Inferring crowd crush accidents in typical high-density pedestrian movement zones via the vision-trajectory fusion neural network.

Saved in:
Bibliographic Details
Title: Inferring crowd crush accidents in typical high-density pedestrian movement zones via the vision-trajectory fusion neural network.
Authors: Thomas Xie CZ; School of Intelligent Systems Engineering, Sun Yat-sen University, China; Guangdong Provincial Key Laboratory of Intelligent Transportation System, China., Zhang H; PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology, China., Zhang Y; Research Centre for Smart Urban Resilience and Firefighting, Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong., Yang X; School of Information and Control Engineering, Qingdao University of Technology, China., He Z; School of Intelligent Systems Engineering, Sun Yat-sen University, China; Guangdong Provincial Key Laboratory of Intelligent Transportation System, China., Tian Y; Department of Computer Science and Engineering, University of Notre Dame, United States. Electronic address: yijun.tian@alumni.nd.edu.
Source: Accident; analysis and prevention [Accid Anal Prev] 2026 Jun; Vol. 230, pp. 108482. Date of Electronic Publication: 2026 Mar 02.
Publication Type: Journal Article
Journal Info: Publisher: Pergamon Press Country of Publication: England NLM ID: 1254476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2057 (Electronic) Linking ISSN: 00014575 NLM ISO Abbreviation: Accid Anal Prev Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-2057
DOI:10.1016/j.aap.2026.108482