Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification
| Autor: | Xinzhi Zhong, Yichuan Peng, Ziqiang Zeng, Yajie Zou, John Ash, Yanxi Hao, Yinhai Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Economics and Econometrics
Engineering Article Subject Strategy and Management Crash computer.software_genre Bayes' theorem 0502 economics and business Hotspot (geology) 0501 psychology and cognitive sciences Cluster analysis 050107 human factors 050210 logistics & transportation business.industry Mechanical Engineering 05 social sciences lcsh:TA1001-1280 Mixture model lcsh:HE1-9990 Computer Science Applications Hierarchical clustering Target site Automotive Engineering Data mining Bayes analysis lcsh:Transportation engineering lcsh:Transportation and communications business computer |
| Zdroj: | Journal of Advanced Transportation, Vol 2017 (2017) |
| ISSN: | 0197-6729 |
| DOI: | 10.1155/2017/5230248 |
| Popis: | Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering) to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy. |
| Databáze: | OpenAIRE |
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