Abstract

Urban transportation networks are vulnerable to various incidents. In order to combat the negative effects due to incident-related congestion, various mitigation strategies have been proposed and implemented. The effectiveness of these congestion mitigation strategies for incident conditions largely depends on the accuracy of information regarding network conditions. Therefore, an efficient and accurate procedure to determine the link states, reflected by flows and density over time, is essential to incident management. This thesis presents a user equilibrium Dynamic Traffic Assignment (DTA) model that incorporates the Cell Transmission Model (CTM) to evaluate the temporal variation of flow and density over links, which reflect the link states of a transportation network. Encapsulation of the CTM equips the model with the capability of accepting inputs of incidents like duration and capacity reduction. Moreover, the proposed model is capable of handling multiple origin-destination (OD) pairs. By using this model, the temporal variation of flows over links can be readily evaluated. The visualized prediction of link density variations is used to investigate the link state relationships. By isolating the effects of an incident, the parallel routes of a specific OD pair display the relationship of substituting for each other, which is consistent with the general expectation regarding such parallel routes. A closer examination of the density variations confirms the existence of a substitution relationship between the unshared links of the two parallel routes. This information regarding link state relationship can be used as general guidance for incident management purposes. Master of Science

Abstract

International audience; In this paper, we extend a previously introduced traffic cell transmission model (CTM) to take electric vehicles (EVs) and charging stations (CSs) into account. The CTM is improved in the following aspects: firstly, traffic demand of multiple origins-destinations (O-D) is considered, whereas the original CTM can not simulate the specific traffic demand of each O-D pair; secondly, CSs are integrated into CTM by developing new queueing cells and charging cells, which could also describe the specific configurations of each CS and model the queueing phenomenon in CSs; third, EVs at different states of charge are considered and, thus, the spatial-temporal charging demands in CSs can be estimated with good approximation.