Integrated predictive control of freeway networks using the extended link transmission model

Journal article (2016)

Authors

M. Hajiahmadi Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Goof Sterk van de Weg Transport and Planning - CEG
CMJ Tampère External organisation
R Corthout External organisation
A. Hegyi Transport and Planning - CEG
Bart De Schutter Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
J. Hellendoorn Delft Center for Systems and Control
Department
Delft Center for Systems and Control
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Published Date

2016

Language

English

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Department

Delft Center for Systems and Control

Abstract

In this paper, the recently developed link transmission model (LTM) is utilized in an online hybrid model-based predictive control (MPC) framework. The model is extended to include the effects of ramp metering and variable speed limits. Next, an integrated freeway traffic control based on the new model is presented in order to minimize the total time spent in the network. The integrated scheme has the capability of controlling large-scale freeway networks in real time as the model is computationally efficient, and it is yet accurate enough for our control purposes. In addition, the extended model is reformulated as a system of linear inequalities with mixed binary and real variables. The reformulated model along with the linearized total travel time objective function establish a mixed-integer linear optimization problem that is more tractable and even faster than the original optimization problem integrated in the MPC scheme. Finally, to investigate the performance of the proposed approaches (nonlinear MPC and the mixed-integer linear counterpart), a freeway network layout based on the Leuven Corridor in Belgium is selected. The extended LTM is calibrated for this network using microsimulation data and then is used for prediction and control of the large network. Microsimulation results show that the proposed methods are able to efficiently improve the total travel time.