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...
Saved in:
Main Authors: | Yajie Zou, Xinzhi Zhong, John Ash, Ziqiang Zeng, Yinhai Wang, Yanxi Hao, Yichuan Peng |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2017-01-01
|
Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2017/5230248 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification
by: Xiaoxue Yang, et al.
Published: (2019-01-01) -
Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm
by: Shuoben Bi, et al.
Published: (2021-01-01) -
Development Status and Hotspot Visualized Analysis of Autonomous Vehicles Based on CiteSpace
by: Lixin Yan, et al.
Published: (2022-01-01) -
Research trends and hotspots in post-stroke speech rehabilitation: A bibliometric analysis
by: Nan Huang, et al.
Published: (2025-04-01) -
Climate Change Hotspots Identification in China through the CMIP5 Global Climate Model Ensemble
by: Huanghe Gu, et al.
Published: (2014-01-01)