Accident Prediction Models for Bus Rapid Transit Systems: Generalized Linear Models Compared with a Neural Network
mode - bus rapid transit, planning - safety/accidents
bus rapid transit (BRT), traffic accidents, predictability indicators
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 socioeconomic 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.
Permission to publish the abstract has been given by Transportation Research Board, Washington, copyright remains with them.
Gómez, F., & Bocarejo, J.P. (2015). Accident Prediction Models for Bus Rapid Transit Systems: Generalized Linear Models Compared with a Neural Network. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2512, pp. 38-45.