Cluster analysis and multi-level modeling for evaluating the impact of rain on aggressive lane-changing characteristics utilizing naturalistic driving data

References

• Ahmed, M. M., Das, A., Khan, M. N., Hammit, B., & Ali, E. (2021). Driver Performance and Behavior in Adverse Weather Conditions: Microsimulation and Variable Speed Limit Implementation of the SHRP2 Naturalistic Driving Study Results. Final Report WY-2105F, Wyoming Department of Transportation.
   Google Scholar
• Ahmed, M. M., Ghasemzadeh, A., Eldeeb, H., Gaweesh, S., Clapp, J., Ksaibati, K., & Young, R. (2015). Driver performance and behavior in adverse weather conditions: An investigation using the SHRP2 naturalistic driving study data—phase 1. Publication FHWA-WY-16/08F. Wyoming Department of Transportation, vol. 307, 1–73.
   Google Scholar
• Ahmed, M., Young, R., Ghasemzadeh, A., Hammit, B., Ali, E., Khan, N., Das, A., & Eldeeb, H. (2017). Implementation of SHRP2 results within the Wyoming connected vehicle variable speed limit system: Phase 2 early findings report and phase 3 proposal. U.S. Department of Transportation.
   Google Scholar
• Ahmed, M. M., Ghasemzadeh, A., Hammit, B., Khan, N., Das, A., Ali, E., Young, R. K., & Eldeeb, H. (2018). Driver performance and behavior in adverse weather conditions: An investigation using the SHRP2 naturalistic driving study data - phase 2. Final Report WY-18/05F, U.S. Department of Transportation.
   Google Scholar
• Ali, Y., Zheng, Z., & Haque, M. M. (2018). Connectivity’s impact on mandatory lane-changing behaviour: Evidences from a driving simulator study. Transportation Research Part C: Emerging Technologies, 93(June), 292–309. doi:10.1016/j.trc.2018.06.008
   Google Scholar
• Ariannezhad, A., & Wu, Y.-J. (2019). Effects of heavy rainfall in different light conditions on crash severity during Arizona’s monsoon season. Journal of Transportation Safety & Security, 11(6), 579–594. doi:10.1080/19439962.2018.1454561
   Web of Science ®Google Scholar
• Bakhit, P. R., Osman, O. A., & Ishak, S. (2017). Detecting imminent lane change maneuvers in connected vehicle environments. Transportation Research Record: Journal of the Transportation Research Board, 2645(1), 168–175. doi:10.3141/2645-18
   Google Scholar
• Blissett, R. (2017). Multilevel modeling in R. Retrieved from https://rpubs.com/rslbliss/r_mlm_ws
   Google Scholar
• Cai, X., Chang, W., Gao, L., & Zhou, C. (2021). Design and application of real-time monitoring system for service status of continuously welded turnout on the high-speed railway bridge. Journal of Transportation Safety & Security, 13(7), 735–758. doi:10.1080/19439962.2019.1673858
   Web of Science ®Google Scholar
• Campbell, K. L. (2012). The SHRP 2 naturalistic driving study: Addressing driver performance and behavior in traffic safety. Transportation Research News, 282, 30–35.
   Google Scholar
• Center for Transportation Research and Education (CTRE). (2015). SHRP2-roadway information database - background. Retrieved from http://www.ctre.iastate.edu/shrp2-rid/
   Google Scholar
• Chen, C., Zhao, X., Liu, H., Ren, G., & Liu, X. (2019). Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations. Journal of Modern Transportation, 27(4), 282–292. doi:10.1007/s40534-019-00197-4
   Web of Science ®Google Scholar
• Chen, R., Gabler, H. C., & Sherony, R. (2017). Predicting driver lane change maneuvers using vehicle kinematic data. 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV), 1–9.
   Google Scholar
• Chen, S., Chen, Y., & Xing, Y. (2022). Comparison and analysis of crash frequency and rate in cross-river tunnels using random-parameter models. Journal of Transportation Safety & Security, 14(2), 280–304. doi:10.1080/19439962.2020.1779420
   Web of Science ®Google Scholar
• Chovan, J. D., Tijerina, L., Alexander, G., & Hendricks, D. L. (1994). Examination of lane change crashes and potential IVHS countermeasures. Final Report, National Highway Traffic Safety Administration, U.S. Department of Transportation (Issue March).
