A Superposition Assessment Method of Road Crash Risk and Congestion Risk: An Empirical Analysis-Reference-Cited by-同舟云学术

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A Superposition Assessment Method of Road Crash Risk and Congestion Risk: An Empirical Analysis

Published:2023-04 Issue:4 Volume:24 Page:4262-4276
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Cheng Zeyang¹^ORCID,Zhang Weihua¹,Lu Jian²,Yu Bo³,Wang Shiguang¹

Affiliation:

1. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China

2. Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China

3. College of Transportation Engineering, Tongji University, Shanghai, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Research Funding Project of Anhui Transportation Holding Group

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10086684/10018119.pdf?arnumber=10018119

Reference69 articles.

1. A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data

2. Simulation and analysis of congestion risk during escalator transfers using a modified social force model

3. Quantitative risk assessment of freeway crash casualty using high-resolution traffic data

4. A residual spatio-temporal architecture for travel demand forecasting

5. Temporal instability and the analysis of highway accident data

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. A traffic dynamic operation risk assessment method using driving behaviors and traffic flow Data: An empirical analysis;Expert Systems with Applications;2024-09

2. Exploring the Effects of Traffic Flow and Driving Behavior on Traffic Instability;2024

3. A Superposition Assessment Framework of Multi-Source Traffic Risks for Mega-Events Using Risk Field Model and Time-Series Generative Adversarial Networks;IEEE Transactions on Intelligent Transportation Systems;2023

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