01506191

Component

http://onlinepubs.trb.org...011/RSS/3/Rosas-Jaimes.pdf

The increase in the number of automobiles in circulation has brought by direct consequence an increase of the interactions among different factors that contribute to the exposure of crashes, particularly in the urban intersections. For example, according to data of the city council of Toluca, in the State of Mexico, 80% of these accidents happens in intersections of urban routes (Hinojosa, 2003). This motivates a fundamental interest in the study of this phenomenon, from where possible policies or instruments could be obtained to reduce this class of incidents. In this communication three final distributions of Bayes Rule are shown (Gregory, 2005) that predict the probability of occurrence of accidents in urban intersections of the Metropolitan Zone of the City of Toluca. The first one is a specification of a Translated Poisson Mixture Model, which presents a weighed average of occurrence rates of observed car accidents. The second one is the One-variable Poisson-Gamma Model, which introduces an effect of random type to the error term of the flow variables, which is an independent variable. Finally, the third one is a Two-variable Possion-Gamma Model, which in addition to the randomness mentioned previously, considers the relation between the frequency of accidents and the vehicular flow. From statistical data of a year of reference, the parameters of these three models are calibrated, with which the final distribution is considered that predicts the occurrence of car accidents in intersections for various temporary horizons. In order to determine which of these three proposed models is more precise, observed data against estimated values by these models are compared, using the correlation coefficient and the GEH as measuring tools for carrying out this comparison. The results of these analyses show that the Two-variable Poisson-Gamma Model is the one that better adjusts to the observed data, reason why it is used to compute an estimation of the number of accidents expected in a set of intersections, and thus to hierarchize their danger, applying then to a case of study.

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01501394

English

19p

2011

3rd International Conference on Road Safety and Simulation

Digital/other

Maps; References; Tables

Bayes' theorem; Crash data; Crash rates; Intersections; Traffic crashes; Urban areas

Poisson gamma models

Toluca (Mexico)

Highways; Planning and Forecasting; Safety and Human Factors; I72: Traffic and Transport Planning; I81: Accident Statistics

TRIS, TRB, ATRI

Jan 29 2014 2:45PM