A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Intersection Based on Real Data using SUMO Simulator

Mahmood, Taha; Ali, Muslim; Durdu, Akif

doi:10.31142/ijtsrd23873

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…The obtained result shows the outperformance of the proposed system as compared to fixed time control. Another two-stage fuzzy logic-based traffic signal control is proposed in [15]. It aims to reduce the average vehicular waiting time at the isolated intersection.…”

Section: The Related Workmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: The Related Workmentioning

confidence: 99%

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

et al. 2021

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“…Recently, multiple intersection traffic signal control has been studied [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], but there is less research of traffic signal control at multiple intersections. Ref [16] proposes an A3C technique with a decentralized coordinated algorithm for multi-intersection traffic signal control.…”

Section: Related Workmentioning

confidence: 99%

“…Traffic flow prediction may predict the future traffic flow in a different way. Most existing traffic signal control studies are trained in virtual simulation [19]. In the real world, traffic flow is especially heavily influenced by variables such as weather, time, and day [20,21].…”

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confidence: 99%

Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

Kim

Jeong

2019

Sensors

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As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Traffic signals may generate bottlenecks due to an unfair timing balance. Facing this problem, adaptive traffic signal controllers have been proposed to compute the phase durations according to conditions monitored from on-road sensors. However, high hardware requirements, as well as complex setups, make the majority of these approaches infeasible for most cities. This paper proposes an adaptive traffic signal fuzzy-logic controller which uses the flow rate, retrieved from simple traffic counters, as a unique input requirement. The controller dynamically computes the cycle duration according to the arrival flow rates, executing a fuzzy inference system guided by the reasoning: the higher the traffic flow, the longer the cycle length. The computed cycle is split into different phases proportionally to the arrival flow rates according to Webster’s method for signalization. Consequently, the controller only requires determining minimum/maximum flow rates and cycle lengths to establish if–then mappings, allowing the reduction of technical requirements and computational overhead. The controller was tested through a microsimulation model of a real isolated intersection, which was calibrated with data collected from a six-month traffic study. Results revealed that the proposed controller with fewer input requirements and lower computational costs has a competitive performance compared to the best and most used approaches, being a feasible solution for many cities.

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A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Intersection Based on Real Data using SUMO Simulator

Cited by 8 publications

References 8 publications

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

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

Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

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

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