   Google Scholar
• Das, A., & Ahmed, M. M. (2021). Exploring the effect of fog on lane-changing characteristics utilizing the SHRP2 naturalistic driving study data. Journal of Transportation Safety & Security, 13(5), 477–502. doi:10.1080/19439962.2019.1645777
   Web of Science ®Google Scholar
• Das, A., Ahmed, M. M., & Ghasemzadeh, A. (2019). Using trajectory-level SHRP2 naturalistic driving data for investigating driver lane-keeping ability in fog: An association rules mining approach. Accident Analysis and Prevention, 129, 250–262.
   Google Scholar
• Das, A., Ghasemzadeh, A., & Ahmed, M. M. (2019). Analyzing the effect of fog weather conditions on driver lane-keeping performance using the SHRP2 naturalistic driving study data. Journal of Safety Research, 68(February), 71–80.
   Google Scholar
• Das, A., Khan, M. N., & Ahmed, M. M. (2020). Nonparametric Multivariate Adaptive Regression Splines Models for Investigating Lane-Changing Gap Acceptance Behavior Utilizing Strategic Highway Research Program 2 Naturalistic Driving Data. Transportation Research Record: Journal of the Transportation Research Board, 2674(5), 223–238.
   Google Scholar
• Das, S., Dutta, A., & Sun, X. (2019). Patterns of rainy weather crashes: Applying rules mining. Journal of Transportation Safety and Security, 12(9), 1083–1105. doi:10.1080/19439962.2019.1572681
   Web of Science ®Google Scholar
• Das, A., Ghasemzadeh, A., & Ahmed, M. M. (2019). Analyzing the effect of fog weather conditions on driver lane-keeping performance using the SHRP2 naturalistic driving study data. Journal of Safety Research, 68(February), 71–80.
   Google Scholar
• Deligianni, S. P., Quddus, M., Morris, A., Anvuur, A., & Reed, S. (2017). Analyzing and modeling drivers’ deceleration behavior from normal driving. Transportation Research Record: Journal of the Transportation Research Board, 2663(1), 134–141. doi:10.3141/2663-17
   Google Scholar
• Everitt, B., & Hothorn, T. (2011). An introduction to applied multivariate analysis with R. New York, USA: Springer Science & Business Media.
   Google Scholar
• Faria, M. V., Baptista, P. C., Farias, T. L., & Pereira, J. M. S. (2020). Assessing the impacts of driving environment on driving behavior patterns. Transportation, 47(3), 1311–1337. doi:10.1007/s11116-018-9965-5
   Web of Science ®Google Scholar
• FHWA. (2022). How do weather events impact roads? Retrieved from https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm
   Google Scholar
• Fitch, G. M., Lee, S. E., Klauer, S., Hankey, J., Sudweeks, J., & Dingus, T. (2009). Analysis of lane-change crashes and near-crashes. Final Report DOT HS 811 147, US Department of Transportation, National Highway Traffic Safety Administration (Issue June).
   Google Scholar
• Galovski, T., & Blanchard, E. B. (2002). Psychological characteristics of aggressive drivers with and without intermittent explosive disorder. Behaviour Research and Therapy, 40(10), 1157–1168. doi:10.1016/S0005-7967(01)00083-3
   PubMed Web of Science ®Google Scholar
• Gan, X., Weng, J., Li, W., & Han, M. (2020). Spatial-temporal varying coefficient model for lane-changing behavior in work zone merging areas. Journal of Transportation Safety & Security, 1–24. doi:10.1080/19439962.2020.1864075
   Web of Science ®Google Scholar
• Ghasemzadeh, A., & Ahmed, M. M. (2018). Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches. Transportation Research Part C: Emerging Technologies, 90(March), 379–392. doi:10.1016/j.trc.2018.03.018
   Google Scholar
• Ghasemzadeh, A., Hammit, B. E., Ahmed, M. M., & Eldeeb, H. (2019). Complementary methodologies to identify weather conditions in naturalistic driving study trips: Lessons learned from the SHRP2 naturalistic driving study & roadway information database. Safety Science, 119, 21–28. doi:10.1016/j.ssci.2019.01.006
   Web of Science ®Google Scholar
• Golbabaei, F., Nejad, F. M., & Noory, A. R. (2014). A microscopic analysis of speed deviation impacts on lane-changing behavior. Transportation Planning and Technology, 37(4), 391–407. doi:10.1080/03081060.2014.897128
   Web of Science ®Google Scholar
• Goldstein, H. (2011). Multilevel statistical models (4th ed.). UK: Wiley.
