Traffic safety analysis of inter-tunnel weaving section with conflict prediction models

References

• Abdel-Aty, M., Uddin, N., & Pande, A. (2005). Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways. Transportation Research Record: Journal of the Transportation Research Board, 1908(1), 51–58. doi:https://doi.org/10.1177/0361198105190800107
   Google Scholar
• Abdel-Aty, M., & Wang, L. (2017). Implementation of variable speed limits to improve safety of congested expressway weaving segments in microsimulation. Transportation Research Procedia, 27, 577–584. doi:https://doi.org/10.1016/j.trpro.2017.12.061
   Google Scholar
• Ahammed, M. A., Hassan, Y., & Sayed, T. A. (2008). Modeling driver behavior and safety on freeway merging areas. Journal of Transportation Engineering, 134(9), 370–377. doi:https://doi.org/10.1061/(ASCE)0733-947X(2008)134:9(370)
   Google Scholar
• American Association of State Highway and Transportation Officials. (2001). A policy on geometric design of highways and streets. Washington, DC. American Association of State Highway and Transportation Officials.
   Google Scholar
• Amundsen, F. H., & Ranes, G. (2000). Studies on traffic accidents in Norwegian road tunnels. Tunnelling and Underground Space Technology, 15(1), 3–11. doi:https://doi.org/10.1016/S0886-7798(00)00024-9
   Web of Science ®Google Scholar
• Bai, L., Liu, P., Chen, Y., Zhang, X., & Wang, W. (2013). Comparative analysis of the safety effects of electric bikes at signalized intersections. Transportation Research Part D: Transport and Environment, 20, 48–54. doi:https://doi.org/10.1016/j.trd.2013.02.001
   Web of Science ®Google Scholar
• Beard, A. N. (2010). Tunnel safety, risk assessment and decision-making. Tunnelling and Underground Space Technology, 25(1), 91–94. doi:https://doi.org/10.1016/j.tust.2009.07.006
   Web of Science ®Google Scholar
• Cai, Q., Saad, M., Abdel-Aty, M., Yuan, J., & Lee, J. (2018). Safety impact of weaving distance on freeway facilities with managed lanes using both microscopic traffic and driving simulations. Transportation Research Record: Journal of the Transportation Research Board, 2672(39), 130–141. doi:https://doi.org/10.1177/0361198118780884
   Web of Science ®Google Scholar
• Caliendo, C., & De Guglielmo, M. L. (2012). Accident rates in road tunnels and social cost evaluation. Procedia - Social and Behavioral Sciences, 53, 166–177. doi:https://doi.org/10.1016/j.sbspro.2012.09.870
   Google Scholar
• Caliendo, C., De Guglielmo, M. L., & Guida, M. (2013). A crash-prediction model for road tunnels. Accident Analysis and Prevention, 55, 107–115. doi:https://doi.org/10.1016/j.aap.2013.02.024
   PubMed Web of Science ®Google Scholar
• Caliendo, C., De Guglielmo, M. L., & Russo, I. (2019). Analysis of crash frequency in motorway tunnels based on a correlated random-parameters approach. Tunnelling and Underground Space Technology, 85, 243–251. doi:https://doi.org/10.1016/j.tust.2018.12.012
   Web of Science ®Google Scholar
• Caliendo, C., Guida, M., & Parisi, A. (2007). A crash-prediction model for multilane roads. Accident Analysis and Prevention, 39(4), 657–670. doi:https://doi.org/10.1016/j.aap.2006.10.012
   PubMed Web of Science ®Google Scholar
• Calvi, A., & D’amico, F. (2013). A study of the effects of road tunnel on driver behavior and road safety using driving simulator. Advances in Transportation Studies Jul 1(30).
