In this paper a new multi-class urban traffic model is proposed based on the features of a single-class urban traffic model and the characteristics of a multi-class freeway traffic model. The heterogeneous traffic flow is represented using the concept of Passenger Car Equivalent (PCE) for congestion and free-flow regimes separately. The proposed multi-class urban traffic model is intended for model-based control applications. The single-class model and the proposed multi-class traffic model are compared with microscopic simulation data obtained using the SUMO (Simulation of Urban MObility) open-source simulator. The two models are calibrated through optimal parameter estimation and their performance is evaluated and compared by taking into account the error index between the models and the simulation data. Simulation results show that the multi-class model gives a significantly better fit.

In this paper, a model predictive control approach for improving the efficiency of bicycling as part of intermodal transportation systems is proposed. Considering a dedicated bicycle lanes infrastructure, the focus in this paper is to optimize the dynamic interaction between bicycles and vehicles at the multimodal urban traffic intersections. In the proposed approach, a dynamic model for the flows, queues, and number of both vehicles and bicycles is explicitly incorporated in the controller. For obtaining a good trade-off between the total time spent by the cyclists and by the drivers, a Pareto analysis is proposed to adjust the objective function of the MPC controller. Simulation results for a two-intersections urban traffic network are presented and the controller is analyzed considering different methods of including in the MPC controller the inflow demands of both vehicles and bicycles.

Road traffic networks are large-scale systems that demand distributed control strategies. Distributed model predictive control (DMPC) arises as a feasible alternative for traffic control. Distributed strategies decompose the whole traffic network into different subnetworks with local optimal controllers that make decisions on actions to be taken by the actuators responsible for traffic control (traffic lights, routing signals, variable speed limits, among others). However, subnetworks are interacting elements of the whole traffic network. Hence, local control decisions made for one sub-network affect and are influenced by the decisions taken for the other subnetworks. Under these circumstances, the DMPC traffic problem can be treated as a game where the rules are provided by the physical system, the players are the local optimal controllers, their strategies are the control sequences, and the payoffs are the local performance indices (such as the total time spent by the users in the network). This configuration allows the achievement of a computational burden reduction, with a compromise between local and global performance. Since DMPC local controllers are able to communicate with each other, the control of the traffic network corresponds to a cooperative game. In this chapter, game-theory-based DMPC is developed and tested for control of urban and motorway networks.