Spatiotemporal features of traffic help reduce automatic accident detection time-Reference-Cited by-同舟云学术

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Spatiotemporal features of traffic help reduce automatic accident detection time

Published:2024-06 Issue: Volume:244 Page:122813
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Moriano Pablo^ORCID,Berres Andy^ORCID,Xu Haowen^ORCID,Sanyal Jibonananda^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference67 articles.

1. Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network;Abdulhai;Transportation Research Part C (Emerging Technologies),1999

2. Application of time-series analysis techniques to freeway incident detection;Ahmed;Transportation Research Record,1982

3. Top 100 truck bottlenecks – 2022;American Transportation Research Institute (ATRI),2023

4. Transport modelling in the age of big data;Anda;International Journal of Urban Sciences,2017

5. Multiscale and multivariate transportation system visualization for shopping district traffic and regional traffic;Berres;Transportation Research Record,2021

Cited by 3 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. Traffic accident severity prediction based on interpretable deep learning model;Transportation Letters;2024-09-13

2. A traffic accident dataset for Chattanooga, Tennessee;Data in Brief;2024-08

3. Machine Learning Techniques for Fatal Accident Prediction;ACC JOURNAL;2024-03-01

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