Traffic signal optimization in large-scale urban road networks: an adaptive-predictive controller using Ising models

• URL: http://arxiv.org/abs/2406.03690v2
• Date: Fri, 12 Jul 2024 08:55:44 GMT
• Title: Traffic signal optimization in large-scale urban road networks: an adaptive-predictive controller using Ising models
• Authors: Daisuke Inoue, Hiroshi Yamashita, Kazuyuki Aihara, Hiroaki Yoshida,
• Abstract summary: We propose a control method called AMPIC that guarantees both scalability and optimality. The proposed method employs model predictive control to solve an optimal control problem at each control interval with explicit consideration of a predictive model of vehicle flow. Results show that AMPIC enables faster vehicle cruising speed with less waiting time than that achieved by classical control methods.
• Score: 4.408586742026574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Realizing smooth traffic flow is important for achieving carbon neutrality. Adaptive traffic signal control, which considers traffic conditions, has thus attracted attention. However, it is difficult to ensure optimal vehicle flow throughout a large city using existing control methods because of their heavy computational load. Here, we propose a control method called AMPIC (Adaptive Model Predictive Ising Controller) that guarantees both scalability and optimality. The proposed method employs model predictive control to solve an optimal control problem at each control interval with explicit consideration of a predictive model of vehicle flow. This optimal control problem is transformed into a combinatorial optimization problem with binary variables that is equivalent to the so-called Ising problem. This transformation allows us to use an Ising solver, which has been widely studied and is expected to have fast and efficient optimization performance. We performed numerical experiments using a microscopic traffic simulator for a realistic city road network. The results show that AMPIC enables faster vehicle cruising speed with less waiting time than that achieved by classical control methods, resulting in lower CO2 emissions. The model predictive approach with a long prediction horizon thus effectively improves control performance. Systematic parametric studies on model cities indicate that the proposed method realizes smoother traffic flows for large city road networks. Among Ising solvers, D-Wave's quantum annealing is shown to find near-optimal solutions at a reasonable computational cost.

Related papers

• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Scalable Multi-Objective Optimization for Robust Traffic Signal Control in Uncertain Environments [7.504173535502228]
  This paper presents a scalable multi-objective optimization approach for robust traffic signal control in dynamic and uncertain urban environments. We propose an algorithm named Adaptive Hybrid Multi-Objective Optimization Algorithm (AHMOA), which addresses the uncertainties of city traffic. Simulations are conducted in different cities including Manhattan, Paris, Sao Paulo, and Istanbul.
  arXiv  Detail & Related papers  (2024-09-20T10:42:16Z)
• Differentiable Predictive Control for Large-Scale Urban Road Networks [1.3414298287600035]
  Transportation is a major contributor to CO2 emissions. This paper presents a novel approach to traffic network control using Differentiable Predictive Control (DPC) Our approach demonstrates a 4 order of magnitude reduction in computation time and an up to 37% improvement in traffic performance.
  arXiv  Detail & Related papers  (2024-06-14T22:42:02Z)
• 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)
• Reinforcement Learning with Model Predictive Control for Highway Ramp Metering [14.389086937116582]
  This work explores the synergy between model-based and learning-based strategies to enhance traffic flow management. The control problem is formulated as an RL task by crafting a suitable stage cost function. An MPC-based RL approach, which leverages the MPC optimal problem as a function approximation for the RL algorithm, is proposed to learn to efficiently control an on-ramp.
  arXiv  Detail & Related papers  (2023-11-15T09:50:54Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics [0.0]
  This work proposes an integral reinforcement learning (IRL) based approach to learning the macroscopic traffic dynamics for adaptive optimal perimeter control. To reduce the sampling complexity and use the available data more efficiently, the experience replay (ER) technique is introduced to the IRL algorithm. The convergence of the IRL-based algorithms and the stability of the controlled traffic dynamics are proven via the Lyapunov theory.
  arXiv  Detail & Related papers  (2022-09-13T04:28:49Z)
• AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles [61.21359293642559]
  The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies. We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
  arXiv  Detail & Related papers  (2022-03-05T10:54:05Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• Safe and Efficient Model-free Adaptive Control via Bayesian Optimization [39.962395119933596]
  We propose a purely data-driven, model-free approach for adaptive control. tuning low-level controllers based solely on system data raises concerns on the underlying algorithm safety and computational performance. We numerically demonstrate for several types of disturbances that our approach is sample efficient, outperforms constrained Bayesian optimization in terms of safety, and achieves the performance optima computed by grid evaluation.
  arXiv  Detail & Related papers  (2021-01-19T19:15:00Z)

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.