In this paper, a control algorithm is presented that integrates connected vehicles in the feedback loop of traffic signal control, which results in highly flexible, signal-group based signalization and speed adaptation of vehicles. The method is based on Model Predictive Control and incorporates a mutual optimization of both traffic signal timings and vehicle trajectories. In light of emerging communication technology, connected vehicles are expected to deliver more detailed data about the current traffic flow compared to stationary detection. This data can be used to influence the signal timing. By capitalizing on the possibility of providing information to connected vehicles, a second means of influence is enabled: Information about future signal timings can be provided to the drivers and hence, further reductions in the number of stops and an increase of traffic flow at the beginning of the green time can be achieved. The complexity increases when both ways of influence are combined, which is often omitted in previous research. This combination is addressed in this paper by introducing an optimized signal control with an integrated speed advisory system. The presented algorithm features an innovative functionality to adjust the predictability of signal timings to account for the reliability of speed advisory messages. A simulation study is carried out as a proof of concept and to evaluate the trade-off between optimality and predictability of the traffic signal control algorithm.     «

In this paper, a control algorithm is presented that integrates connected vehicles in the feedback loop of traffic signal control, which results in highly flexible, signal-group based signalization and speed adaptation of vehicles. The method is based on Model Predictive Control and incorporates a mutual optimization of both traffic signal timings and vehicle trajectories. In light of emerging communication technology, connected vehicles are expected to deliver more detailed data about the curre...     »