Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme

Jiang, Taiping; Wang, Zili; Chen, Fuyang

doi:10.1155/2021/6693562

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…Based on achieved results, the method over perform the fixed-time traffic signal control. Further two-stage fuzzy logic-based traffic signal control is suggested in [16]. It can be considered as an optimized version of two-stage fuzzy logicbased traffic signal control.…”

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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“…This adaptative learning process allows the algorithm to acquire an optimal strategy for resolving TSP encompasses three types: passive priority, active priority, and real-time priority. The approaches used for optimizing passive priority and active priority are not wellsuited for TSP at arterial intersections [5]. Previous studies on active TSP at arterial intersections predominantly focused on achieving maximum "green wave" and minimizing delay [6].…”

Section: Introductionmentioning

confidence: 99%

Coordination Optimization of Real-Time Signal Priority of Self-Driving Buses at Arterial Intersections Considering Private Vehicles

Li,

Zhang

2023

Applied Sciences

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Transit Signal Priority (TSP) is a system designed to grant right-of-way to buses, yet it can lead to delays for private vehicles. With the rapid advancement of network technology, self-driving buses have the capability to efficiently acquire road information and optimize the coordination between vehicle arrival and signal timing. However, the complexity of arterial intersections poses challenges for conventional algorithms and models in adapting to real-time signal priority. In this paper, a novel real-time signal-priority optimization method is proposed for self-driving buses based on the CACC model and the powerful deep Q-network (DQN) algorithm. The proposed method leverages the DQN algorithm to facilitate rapid data collection, analysis, and feedback in self-driving scenarios. Based on the arrival states of both the bus and private vehicles, appropriate actions are chosen to adjust the current-phase green time or switch to the next phase while calculating the duration of the green light. In order to optimize traffic balance, the reward function incorporates an equalization reward term. Through simulation analysis using the SUMO framework with self-driving buses in Zhengzhou, the results demonstrate that the DQN-controlled self-driving TSP optimization method reduces intersection delay by 27.77% and 30.55% compared to scenarios without TSP and with traditional active transit signal priority (ATSP), respectively. Furthermore, the queue length is reduced by 33.41% and 38.21% compared to scenarios without TSP and with traditional ATSP, respectively. These findings highlight the superior control effectiveness of the proposed method, particularly during peak hours and in high-traffic volume scenarios.

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“…The use of fixed interval control and operated control cannot be entirely adequate [4][5][6]. In the last decade, numerous researchers applied fuzzy logic in traffic signal control, including type-1 and type-2 fuzzy systems [7]. Fuzzy logic relies on scientific interpretations of personal knowledge and its experiments.…”

Section: Introductionmentioning

confidence: 99%

Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

2021

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Due to the increasing use of private cars for urbanization and urban transport, the travel time of urban transportation is increasing. People spend a lot of time in the streets, and the queue length of waiting increases accordingly; this has direct effects on fuel consumption too. Traffic flow forecasts and traffic light schedules were studied separately in the urban traffic system. This paper presents a new stable TS (Takagi–Sugeno) fuzzy controller for urban traffic. The state-space dynamics are utilized to formulate both the vehicle’s average waiting time at an isolated intersection and the length of queues. A fuzzy intelligent controller is designed for light control based upon the length of the queue, and eventually, the system’s stability is proved using the Lyapunov theorem. Moreover, the input variables are the length of queue and number of input or output vehicles from each lane. The simulation results describe the appearance of the proposed controller. An illustrative example is also given to show the proposed method’s effectiveness; the suggested method is more efficient than both the conventional fuzzy traffic controllers and the fixed time controller.

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Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme

Cited by 9 publications

References 32 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

Coordination Optimization of Real-Time Signal Priority of Self-Driving Buses at Arterial Intersections Considering Private Vehicles

Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

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