   Google Scholar
• Higgs, B., & Abbas, M. (2013 A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Itsc, 857–862. doi:10.1109/ITSC.2013.6728339
   Google Scholar
• Hill, C., Elefteriadou, L., & Kondyli, A. (2015). Exploratory analysis of lane changing on freeways based on driver behavior. Journal of Transportation Engineering, 141(4), 04014090. doi:10.1061/(ASCE)TE.1943-5436.0000758
   Google Scholar
• Hossain, M. J., Ivan, J. N., Zhao, S., Wang, K., Sharmin, S., Ravishanker, N., & Jackson, E. (2022). Considering demographics of other involved drivers in predicting the highest driver injury severity in multi-vehicle crashes on rural two-lane roads in California. Journal of Transportation Safety & Security, 1–16. doi:10.1080/19439962.2022.2033899
   Web of Science ®Google Scholar
• InSight SHRP2 NDS. (2019). SHRP2 NDS data access. Retrieved from https://insight.shrp2nds.us/
   Google Scholar
• Islam, M., & Mannering, F. (2020). A temporal analysis of driver-injury severities in crashes involving aggressive and non-aggressive driving. Analytic Methods in Accident Research, 27, 100128. doi:10.1016/j.amar.2020.100128
   Web of Science ®Google Scholar
• Jackson, T. L., & Sharif, H. O. (2016). Rainfall impacts on traffic safety: Rain-related fatal crashes in Texas. Geomatics, Natural Hazards and Risk, 7(2), 843–860. doi:10.1080/19475705.2014.984246
   Web of Science ®Google Scholar
• Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis (Vol. 344). Hoboken, New Jersey, USA: John Wiley & Sons.
   Google Scholar
• Khan, M. N., Ghasemzadeh, A., & Ahmed, M. M. (2018). Investigating the impact of fog on freeway speed selection using the shrp2 naturalistic driving study data. Transportation Research Record: Journal of the Transportation Research Board, 2672(16), 93–104.
   Google Scholar
• Khan, M. N., Das, A., & Ahmed, M. M. (2020). Non-parametric association rules mining and parametric ordinal logistic regression for an in-depth investigation of driver speed selection behavior in adverse weather using SHRP2 naturalistic driving study data. Transportation Research Record: Journal of the Transportation Research Board, 2674(11), 101–119.
   Google Scholar
• Khattak, Z. H., Fontaine, M. D., Li, W., Khattak, A. J., & Karnowski, T. (2021). Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment. Accident; Analysis and Prevention, 156(September 2020), 106086. doi:10.1016/j.aap.2021.106086
   PubMedGoogle Scholar
• Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2004). Applied linear regression models (4th ed.). McGraw-Hill, Chicago, IL, USA.
   Google Scholar
• Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2019). Package ‘lmerTest’.
   Google Scholar
• Lee, S. E., Olsen, E. C. B., & Wierwille, W. W. (2004). A comprehensive examination of naturalistic lane-changes. Final Report DOT Hs, 809(702), March, 1–236.
   Google Scholar
• Li, X.-G., Jia, B., Gao, Z.-Y., & Jiang, R. (2006). A realistic two-lane cellular automata traffic model considering aggressive lane-changing behavior of fast vehicle. Physica A: Statistical Mechanics and Its Applications, 367, 479–486. doi:10.1016/j.physa.2005.11.016
   Web of Science ®Google Scholar
• Liu, B. S., & Lee, Y. H. (2005). Effects of car-phone use and aggressive disposition during critical driving maneuvers. Transportation Research Part F: Traffic Psychology and Behaviour, 8(4–5), 369–382. doi:10.1016/j.trf.2005.04.019
   Web of Science ®Google Scholar
• MathWorks. (2021). Fillmissing. Retrieved from https://www.mathworks.com/help/matlab/ref/fillmissing.html
   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(June 2020), 102917. doi:10.1016/j.trc.2020.102917
   Google Scholar
• Moridpour, S., Sarvi, M., & Rose, G. (2010). Modeling the lane-changing execution of multiclass vehicles under heavy traffic conditions. Transportation Research Record: Journal of the Transportation Research Board, 2161(1), 11–19. doi:10.3141/2161-02
   Web of Science ®Google Scholar
• NHTSA. (2019). Traffic safety facts: A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system. U.S. Department of Transportation.
