Incorporating Nonlocal Traffic Flow Model in Physics-informed Neural Networks

• URL: http://arxiv.org/abs/2308.11818v1
• Date: Tue, 22 Aug 2023 22:41:33 GMT
• Title: Incorporating Nonlocal Traffic Flow Model in Physics-informed Neural Networks
• Authors: Archie J. Huang, Animesh Biswas, Shaurya Agarwal
• Abstract summary: We propose a novel PIDL framework that incorporates the nonlocal LWR model. We introduce both fixed-length and variable-length kernels and develop the required mathematics. The results demonstrate improvements over the baseline PIDL approach using the local LWR model.
• Score: 0.15346678870160887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research contributes to the advancement of traffic state estimation methods by leveraging the benefits of the nonlocal LWR model within a physics-informed deep learning framework. The classical LWR model, while useful, falls short of accurately representing real-world traffic flows. The nonlocal LWR model addresses this limitation by considering the speed as a weighted mean of the downstream traffic density. In this paper, we propose a novel PIDL framework that incorporates the nonlocal LWR model. We introduce both fixed-length and variable-length kernels and develop the required mathematics. The proposed PIDL framework undergoes a comprehensive evaluation, including various convolutional kernels and look-ahead windows, using data from the NGSIM and CitySim datasets. The results demonstrate improvements over the baseline PIDL approach using the local LWR model. The findings highlight the potential of the proposed approach to enhance the accuracy and reliability of traffic state estimation, enabling more effective traffic management strategies.

Related papers

• Strada-LLM: Graph LLM for traffic prediction [62.2015839597764]
  A considerable challenge in traffic prediction lies in handling the diverse data distributions caused by vastly different traffic conditions. We propose a graph-aware LLM for traffic prediction that considers proximal traffic information. We adopt a lightweight approach for efficient domain adaptation when facing new data distributions in few-shot fashion.
  arXiv  Detail & Related papers  (2024-10-28T09:19:29Z)
• Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurations [4.2185937778110825]
  This study introduces a Local Correction Factor (LCF) that integrates MFD-derived network mean speed with network configurations to accurately estimate the individual speed of a link. We use a novel deep learning framework to capture both spatial configurations and temporal dynamics of the network. Our model enhances the precision of link-level traffic speed estimations while preserving the computational benefits of aggregate models.
  arXiv  Detail & Related papers  (2024-05-23T07:37:33Z)
• 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)
• Learning "Look-Ahead" Nonlocal Traffic Dynamics in a Ring Road [2.2481284426718533]
  We develop a physics-informed neural network to learn the fundamental diagram and look-ahead kernel. We show that the learned nonlocal LWR yields a more accurate prediction of traffic wave propagation in three different scenarios.
  arXiv  Detail & Related papers  (2023-12-05T14:00:32Z)
• Large-Scale OD Matrix Estimation with A Deep Learning Method [70.78575952309023]
  The proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset.
  arXiv  Detail & Related papers  (2023-10-09T14:30:06Z)
• Physics Informed Deep Learning: Applications in Transportation [1.1929584800629671]
  Recent development in machine learning - physics-informed deep learning (PIDL) - presents unique advantages in transportation applications such as traffic state estimation. In this paper, we first explain the conservation law from the traffic flow theory as physics'', then present the architecture of a PIDL neural network and demonstrate its effectiveness in learning traffic conditions of unobserved areas.
  arXiv  Detail & Related papers  (2023-02-23T21:07:00Z)
• TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification [4.215251065887861]
  We propose TrafficFlowGAN, a physics-informed flow based generative adversarial network (GAN) for uncertainty quantification (UQ) of dynamical systems. This flow model is trained to maximize the data likelihood and to generate synthetic data that can fool a convolutional discriminator. To the best of our knowledge, we are the first to propose an integration of flow, GAN and PIDL for the UQ problems.
  arXiv  Detail & Related papers  (2022-06-19T03:35:12Z)
• Physics-Informed Deep Learning for Traffic State Estimation [3.779860024918729]
  Traffic state estimation (TSE) reconstructs the traffic variables (e.g., density) on road segments using partially observed data. This paper introduces a physics-informed deep learning (PIDL) framework to efficiently conduct high-quality TSE with small amounts of observed data.
  arXiv  Detail & Related papers  (2021-01-17T03:28:32Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)
• Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning [119.97450366894718]
  A novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station. A channel estimation approach is developed to measure the channel state information (CSI) in real-time. A distributional reinforcement learning (DRL) approach is proposed to learn the optimal IR reflection and maximize the expectation of downlink capacity.
  arXiv  Detail & Related papers  (2020-02-24T22:18:54Z)
• FPCR-Net: Feature Pyramidal Correlation and Residual Reconstruction for Optical Flow Estimation [72.41370576242116]
  We propose a semi-supervised Feature Pyramidal Correlation and Residual Reconstruction Network (FPCR-Net) for optical flow estimation from frame pairs. It consists of two main modules: pyramid correlation mapping and residual reconstruction. Experiment results show that the proposed scheme achieves the state-of-the-art performance, with improvement by 0.80, 1.15 and 0.10 in terms of average end-point error (AEE) against competing baseline methods.
  arXiv  Detail & Related papers  (2020-01-17T07:13:51Z)

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.