   Google Scholar
• Calvi, A., De Blasiis, M. R., & Guattari, C. (2012). An empirical study of the effects of road tunnel on driving performance. Procedia - Social and Behavioral Sciences, 53, 1098–1108. doi:https://doi.org/10.1016/j.sbspro.2012.09.959
   Google Scholar
• Chen, D., & Ahn, S. (2018). Capacity-drop at extended bottlenecks: Merge, diverge, and weave. Transportation Research Part B: Methodological, 108, 1–20. doi:https://doi.org/10.1016/j.trb.2017.12.006
   Web of Science ®Google Scholar
• Chen, H., Zhou, H., Zhao, J., & Hsu, P. (2011). Safety performance evaluation of left-side off-ramps at freeway diverge areas. Accident Analysis and Prevention, 43(3), 605–612. doi:https://doi.org/10.1016/j.aap.2010.08.019
   PubMedGoogle Scholar
• Chin, H. C., & Quddus, M. A. (2003). Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections. Accident Analysis and Prevention, 35(2), 253–259. doi:https://doi.org/10.1016/s0001-4575(02)00003-9
   PubMed Web of Science ®Google Scholar
• Cooper, P. J. (1984). Experience with traffic conflicts in Canada with emphasis on “post encroachment time” techniques. In International calibration study of traffic conflict techniques (pp. 75–96). Berlin: Springer. Erik Asmussen.
   Google Scholar
• De Blasiis, M. R., Diana, S., & Veraldi, V. (2018). Safety audit for weaving maneuver: A driver simulation safety analysis. Journal of Transportation Safety & Security, 10(1-2), 159–175. doi:https://doi.org/10.1080/19439962.2017.1323060
   Web of Science ®Google Scholar
• El-Basyouny, K., & Sayed, T. (2009). Accident prediction models with random corridor parameters. Accident Analysis and Prevention, 41(5), 1118–1123. doi:https://doi.org/10.1016/j.aap.2009.06.025
   PubMed Web of Science ®Google Scholar
• Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC. London.
   Google Scholar
• Gettman, D., & Head, L. (2003). Surrogate safety measures from traffic simulation models. Transportation Research Record: Journal of the Transportation Research Board, 1840(1), 104–115. doi:https://doi.org/10.3141/1840-12
   Google Scholar
• Golob, T. F., Recker, W. W., & Alvarez, V. M. (2004). Safety aspects of freeway weaving sections. Transportation Research Part A: Policy and Practice, 38(1), 35–51. doi:https://doi.org/10.1016/j.tra.2003.08.001
   Web of Science ®Google Scholar
• Hayward, J. (1971). Near misses as a measure of safety at urban intersections. Doctoral Thesis, The Pensilvania State University, Department of Civil Engineering, Pennsylvania Transportation and Traffic Safety Center.
   Google Scholar
• Hidas, P. (2005). Modelling vehicle interactions in microscopic simulation of merging and weaving. Transportation Research Part C: Emerging Technologies, 13(1), 37–62. doi:https://doi.org/10.1016/j.trc.2004.12.003
   Web of Science ®Google Scholar
• Highway Capacity Manual. (2010). HCM2010. Washington, DC: Transportation Research Board, National Research Council, 1207 p.
   Google Scholar
• Hossain, M., & Muromachi, Y. (2013). A real-time crash prediction model for the ramp vicinities of urban expressways. IATSS Research, 37(1), 68–79. doi:https://doi.org/10.1016/j.iatssr.2013.05.001
   Google Scholar
• Huang, H., & Abdel-Aty, M. (2010). Multilevel data and Bayesian analysis in traffic safety. Accident Analysis and Prevention, 42(6), 1556–1565. doi:https://doi.org/10.1016/j.aap.2010.03.013
   PubMed Web of Science ®Google Scholar
• Kim, K., & Park, B. J. (2018). Safety features of freeway weaving segments with a buffer-separated high-occupancy-vehicle (HOV) lane. International Journal of Injury Control and Safety Promotion, 25(3), 284–292. doi:https://doi.org/10.1080/17457300.2018.1431943
   PubMed Web of Science ®Google Scholar
• Laval, J. A., & Daganzo, C. F. (2006). Lane-changing in traffic streams. Transportation Research Part B: Methodological, 40(3), 251–264. doi:https://doi.org/10.1016/j.trb.2005.04.003
   Web of Science ®Google Scholar
• Laval, J. A., & Leclercq, L. (2008). Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model. Transportation Research Part B: Methodological, 42(6), 511–522. doi:https://doi.org/10.1016/j.trb.2007.10.004
   Web of Science ®Google Scholar
• Lee, J., & Cassidy, M. J. (2009). An empirical and theoretical study of freeway weave bottlenecks. California PATH Program, University of California, Berkeley, 70 p.
