An agent-based learning towards decentralized and coordinated traffic signal control

El-Tantawy, Samah; Abdulhai, Baher

doi:10.1109/itsc.2010.5625066

Cited by 83 publications

(48 citation statements)

References 9 publications

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“…The objective was to create decentralized and coordinated traffic signal control systems that respond in real time to the volume fluctuations in a given transportation network (El-Tantawy and Abdulhai, 2010). Other game theory applications involved ramp metering and speed harmonization (Ghods and Kian, 2008;Li and Fan, 2008) and planning for freight transportation network (Xiao and Yang, 2007).…”

Section: Introductionmentioning

confidence: 99%

Modeling lane-changing behavior in a connected environment: A game theory approach

Talebpour

Mahmassani

Hamdar

2015

Transportation Research Part C: Emerging Technologies

183

120

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a b s t r a c tVehicle-to-Vehicle communications provide the opportunity to create an internet of cars through the recent advances in communication technologies, processing power, and sensing technologies. A connected vehicle receives real-time information from surrounding vehicles; such information can improve drivers' awareness about their surrounding traffic condition and lead to safer and more efficient driving maneuvers. Lane-changing behavior, as one of the most challenging driving maneuvers to understand and to predict, and a major source of congestion and collisions, can benefit from this additional information. This paper presents a lane-changing model based on a game-theoretical approach that endogenously accounts for the flow of information in a connected vehicular environment. A calibration approach based on the method of simulated moments is presented and a simplified version of the proposed framework is calibrated against NGSIM data. The prediction capability of the simplified model is validated. It is concluded the presented framework is capable of predicting lane-changing behavior with limitations that still need to be addressed. Finally, a simulation framework based on the fictitious play is proposed. The simulation results revealed that the presented lane-changing model provides a greater level of realism than a basic gap-acceptance model.

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Section: Introductionmentioning

confidence: 99%

Modeling lane-changing behavior in a connected environment: A game theory approach

Talebpour

Mahmassani

Hamdar

2015

Transportation Research Part C: Emerging Technologies

183

120

View full text Add to dashboard Cite

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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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“…Numerous algorithms with plausible convergence speed and easilycustomized parameters are used to solve the single-agent RL task; the most notable of which is the Q-learning approach of Watkins [13] that has shown promising result in the field of transportation [14,15].…”

Section: Reinforcement Learningmentioning

confidence: 99%

Application of reinforcement learning with continuous state space to ramp metering in real-world conditions

Rezaee

Abdulhai

Abdelgawad

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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Abstract² In this paper we introduce a new approach to Freeway Ramp Metering (RM) based on Reinforcement Learning (RL) with focus on real-life experiments in a case study in the City of Toronto. Typical RL methods consider discrete state representation that lead to slow convergence in complex problems. Continuous representation of state space has the potential to significantly improve the learning speed and therefore enables tackling large-scale complex problems. A robust approach based on local regression, named k nearest neighbors temporal difference (kNN-TD), is employed to represent state space continuously in the RL environment. The performance of the new algorithm is compared against the ALINEA controller and typical RL methods using a microsimulation testbed in Paramics. The results show that RM using the kNN-TD method can reduce total network travel time by 44% compared to the do-nothing case (without RM) and by 17% compared to ALINEA.

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An agent-based learning towards decentralized and coordinated traffic signal control

Cited by 83 publications

References 9 publications

Modeling lane-changing behavior in a connected environment: A game theory approach

Modeling lane-changing behavior in a connected environment: A game theory approach

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

Application of reinforcement learning with continuous state space to ramp metering in real-world conditions

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