This paper extends an existing linear quadratic model predictive control (LQMPC) approach to multi-destination traffic networks, where the correct origin-destination (OD) relations are preserved. In the literature, the LQMPC approach has been presented for efficient routing and intersection signal control. The optimization problem in the LQMPC has a linear quadratic formulation that can be solved quickly, which is beneficial for a real-time application. However, the existing LQMPC approach does not preserve OD relations and thus may send traffic to wrong destinations. This problem is tackled by a heuristic method presented is this paper. We present two macroscopic models: 1) a non-linear route-specific model which keeps track of traffic dynamics for each OD pair and 2) a linear model that aggregates all route traffic states, which can be embedded into the LQMPC framework. The route-specific model predicts traffic dynamics and provides information to the LQMPC before the optimization and evaluates the optimal solutions after the optimization. The information obtained from the route-specific model is formulated as constraints in the LQMPC to narrow the solution space and exclude unrealistic solutions that would lead to flows that are inconsistent with the OD relations. The extended LQMPC approach is tested in a synthetic network with multiple bottlenecks. The simulation of the LQMPC approach achieves a total time spent close to the system optimum, and the computation time remains tractable.

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In this paper we develop a fast model predictive control (MPC) approach for variable speed limit coordination to resolve freeway jam waves. Existing MPC approaches that are based on the second-order traffic flow models suffer from high computation load due to the non-linear and non-convex optimization formulation. In recent years, simplified MPC approaches which are based on discrete first-order traffic flow models have attracted more and more attention because they are beneficial for real-time applications. In literature, the type of traffic jam resolved by these approaches is limited to the standing queue in which the jam head is fixed at the bottleneck. Another type of traffic jam known as the jam wave, has been neglected by the discrete first-order model-based MPC approaches. To fill this gap, we develop a fast MPC approach based on a more accurate discrete first-order model. The model keeps the linear property of the classical discrete first-order model, meanwhile takes traffic flow features of jam waves propagation into consideration. A classical non-linear MPC and a recently proposed linear MPC are compared with the proposed MPC in terms of computation speed and jam wave resolution by a benchmark problem. Simulation results show that the proposed MPC resolves the jam wave with a real-time feasible computation speed.

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Traffic congestion has become a global issue that has a significant impact on our societys productivity. Its negative effects not only lie in the travel delays and unsafe conditions that it brings to road users, but also many aspects of our lives such as the air we all breathe. Construction and traffic management are typical alternatives for traffic researchers and practitioners to reduce congestion. Traffic management, which intends to make a better use of existing infrastructure, is more economical and environmentally friendly and becoming an increasingly preferred option. Dynamic traffic control proves to be efficient in the management of network traffic flows. This thesis focuses on the development of dynamic traffic control strategies to reduce congestion. Advanced dynamic traffic control strategies using model predictive control (MPC) approaches can considerably reduce traffic congestion. MPC for traffic systems utilizes a traffic model to predict traffic states evolutions based on the current states of the system, and determines the optimal control actions that result in the optimum value of an objective function. This feature enables the controller to take advantage of potentially larger future gains at a current (smaller) cost, so as to avoid myopic control actions...@en

This paper validates the prediction model embedded in a model predictive controller (MPC) of variable speed limits (VSLs). The MPC controller was designed based on an extended discrete first-order model with a triangular fundamental diagram. In our previous work, the extended discrete first-order model was designed to reproduce the capacity drop and the propagation of jam waves, and it was validated with reasonable accuracy without the presence of VSLs. As VSLs influence traffic dynamics, the dynamics including VSLs needs to be validated, before it can be applied as a prediction model in MPC. For conceptual illustrations, we use two synthetic examples to show how the model reproduces the key mechanisms of VSLs that are applied by existing VSL control approaches. Furthermore, the model is calibrated by use of real traffic data from Dutch freeway A12, where the field test of a speed limit control algorithm (SPECIALIST) was conducted. In the calibration, the original model is extended by using a quadrangular fundamental diagram which keeps the linear feature of the model and represents traffic states at the under-critical branch more accurately. The resulting model is validated using various traffic data sets. The accuracy of the model is compared with a second-order traffic flow model. The performance of two models is comparable: both models reproduce accurate results matching with real data. Flow errors of the calibration and validation are around 10%. The extended discrete first-order model-based MPC controller has been demonstrated to resolve freeway jam waves efficiently by synthetic cases. It has a higher computation speed comparing to the second-order model-based MPC.

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This paper proposes an extension of the discrete Lighthill–Whitham–Richards model of the cell transmission model type to reproduce capacity drop and the propagation of jam waves. Recent studies have tried to incorporate the capacity drop into discrete first-order traffic flow models for traffic optimization purposes. It was found that the inflow to a discharging cell predicted by these models might have been overestimated, and this overestimation influenced the propagation of a jam wave. An empirical analysis was carried out to confirm this assumption. It was found that the extent of the flow reduction depended on the state difference between the targeting cell and its upstream cell. On the basis of these findings, a new mathematical model formulation is given. Simulations with both a hypothetical freeway stretch and a real-life freeway stretch are performed to test the behavior of the proposed model. The previously mentioned models are also simulated for comparison. The simulation results indicate that the proposed model is better able to reproduce jam waves. In addition, the proposed model can be used in a linear model predictive control framework and formulated as a linear optimization problem, which may be beneficial for a real-life, real-time application.@en

Capacity drop is a common phenomenon that typically occurs at the downstream of freeway congestion area, which decreases traffic operation efficiency significantly. It can be avoided by some traffic control measures such as ramp metering. In this paper, a generic first-order traffic flow model is proposed, to reproduce the capacity drop at both on-ramp bottleneck and lane drop bottleneck. Based on this model, a linear quadratic model predictive control strategy for the integration of dynamic route guidance and ramp metering is presented. The objective is to minimize the total time spend of a traffic network. Due to the linearity of the optimization problem, the problem can be solved efficiently, which makes the problem suitable for real-time traffic control. A hypothetical traffic network containing both on-ramp bottleneck and lane drop bottleneck is utilized as a test bed, to demonstrate the effectiveness of the proposed control strategy.@en