control (-Support) model and psychological well-being. Work & Stress, 24(1), 1–35. doi:10.1080/02678371003683747
   Web of Science ®Google Scholar
• Im, D. (2018). Analysis of traffic crashes based on digital tachograph and driver’s personal traits -Focused on expressway bus drivers [Doctoral dissertation, Seoul National University].
   Google Scholar
• Jones, D. R. (2001). A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21(4), 345–383. doi:10.1023/A:1012771025575
   Web of Science ®Google Scholar
• Kim, H., & Kim, H. (2014). Predicting loan defaults with gradient boosting and balanced classification. The Journal of Korean Institute of Information Technology, 12(1), 155–164. doi:10.14801/kiitr.2014.12.1.155
   Google Scholar
• Kim, M., Park, S., & Park, B. (2017, March) A study of children play educational environment based on u-Healthcare system. In International Conference on Information Science and Applications (pp. 751–756). Springer.
   Google Scholar
• Korea Transportation Safety Authority. (2017). Prediction of hazardous sections of Gyeongbu expressway using DTG data.
   Google Scholar
• La, Q. N., Van Duong, D., Lee, A. H., & Meuleners, L. B. (2017). Factors underlying bus-related crashes in Hanoi, Vietnam. Transportation Research Part F: Traffic Psychology and Behaviour, 46, 426–437. doi:10.1016/j.trf.2016.06.023
   Web of Science ®Google Scholar
• Law, T. H., Daud, M. S., Hamid, H., & Haron, N. A. (2017). Development of safety performance index for intercity buses: An exploratory factor analysis approach. Transport Policy, 58, 46–52. doi:10.1016/j.tranpol.2017.05.003
   Web of Science ®Google Scholar
• Li, L. P., Liu, Z. G., Zhu, H. Y., Zhu, L., & Huang, Y. C. (2019). Functional near-infrared spectroscopy in the evaluation of urban rail transit drivers' mental workload under simulated driving conditions. Ergonomics, 62(3), 406–419. doi:10.1080/00140139.2018.153509330307379
   PubMed Web of Science ®Google Scholar
• Lyu, Z., Yu, Y., Samali, B., Rashidi, M., Mohammadi, M., Nguyen, T. N., & Nguyen, A. (2022). Back-propagation neural network optimized by K-fold cross-validation for prediction of torsional strength of reinforced Concrete beam. Materials, 15(4), 1477. doi:10.3390/ma15041477
   PubMed Web of Science ®Google Scholar
• Ma, X., Dai, Z., He, Z., Ma, J., & Wang, Y. Wang, Y. (2017). Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17(4), 818.doi: 10.3390/s17040818
   PubMed Web of Science ®Google Scholar
• Ma, M., Yan, X., Huang, H., & Abdel-Aty, M. (2010). Safety of public transportation occupational drivers: Risk perception, attitudes, and driving behavior. Transportation Research Record: Journal of the Transportation Research Board, 2145(1), 72–79. doi:10.3141/2145-09
   Google Scholar
• Machová, K., Puszta, M., Barčák, F., & Bednár, P. (2006). A comparison of the bagging and the boosting methods using the decision trees classifiers. Computer Science and Information Systems, 3(2), 57–72. doi:10.2298/CSIS0602057M
   Google Scholar
• Mallia, L., Lazuras, L., Violani, C., & Lucidi, F. (2015). Crash risk and aberrant driving behaviors among bus drivers: The role of personality and attitudes towards traffic safety. Accident; Analysis and Prevention, 79, 145–151. doi:10.1016/j.aap.2015.03.034
   PubMed Web of Science ®Google Scholar
• Meng, F., Li, S., Cao, L., Li, M., Peng, Q., Wang, C., & Zhang, W. (2015). Driving fatigue in professional drivers: A survey of truck and taxi drivers. Traffic Injury Prevention, 16(5), 474–483. 10.1080/15389588.2014.97394525357206
   PubMed Web of Science ®Google Scholar
• National Academies of Sciences, Engineering, and Medicine. (2016). Commercial motor vehicle driver fatigue, long-term health, and highway safety: Research needs.
   Google Scholar
• Oh, Y., & Kang, D. (2018). Advanced traffic safety engineering. Gyeonggido. Cheongmoongak.
   Google Scholar
• Park, H., Haghani, A., Samuel, S., & Knodler, M. A. (2018). Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accident; Analysis and Prevention, 112, 39–49. doi:10.1016/j.aap.2017.11.025
   PubMed Web of Science ®Google Scholar
• Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41(1), 203–212. doi:10.1016/j.jrp.2006.02.001
   Web of Science ®Google Scholar
• Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & De Freitas, N. (2016). Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104(1), 148–175. doi:10.1109/JPROC.2015.2494218
   Web of Science ®Google Scholar
• Shen, J., Gu, H., Feng, X., Yu, M., & Zheng, C. (2022). Investigation of factors contributing to bus-crash severity based on extended hierarchical ordered probit model with heteroscedasticity. Journal of Transportation Safety & Security, 14(9), 1531–1551. doi:10.1080/19439962.2021.1941460
   Web of Science ®Google Scholar
• Shinar, D. (2019). Crash causes, countermeasures, and safety policy implications. Accident; Analysis and Prevention, 125, 224–231. doi:10.1016/j.aap.2019.02.015
   PubMed Web of Science ®Google Scholar
• Siegrist, J. (2016). Effort-reward imbalance model. In Stress: Concepts, cognition, emotion, and behavior (pp. 81–86). Elsevier: Academic Press.
   Google Scholar
• Spyropoulou, I., Penttinen, M., Karlaftis, M., Vaa, T., & Golias, J. (2008). ITS solutions and accident risks: Prospective and limitations. Transport Reviews, 28(5), 549–572. doi:10.1080/01441640801886605
   Web of Science ®Google Scholar
• Stipancic, J., Miranda-Moreno, L., Saunier, N., & Labbe, A. (2018). Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent gaussian spatial models. Accident; Analysis and Prevention, 120, 174–187. doi:10.1016/j.aap.2018.07.01330142497
   PubMed Web of Science ®Google Scholar
• Traffic Accident Analysis System (TAAS). http://taas.koroad.or.kr/
   Google Scholar
• Useche, S. A., Ortiz, V. G., & Cendales, B. E. (2017). Stress-related psychosocial factors at work, fatigue, and risky driving behavior in bus rapid transport (BRT) drivers. Accident; Analysis and Prevention, 104, 106–114. doi:10.1016/j.aap.2017.04.023
   PubMed Web of Science ®Google Scholar
• Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019). A review of deep learning for renewable energy forecasting. Energy Conversion and Management, 198, 111799. doi:10.1016/j.enconman.2019.111799
   Web of Science ®Google Scholar
• Wang, Y., Li, L., Feng, L., & Peng, H. (2014). Professional drivers’ views on risky driving behaviors and accident liability: A questionnaire survey in Xining, China. Transportation Letters, 6(3), 126–135. doi:10.1179/1942787514Y.0000000019
   Web of Science ®Google Scholar
• Wang, Z., & de Freitas, N. (2014). Theoretical analysis of Bayesian optimisation with unknown Gaussian process hyper-parameters. arXiv preprint arXiv:1406.7758.
   Google Scholar