Detection of distracted driving through the analysis of real-time driver, vehicle, and roadway volatilities

References

• Ahangari, S., Jeihani, M., & Jarju, A. (2018). Drivers’ behavior analysis under rainy weather conditions using a driving simulator. Transportation Research Board 97th Annual Meeting.
   Google Scholar
• Arvin, R., & Khattak, A. J. (2020). Harnessing big data generated by connected vehicles to monitor safety performance: Application of geographically weighted negative binomial regression. Paper Presented at the Transportation Research Board 99th Annual Meeting.
   Google Scholar
• Arvin, R., Kamrani, M., & Khattak, A. J. (2019). The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data. Accident; Analysis and Prevention, 132, 105226. https://doi.org/10.1016/j.aap.2019.07.002
   PubMed Web of Science ®Google Scholar
• Arvin, R., Khattak, A. J., & Qi, H. (2021). Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods. Accident; Analysis and Prevention, 151, 105949. https://doi.org/10.1016/j.aap.2020.105949
   PubMed Web of Science ®Google Scholar
• Bejani, M. M., & Ghatee, M. (2018). A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data. Transportation Research Part C: Emerging Technologies, 89, 303–320. https://doi.org/10.1016/j.trc.2018.02.009
   Web of Science ®Google Scholar
• Blana, E., & Golias, J. (2002). Differences between vehicle lateral displacement on the road and in a fixed-base simulator. Human Factors, 44(2), 303–313. https://doi.org/10.1518/0018720024497899
   PubMed Web of Science ®Google Scholar
• Blanco, M., Biever, W. J., Gallagher, J. P., & Dingus, T. A. (2006). The impact of secondary task cognitive processing demand on driving performance. Accident; Analysis and Prevention, 38(5), 895–906. https://doi.org/10.1016/j.aap.2006.02.015
   PubMed Web of Science ®Google Scholar
• Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Cao, S., & Liu, Y. (2013). Concurrent processing of vehicle lane keeping and speech comprehension tasks. Accident; Analysis and Prevention, 59, 46–54. https://doi.org/10.1016/j.aap.2013.04.038
   PubMed Web of Science ®Google Scholar
• Chisholm, S. L., Caird, J. K., & Lockhart, J. (2008). The effects of practice with MP3 players on driving performance. Accident; Analysis and Prevention, 40(2), 704–713. https://doi.org/10.1016/j.aap.2007.09.014
   PubMed Web of Science ®Google Scholar
• Choudhary, P., & Velaga, N. R. (2017). Mobile phone use during driving: Effects on speed and effectiveness of driver compensatory behaviour. Accident; Analysis and Prevention, 106, 370–378. https://doi.org/10.1016/j.aap.2017.06.021
   PubMed Web of Science ®Google Scholar
• Choudhary, P., & Velaga, N. R. (2019). Effects of phone use on driving performance: A comparative analysis of young and professional drivers. Safety Science, 111, 179–187. https://doi.org/10.1016/j.ssci.2018.07.009
   Web of Science ®Google Scholar
• Dorr, D., Grabengiesser, D., & Gauterin, F. (2014). Online driving style recognition using fuzzy logic. 17th International IEEE Conference on Intelligent Transportation Systems.
   Google Scholar
• Feng, Y., Pickering, S., Chappell, E., Iravani, P., & Brace, C. (2018). Driving style analysis by classifying real-world data with support vector clustering. 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE).
   Google Scholar
• GPS.gov (2020). Official U.S. Government information about the Global Positioning System (GPS) and related topics, GPS Accuracy. Retrieved from https://www.gps.gov/systems/gps/performance/accuracy/#speed
   Google Scholar
• He, J., Chaparro, A., Nguyen, B., Burge, R. J., Crandall, J., Chaparro, B., Ni, R., & Cao, S. (2014). Texting while driving: Is speech-based text entry less risky than handheld text entry? Accident; Analysis and Prevention, 72, 287–295. https://doi.org/10.1016/j.aap.2014.07.014
   PubMed Web of Science ®Google Scholar
• Irwin, C., Monement, S., & Desbrow, B. (2015). The influence of drinking, texting, and eating on simulated driving performance. Traffic Injury Prevention, 16(2), 116–123. https://doi.org/10.1080/15389588.2014.920953
   PubMed Web of Science ®Google Scholar
• Khattak, A. J., Ahmad, N., Wali, B., & Dumbaugh, E. (2021). A taxonomy of driving errors and violations: Evidence from the naturalistic driving study. Accident; Analysis and Prevention, 151, 105873. https://doi.org/10.1016/j.aap.2020.105873
   PubMed Web of Science ®Google Scholar
• Krogh, A. (2008). What are artificial neural networks? In Nature biotechnology (Vol. 26). http://www.r-project.org/ https://doi.org/10.1038/nbt1386
   Google Scholar
• Lee, J. Y., Lee, J. D., Bärgman, J., Lee, J., & Reimer, B. (2018). How safe is tuning a radio?: Using the radio tuning task as a benchmark for distracted driving. Accident; Analysis and Prevention, 110, 29–37. https://doi.org/10.1016/j.aap.2017.10.009
   PubMed Web of Science ®Google Scholar
• Leung, S., Croft, R. J., Jackson, M. L., Howard, M. E., & Mckenzie, R. J. (2012). A comparison of the effect of mobile phone use and alcohol consumption on driving simulation performance. Traffic Injury Prevention, 13(6), 566–574. https://doi.org/10.1080/15389588.2012.683118
   PubMed Web of Science ®Google Scholar
• Liu, Y. C., & Ou, Y. K. (2011). Effects of age and the use of hands-free cellular phones on driving behavior and task performance. Traffic Injury Prevention, 12(6), 550–558. https://doi.org/10.1080/15389588.2011.607197
   PubMed Web of Science ®Google Scholar
• Mahdinia, I., Arvin, R., Khattak, A. J., & Ghiasi, A. (2020). Safety, energy, and emissions impacts of adaptive cruise control and cooperative adaptive cruise control. Transportation Research Record: Journal of the Transportation Research Board, 2674(6), 253–267. https://doi.org/10.1177/0361198120918572
   Web of Science ®Google Scholar
• Mantouka, E. G., Barmpounakis, E. N., & Vlahogianni, E. I. (2019). Identifying driving safety profiles from smartphone data using unsupervised learning. Safety Science, 119, 84–90. https://doi.org/10.1016/j.ssci.2019.01.025
   Web of Science ®Google Scholar
• Metz, B., Landau, A., & Hargutt, V. (2015). Frequency and impact of hands-free telephoning while driving – Results from naturalistic driving data. Transportation Research Part F: Traffic Psychology and Behaviour, 29, 1–13. https://doi.org/10.1016/j.trf.2014.12.002
   Web of Science ®Google Scholar
• Mohammadnazar, A., Arvin, R., & Khattak, A. J. (2021). Classifying travelers’ driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning. Transportation Research Part C: Emerging Technologies, 122, 102917. https://doi.org/10.1016/j.trc.2020.102917
   Web of Science ®Google Scholar
• Murphey, Y. L., Milton, R., & Kiliaris, L. (2009). Driver’s style classification using jerk analysis. 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems.
