An Empirical Analysis of Fixed and Fuzzy-Based Traffic Congestion Control System

Abstract

Traffic Management in an optimum way seems to be an effective way to reduce traffic congestion over various intersections. The core idea behind this optimality is to provide green time for dynamic traffic flow changes in urban areas. As the vehicles are waiting in the queue during red light time, an effective control system is required to reduce the waiting time. In a fixed time/conventional traffic system green light is turned on for a fixed time in each direction. Such systems are generally pre-programmed or the fixed delay in each direction can be controlled manually and hence requires a human operator to make the desired changes, as and when required. Also, a human operator will change this for a limited number of times in a day. However, this process can be automated by using fuzzy control systems. In the fuzzy controlled traffic systems, the on time of green light is adjusted (during each transition of traffic lights) depending on the different input parameters such as Queue length, Time of the day, Arrival Rate and Waiting Time, etc. Adjustment in number of transitions indicates the flexibility/adaptive nature of fuzzy controlled system. In this paper, the novelty in the field of traffic engineering is introduced by computing the relative significance of identified parameters. Various fuzzy models with three input parameters, i.e., Arrival rate, Queue length and Waiting Time are implemented and comparative performance analysis of the seven fuzzy models hence obtained, is presented. The performance of all the implemented fuzzy models is also compared with the conventional traffic system. A traffic simulator is implemented in MATLAB to generate the real-time traffic conditions, each system is simulated and compared for all possible combinations of traffic density. Fuzzy model with two input parameters Queue length and Waiting Time outperforms the other systems and provides 23.69% average improvement in the delay observed by the vehicles waiting in the queue.