This research sought to model traffic accidents in the bus rapid transit (BRT) system in Bogotá, Colombia. For each BRT station, 35 variables related to system flows, infrastructure, service, surroundings, and socio-economic context were tested. After a selection process, a set of 11 explanatory variables was obtained and used in the development of generalized linear models (Poisson and negative binomial models) and a neural network model. The results showed that the neural network model had better predictability indicators than did those obtained by the Poisson and negative binomial models. Additionally, the negative binomial regression model did not produce better predictions than did the Poisson regression model. Finally, a scenario analysis was developed from the most relevant variables: bus flow, number of accesses, and proximity to at-grade vehicular intersections.
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References
1.
Universidad de los Andes and Corporación Fondo de Prevención Vial. Anuario Estadístico de Accidentalidad Vial en Colombia 2010.Corporación Fondo de Prevención Vial, Bogotá, Colombia, 2012.
2.
Universidad de los Andes and Asociación Nacional de Empresarios de Colombia. Observatorio de Movilidad 2012.Asociación Nacional de Empresarios de Colombia, Bogotá, Colombia, 2013.
3.
RodríguezD. A., BrissonE. M., and EstupiñánN.The Relationship Between Segment-Level Built Environment Attributes and Pedestrian Activity Around Bogota's BRT Stations. Transportation Research Part D: Transport and Environment, Vol. 14, No. 7, 2009, pp. 470–478.
4.
JiangY., ZegrasC., and MehndirattaS.. Walk the Line: Station Context, Corridor Type, and Bus Rapid Transit Walk Access in Jinan, China. Journal of Transport Geography, Vol. 20, No. 1, 2012, pp. 1–14.
5.
HidalgoD., and PaiM.. Delhi Bus Corridor: An Evaluation.EMBARQ India, Mumbai, 2009.
6.
DengT., and NelsonJ. D.. Bus Rapid Transit Implementation in Beijing: An Evaluation of Performance and Impacts. Research in Transportation Economics, Vol. 39, No. 1, 2013, pp. 108–113.
7.
BocarejoJ. P., VelasquezJ. M., DíazC. A., and TafurL. E.. Impact of Bus Rapid Transit Systems on Road Safety: Lessons from Bogotá, Colombia. In Transportation Research Record: Journal of the Transportation Research Board, No. 2317, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 1–7.
8.
BerhanuG.Models Relating Traffic Safety with Road Environment and Traffic Flows on Arterial Roads in Addis Ababa. Accident Analysis and Prevention, Vol. 36, No. 5, 2004, pp. 697–704.
9.
ChenF., and ChenS.. Injury Severities of Truck Drivers in Single- and Multi-Vehicle Accidents on Rural Highways. Accident Analysis and Prevention, Vol. 43, No. 5, 2011, pp. 1677–1688.
10.
KaplanS., and PratoC. G.. Risk Factors Associated with Bus Accident Severity in the United States: A Generalized Ordered Logit Model. Journal of Safety Research, Vol. 43, No. 3, 2012, pp. 171–180.
11.
XieY., LordD., and ZhangY.. Predicting Motor Vehicle Collisions Using Bayesian Neural Network Models: An Empirical Analysis. Accident Analysis and Prevention, Vol. 39, No. 5, 2007, pp. 922–933.
12.
OhJ., WashingtonS. P., and NamD.. Accident Prediction Model for Railway–Highway Interfaces. Accident Analysis and Prevention, Vol. 38, No. 2, 2006, pp. 346–356.
13.
GreibeP.Accident Prediction Models for Urban Roads. Accident Analysis and Prevention, Vol. 35, No. 2, 2003, pp. 273–285.
14.
LordD., WashingtonS. P., and IvanJ. N.. Poisson, Poisson-Gamma, and Zero-Inflated Regression Models of Motor Vehicle Crashes: Balancing Statistical Fit and Theory. Accident Analysis and Prevention, Vol. 37, No. 1, 2005, pp. 35–46.
15.
UsmanT., FuL., and Miranda-MorenoL. F.. Quantifying Safety Benefit of Winter Road Maintenance: Accident Frequency Modeling. Accident Analysis and Prevention, Vol. 42, No. 6, 2010, pp. 1878–1887.
16.
DelenD., ShardaR., and BessonovM.. Identifying Significant Predictors of Injury Severity in Traffic Accidents Using a Series of Artificial Neural Networks. Accident Analysis and Prevention, Vol. 38, No. 3, 2006, pp. 434–444.
17.
ChangL.-Y.Analysis of Freeway Accident Frequencies: Negative Binomial Regression Versus Artificial Neural Network. Safety Science, Vol. 43, No. 8, 2005, pp. 541–557.
18.
LordD., and ManneringF.. The Statistical Analysis of Crash-Frequency Data: A Review and Assessment of Methodological Alternatives. Transportation Research Part A: Policy and Practice, Vol. 44, No. 5, 2010, pp. 291–305.
19.
TransMilenioS.A.Plan Marco 2010 Sistema TransMilenio.Alcaldía Mayor de Bogotá D.C., Bogotá, Colombia, 2011.
20.
Universidad de los Andes and Corporación Fondo de Prevención Vial. Propuesta Metodológica para la Elaboración de Mapas de Riesgo de Accidentalidad Vial: Aplicación a Cinco Ciudades de Colombia.Corporación Fondo de Prevención Vial, Bogotá, Colombia, 2011.
