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ABSTRACT
The authors performed a city-level hot-spot identification by using the 4-year data of 265 cities in California. It is intended to equip road safety professionals with more useful information to compare the safety performance of city as a whole. Potential for safety improvement (PSI) was adopted as a measure of crash risk to compare alternate identification of hot spot (HSID) methods, including the Empirical Bayes (EB) and three full Bayesian (FB) alternatives, negative binomial, Poisson log-normal, and the Poisson temporal random effect, for ranking the safety performance of cities. Five evaluation tests which contain the Site Consistency Test, the Method Consistency Test, the Total Rank Difference Test, the Total Performance Difference Test, and the Total Score Test were applied to evaluate the performance of the four HSID methods. Moreover, two cutoff levels, top 5% and 10% cities, were employed for more reliable results. Overall, the study results are consistent with the results of previous quantitative evaluations focused on micro-level HSID. The three FB approaches significantly outperform the EB counterpart. The method accounting for temporal random effect produces more reliable HSID results than those without considering the serial correlations in collision counts.
Acknowledgments
This research received the grant from University of California Center on Economic Competitiveness in Transportation (UCCONNECT) summer research program.