Reinforcement Learning Ramp Metering without Complete Information

Wang, Xing-Ju; Gao, Gui‐Feng

doi:10.1155/2012/208456

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Several intelligent methods have been proposed for ramp metering including reinforcement learning (RL) [41] and artificial neural networks (ANN) [42]. Research presented in [18,40] and [43][44][45][46][47][48][49][50][51][52][53][54] describe some of the models that use reinforcement learning. Most of the reinforcement learning models use Q-learning [49][50][51] or some methods based on Q-learning.…”

Section: Ramp Metering Algorithmsmentioning

confidence: 99%

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

Ghanbartehrani

Sanandaji

Mokhtari³

et al. 2020

MAKE

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The random nature of traffic conditions on freeways can cause excessive congestion and irregularities in the traffic flow. Ramp metering is a proven effective method to maintain freeway efficiency under various traffic conditions. Creating a reliable and practical ramp metering algorithm that considers both critical traffic measures and historical data is still a challenging problem. In this study we use simple machine learning approaches to develop a novel real-time ramp metering algorithm. The proposed algorithm is computationally simple and has minimal data requirements, which makes it practical for real-world applications. We conduct a simulation study to evaluate and compare the proposed approach with an existing traffic-responsive ramp metering algorithm.

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Section: Ramp Metering Algorithmsmentioning

confidence: 99%

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

Ghanbartehrani

Sanandaji

Mokhtari³

et al. 2020

MAKE

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“…Peng et al [16] presented a new macro model of traffic flow considering the anticipation optimal velocity. Besides, some microscopic models of driver-vehicle behavior have also been proposed, which include the early linear models proposed by Chandler et al [17], the early nonlinear models presented by Pipes [18] and many recent remarkable works of Wang et al [19], Zhou et al [20], Tang et al [21], Hu et al [22], and so on. They treat each individual vehicle as a particle and regard traffic as a complex system of interacting particles.…”

Section: Introductionmentioning

confidence: 99%

Phase Plane Analysis Method of Nonlinear Traffic Phenomena

Shi

Liu

2015

Journal of Control Science and Engineering

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A new phase plane analysis method for analyzing the complex nonlinear traffic phenomena is presented in this paper. This method makes use of variable substitution to transform a traditional traffic flow model into a new model which is suitable for the analysis in phase plane. According to the new model, various traffic phenomena, such as the well-known shock waves, rarefaction waves, and stop-and-go waves, are analyzed in the phase plane. From the phase plane diagrams, we can see the relationship between traffic jams and system instability. So the problem of traffic flow could be converted into that of system stability. The results show that the traffic phenomena described by the new method is consistent with that described by traditional methods. Moreover, the phase plane analysis highlights the unstable traffic phenomena we are chiefly concerned about and describes the variation of density or velocity with time or sections more clearly.

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“…The basic RL algorithm named -learning was adopted by this system to alleviate traffic congestion caused by incidents. After this work, several -learning systems considering both local (e.g., [23,24]) and coordinated (e.g., [25,26]) control problems were proposed. However, -learning can only learn from real interactions with the traffic operation and cannot make full use of historical data (or models).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Ramp Control for Incident Response Using Dyna-QArchitecture

Lü

Zhao

Gong

2015

Mathematical Problems in Engineering

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Reinforcement learning (RL) has shown great potential for motorway ramp control, especially under the congestion caused by incidents. However, existing applications limited to single-agent tasks and based onQ-learning have inherent drawbacks for dealing with coordinated ramp control problems. For solving these problems, a Dyna-Qbased multiagent reinforcement learning (MARL) system named Dyna-MARL has been developed in this paper. Dyna-Qis an extension ofQ-learning, which combines model-free and model-based methods to obtain benefits from both sides. The performance of Dyna-MARL is tested in a simulated motorway segment in the UK with the real traffic data collected from AM peak hours. The test results compared with Isolated RL and noncontrolled situations show that Dyna-MARL can achieve a superior performance on improving the traffic operation with respect to increasing total throughput, reducing total travel time and CO2emission. Moreover, with a suitable coordination strategy, Dyna-MARL can maintain a highly equitable motorway system by balancing the travel time of road users from different on-ramps.

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Reinforcement Learning Ramp Metering without Complete Information

Cited by 4 publications

References 15 publications

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

Phase Plane Analysis Method of Nonlinear Traffic Phenomena

Intelligent Ramp Control for Incident Response Using Dyna-QArchitecture

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