Modeling Framework to Identify an Affected Area for Developing Traffic Management StrategiesAuthor/Presenter: Memarian, Arezoo; Mattingly, Stephen P.; Rosenberger, Jay M.; Williams, James C.; Ardekani, Siamak A.; Hashemi, Hossein
When a traffic incident occurs, congestion starts to disseminate around the incident location. Considering a suitable area to assess the impact of incidents and develop traffic network prediction models for evaluating traffic management schemes remains a challenging question. This study aims at developing a modeling framework to identify an affected area around the incident. For this purpose, linear regression models are presented to predict the maximum distance from a closed link to a link with a specified expected increase in travel time. Nine different models are presented to investigate the effects of the network topology and demand on the size of the affected area around the disruption. The models demonstrate that traffic volume on the closed link, a link’s area type, and the travel time on the first and second alternate paths with lowest travel times predict the radius of the affected area. This study will help traffic network managers reduce the complexity of their models by allowing them to use a subnetwork instead of the entire network.
Publication Date: October 2018
Full Text URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Incident Management; Mathematical Models; Temporary Traffic Control