   Google Scholar
• Natasha, M., Kountouriotis, G. K., Tomlinson, A., & Carsten, O. (2015). Performance on the Detection Response Task during driving: Separating the manual and cognitive element of the secondary task. 4th International Driver Distraction and Inattention Conference. https://www.researchgate.net/publication/289531959
   Google Scholar
• Oviedo-Trespalacios, O., Haque, M. M., King, M., & Washington, S. (2017). Effects of road infrastructure and traffic complexity in speed adaptation behaviour of distracted drivers. Accident; Analysis and Prevention, 101, 67–77. https://doi.org/10.1016/j.aap.2017.01.018
   PubMed Web of Science ®Google Scholar
• Parmar, A., Katariya, R., & Patel, V. (2018). A review on Random Forest: An ensemble classifier. International Conference on Intelligent Data Communication Technology and Internet of Things (ICICI) (pp. 758–763).
   Google Scholar
• Regan, M., Lee, J., & Young, K. (2008). Driver distraction: Theory, effects, and mitigation. CRC Press.
   Google Scholar
• Rossana, R. J., & Seater, J. J. (1995). Temporal aggregation and economic time series. Journal of Business & Economic Statistics, 13(4), 441–451. https://doi.org/10.1080/07350015.1995.10524618
   Web of Science ®Google Scholar
• Rumschlag, G., Palumbo, T., Martin, A., Head, D., George, R., & Commissaris, R. L. (2015). The effects of texting on driving performance in a driving simulator: The influence of driver age. Accident; Analysis and Prevention, 74, 145–149. https://doi.org/10.1016/j.aap.2014.10.009
   PubMed Web of Science ®Google Scholar
• Sajid Hasan, A., Patel, D., Alfaris, R., & Jalayer, M. (2022). Identifying distracted-driving events from on-road observations using a moving vehicle: A case study in New Jersey. Accident; Analysis and Prevention, 177, 106827. https://doi.org/10.1016/j.aap.2022.106827
   PubMed Web of Science ®Google Scholar
• Santos, J., Merat, N., Mouta, S., Brookhuis, K., & De Waard, D. (2005). The interaction between driving and in-vehicle information systems: Comparison of results from laboratory, simulator and real-world studies. Transportation Research Part F: Traffic Psychology and Behaviour, 8(2), 135–146. https://doi.org/10.1016/j.trf.2005.04.001
   Web of Science ®Google Scholar
• Silvestrini, A., & Veredas, D. (2008). Temporal aggregation of univariate and multivariate time series models: A survey. Journal of Economic Surveys, 22(3), 458–497. https://doi.org/10.1111/j.1467-6419.2007.00538.x
   Web of Science ®Google Scholar
• Spiliotis, E., Petropoulos, F., Kourentzes, N., & Assimakopoulos, V. (2020). Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption. Applied Energy, 261(, 114339. https://doi.org/10.1016/j.apenergy.2019.114339
   Web of Science ®Google Scholar
• Stewart, T. (2023, April). Overview of motor vehicle traffic crashes in 2021. National Highway Traffic Safety Administration.
   Google Scholar
• Suzdaleva, E., & Nagy, I. (2018). An online estimation of driving style using data-dependent pointer model. Transportation Research Part C: Emerging Technologies, 86, 23–36. https://doi.org/10.1016/j.trc.2017.11.001
   Web of Science ®Google Scholar
• Thapa, R., Codjoe, J., Ishak, S., & McCarter, K. S. (2015). Post and during event effect of cell phone talking and texting on driving performance—A driving simulator study. Traffic Injury Prevention, 16(5), 461–467. https://doi.org/10.1080/15389588.2014.969803
   PubMed Web of Science ®Google Scholar
• Wali, B., Khattak, A. J., & Karnowski, T. (2019). Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility. Accident; Analysis and Prevention, 132, 105277. https://doi.org/10.1016/j.aap.2019.105277
   PubMed Web of Science ®Google Scholar
• World Health Organization (2010). Mobile phone use : A growing problem of driver distraction. Author.
   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
• Young, K. L., & Salmon, P. M. (2012). Examining the relationship between driver distraction and driving errors: A discussion of theory, studies and methods. Safety Science, 50(2), 165–174. https://doi.org/10.1016/j.ssci.2011.07.008
   Web of Science ®Google Scholar