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.@en

In the context of transit systems’ management, uncertainty is present in different inputs required for the operator to make dispatch decisions properly. At the operational level, traffic congestion, passenger demand, non-recurrent congestion due to incidents, among multiple possible facts, make these systems very difficult to control without efficient tools or systematic rules to ameliorate the effect on the level of service caused by unexpected randomness in these features of the real timeoperation. One efficient way to deal with such situations is to impose real-time control strategies to set the service frequencies trying to minimize the effects of the different disturbances in the operation of such transit systems. In this sense, the literature is extensive in developing flexible control strategies, depending on the specific features of the problem. The most studied strategy is holding (Eberlein, 2001; Yu and Yang, 2007), in which vehicles are held at specific stations for a certain time, mostly trying to keep the headway between successive buses as close as possible to a predefined value. Moreover, and as a complement to holding-type strategies, rules based on expressing or stop-skipping are formulated in order to somehow speed up vehicles instead of delay them as in holding (Fu et al., 2003). In addition, if the dynamic control of the systems involves synchronization of routes at specific stations in order to allow travelers to transfer from one line to another, the resulting optimization problem becomes more complicated and hard to implement in a real setting (Nesheli and Ceder, 2015; Kim and Schonfeld, 2014). @en

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.

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