The impact of motion prediction methods on surrogate safety analysis: A case study of left-turn and opposite-direction interactions at a signalized intersection in Montreal

References

• Archer, J. (2005). Indicators for traffic safety assessment and prediction and their application in micro-simulation modelling:A study of urban and suburban intersections (Unpublished doctoral dissertation). Sweden Royal Institute of Technology, Stockholm:.
   Google Scholar
• Gettman, D., Pu, L., Sayled, T., & Shelby, S. G. (2008). Surrogate safety assessment model and validation: Final report. Tech. rep. Washington, DC: Federal Highway Administration.
   Google Scholar
• Hydén, C., & Amundsen, F. H. (1977). Proceedings: First workshop on traffic conflicts, Oslo 77. s.l. Norwegian Council for Scientific and Industrial Research.
   Google Scholar
• Jackson, S., Miranda-Moreno, L., St-Aubin, P., & Saunier, N. (2013). A flexible, mobile video camera system and open source video analysis software for road safety and behavioural analysis. Transportation Research Record, 2365, 90–98.
   Web of Science ®Google Scholar
• Laureshyn, A. (2010). Application of automated video analysis to road user behaviour (unpublished doctoral dissertation). Lund University, Lund.
   Google Scholar
• Laureshyn, A., Svensson, Å., & Hydén, C. ( 2010, 6 November). Evaluation of traffic safety,based on micro-level behavioural data: Theoretical framework and first implementation. Accident Analysis and Prevention, 42, 1647–1646.
   PubMed Web of Science ®Google Scholar
• Mohamed, M. G., Saunier, N., Miranda-Moreno, L., & Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian-vehicle crashes in New York, US and Montreal, Canada. Safety Science, 54, 27–37. doi:10.1016/j.ssci.2012.11.001.
   Web of Science ®Google Scholar
• Mohamed, M. G. (2014). Behaviour Analysis. Retrieved from https://bitbucket.org/mohamedgomaa/behaviour-analysis.
   Google Scholar
• Mohamed, M. G., & Saunier, N. (2015). Behaviour analysis using a multi-level motion pattern learning framework. Transportation Research Record: Journal of the Transportation Research Board, 2528 ,116–127.
   Web of Science ®Google Scholar
• Mohamed, M., & Saunier, N. (2013). Motion prediction methods for surrogate safety analysis. Transportation Research Record: Journal of the Transportation Research Board, 2386, 168–178.
   Web of Science ®Google Scholar
• Morris, B., & Trivedi, M. (2009). Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. IEEE Computer Society Conference on, Computer Vision and Pattern Recognition, 312–319.
   Google Scholar
• Najm, W. G., Smith, J. D., & Smith, D. L. (2001). Analysis of crossing path crashes. Tech. rep . Retrieved from https://trid.trb.org/view.aspx?id=718155
   Google Scholar
• Nicholson, A. J. (1985). The variability of accident counts. Accident Analysis & Prevention, 17, 47–56.
   PubMed Web of Science ®Google Scholar
• Ragland, D. R., & Zabyshny, A. A. (2003). Intersection decision support project: Taxonomy of crossing-path crashes at intersections using GES 2000 data. Berkeley, CA: Safe Transportation Research & Education Center.
   Google Scholar
• Road Safety Manual (RSM). (2003). Road safety manual. La Grande Arche: PIARC Technical Committee on Road Safety - World Road Safety.
   Google Scholar
• Saunier, N. (2014). Traffic intelligence software. Retrieved from https://bitbucket.org/Nicolas/trafficintelligence.
   Google Scholar
• Saunier, N., & Mohamed, M. G. (2014). Clustering surrogate safety indicators to understand collision processes. Washington, DC: Transportation Research Board, 93rd meeting. 14–2380.
   Google Scholar
• Saunier, N., & Sayed, T. (2006). A feature-based tracking algorithm for vehicles in intersections. Computer and Robot Vision, The 3rd Canadian Conference, Quebec, Canada.
   Google Scholar
• Saunier, N., Sayed, T., & Ismail, K. (2010). Large scale automated analysis of vehicle interactions and collisions. Transportation Research Record, 2147, 42–50.
   Web of Science ®Google Scholar
• Saunier, N., Sayed, T., & Lim, C. (2007). Probabilistic collision prediction for vision-based automated road safety analysis. In The 10th International IEEE Conference on Intelligent Transportation Systems (pp. 872–878). Seattle, WA: IEEE. doi:10.1109/ITSC.2007.4357793.
   Google Scholar
• Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36, 1627–1639.
   Web of Science ®Google Scholar
• St-Aubin, P., Saunier, N., & Miranda-Moreno, L. F. (2014). Road user collision prediction using motion patterns applied to surrogate safety analysis. Transportation Research Board Annual Meeting Compendium of Papers, 14–5363.
   Google Scholar
• St-Aubin, P., Saunier, N., & Miranda-Moreno, L. (2015). Large-scale automated proactive road safety analysis using video data. Transportation Research Part C: Emerging Technologies, 58(B), 363–379.
   Web of Science ®Google Scholar
• Svensson, Å. (1998). A method for analysing the traffic process in a safety perspective (Unpublished doctoral dissertation). Lund Institute of Technology, Lund.
   Google Scholar
• Tarko, A., Davis, G. A., Saunier, N., Sayed, T., & Washington, S. (2009). Surrogate measures of safety (White paper). ANB20(3) Subcommittee on Surrogate Measures of Safety.
   Google Scholar
• Vlachos, M., Kollios, G., & Gunopulos, D. (2002). Discovering similar multidimensional trajectories. In Proceedings of the 18th IEEE International Conference on Data Engineering ( pp. 673-684). IIIE. ISSN: 1063-6382 doi:10.1109/ICDE.2002.994784.
   Google Scholar
• Zaki, M. H., Sayed, T., Ismail, K., & Alrukaibi, F. (2012). Use of computer vision to identify pedestrians' nonconforming behavior at urban intersections. Transportation Research Record, 2279, 54–64.
   Web of Science ®Google Scholar