Holonic multi-agent system for traffic signals control

Abdoos, Monireh; Mozayani, Nasser; Bazzan, Ana L. C.

doi:10.1016/j.engappai.2013.01.007

Cited by 129 publications

(58 citation statements)

References 25 publications

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“…The proposed method has been tested on a large network which is partitioned into some regions with various number of intersections in [32]. To simply description of the proposed method for traffic signals control, we use a regular 4 × 4 grid network for experimental results as shown in Fig.…”

Section: Experimental Results: Application To Traffic Signals Controlmentioning

confidence: 99%

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Towards reinforcement learning for holonic multi-agent systems

Abdoos

Mozayani

Bazzan

2015

IDA

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Holonic multi-agent system (HMAS) offers a promising approach to model complex systems. HMAS is based on self-similar entities defined in a holarchical organization. Although some models and frameworks have been proposed for holonic systems, there is no general reinforcement learning method that can be easily implemented in HMAS. This paper presents a reinforcement learning method for HMAS. The holons in different levels have direct effect on learning process of each other through communication. For hierarchical communications between holons, abstract data flows are defined that are used for state estimation, action selection and reward calculation. The proposed learning method includes a self-similar structure in which the learning processes of the holons are independent of their actual positions in the holarchy. A real-world application is also used to show that how the holons can be implemented in practice. Experimental results show that the proposed holonic reinforcement learning method improves the performance.

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Section: Experimental Results: Application To Traffic Signals Controlmentioning

confidence: 99%

“…8. However the readers can find more details about the holonic multi-agent system for traffic signals control in [32].…”

Section: Experimental Results: Application To Traffic Signals Controlmentioning

confidence: 99%

Towards reinforcement learning for holonic multi-agent systems

Abdoos

Mozayani

Bazzan

2015

IDA

Self Cite

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“…In this regard, it seems better that the controller produce the appropriate value of green time for each phase at the start of that phase instead of the start of the cycle. Their next work (Abdoos, Mozayani, & Bazzan, 2013), presents a holonic multi-agent. The structure of each controller is similar to Abdoos et al (2011).…”

Section: Intelligent Traffic Signal Controllersmentioning

confidence: 99%

Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network

Araghi

Khosravi

Creighton

2015

Expert Systems with Applications

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“…Three fixed-time controllers are also designed to evaluate the performance of the FLCs with them. In some studies fixed predefined green times are used based on traffic congestion [13] [14] which reduces the flexibility of cycle times. In some other works just extension or termination of the green phase are computable [15]- [18].…”

Section: Introductionmentioning

confidence: 99%

Optimal fuzzy traffic signal controller for an isolated intersection

Araghi

Khosravi

Creighton

et al. 2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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This paper aims at optimally adjusting a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. Fuzzy logic system (FLS) is the method applied to develop the intelligent traffic timing controller. For this purpose, an intersection is considered and simulated as an intelligent agent that learns how to set green times in each cycle based on the traffic information. The FLS controller (FLC) uses genetic algorithm to tune its parameters during learning phase. Finally, The performance of the intelligent FLC is compared with the performance of a FLC with predefined parameters and three simple fixed-time controller. Simulation results indicate that intelligent FLC significantly reduces the total delay in the network compared to the fixed-time method and FLC with manual parameter setting.

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Holonic multi-agent system for traffic signals control

Cited by 129 publications

References 25 publications

Towards reinforcement learning for holonic multi-agent systems

Towards reinforcement learning for holonic multi-agent systems

Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network

Optimal fuzzy traffic signal controller for an isolated intersection

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