Browsing by Author "Ali, Muzamil Eltejani Mohammed"

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Citation - WoS: 24
Citation - Scopus: 40
An Adaptive Method for Traffic Signal Control Based on Fuzzy Logic With Webster and Modified Webster Formula Using Sumo Traffic Simulator
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Ali, Muzamil Eltejani Mohammed; Durdu, Akif; Çeltek, Seyit Alperen; Yılmaz, Alper
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 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 overperform the fixed time and fuzzy logic-based traffic control methods in terms of average vehicular delay, speed, and travel time.
Citation - WoS: 8
Citation - Scopus: 10
Evaluating Action Durations for Adaptive Traffic Signal Control Based on Deep Q-Learning
(SPRINGER, 2021) Çeltek, Seyit Alperen; Durdu, Akif; Ali, Muzamil Eltejani Mohammed
Adaptive traffic signal control is the control technique that adjusts the signal times according to traffic conditions and manages the traffic flow. Reinforcement learning is one of the best algorithms used for adaptive traffic signal controllers. Despite many successful studies about Reinforcement Learning based traffic control, there remains uncertainty about what the best actions to actualize adaptive traffic signal control. This paper seeks to understand the performance differences in different action durations for adaptive traffic management. Deep Q-Learning has been applied to a traffic environment for adaptive learning. This study evaluates five different action durations. Also, this study proposes a novel approach to the Deep Q-Learning based adaptive traffic control system for determine the best action. Our approach does not just aim to minimize delay time by waiting time during the red-light signal also aims to decrease delay time caused by vehicles slowing down when approaching the intersection and caused by the required time to accelerate after the green light signal. Thus the proposed strategy uses not just information of intersection also uses the data of adjacent intersection as an input. The performances of these methods are evaluated in real-time through the Simulation of Urban Mobility traffic simulator. The output of this paper indicate that the short action times increase the traffic control system performances despite more yellow signal duration. The results clearly shows that proposed method decreases the delay time.
Citation - WoS: 4
Citation - Scopus: 3
Fuzzy Logic and Webster's Optimal Cycle Based Decentralized Coordinated Adaptive Traffic Control Method
(KAUNAS UNIV TECHNOLOGY, 2020) Ali, Muzamil Eltejani Mohammed; Durdu, Akif; Çeltek, Seyit Alperen; Gültekin, Seyfettin Sinan
Traffic control systems for an urban traffic management play an important role in reducing congestion and the negative effects of social and economic aspects. In this paper, the coordinated control method for an arterial road network is proposed. The proposed method is based on fuzzy logic and Webster optimum cycle formula. It is a cyclic method, which means that all-feasible phases at the intersection are get at least a minimum green signal during each cycle. These minimum green times can be used for pedestrian crossing purposes. This method eliminates the starvation that occurs at minor roads due to the non-cyclic strategy. The proposed method is investigated in both coordinated and isolated circumstances. It is compared with non-optimized fixed time control and the cyclic backpressure strategy suggested in the literature. The cyclic backpressure strategy was selected due to its similarity with our proposed method. Based on the obtained results, the adaptive fuzzy logic and Webster based coordinated method outperforms the other methods in terms of the average of waiting time, travel time, travel speed, and queue lengths. In addition, the result achieved from a coordinated situation slightly superior that obtained from isolated situation, which means the proposed method provides good performance both in an isolated and coordinated situations.
Citation - WoS: 47
Citation - Scopus: 67
Real-Time Traffic Signal Control With Swarm Optimization Methods
(ELSEVIER SCI LTD, 2020) Çeltek, Seyit Alperen; Durdu, Akif; Ali, Muzamil Eltejani Mohammed
Real-time traffic signal control is the control methods that control the traffic signal according to the instant traffic situation. In this paper, it is suggested to optimize the traffic control problem with the swarm-based heuristic optimization algorithms. The proposed methods are tested with the real traffic data obtained from Kilis city in Turkey. The performance is evaluated in real-time via the SUMO traffic simulator. The obtained results are compared with the actual traffic measurement data, and the success of the proposed method is expressed numerically. Finally, it is proved that the particle swarm optimization algorithm and its variance algorithm could be used successfully to optimize the traffic signals control in real traffic. In this study, Social Learning-Particle Swarm Optimization is used as a traffic signal optimizer for the first time in the known literature. (C) 2020 Elsevier Ltd. All rights reserved.
A Two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Intersection Based on Real Data Using Sumo Simulator
(2019) Mahmood, Taha; Ali, Muzamil Eltejani Mohammed; Durdu, Akif
In this paper, a two-stage fuzzy logic system has been proposed to control an isolated intersection adaptively. The aim of this work is to minimize the average waiting time for a different traffic flow rates in real time means. In the first stage, the system consists of two modules named next phase selection module and the green phase extension module. In the second stage the system consists of the decision named module. The study was performed using SUMO traffic simulator. A comparison is made between a fuzzy logic controller and a conventional fixedtime controller. As a result, fuzzy logic controller has shown better performance.