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Model predictive traffic control: Efficiency versus accuracy

Subject: Model predictive traffic control: Efficiency versus accuracy
Staff Mentor: prof. B. De Schutter
Other Mentor(s): Mohammad Hajiahmadi
Keywords: Optimal and model predictive control; Automotive and intelligent transportation systems; Distributed and large-scale systems
Description: Description:

With the increasing number of vehicles, the freeways are becoming more and more congested. This along with increasingly stringent traffic requirements necessitates use of efficient large-scale traffic management and control algorithms. One particular solution to this problem is based on Model Predictive Control (MPC), where a finite-horizon constrained optimal control problem is solved in a receding horizon fashion. However, in the MPC framework a model of the process is required to predict the behavior of it in a prediction horizon. For traffic networks, a wide range of traffic flow models have been developed. Among these, a model that can provide accurate predictions of the traffic states while it has low computational complexity is needed. The METANET model is a second-order model that is able to model the traffic network with good accuracy. However, for the simple case study shown in Figure, it has been shown that finding optimal control inputs in the MPC framework based on the METANET, takes considerable time. One way to overcome this problem especially for large-scale traffic networks is to use first-order models like the Cell Transmission Model and the Link Transmission Model. These models are mostly used for dynamic traffic assignment problems. But in this project we consider using them for control purposes.


In this project we consider modeling a case study with these different macroscopic traffic flow models: METANET, CTM, and LTM. In the first step, these models should be carefully studied and next be implemented in MATLAB for a traffic network case study. The evolution of traffic states (flow, density, velocity) should be determined and compared for the 3 models. Computation time should be calculated and compared. After this step, a model predictive controller for minimizing the total time that vehicles spend in the traffic network will be implemented. As a prediction model in the MPC framework, any of the afforementioned models can be used. The aim is to design an MPC controller for the case study modeled by each of the 3 models (METANET, CTM, and LTM) and next, to compare the performance of the MPC controllers in terms of computational efficiency and total cost. As an extension to this work, a real traffic network can be modeled by the LTM and the model can be calibrated using real data.

Traffic network case study

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Last modified: 25 May 2012, 6:25 UTC
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