Traffic data reconstruction based on Markov random field modeling

TL;DR

This paper introduces a novel traffic data reconstruction method using Markov random field modeling, mean-field approximation, and machine learning, validated on realistic simulated traffic data from Sendai, Japan.

Contribution

It presents a new approach combining Markov random fields with machine learning for reconstructing incomplete traffic data, improving accuracy over existing methods.

Findings

Effective reconstruction of incomplete traffic data demonstrated

Method outperforms traditional approaches in simulations

Validated on real-world road network data

Abstract

We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from various traffic sensors. Our approach is based on Markov random field modeling of road traffic. The reconstruction is achieved by using mean-field method and a machine learning method. We numerically verify the performance of our method using realistic simulated traffic data for the real road network of Sendai, Japan.