An accident prediction model for urban road networks

References

• AASHTO. (2010). The highway safety manual. Washington, DC: American Association of State Highway Transportation Professionals.
   Google Scholar
• Ackaah, W., & Salifu, M. (2011). Crash prediction model for two-lane rural highways in the Ashanti region of Ghana. IATTS Research, 35, 34–40.
   Google Scholar
• Aguero-Valverde, J. (2013). Multivariate spatial models of excess crash frequency at area level: Case of Costa Rica. Accident Analysis & Prevention, 59, 365–373.
   PubMed Web of Science ®Google Scholar
• Barbosa, H., Cunto, F., Bezerra, B., & Nodari, C. (2014). Safety performance model for urban intersection in Brasil. Accident Analysis and Prevention, 70, 258–266.
   PubMed Web of Science ®Google Scholar
• Bauer, K. M., & Harwood, D. W. (2000). Statistical models of at-grade intersection accidents—addendum (FHWA-RD-99-094). Washington, DC: U.S. Department of Transportation.
   Google Scholar
• Cafiso, S., Di Graziano, A., Di Silvestro, G., La Cava, G., & Persaud, B. (2010). Development of comprehensive accident models for two-lane rural highways using exposure, geometry, consistency and context variables. Accident Analysis and Prevention, 42, 1072–1079.
   PubMed Web of Science ®Google Scholar
• Caliendo, C., De Guglielmo, M. L., & Guida, M. (2013). A crash-prediction model for road tunnels. Accident Analysis & Prevention, 55, 107–115.
   PubMed Web of Science ®Google Scholar
• Canale, S., Leonardi, S., & Pappalardo, G. (2005). The reliability of the urban road network: Accident forecast models. In Proceedings of 3rd International SIIV (pp. 1–22). Bari, Italy, September 22–24, 2005.
   Google Scholar
• Dong, C., Clarke, D. B., Yan, X., Khattak, A., & Huang, B. (2014). Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections. Accident Analysis & Prevention, 70, 320–329.
   PubMed Web of Science ®Google Scholar
• Dong, C., Richards, S. H., Clarke, D. B., Zhou, X., & Ma, Z. (2014). Examining signalized intersection crash frequency using multivariate zero-inflated Poisson regression. Safety Science, 70, 63–69.
   Web of Science ®Google Scholar
• El-Basyouny, K., & Sayed, T. (2009). Collision prediction models using multivariate Poisson-lognormal regression. Accident Analysis & Prevention, 41(4), 820–828.
   PubMed Web of Science ®Google Scholar
• Eustace, D., Aylo, A., & Mergia, W. Y. (2015). Crash frequency analysis of left-side merging and diverging areas on urban freeway segments—A case study of I-75 through downtown Dayton, Ohio. Transportation Research Part C: Emerging Technologies, 50, 78–85.
   Web of Science ®Google Scholar
• Geedipally, S. R., Lord, D., & Dhavala, S. S. (2012). The negative binomial-Lindley generalized linear model: Characteristics application using crash data. Accident Analysis and Prevention, 45, 258–265.
   PubMed Web of Science ®Google Scholar
• Greibe, P. (2003). Accident prediction models for urban roads. Accident Analysis & Prevention, 35(2), 273–285.
   PubMed Web of Science ®Google Scholar
• Hosseinpour, M., Yahaya, A. S., & Sadullah, A. F. (2014). Exploring the effects of roadway characteristics on the frequency severity of head-on crashes: Case studies from Malaysian federal roads. Accident Analysis and Prevention, 62, 209–222.
   PubMed Web of Science ®Google Scholar
• Husman, J. A., Hall, B., & Griliches, Z. (1984). Econometric models for count data with an application to the patents-R&D relationship. Econometrica, 52(4), 909–938.
   Web of Science ®Google Scholar
• Johansson, P. (1996). Speed limitation and motorway casualties: A time series count data regression approach. Accident Analysis & Prevention, 28(1), 73–87.
   PubMed Web of Science ®Google Scholar
• Kumara, S., & Chin, H. (2003). Modeling accident occurrence at signalized tee intersections with special emphasis on excess zeros. Traffic Injury Prevention, 4, 53–57.
   PubMedGoogle Scholar
• Lord, D., & Mannering, F. (2010). The statistical analysis of crash frequency data: A review and assessment of methodological alternatives. Transportation Research Part A, 44, 291–305.
   Google Scholar
• Lord, D., & Persaud, B. N. (2000). Accident prediction models with and without trend: application of the generalized estimating equation (GEE) procedure. Transportation Research Board, 00–0496, 14.
   Google Scholar
• Lord, D., Washington, S. P., & Ivan, J. N. (2005). Poisson, Poisson-gamma and zero inflated regression models of motor vehicle crashes: Balancing statistical fit and theory. Accident Analysis and Prevention, 37(1), 35–46.
   PubMed Web of Science ®Google Scholar
• Ma, M., Yan, X., Abdel-Aty, M., Huang, H., & Wang, X. (2010). Safety analysis of urban arterials under mixed-traffic patterns in Beijing. Transportation Research Record, 2193, 105–115.
