Decision support tools to support the operations of traffic management centers (TMC)
Mohammed Hadi, Chengjun Zhan, Yan Xiao, Hujing Qiang
Abstract
Mohammed Hadi, Chengjun Zhan, Yan Xiao, Hujing Qiang
Abstract
The goal of this project is to develop decision support tools to support traffic management operations based on collected intelligent transportation system (ITS) data. The project developments are in accordance with the needs of traffic management centers (TMCs) in Florida, as identified in this project. The project developments include new models to estimate travel time based on point detectors. These models were compared with existing travel time estimation methods including the one used in the SunGuide software. The results indicate that all of the tested methods perform at acceptable and comparable levels at low congestion levels. However, their performances vary with the increase in congestion levels. The comparison with other estimation methods shows that the developed models perform well in all cases. The developments of this study include a method to estimate traffic diversion based on the traffic detector and incident data. In addition, this study developed a method to determine the time lag between incident occurrence and the time it is recorded in the SunGuide database. This study also developed methods to estimate freeway secondary crashes, potential incident impacts on mobility, and a new method to allow incidents to be classified into categories based on primary incident attributes and impacts.
OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The goal of this project is to develop decision support tools to support traffic management operations based on collected intelligent transportation system (ITS) data. The project developments are in accordance with the needs of traffic management centers (TMCs) in Florida, as identified in this project. The project developments include new models to estimate travel time based on point detectors. These models were compared with existing travel time estimation methods including the one used in the SunGuide software. The results indicate that all of the tested methods perform at acceptable and comparable levels at low congestion levels. However, their performances vary with the increase in congestion levels. The comparison with other estimation methods shows that the developed models perform well in all cases. The developments of this study include a method to estimate traffic diversion based on the traffic detector and incident data. In addition, this study developed a method to determine the time lag between incident occurrence and the time it is recorded in the SunGuide database. This study also developed methods to estimate freeway secondary crashes, potential incident impacts on mobility, and a new method to allow incidents to be classified into categories based on primary incident attributes and impacts.
Key concepts: Incident management, Decision support system, Traffic congestion, Transport engineering, Computer science, Estimation, Software, Operations research