   Google Scholar
• Li, Y., Xu, C., Xing, L., & Wang, W. (2017). Evaluation of impacts of different car-following types on rear-end crash risk at freeway weaving sections using vehicle trajectory data (No. 17-01540). In Proceedings of the Transportation Research Board 96th Annual Meeting. Washington DC. pp. 1–13.
   Google Scholar
• Liu, P., Chen, H., Lu, J. J., & Cao, B. (2010). How lane arrangements on freeway mainlines and ramps affect safety of freeways with closely spaced entrance and exit ramps. Journal of Transportation Engineering, 136(7), 614–622. doi:https://doi.org/10.1061/(ASCE)TE.1943-5436.0000127
   Web of Science ®Google Scholar
• Liu, P., Lu, J. J., & Chen, H. (2008). Safety effects of the separation distances between driveway exits and downstream U-turn locations. Accident Analysis and Prevention, 40(2), 760–767. doi:https://doi.org/10.1016/j.aap.2007.09.011
   PubMed Web of Science ®Google Scholar
• Mao, X., Yuan, C., Gan, J., & Zhang, S. (2019). Risk factors affecting traffic accidents at urban weaving sections: Evidence from China. International Journal of Environmental Research and Public Health, 16(9), 1542. doi:https://doi.org/10.3390/ijerph16091542
   PubMed Web of Science ®Google Scholar
• Meng, Q., & Qu, X. (2012). Estimation of rear-end vehicle crash frequencies in urban road tunnels. Accident Analysis and Prevention, 48, 254–263. doi:https://doi.org/10.1016/j.aap.2012.01.025
   PubMed Web of Science ®Google Scholar
• Meng, Q., Qu, X., Wang, X., Yuanita, V., & Wong, S. C. (2011). Quantitative risk assessment modeling for nonhomogeneous urban road tunnels. Risk Analysis, 31(3), 382–403. doi:https://doi.org/10.1111/j.1539-6924.2010.01503.x
   PubMedGoogle Scholar
• Meng, Q., Qu, X., Yong, K. T., & Wong, Y. H. (2011). QRA model-based risk impact analysis of traffic flow in urban road tunnels . Risk Analysis, 31(12), 1872–1882. doi:https://doi.org/10.1111/j.1539-6924.2011.01624.x
   PubMedGoogle Scholar
• Pan, T. L., Lam, W. H., Sumalee, A., & Zhong, R. X. (2016). Modeling the impacts of mandatory and discretionary lane-changing maneuvers. Transportation Research Part C: Emerging Technologies, 68, 403–424. doi:https://doi.org/10.1016/j.trc.2016.05.002
   Web of Science ®Google Scholar
• Parker, M. R., Jr., & Zegeer, C. V. (1989). Traffic conflict techniques for safety and operations: Observers manual (No. FHWA-IP-88-027, NCP 3A9C0093). Federal Highway Administration, U.S. Department of Transportation.
   Google Scholar
• PIARC Technical Committee C3.3 Road Tunnel Operation. (2008). Risk analysis for road tunnels, May 2008. Retrieved from http://publications.piarc.org/ressources/publicationsfiles/4/2234,TM2008R02-WEB.pdf
   Google Scholar
• Pulugurtha, S. S., & Bhatt, J. (2010). Evaluating the role of weaving section characteristics and traffic on crashes in weaving areas. Traffic Injury Prevention, 11(1), 104–113. doi:https://doi.org/10.1080/15389580903370039
   PubMed Web of Science ®Google Scholar
• Qi, Y., Chen, X., & Liu, J. (2015). Safety performance of freeway weaving sections (No. 15-1271). In Proceedings of the Transportation Research Board 94th Annual Meeting. Washington, DC. pp. 1–15.
