Large-Scale OD Matrix Estimation with A Deep Learning Method

TL;DR

This paper introduces a deep learning-based approach for large-scale OD matrix estimation that infers structural constraints from traffic data, eliminating reliance on outdated priors and enabling real-time, scalable solutions.

Contribution

The paper proposes a novel integration of deep learning with numerical optimization for OD matrix estimation, improving accuracy and real-time performance without dependence on prior matrices.

Findings

Demonstrated good generalization on synthetic data

Verified stability on real traffic data

Confirmed benefits of combining neural networks with optimization

Abstract

The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS). It involves adjusting an initial OD matrix by regressing the current observations like traffic counts of road sections (e.g., using least squares). However, the OD estimation problem lacks sufficient constraints and is mathematically underdetermined. To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints. However, this approach is highly dependent on the existing prior matrix, which may be outdated. Others add structural constraints through sensor data, such as vehicle trajectory and speed, which can reflect more current structural constraints in real-time. Our proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. This approach combines the advantages of both deep learning and numerical optimization algorithms. The neural network(NN) learns to infer structural constraints from probe traffic flows, eliminating dependence on prior information and providing real-time performance. Additionally, due to the generalization capability of NN, this method is economical in engineering. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset. Subsequently, we verified the stability of our method on real traffic data. Our experiments provided confirmation of the benefits of combining NN and numerical optimization.