This study presents a distributed gradient-based approach to solve system optimal dynamic traffic assignment (SODTA) formulated based on the cell transmission model. The algorithm distributes SODTA into local sub-problems, who find values for their decision variables within an intersection. Each sub-problem communicates with its immediate neighbors reach consensus of common variables. A receive...

We propose a model of dynamic traffic assignment where strategic choices are an integral part of user behaviour. The model provides a discrete-time description of flow variation through a road network involving arcs with rigid capacities. A driver’s strategy assigns, to each node of the network, a set of arcs in the forward star of that node, sorted according to a preference order. The main ele...

We are interested in improving the quality of frequency assignment via a more accurate modeling of the traffic over the network. For this purpose, we propose here an original model for FAP, which takes into account both spatial and temporal variation of the traffic. The proposed model is assessed with a hybrid genetic algorithm and compared against a classical model. Experimental results on bot...

Accurate calibration of demand and supply simulators within a Dynamic Traffic Assignment (DTA) system is critical for the provision of consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as Automatic Vehicle Identification (AVI) technology provide a rich source of disaggregate traffic data. This thesis presents a methodology for calibration...

This paper presents a pre-trip demand simulator with predictive capabilities and an explicit simulation of the response of travelers to real-time pretrip information. The demand simulator is an extension of dynamic OD estimation models aimed at explicitly capturing the effect of information on demand. This is achieved by explicitly simulating driver behavior in response to both descriptive and ...

The estimation of trip lengths has been proven to be a key feature for the application aggregated traffic models based on Macroscopic Fundamental Diagram. paths and distances traveled by vehicles in regional networks vary over time, due changes conditions. In this paper, we develop methodological framework explicitly determine traffic-dependent estimate their travel distances. This is incorpora...

In this paper, we address the question of dynamic network provisioning for time-varying traffic rates, with the objective of maximizing the system throughput. We assume that the network is capable of providing bandwidth guaranteed traffic tunnels for an ingress-egress pair and present an approach that (1) updates the tunnel routes and (2) adjusts the tunnel bandwidths, in an incremental, adapti...

In traditional traffic assignment procedure, each traffic analysis zone is represented by one point in its geometric center which is connected to the network by several connectors. Results of studies show that different connector patterns would result up to 10% change in estimated volume and up to 20% change in total travel time. Also the different patterns of connectors can change the priority...

This chapter demonstrates an application of agent-based selection dynamics to the traffic assignment problem. We introduce an evolutionary dynamic approach that acquires payoff data from multi-agent reinforcement learning to enable a adaptive optimization of traffic assignment, provided that classical theories of traffic user equilibrium pose the problem as one of global optimization. We then s...