عنوان مقاله [English]
One of the conventional alternatives for improving the performance of the networks is building new roads, which nowadays is not considered as a proper alternative especially for congested urban areas. Using the intelligent transportation systems (ITS) for traffic control and congestion reduction, as a powerful alternative, has attracted a lot of attention in the past two decades. Advanced traveler information systems (ATIS) are typical ITS applications, which provide the travelers and the traffic control system operators with information in order to enhance the safety and performance of roadway facilities. The basic requirement of implementing and applying the ATIS is the modeling of the time-varying traffic flows in the network. Therefore, there has been an extensive focus on developing the dynamic traffic assignment (DTA) models. The DTA models can capture the dynamic characteristics of the traffic flow by predicting the pattern of time-varying flows, when the time-varying travel demand is given. In this paper, an analytical multi-class DTA model is proposed which defines temporal path-link incidence and path-link fraction variables to explain the relationship between the link and path flows and travel times. This model applies the BPR performance function, while confining link flows to the link capacities and considering link queuing delays by employing link capacity constraints. Also, an algorithm is developed which rapidly converges to the optimal solution for large scale problems. In addition, the algorithm uses dynamic penalty functions to deal with the capacity constraints, whereby the queuing delays for each link and each time interval can be easily calculated. The suggested algorithm is applied to the DTA test problem of Tehran network, showing that it is able to efficiently solve the problem. The application of the algorithm for evaluating some multi-class ITS policies in Tehran is also investigated. Finally, the comparison of the dynamic versus static results reveals that there is a significant difference between them.