Learning Traffic Flow Dynamics using Random Fields

Abstract

This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories. The dynamics are represented by a sequence of factor graphs, which enable learning of traffic dynamics from limited Lagrangian measurements using an efficient message passing technique. The approach ensures that estimated speeds and traffic densities are non-negative with probability one. The estimation technique is tested using vehicle trajectory datasets generated using an independent microscopic traffic simulator and is shown to efficiently reproduce traffic conditions with probe vehicle penetration levels as little as 10\%. The proposed algorithm is also compared with state-of-the-art traffic state estimation techniques developed for the same purpose and it is shown that the proposed approach can outperform the state-of-the-art techniques in terms reconstruction accuracy.

References in corpus (3)

• Traffic state estimation using stochastic Lagrangian dynamics
• Sparse Travel Time Estimation from Streaming Data
• Large Scale Estimation in Cyberphysical Systems using Streaming Data: a Case Study with Smartphone Traces

Cited by in corpus (12)

• Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems
• Traffic Data Imputation using Deep Convolutional Neural Networks
• Backpressure Control with Estimated Queue Lengths for Urban Network Traffic
• Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
• Noticeability Versus Impact in Traffic Signal Tampering
• Physics-Informed Deep Learning for Traffic State Estimation
• Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation
• Learning Traffic Speed Dynamics from Visualizations
• Generalized adaptive smoothing based neural network architecture for traffic state estimation
• Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning
• Short-Term Traffic Forecasting Using High-Resolution Traffic Data
• Generalized Adaptive Smoothing Using Matrix Completion for Traffic State Estimation