   Google Scholar
• Sarvi, M. (2013). Freeway weaving phenomena observed during congested traffic. Transportmetrica A: Transport Science, 9(4), 299–315. doi:https://doi.org/10.1080/18128602.2011.574649
   Web of Science ®Google Scholar
• Sayed, T., Zaki, M. H., & Autey, J. (2013). Automated safety diagnosis of vehicle–bicycle interactions using computer vision analysis. Safety Science, 59, 163–172. doi:https://doi.org/10.1016/j.ssci.2013.05.009
   Web of Science ®Google Scholar
• Tilg, G., Yang, K., & Menendez, M. (2018). Evaluating the effects of automated vehicle technology on the capacity of freeway weaving sections. Transportation Research Part C: Emerging Technologies, 96, 3–21. doi:https://doi.org/10.1016/j.trc.2018.09.014
   Web of Science ®Google Scholar
• van Beinum, A., Farah, H., Wegman, F., & Hoogendoorn, S. (2018). Driving behaviour at motorway ramps and weaving segments based on empirical trajectory data. Transportation Research Part C: Emerging Technologies, 92, 426–441. doi:https://doi.org/10.1016/j.trc.2018.05.018
   Web of Science ®Google Scholar
• Wang, L., Abdel-Aty, M., Shi, Q., & Park, J. (2015). Real-time crash prediction for expressway weaving segments. Transportation Research Part C: Emerging Technologies, 61, 1–10. doi:https://doi.org/10.1016/j.trc.2015.10.008
   Web of Science ®Google Scholar
• Xu, C., Liu, P., Wang, W., & Li, Z. (2014). Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service. Transportation Research Part A: Policy and Practice, 69, 58–70. doi:https://doi.org/10.1016/j.tra.2014.08.011
   Web of Science ®Google Scholar
• Xu, C., Tarko, A. P., Wang, W., & Liu, P. (2013). Predicting crash likelihood and severity on freeways with real-time loop detector data. Accident; Analysis and Prevention, 57, 30–39. doi:https://doi.org/10.1016/j.aap.2013.03.035
   PubMed Web of Science ®Google Scholar
• Xu, C., Wang, W., & Liu, P. (2013). A genetic programming model for real-time crash prediction on freeways. IEEE Transactions on Intelligent Transportation Systems, 14(2), 574–586. doi:https://doi.org/10.1109/TITS.2012.2226240
   Web of Science ®Google Scholar
• Yu, R., Abdel-Aty, M., & Ahmed, M. (2013). Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors. Accident Analysis and Prevention, 50, 371–376. doi:https://doi.org/10.1016/j.aap.2012.05.011
   PubMed Web of Science ®Google Scholar
• Yuan, J., Abdel-Aty, M., Cai, Q., & Lee, J. (2019). Investigating drivers' mandatory lane change behavior on the weaving section of freeway with managed lanes: A driving simulator study. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 11–32. doi:https://doi.org/10.1016/j.trf.2018.12.007
   Web of Science ®Google Scholar
• Yun, M., Zhao, J., Zhao, J., Weng, X., & Yang, X. (2017). Impact of in-vehicle navigation information on lane-change behavior in urban expressway diverge segments. Accident Analysis and Prevention, 106, 53–66. doi:https://doi.org/10.1016/j.aap.2017.05.025
   PubMed Web of Science ®Google Scholar
• Zhang, C., Yan, X., An, M., & Zhao, H. (2015). Spatial influence analysis of traffic safety in diverging areas between freeway segments and off ramps. Discrete Dynamics in Nature and Society, 2015, 1–9. doi:https://doi.org/10.1155/2015/357579
   Google Scholar
• Zhao, J., & Liu, Y. (2016). Integrated signal optimization and non-traditional lane assignment for urban freeway off-ramp congestion mitigation. Transportation Research Part C: Emerging Technologies, 73, 219–238. doi:https://doi.org/10.1016/j.trc.2016.11.003
   Web of Science ®Google Scholar
• Zheng, L., & Ismail, K. (2017). A generalized exponential link function to map a conflict indicator into severity index within safety continuum framework. Accident Analysis and Prevention, 102, 23–30. doi:https://doi.org/10.1016/j.aap.2017.02.013
   PubMed Web of Science ®Google Scholar
• Zhu, S., Xu, Y., Yan, Y., & Luo, S. (2008, October). Study on traffic safety, efficiency and intervention in weaving area. Paper presented at the 2008 IEEE International Conference on Service Operations and Logistics, and Informatics (Vol. 2, pp. 2806–2809). IEEE. Beijing, China.
   Google Scholar