Study of work zone traffic safety under adverse driving conditions with a microscopic traffic simulation approach-Reference-Cited by-同舟云学术

Study of work zone traffic safety under adverse driving conditions with a microscopic traffic simulation approach

Published:2020-09 Issue: Volume:145 Page:105698
ISSN:0001-4575
Container-title:Accident Analysis & Prevention
language:en
Short-container-title:Accident Analysis & Prevention

Author:

Hou Guangyang,Chen Suren

Funder

National Institute of Standards and Technology

U.S. Department of Transportation

Publisher

Elsevier BV

Subject

Public Health, Environmental and Occupational Health,Safety, Risk, Reliability and Quality,Human Factors and Ergonomics

Reference53 articles.

1. Optimizing the planning of highway work zones to maximize safety and mobility;Abdelmohsen;J. Manage. Eng.,2018

2. Impacts of weather on urban freeway traffic flow characteristics and facility capacity;Agarwal;Proc., Proceedings of the 2005 Mid-Continent Transportation Research Symposium,2005

3. Calibrating VISSIM to adverse weather conditions;Asamer,2011

4. Validation of a driving simulator for work zone design;Bella;Transp. Res. Rec.,2005

5. Timing traffic signal change intervals based on driver behavior;Chang;Transp. Res. Rec.,1985

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