A model for identifying and ranking dangerous accident locations: a case study in Flanders

被引：11

作者：

Brijs, Tom

Van den Bossche, Filip

Wets, Geert

Karlis, Dimitris

机构：

[1] Limburgs Univ Ctr, Transportat Res Inst, B-3590 Diepenbeek, Belgium

[2] Athens Univ Econ & Business, Dept Stat, Athens, Greece

来源：

STATISTICA NEERLANDICA | 2006年 / 60卷 / 04期

关键词：

Gibbs sampling; Markov Chain Monte Carlo; empirical Bayes; road accidents; multivariate Poisson distribution;

D O I：

10.1111/j.1467-9574.2006.00341.x

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：457 / 476

页数：20