Optimized two-stage fuzzy control for urban traffic signals at isolated intersection and Paramics simulation

Yang, Wenchen; Zhang, Lun; He, Zhaocheng; Zhuang, Lijian

doi:10.1109/itsc.2012.6338691

Cited by 13 publications

(13 citation statements)

References 13 publications

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“…Recently, many fuzzy logic-based algorithms [23,24] have been studied as machine learning algorithms. Fuzzy logic-based adaptive traffic control studies [25][26][27] generally improve the performance of existing studies. Arif A.…”

Section: Related Workmentioning

confidence: 98%

“…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%

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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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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

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

Kim

Jeong

2019

Sensors

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“…A golden ratio-based heuristic genetic algorithm (GRGA) has been proposed to yield approximate solutions for expanded capacities of urban road network [ 14 ]. The proposed heuristic is able to find the closest solution to the best solution by introducing golden ratio (GR) to enhance the local optimal capability of an improved real-coded genetic algorithm [ 15 ].…”

Section: Solution Algorithmmentioning

confidence: 99%

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

Zhang

Yang

et al. 2015

Computational Intelligence and Neuroscience

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This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity.

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“…With the rapid development of com-puter technology, artificial intelligent techniques, such as fuzzy logic [19], [20], neural networks [21], [22], evolutionary algorithms [23], [24], reinforcement learning [25], [26] and multi-agent technology [27], [28] are applied and expected to play important roles in more complicated traffic control systems. The results showed that intelligent control system has better performance and is more cost effective compared to a conventional fixed-time control system [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Traffic Control Agency Decision Behavior for Multimodal Manual Signal Control Under Event Occurrences

Ding

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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Traffic control agencies (TCAs), including police officers, firefighters, or other traffic law enforcement officers, can override automatic traffic signal control and manually control the traffic at an intersection. TCA-based traffic signal control is crucial to mitigate nonrecurrent traffic congestion caused by planned and unplanned events. Understanding and predicting TCA behaviors is significant to optimize event traffic management and operations. In this paper, we propose a pressure-based human behavior model to mimic TCA's decision-making behavior. The model calculates TCA's pressure based on two attributes: vehicle and pedestrian queue dynamics and the red time duration for each phase. When TCA's pressure on each phase meet certain criteria and the minimal green is satisfied, TCA will terminate the current phase and switch to another phase. In order to study TCA behavior systematically, we first build a manual signal control simulator based on a microscopic traffic simulation tool. Supported by the manual control simulator, a series of human subject experiments have been conducted with real-world TCAs. Experiment data are divided into training data and test data. The proposed behavior model is then calibrated by training data, and the model is validated by both offline segment-based phase and duration prediction and online VISSIM-based simulation. Further, we test the model with videotaped TCA behavior data at a real-world intersection. Both validation results support the effectiveness of proposed behavior model. Index Terms-Human behavior modeling, multimodal event traffic, traffic signal control.

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Optimized two-stage fuzzy control for urban traffic signals at isolated intersection and Paramics simulation

Cited by 13 publications

References 13 publications

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

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

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

Modeling Traffic Control Agency Decision Behavior for Multimodal Manual Signal Control Under Event Occurrences

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