Fuzzy Group-Based Intersection Control via Vehicular Networks for Smart Transportations

Abstract

Vehicular network has been recently used to achieve high efficient and flexible traffic scheduling at intersection roads for smart transportation systems. Different from existing works, where traffic signal is used to schedule waiting vehicles at each lane, we propose to divide vehicles in the same lane into small groups and schedule vehicle groups via wireless communication rather than traffic lights. Such direct scheduling of vehicles can reduce waiting time and improve fairness, especially when the traffic volume in different lanes is imbalanced. The key challenge in such a design lies in determining appropriate size of groups with respect to real-time traffic conditions. To cope with this issue, we propose a neuro-fuzzy network-based grouping mechanism, where the network is trained using reinforcement learning technique. Also, vehicle groups are scheduled via a neuro-fuzzy network. Simulations using ns3 are conducted to evaluate the performance of our algorithm and compare it with similar works. The results show that our algorithm can reduce waiting time and at the same time improve fairness in various cases, and the advantage against traffic light algorithms can be up to 40%.