基于安全可提高空间的事故多发信控交叉口判别

Signalized Intersection Hotspot Identification Based on Potential for Safety Improvement

摘要:

贝叶斯安全可提高空间判别方法通过计算贝叶斯方法估计的事故数与具有相同道路交通特征地点的事故期望的差值，对事故多发信控交叉口进行判别；差值越大，表明安全改善潜能越大.贝叶斯方法结合事故观测值和具有相同道路交通特征地点的事故期望估计事故数，消除了事故观测值的随机波动特性.基于全贝叶斯安全可提高空间方法判别上海市事故多发信控交叉口，并与事故绝对数方法、经验贝叶斯方法、全贝叶斯方法、经验贝叶斯安全可提高方法的判别结果进行对比，发现该判别方法最优.

Abstract:

The Bayesian based potential for safety improvement method identifies signalized intersection hotspots using the difference between the Bayesian posteriors of crash number and the expected crash number for similar sites which are obtained from the regression model. As the difference increases, the potential for safety improvement increases. In addition, the Bayesian method combines clues from both the observed crash frequency of a specific site and the expected crash number for similar sites to estimate the crash number which can overcome the problem associated with the fluctuation of observed crashes. Based on signalized crash data at intersections in Shanghai, the full Bayesian based potential for safety improvement method is compared with the crash frequency method, the empirical Bayesian method, the full Bayes method and the empirical Bayesian based potential for safety improvement method. The results show that the proposed full Bayesian potential for safety improvement is superior to other methods.