Abstract
Traffic congestion is a worsening problem in metropolitan areas which will require integrated regional traffic control systems to improve traffic conditions. This paper presents a regional traffic control system which can detect incident conditions and provide integrated traffic management during nonrecurrent congestion events. The system combines advanced artificial intelligence techniques with a traffic performance model based on HCM equations. Preliminary evaluation of the control system using traffic microsimulation demonstrates that it has the potential to improve system conditions during traffic incidents. In addition, several enhancements were identified which will make the system more robust in a real traffic control setting. An assessment of the control system elements indicates that there are no substantial technical barriers in implementing this system in a large traffic network.
Highlights
Traffic congestion occurs when there is insufficient capacity to meet the prevailing demand for the transportation infrastructure and incurs consequences that include increased delay, pollution, and wasted fuel
Regional traffic control can be considered a macroscopic approach to traffic management and is a means to deal with both recurrent and nonrecurrent congestion events
Interstate incident management ended 10 minutes after the incident was removed in scenarios 3 and 4 and indicates that incident conditions were isolated to the vicinity of the incident with negligible queue growth
Summary
Traffic congestion occurs when there is insufficient capacity to meet the prevailing demand for the transportation infrastructure and incurs consequences that include increased delay, pollution, and wasted fuel. Transportation planners have placed considerable focus on traffic management solutions which more effectively utilize the existing transportation infrastructure These efforts are aided by Intelligent Transportation Systems (ITS) technologies which are a defined as “advanced electronics and communication technologies to enhance the capacity and efficiency of the surface transportation system” [1]. Traffic managers have several choices during nonrecurrent events They can implement predefined control plans, or they can develop traffic control plans on an ad hoc basis. Ad hoc plan development can be difficult because of network dynamics under congestion conditions and the complex interactions between control measures [3] In this situation, the effectiveness of traffic management becomes highly dependent on the traffic manager’s experience. Results show that the control system has the potential to improve system performance during nonrecurrent events
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