Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification
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 o...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/5230248 |
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| _version_ | 1832565228071550976 |
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| author | Yajie Zou Xinzhi Zhong John Ash Ziqiang Zeng Yinhai Wang Yanxi Hao Yichuan Peng |
| author_facet | Yajie Zou Xinzhi Zhong John Ash Ziqiang Zeng Yinhai Wang Yanxi Hao Yichuan Peng |
| author_sort | Yajie Zou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-45fa4d4a46234cfbac00009488227c5f |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-45fa4d4a46234cfbac00009488227c5f2025-02-03T01:08:48ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/52302485230248Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot IdentificationYajie Zou0Xinzhi Zhong1John Ash2Ziqiang Zeng3Yinhai Wang4Yanxi Hao5Yichuan Peng6The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaUniversity of Washington, P.O. Box 352700, Seattle, WA 98195-2700, USAUncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaCollege of Urban Railway Transportation, Shanghai University of Engineering Science, 333 Longteng Road, Shanghai 201620, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaHotspot 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.http://dx.doi.org/10.1155/2017/5230248 |
| spellingShingle | Yajie Zou Xinzhi Zhong John Ash Ziqiang Zeng Yinhai Wang Yanxi Hao Yichuan Peng Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification Journal of Advanced Transportation |
| title | Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification |
| title_full | Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification |
| title_fullStr | Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification |
| title_full_unstemmed | Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification |
| title_short | Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification |
| title_sort | developing a clustering based empirical bayes analysis method for hotspot identification |
| url | http://dx.doi.org/10.1155/2017/5230248 |
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