Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning

• URL: http://arxiv.org/abs/2210.02612v2
• Date: Wed, 17 Jan 2024 03:15:15 GMT
• Title: Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning
• Authors: Chaolun Ma, Bruce Wang, Zihao Li, Ahmadreza Mahmoudzadeh, Yunlong Zhang
• Abstract summary: This study introduces a unified framework using Lyapunov control theory, defining specific Lyapunov functions respectively. Building on insights from Lyapunov theory, this study designs a reward function for the Reinforcement Learning (RL)-based network signal control. The proposed algorithm is compared with several traditional and RL-based methods under pure passenger car flow and heterogenous traffic flow including freight.
• Score: 9.797994846439527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In traffic signal control, flow-based (optimizing the overall flow) and pressure-based methods (equalizing and alleviating congestion) are commonly used but often considered separately. This study introduces a unified framework using Lyapunov control theory, defining specific Lyapunov functions respectively for these methods. We have found interesting results. For example, the well-recognized back-pressure method is equal to differential queue lengths weighted by intersection lane saturation flows. We further improve it by adding basic traffic flow theory. Rather than ensuring that the control system be stable, the system should be also capable of adaptive to various performance metrics. Building on insights from Lyapunov theory, this study designs a reward function for the Reinforcement Learning (RL)-based network signal control, whose agent is trained with Double Deep Q-Network (DDQN) for effective control over complex traffic networks. The proposed algorithm is compared with several traditional and RL-based methods under pure passenger car flow and heterogenous traffic flow including freight, respectively. The numerical tests demonstrate that the proposed method outperforms the alternative control methods across different traffic scenarios, covering corridor and general network situations each with varying traffic demands, in terms of the average network vehicle waiting time per vehicle.

Related papers

• Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion [2.733700237741334]
  This paper explores the use of Reinforcement Learning to enhance traffic signal operations at intersections. We introduce two RL-based algorithms: a turn-based agent, which dynamically prioritizes traffic signals based on real-time queue lengths, and a time-based agent, which adjusts signal phase durations according to traffic conditions. Simulation results demonstrate that both RL algorithms significantly outperform conventional traffic signal control systems.
  arXiv  Detail & Related papers  (2024-08-28T12:35:56Z)
• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• Improving the generalizability and robustness of large-scale traffic signal control [3.8028221877086814]
  We study the robustness of deep reinforcement-learning (RL) approaches to control traffic signals. We show that recent methods remain brittle in the face of missing data. We propose using a combination of distributional and vanilla reinforcement learning through a policy ensemble.
  arXiv  Detail & Related papers  (2023-06-02T21:30:44Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• Comparative analysis of machine learning methods for active flow control [60.53767050487434]
  Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, bench-marking some of their most representative algorithms against global optimization techniques.
  arXiv  Detail & Related papers  (2022-02-23T18:11:19Z)
• A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization [14.455497228170646]
  Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. This paper first proposes a multi-agent deep deterministic policy gradient (MADDPG) method by extending the actor-critic policy gradient algorithms.
  arXiv  Detail & Related papers  (2021-07-13T14:11:04Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles [109.71532364079711]
  A new federated learning (FL) framework is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs) A novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, and the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal controller.
  arXiv  Detail & Related papers  (2021-02-05T19:57:47Z)
• MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control [54.162449208797334]
  Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. We propose a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method to learn the decentralized policy for each intersection that considers neighbor information in a latent way.
  arXiv  Detail & Related papers  (2021-01-04T03:06:08Z)
• Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond [1.3999481573773072]
  We study adaptive data-guided traffic planning and control using Reinforcement Learning (RL) We propose a novel DQN-based algorithm for Traffic Control (called TC-DQN+) as a tool for fast and more reliable traffic decision-making.
  arXiv  Detail & Related papers  (2020-07-21T17:26:20Z)
• Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal Control [8.380832628205372]
  We design a multiagent coordination framework based on Deep Reinforcement Learning methods for traffic signal control. Specifically, we propose the Spatial Differentiation method for coordination which uses the temporal-spatial information in the replay buffer to amend the reward of each action.
  arXiv  Detail & Related papers  (2020-02-27T02:16:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.