Investigating the relationship between freeway rear-end crash rates and macroscopically modeled reaction time

References

• Abdel-Aty, Mohamed, Nizam Uddin, Anurag Pande, M. Fathy Abdalla, and Liang Hsia. 2004. “Predicting Freeway Crashes from Loop Detector Data by Matched Case-Control Logistic Regression.” Transportation Research Record 1897 (1): 88–95. doi:10.3141/1897-12.
   Google Scholar
• Ahmed, Ishtiak, Nagui M. Rouphail, and Shams Tanvir. 2018. “Characteristics and Temporal Stability of Recurring Bottlenecks.” Transportation Research Record 2672 (42): 235–246. doi:10.1177/0361198118798991.
   Google Scholar
• Ahmed, Ishtiak, Billy M. Williams, and M. Shoaib Samandar. 2018. “Application of a Discontinuous Form of Macroscopic Gazis-Herman-Rothery Model to Steady-State Freeway Traffic Stream Observations.” Transportation Research Record 2672 (42): 51–62. doi:10.1177/0361198118799166.
   Google Scholar
• Ahn, Soyoung, Jorge Laval, and Michael J. Cassidy. 2010. “Effects of Merging and Diverging on Freeway Traffic Oscillations.” Transportation Research Record 2188 (1): 1–8. doi:10.3141/2188-01.
   Google Scholar
• American Association of State Highway and Transportation Officials. 2010. Highway Safety Manual. Vol. 1. Washington, DC: AASHTO.
   Google Scholar
• Brill, Edward A. 1972. “Car-Following Model Relating Reaction Times and Temporal Headways to Accident Frequency.” Transportation Science 6 (4): 343–353. doi:10.1287/trsc.6.4.343.
   Google Scholar
• Chandler, Robert E., Robert Herman, and Elliott W. Montroll. 1958. “Traffic Dynamics: Studies in Car Following.” Operations Research 6 (2): 165–184. doi:10.1287/opre.6.2.165.
   Google Scholar
• Chatterjee, Indrajit, and Gary A. Davis. 2016. “Analysis of Rear-End Events on Congested Freeways by Using Video-Recorded Shock Waves.” Transportation Research Record 2583 (1): 110–118. doi:10.3141/2583-14.
   Google Scholar
• Chou, Chih Sheng, and Andrew P. Nichols. 2015. “Deriving a Surrogate Safety Measure for Freeway Incidents Based on Predicted End-of-Queue Properties.” IET Intelligent Transport Systems 9 (1): 22–29. doi:10.1049/iet-its.2013.0199.
   Google Scholar
• Davis, Gary A., and Tait Swenson. 2006. “Collective Responsibility for Freeway Rear-Ending Accidents?. An Application of Probabilistic Causal Models.” Accident Analysis and Prevention 38 (4): 728–736. doi:10.1016/j.aap.2006.01.003.
   Google Scholar
• Edie, Leslie C. 1961. “Car-Following and Steady-State Theory for Noncongested Traffic.” Operations Research 9 (1): 66–76. doi:10.1287/opre.9.1.66.
   Google Scholar
• Gazis, Denos C., Robert Herman, and Richard W. Rothery. 1961. “Nonlinear Follow-the-Leader Models of Traffic Flow.” Operations Research 9 (4): 545–567. doi:10.1287/opre.9.4.545.
   Google Scholar
• Hauer, Ezra. 2015. The Art of Regression Modeling in Road Safety. New York: Springer.
   Google Scholar
• Kim, Taewan, and H. Michael Zhang. 2011. “Interrelations of Reaction Time, Driver Sensitivity, and Time Headway in Congested Traffic.” Transportation Research Record 2249 (1): 52–61. doi:10.3141/2249-08.
   Google Scholar
• Li, Ye, Dan Wu, Jaeyoung Lee, Min Yang, and Yuntao Shi. 2020. “Analysis of the Transition Condition of Rear-End Collisions Using Time-to-Collision Index and Vehicle Trajectory Data.” Accident Analysis and Prevention 144. doi:10.1016/j.aap.2020.105676.
   Google Scholar
• Liu, C., B. M. Williams, and N. M. Rouphail. 2012. “Temporal Stability of Freeway Macroscopic Traffic Stream Models.” Transportation Research Record 2315 (1): 131–140.
   Google Scholar
• Lord, Dominique, Abdelaziz Manar, and Anna Vizioli. 2005. “Modeling Crash-Flow-Density and Crash-Flow-V/C Ratio Relationships for Rural and Urban Freeway Segments.” Accident Analysis and Prevention 37 (1): 185–199. doi:10.1016/j.aap.2004.07.003.