   Google Scholar
• Park, H. S., & Jun, C. H. (2009). A simple and fast algorithm for K-medoids clustering. Expert Systems with Applications, 36(2), 3336–3341. doi:10.1016/j.eswa.2008.01.039
   Web of Science ®Google Scholar
• Qi, X., Wu, G., Boriboonsomsin, K., & Barth, M. J. (2016). Empirical study of lane-changing characteristics on high-occupancy-vehicle facilities with different types of access control based on aerial survey data. Journal of Transportation Engineering, 142(1), 04015034. doi:10.1061/(ASCE)TE.1943-5436.0000803
   Google Scholar
• Salvucci, D. D., & Liu, A. (2002). The time course of a lane change: Driver control and eye-movement behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 5(2), 123–132. doi:10.1016/S1369-8478(02)00011-6
   Google Scholar
• Shangguan, Q., Wang, J., Fu, T., & Fang, S. (2021). Quantification of cut-in risk and analysis of its influencing factors: A study using random parameters ordered probit model. Journal of Transportation Safety & Security, 1–26. doi:10.1080/19439962.2021.1994683
   Web of Science ®Google Scholar
• Shao, H., Zhang, M., Feng, T., & Dong, Y. (2020). A discretionary lane-changing decision-making mechanism incorporating drivers’ heterogeneity: A signalling game-based approach. Journal of Advanced Transportation, 2020, 1–16. doi:10.1155/2020/8892693
   Web of Science ®Google Scholar
• Sun, D., & Elefteriadou, L. (2010). Research and implementation of lane-changing model based on driver behavior. Transportation Research Record: Journal of the Transportation Research Board, 2161(1), 1–10. doi:10.3141/2161-01
   Google Scholar
• Sun, D., & Elefteriadou, L. (2011). Lane-changing behavior on urban streets: A focus group-based study. Applied Ergonomics, 42(5), 682–691. doi:10.1016/j.apergo.2010.11.001
   PubMed Web of Science ®Google Scholar
• Toledo, T., & Zohar, D. (2007). Modeling duration of lane changes. Transportation Research Record: Journal of the Transportation Research Board, 1999(1), 71–78. doi:10.3141/1999-08
   Google Scholar
• Wang, C., Li, Z., Fu, R., Zhang, M., & Sun, Q. (2019). Lane change safety assessment of coaches in naturalistic driving state. Safety Science, 119(December 2017), 126–132. doi:10.1016/j.ssci.2018.09.009
   Google Scholar
• Wang, J., Dang, R., Zhang, F., Wang, J., Yi, S., & Li, K. (2013). Analysis of Chinese driver’s lane change characteristic based on real vehicle tests in highway. Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), 100084(October), 1917–1922. doi:10.1109/ITSC.2013.6728509
   Google Scholar
• Wang, X., Yang, M., & Hurwitz, D. (2019). Analysis of cut-in behavior based on naturalistic driving data. Accident; Analysis and Prevention, 124(March), 127–137. doi:10.1016/j.aap.2019.01.006
   PubMedGoogle Scholar
• Wu, L., Dadashova, B., Geedipally, S., Pratt, M. P., & Shirazi, M. (2021). Using naturalistic driving study data to explore the association between horizontal curve safety and operation on rural two-lane highways. Journal of Transportation Safety & Security, 13(8), 896–913. doi:10.1080/19439962.2019.1697776
   Web of Science ®Google Scholar
• Yang, Q., & Koutsopoulos, H. N. (1996). A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transportation Research Part C: Emerging Technologies, 4(3), 113–129. doi:10.1016/S0968-090X(96)00006-X
   Web of Science ®Google Scholar
• You, F., Zhang, R., Lie, G., Wang, H., Wen, H., & Xu, J. (2015). Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system. Expert Systems with Applications, 42(14), 5932–5946. doi:10.1016/j.eswa.2015.03.022
   Web of Science ®Google Scholar
• Zhang, W., Wang, C., Chen, Q., Liu, J., Feng, Z., Wang, K., & Shen, Y. (2022). Lane-change behavior in low illumination: Research based on a questionnaire investigation. Journal of Transportation Safety & Security, 14(1), 130–151. doi:10.1080/19439962.2020.1744050
   Web of Science ®Google Scholar
• Zheng, Z., Ahn, S., Chen, D., & Laval, J. (2013). The effects of lane-changing on the immediate follower: Anticipation, relaxation, and change in driver characteristics. Transportation Research Part C: Emerging Technologies, 26, 367–379. doi:10.1016/j.trc.2012.10.007
   Web of Science ®Google Scholar
• Zhou, Y., Fu, R., & Wang, C. (2020). Learning the car-following behavior of drivers using maximum entropy deep inverse reinforcement learning. Journal of Advanced Transportation, 2020, 1–13. doi:10.1155/2020/4752651
   Web of Science ®Google Scholar