A dynamic traffic simulation/assignment model in the context of Advanced Driver Information Systems

Abstract

Growing congestion problems of many metropolitan areas which cause excessive traffic delay, instability of travel time generated the need for the development of an Intelligent Vehicle Highway System (IVHS) program that is capable of making significant improvements in mobility, highway safety, and productivity on highways and urban streets. The success of such real time control system highly depends on the new methods that address dynamic traffic assignment. Thus there is an urgent need for an effective dynamic assignment model.

The main objective of this research is to emphasize the importance of dynamic traffic assignment for Advanced Driver Information Systems (ADIS) which is one of the major components of IVHS and to present a practical traffic assignment model that is capable of running in real time and which can accurately predict link travel times, queue build up, and network performance. DYNTRAS (DYNamic Traffic Simulation Assignment), a simulation /assignment model is developed. The model uses an incremental loop that assigns a portion of the Origin-Destination matrix, and simulates the movement of the vehicles. Then, it updates travel times and assigns an additional portion of the O-D matrix. In contrast to traditional traffic assignment models like "capacity restraint" and “incremental assignment" techniques that do not consider time dimension, DYNTRAS incorporates time as a third dimension by keeping track of the vehicle movements in time. As a result, it is capable of predicting time-dynamic impacts of congestion and effects of diverted traffic on traffic flow more realistically.

The model is applied to a test network. “Several experimental factors are varied to test the sensitivity of the model. The results obtained are presented and general conclusions are derived. The differences between dynamic and static traffic assignment results are also discussed by considering results obtained from both methods.

The model needs to be calibrated using real traffic data. According to the results obtained, it needs to be validated. In addition, its long computation time should be reduced to be able to use it for real time applications.