   Google Scholar
• May, A. D. 1990. Traffic Flow Fundamentals.
   Google Scholar
• Misener, J. A., H. S. J. Tsao, B. Song, and A. Steinfeld. 2000. “Emergence of a Cognitive Car-Following Driver Model: Application to Rear-End Crashes with a Stopped Lead Vehicle.” Transportation Research Record 1724 (1): 29–38. doi:10.3141/1724-05.
   Google Scholar
• North Carolina Department of Transportation. 2020. https://connect.ncdot.gov/resources/safety/Pages/TEAAS-Crash-Data.
   Google Scholar
• Oh, Cheol, and Taejin Kim. 2010. “Estimation of Rear-End Crash Potential Using Vehicle Trajectory Data.” Accident Analysis and Prevention 42 (6): 1888–1893. doi:10.1016/j.aap.2010.05.009.
   Google Scholar
• Pande, Anurag, and Mohamed Abdel-Aty. 2006. “Comprehensive Analysis of the Relationship Between Real-Time Traffic Surveillance Data and Rear-End Crashes on Freeways.” Transportation Research Record 1953 (1): 31–40. doi:10.3141/1953-04.
   Google Scholar
• Rahman, Moynur, and Min Wook Kang. 2020. “Safety Evaluation of Drowsy Driving Advisory System: Alabama Case Study.” Journal of Safety Research 74: 45–53. doi:10.1016/j.jsr.2020.04.005.
   Google Scholar
• Smith, Ralph C. 2013. Uncertainty Quantification: Theory, Implementation, and Applications. Philadelphia: Siam.
   Google Scholar
• Srinivasan, Raghavan, Michael Colety, Geni Bahar, Brent Crowther, and Matt Farmen. 2016. “Estimation of Calibration Functions for Predicting Crashes on Rural Two-Lane Roads in Arizona.” Transportation Research Record 2583 (1): 17–24. doi:10.3141/2583-03.
   Google Scholar
• Tanaka, Mitsuru, Prakash Ranjitkar, and Takashi Nakatsuji. 2008. “Asymptotic Stability and Vehicle Safety in Dynamic Car-Following Platoon.” Transportation Research Record 2088 (1): 198–207. doi:10.3141/2088-21.
   Google Scholar
• Tanvir, Shams, R. T. Chase, and N. M. Roupahil. 2019. “Development and Analysis of Eco-Driving Metrics for Naturalistic Instrumented Vehicles.” Journal of Intelligent Transportation Systems, 1–14. doi:10.1080/15472450.2019.1615486.
   Google Scholar
• Touran, Ali, Mark A. Brackstone, and Mike McDonald. 1999. “A Collision Model for Safety Evaluation of Autonomous Intelligent Cruise Control.” Accident Analysis and Prevention 31 (5): 567–578. doi:10.1016/S0001-4575(99)00013-5.
   Google Scholar
• Transportation Research Board. 2016. Highway Capacity Manual. 6th ed. Washington, DC: The National Academies of Sciences, Engineering, and Medicine.
   Google Scholar
• University of Virginia Library, Charlottesville. “Understanding Diagnostic Plots for Linear Regression Analysis”. https://data.library.virginia.edu/diagnostic-plots/.
   Google Scholar
• USDOT, 2006. Next Generation Simulation (NGSIM). https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm.
   Google Scholar
• Wu, Ning. 2002. “A New Approach for Modeling of Fundamental Diagrams.” Transportation Research Part A: Policy and Practice 36 (10): 867–884. doi:10.1016/S0965-8564(01)00043-X.
   Google Scholar
• Xu, Tu, and Jorge A. Laval. 2019. “Analysis of a Two-Regime Stochastic Car-Following Model: Explaining Capacity Drop and Oscillation Instabilities.” Transportation Research Record 2673 (10): 610–619. doi:10.1177/0361198119850464.
   Google Scholar
• Xu, Chengcheng, Wei Wang, Pan Liu, and Fangwei Zhang. 2015. “Development of a Real-Time Crash Risk Prediction Model Incorporating the Various Crash Mechanisms Across Different Traffic States.” Traffic Injury Prevention 16 (1): 28–35. doi:10.1080/15389588.2014.909036.
   Google Scholar
• Zielke, Benjamin A., Robert L. Bertini, and Martin Treiber. 2008. “Empirical Measurement of Freeway Oscillation Characteristics: An International Comparison.” Transportation Research Record 2088 (1): 57–67. doi:10.3141/2088-07.
   Google Scholar