User throughput optimization for signalized intersection in a connected vehicle environment

Development of connected vehicles has provided different opportunities for traffic management based on high-resolution data. However, dominant methods are focused on vehicle-based strategies. The aim of this research is the development of a user-based signal timing (UST) strategy aiming at maximizing user throughput in a connected vehicle environment. The inputs of the proposed optimization algorithm are position, speed, and length of connected vehicles, as well as the number of passengers for each of vehicles, while the output is the optimum green time duration for each phase of signal timing. A microscopic simulation environment is used to collect data and validate the model employed within the algorithm. Then, the proposed optimization problem is solved by genetic algorithm method. The results obtained via UST optimization are compared with a vehicle-based optimization strategy, which is solved by the same algorithm. Results show significant increase in user throughput and share of vehicles with higher number of users on-board when UST is employed. The UST algorithm can be also implemented as transit signal priority strategy and supportive policy for ride-sharing.