Physics-Informed Deep Learning for Traffic State Estimation
- URL: http://arxiv.org/abs/2101.06580v1
- Date: Sun, 17 Jan 2021 03:28:32 GMT
- Title: Physics-Informed Deep Learning for Traffic State Estimation
- Authors: Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, Qiang Du
- Abstract summary: 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.
- Score: 3.779860024918729
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Traffic state estimation (TSE), which reconstructs the traffic variables
(e.g., density) on road segments using partially observed data, plays an
important role on efficient traffic control and operation that intelligent
transportation systems (ITS) need to provide to people. Over decades, TSE
approaches bifurcate into two main categories, model-driven approaches and
data-driven approaches. However, each of them has limitations: the former
highly relies on existing physical traffic flow models, such as
Lighthill-Whitham-Richards (LWR) models, which may only capture limited
dynamics of real-world traffic, resulting in low-quality estimation, while the
latter requires massive data in order to perform accurate and generalizable
estimation. To mitigate the limitations, this paper introduces a
physics-informed deep learning (PIDL) framework to efficiently conduct
high-quality TSE with small amounts of observed data. PIDL contains both
model-driven and data-driven components, making possible the integration of the
strong points of both approaches while overcoming the shortcomings of either.
This paper focuses on highway TSE with observed data from loop detectors, using
traffic density as the traffic variables. We demonstrate the use of PIDL to
solve (with data from loop detectors) two popular physical traffic flow models,
i.e., Greenshields-based LWR and three-parameter-based LWR, and discover the
model parameters. We then evaluate the PIDL-based highway TSE using the Next
Generation SIMulation (NGSIM) dataset. The experimental results show the
advantages of the PIDL-based approach in terms of estimation accuracy and data
efficiency over advanced baseline TSE methods.
Related papers
- Towards Responsible and Reliable Traffic Flow Prediction with Large Language Models [36.869371885656236]
We propose a large language model (LLM) to generate responsible traffic predictions.
By transferring multi-modal traffic data into natural language descriptions, R2T-LLM captures complex spatial-temporal patterns and external factors from comprehensive traffic data.
Empirically, R2T-LLM shows competitive accuracy compared with deep learning baselines, while providing an intuitive and reliable explanation for predictions.
arXiv Detail & Related papers (2024-04-03T07:14:15Z) - TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models [27.306180426294784]
We introduce TPLLM, a novel traffic prediction framework leveraging Large Language Models (LLMs)
In this framework, we construct a sequence embedding layer based on Conal Neural Networks (LoCNNs) and a graph embedding layer based on Graph Contemporalal Networks (GCNs) to extract sequence features and spatial features.
Experiments on two real-world datasets demonstrate commendable performance in both full-sample and few-shot prediction scenarios.
arXiv Detail & Related papers (2024-03-04T17:08:57Z) - 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) - Unsupervised Domain Adaptation for Self-Driving from Past Traversal
Features [69.47588461101925]
We propose a method to adapt 3D object detectors to new driving environments.
Our approach enhances LiDAR-based detection models using spatial quantized historical features.
Experiments on real-world datasets demonstrate significant improvements.
arXiv Detail & Related papers (2023-09-21T15:00:31Z) - Reinforcement Learning with Human Feedback for Realistic Traffic
Simulation [53.85002640149283]
Key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge.
This study identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models.
arXiv Detail & Related papers (2023-09-01T19:29:53Z) - Incorporating Nonlocal Traffic Flow Model in Physics-informed Neural
Networks [0.15346678870160887]
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.
arXiv Detail & Related papers (2023-08-22T22:41:33Z) - Estimating Link Flows in Road Networks with Synthetic Trajectory Data
Generation: Reinforcement Learning-based Approaches [7.369475193451259]
This paper addresses the problem of estimating link flows in a road network by combining limited traffic volume and vehicle trajectory data.
We propose a novel generative modelling framework, where we formulate the link-to-link movements of a vehicle as a sequential decision-making problem.
To ensure the generated population vehicle trajectories are consistent with the observed traffic volume and trajectory data, two methods based on Inverse Reinforcement Learning and Constrained Reinforcement Learning are proposed.
arXiv Detail & Related papers (2022-06-26T13:14:52Z) - Truncated tensor Schatten p-norm based approach for spatiotemporal
traffic data imputation with complicated missing patterns [77.34726150561087]
We introduce four complicated missing patterns, including missing and three fiber-like missing cases according to the mode-drivenn fibers.
Despite nonity of the objective function in our model, we derive the optimal solutions by integrating alternating data-mputation method of multipliers.
arXiv Detail & Related papers (2022-05-19T08:37:56Z) - A Physics-Informed Deep Learning Paradigm for Traffic State Estimation
and Fundamental Diagram Discovery [3.779860024918729]
This paper contributes an improved paradigm, called physics-informed deep learning with a fundamental diagram learner (PIDL+FDL)
PIDL+FDL integrates ML terms into the model-driven component to learn a functional form of a fundamental diagram (FD), i.e., a mapping from traffic density to flow or velocity.
We demonstrate the use of PIDL+FDL to solve popular first-order and second-order traffic flow models and reconstruct the FD relation.
arXiv Detail & Related papers (2021-06-06T14:54:32Z) - 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) - Deep traffic light detection by overlaying synthetic context on
arbitrary natural images [49.592798832978296]
We propose a method to generate artificial traffic-related training data for deep traffic light detectors.
This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds.
It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
arXiv Detail & Related papers (2020-11-07T19:57:22Z)
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.