An Adaptive Method for Traffic Signal Control Based on Fuzzy Logic With Webster and Modified Webster Formula Using SUMO Traffic Simulator

Abstract

In the past, the Webster optimal cycle time formula was limited to calculate the optimal cycle from historical data for fixed-time traffic signal control. This paper focuses on the design of an adaptive traffic signal control based on fuzzy logic with Webster and modified Webster's formula. These formulas are used to calculate the optimal cycle time depending on the current traffic situation which applying in the next cycle. The alternation of the traffic condition between two successive cycles is monitored and handled through the fuzzy logic system to compensate the fluctuation. The obtained optimal cycle time is used to determine adaptively the effective phase green times i.e. is used to determine adaptively the maximum allowable extension limit of the green phase in the next cycle. The SUMO traffic simulator is used to compare the results of the proposed adaptive control methods with fuzzy logic based traffic control, and a fixed-time Webster and modified Webster based traffic control methods. The proposed methods are tested on an isolated intersection. In this study, real field-collected data obtained from three, four, and five approaches intersections in Kilis/Turkey are used to test the performance of the proposed methods. In addition, to examine the efficiency of the proposed techniques at heavy demands, the arbitrary demands are generated by SUMO for a four approaches intersection. The obtained simulation results indicate that the proposed methods over perform the fixed time and fuzzy logic based traffic control methods in terms of average vehicular delay, speed, and travel time.INDEX TERMS Fuzzy logic control, Webster's formula, modified Webster's formula, adaptive traffic control, fixed-time traffic control, SUMO simulator.

Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems

“…Techniques such as wireless communications [31], [32] and edge cloud computing [33] can significantly improve the data transmission rate and computing efficiency. Conventional methods mainly include classic optimization based methods [10]- [19], evolutionary algorithms [34]- [36], and fuzzy logic [37], [38].…”

Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal

“…Ali et al [34] proposed an adaptive method based on FL and the modified Webster formula [62,63]. Using a fuzzy system, the method dynamically adjusted the green interval during its execution.…”

“…The calibrated microsimulation model was used as a test bench to compare the proposed adaptive FL controller against five approaches: (1) the existing Pre-Timed controller (see Figure 6), (2) a Time-Gap controller [78,79], (3) the Time-Delay controller proposed by Oertel and Wagner [43,44], (4) an FL Green-Extension controller based on the works of Nasser et al [23] and Shiri and Maleki [58], and (5) the adaptive FL-based method with modified Webster's formula proposed by Ali et al [34], which is hereinafter referred to as the FL Phase-Adjustment controller. From the in situ retrieved traffic volumes, eleven levels of arrival flow rate were simulated to mimic the observed variations from free-flow to congested traffic conditions.…”

“…Artificial intelligence methods have gained popularity to design adaptive controllers capable of addressing unpredictable traffic conditions. Numerous approaches have been proposed based on reinforcement learning (RL) [13][14][15], neural networks (NN) [16][17][18], deep reinforcement learning (DRL) [19][20][21][22], and fuzzy logic (FL) [12,[23][24][25][26][27][28][29][30][31][32][33][34]. Due to their accuracy, computational requirements, as well as the supported amounts of states and actions, NN, DRL, and FL methods have demonstrated the best performances [35][36][37].…”

“…The simulation scenario was calibrated on data collected from a sixmonth traffic survey. Using the simulated intersection as a test bench, the proposed controller (Proposed Adaptive FL) was compared against five approaches: (a) the existing pre-timed controller (Pre-Timed), (b) a time-gap-based actuated controller (Time-Gap), (c) a time-delay-based actuated controller (Time-Delay) [43,44], (d) a fuzzy system for green extension (FL Green-Extension), and (e) an adaptive FL-based method with modified Webster's formula (FL Phase-Adjustment) [34]. Simulation results show that the performance of the proposed controller is competitive with the most sophisticated and effective approaches, even without needing complex or expensive requirements for deployment and setup.…”