   Web of Science ®Google Scholar
• Maher, M. J. (1990). A bivariate negative binomial model to explain traffic accident migration. Accident Analysis & Prevention, 22(5), 487–498.
   PubMed Web of Science ®Google Scholar
• Malyshkina, N. V., & Mannering, F. L. (2010). Zero state Markov switching count-data models: An empirical assessment. Accident Analysis and Prevention, 42(1), 131–139.
   PubMed Web of Science ®Google Scholar
• Mannering, F. L., & Bhat, C. R. (2014). Analytic methods in accident research: Methodological frontier and future direction. Analytic Methods in Accident Research, 1, 1–22.
   Google Scholar
• Marchionna, A., Perco, P., & Falconetti, N. (2012). Evaluation of the applicability of IHSDM crash prediction module on Italian two-lane rural roads. SIIV—5th International Congress—Sustainability of Road Infrastructures, 53, 933–942. Roma: Procedia—Social and Behavioral Sciences.
   Google Scholar
• Marchionna, A., Perco, P., & Tavernar, F. C. (2008). Transferability of accident prediction models for urban intersections. Proceedings of the 17th National SIIV Congress, Enna, Italy, 10–12 September 10–12, 2008.
   Google Scholar
• Martinelli, F., La Torre, F., & Vada, P. (2009). Calibration of the highway safety manual's accident prediction model for Italian secondary road network. Transportation Research Record: Journal of the Transportation Research Board, 2103, 1–9.
   Google Scholar
• Miaou, S. (1994). The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regression. Accident Analysis and Prevention, 26(4), 471–482.
   PubMed Web of Science ®Google Scholar
• Mohammadi, M. A., Samaranayake, V. A., & Bham, G. H. (2014). Crash frequency modeling using negative binomial models: An application of generalized estimating equation to longitudinal data. Analytic Methods in Accident Research, 2, 52–69.
   Google Scholar
• Ng, C. M., Ong, S.-H., & Srivastava, H. M. (2010). A class of bivariate negative binomial distributions with different index parameters in the marginals. Applied Mathematics and Computation, 217(7), 3069–3087.
   Web of Science ®Google Scholar
• Rakha, H., Arafeh, M., Abdel-Salam, A. G., Guo, F., & Flintsch, A. M. (2010). Linear regression crash prediction models: Issue and proposed solutions. Washington, DC: Virginia Tech Transportation Institute—U.S. Department of Transportation.
   Google Scholar
• Sacchi, E., Persaud, B., & Bassani, M. (2012). Assessing international transferability of highway safety manual crash prediction algorithm and its components. Transportation Research Record, 2279, 90–98.
   Google Scholar
• Savolainen, P. T., Mannering, F., Lord, D., & Quddus, M. A. (2011). The statistical analysis of crash-injury severities: A review and assessment of methodological alternatives. Accident Analysis and Prevention, 43(5), 1666–1676.
   PubMed Web of Science ®Google Scholar
• Šenk, P., Ambros, J., Pokorný, P., & Striegler, R. (2013). Use of accident prediction models in identifying hazardous road locations. Transactions on Transport Sciences, 5(4), 223–232.
   Google Scholar
• Shankar, V. N., Albin, R. B., Milton, J. C., & Mannering, F. L. (1998). Evaluating median crossover likelihoods with clustered accident counts: An empirical inquiry using the random effects negative binomial model. Transportation Research Record, 1635, 44–48.
   Google Scholar
• Shankar, V., Mannering, F., & Barfield, W. (1995). Effects of roadway geometrics and environmental factors of rural freeway accident frequencies. Accident Analysis and Prevention, 27(3), 371–389.
   PubMed Web of Science ®Google Scholar
• Shankar, V., Milton, J., & Mannering, F. (1997). Modeling accident frequencies as zero-altered probability process an empirical inquiry. Accident Analysis and Prevention, 29(6), 829–837.
   PubMed Web of Science ®Google Scholar
• Summersgill, I., Kennedy, J., Hall, R. D., Hickford, A. J., & Barnad, S. R. (2001). Accidents at junctions on one-way urban roads. Wokingham, England: Transport Research Laboratory.
   Google Scholar
• Van Petegem, J. H., & Wegman, F. (2014). Analyzing road design risk factors for run-off-road crashes in the Netherlands with crash prediction models. Journal of Safety Research, 49, 121–127.
   PubMed Web of Science ®Google Scholar
• Wang, K., Lee, A. H., Yau, K. K., & Carrivick, P. J. (2003). A bivariate zero-inflated Poisson regression model to analyze occupational injuries. Accident Analysis & Prevention, 35(4), 625–629.
   PubMed Web of Science ®Google Scholar
• Wang, X., & Abdel-Aty, M. (2006). Temporal and Spatial analyses of rear-end crashes at signalized intersection. Accident Analysis and Prevention, 38(6), 1137–1150.
   PubMed Web of Science ®Google Scholar
• Ward, J. H. Jr. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244.
   Web of Science ®Google Scholar