Traffic light control in non-stationary environments based on multi agent Q-learning

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

doi:10.1109/itsc.2011.6083114

Cited by 154 publications

(77 citation statements)

References 19 publications

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“…It operates in ISM bands (868 MHz in Europe, 915 MHz in USA and Australia, 2.4GHz in rest of the world). Data transmission rates vary from 20 Kilobits/second in the 868 MHz frequency band to 250 Kilobits/second in the 2.4 GHz frequency band [3][4]. The ZigBee uses 11 channels in case of 868/915 MHz radio frequency and 16 channels in case of 2.4 GHz radio frequency.…”

Section: Introductionmentioning

confidence: 99%

Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection

2015

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Abstract-This paper presents an intelligent traffic control system to pass emergency vehicles smoothly. Each individual vehicle is equipped with special RFID tag(placed at a strategic location), which makes it impossible to remove or destroy. We use RFID reader, NSK EDK-125-TTL and PIC16F877A system-onchip to read the RFID tags attached to the vehicle. It counts number of vehicles that passes on a particular path during a specified duration. It also determines the network congestion, and hence the green light duration for that path. If the RFID-tag-read belongs to the stolen vehicle, then a message is sent using GSM SIM300 to the police control room. Also, when an ambulance is approaching the junction, it will communicate to the traffic controller in the junction to turn on the green light. This module uses ZigBee modules on CC2500 and PIC16F877A system-onchip for wireless communications between the ambulance and traffic controller. The prototype was tested under different combinations of inputs in our wireless communication laboratory and experimental results were found as expected.

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Section: Introductionmentioning

confidence: 99%

Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection

2015

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“…Their next work (Abdoos, Mozayani, & Bazzan, 2013), presents a holonic multi-agent. The structure of each controller is similar to Abdoos et al (2011). The result of their research revealed that the performance of the individual Q-learning and holonic Q-learning is almost the same.…”

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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“…Although the use of several approaches to investigate traffic management issues, the majority of these propositions are based on the use of reinforcement and Qlearning techniques [7][8][9][10][11][12][13][14][15][16]. Among those works, the timearrival estimation technique introduced in [7] proposed a prediction engine system that built its visions and decisions based on the context behaviors of drivers and vehicles.…”

Section: Related Workmentioning

confidence: 99%

Context-aware multi-modal traffic management in ITS: A Q-learning based algorithm

Said

Soua

Abd-Elrahman

et al. 2015

2015 International Wireless Communications and Mobile Computing Conference (IWCMC)

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Multi-modal traffic management in IntelligentTransportation Systems (ITS) aims to provide a more efficient traffic regulation to passengers and reduce congestion and obstruction in the roads. In spite of the outstanding progress made in this research filed; traffic management still a very challenging problem regarding the multiple factors that have to be taken into account in any proposed solution. To tackle this problem, this paper introduces a collaborative model based context awareness multi-modal traffic management aiming at providing an efficient way to manage the traffic inside a transportation station. In this model (Multi-Layers Stations: the stations that have different intersections for different means of transport), the traffic management is based on a Q-learning technique that takes into account the context awareness parameters to provide more potent decisions. The learning technique offers the opportunity to the system (transportation station) to adapt dynamically its decision (choice of the best transportation mean) based on feedbacks provided by the passengers traveling from that specified station and thus optimize their journey through the transportation network. The efficacy of our proposed technique is validated through extensive simulations for different layers of transport means like metros, trains, and buses. Our proposal holds for any ITS system decisions provided the availability of real-time traces about the passengers passing by any station.

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Traffic light control in non-stationary environments based on multi agent Q-learning

Cited by 154 publications

References 19 publications

Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection

Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection

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

Context-aware multi-modal traffic management in ITS: A Q-learning based algorithm

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