Robust spatiotemporal crash risk prediction with gated recurrent convolution network and interpretable insights from SHapley additive explanations-Reference-Cited by-同舟云学术

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Robust spatiotemporal crash risk prediction with gated recurrent convolution network and interpretable insights from SHapley additive explanations

Published:2024-01 Issue: Volume:127 Page:107379
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Kashifi Mohammad Tamim^ORCID

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference53 articles.

1. Comparison of road crashes incidence and severity between some French counties;Amoros;Accid. Anal. Prev.,2003

2. inTformer: A Time-Embedded Attention-Based Transformer for Crash Likelihood Prediction at Intersections Using Connected Vehicle Data;Anik,2023

3. NOAA's U.S. Climate Normals (1981-2010);Arguez,2020

4. Red-light running traffic violations : a novel time-based method for determining a fine structure;Baratian-ghorghi;Transport. Res. Part A,2016

5. Real-time crash prediction in an urban expressway using disaggregated data;Basso;Transport. Res. C Emerg. Technol.,2018

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Hybrid Attention Framework for Road Crash Emergency Response Management;IEEE Transactions on Intelligent Transportation Systems;2024-08

2. Traffic signal optimization framework using interpretable machine learning technique under heterogeneous-autonomy traffic environment;Neural Computing and Applications;2024-04-28

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