Inferring origin-destination distribution of agent transfer in a complex network using deep gated recurrent units

TL;DR

This paper introduces a deep neural network framework with gated recurrent units (DNNGRU) for predicting origin-destination distributions in complex networks, overcoming underdetermination issues and outperforming existing methods.

Contribution

The paper presents a network-free deep learning approach using DNNGRU trained on time-series data to improve OD prediction accuracy in complex networks.

Findings

DNNGRU outperforms existing methods across various scenarios.

Prediction accuracy depends on network topology and path overlap.

DNNGRU achieves near-optimal performance compared to exact methods.

Abstract

Predicting the origin-destination (OD) probability distribution of agent transfer is an important problem for managing complex systems. However, prediction accuracy of associated statistical estimators suffer from underdetermination. While specific techniques have been proposed to overcome this deficiency, there still lacks a general approach. Here, we propose a deep neural network framework with gated recurrent units (DNNGRU) to address this gap. Our DNNGRU is \emph{network-free}, as it is trained by supervised learning with time-series data on the volume of agents passing through edges. We use it to investigate how network topologies affect OD prediction accuracy, where performance enhancement is observed to depend on the degree of overlap between paths taken by different ODs. By comparing against methods that give exact results, we demonstrate the near-optimal performance of our DNNGRU, which we found to consistently outperform existing methods and alternative neural network architectures, under diverse data generation scenarios.