Maximizing User Throughput at Signalized Intersections in a Connected Vehicle Environment

Connected vehicle (CV) technology is expected to bring unprecedented opportunities to share, collect, and exploit various information on vehicles and their occupants. This capability can be exploited to develop novel traffic management strategies, replacing or complementing the current dominant strategies, which have been mainly designed to facilitate vehicle flow. Assuming that CVs are able to transmit on-board users and vehicle data, the authors propose a user-based signal timing optimisation (UBSTO) strategy, designed to optimize user throughput for signalized intersections. In the CV environment, the inputs of the proposed algorithm consist of position and speed of CVs, as well as the number of passengers travelling in each vehicle, while the output is the optimum green time duration for each signal phase. In addition, the authors proposed strategy is able to adapt the cycle length to the traffic volume condition. In case of missing users data, the same strategy can also operate in vehicle-based mode, where the objective is vehicle-throughput maximization. The performance of the proposed strategy is compared with a fully actuated controller (FAC) in microscopic simulation for several scenarios, including different CV penetration rates. The authors' findings show that UBSTO can effectively increase user throughput and decrease average user delay in comparison with FAC, while also prioritizing vehicles with higher number of users on-board. These findings have implications for further development of prioritization strategies for public transport and ride-sharing vehicles.