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A model for identifying and ranking dangerous accident locations: a case study in Flanders
Author(s) -
Brijs Tom,
Bossche Filip Van den,
Wets Geert,
Karlis Dimitris
Publication year - 2006
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2006.00341.x
Subject(s) - ranking (information retrieval) , computer science , road accident , markov chain monte carlo , bayesian probability , accident (philosophy) , black spot , operations research , bayesian inference , transport engineering , econometrics , engineering , mathematics , philosophy , epistemology , artificial intelligence , horticulture , biology , machine learning
These days, road safety has become a major concern in most modern societies. In this respect, the determination of road locations that are more dangerous than others (black spots or also called sites with promise) can help in better scheduling road safety policies. The present paper proposes a multivariate model to identify and rank sites according to their total expected cost to the society. Bayesian estimation of the model via a Markov Chain Monte Carlo approach is discussed in this paper. To illustrate the proposed model, accident data from 23,184 accident locations in Flanders (Belgium) are used and a cost function proposed by the European Transport Safety Council is adopted to illustrate the model. It is shown in the paper that the model produces insightful results that can help policy makers in prioritizing road infrastructure investments.