01550157

Component

https://doi.org/10.3141/2512-05

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.

01589161

15-0951

English

pp 38–45

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369343

Print

Figures (6) ; References (20) ; Tables (6)

Bus rapid transit; Bus transportation; Bus travel; Crash characteristics; Intersections; Mathematical models; Neural networks; Traffic crashes; Traffic flow

Bogota (Colombia)

Public Transportation; Safety and Human Factors; I81: Accident Statistics; I82: Accidents and Transport Infrastructure

TRIS, TRB, ATRI

Dec 30 2014 12:24PM

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