Congestion Forecast for Trains with Railroad-Graph-based Semi-Supervised Learning using Sparse Passenger Reports

TL;DR

This paper introduces SURCONFORT, a semi-supervised learning approach utilizing a railway graph to improve train congestion forecasting from sparse passenger reports, significantly outperforming existing methods.

Contribution

The paper proposes a novel semi-supervised learning method with a railway network graph to enhance congestion prediction from limited passenger reports.

Findings

SURCONFORT improved forecasting accuracy by 14.9% over state-of-the-art methods.

The method effectively leverages unlabeled data and station relationships.

Graph regularization enhances model stability with sparse labels.

Abstract

Forecasting rail congestion is crucial for efficient mobility in transport systems. We present rail congestion forecasting using reports from passengers collected through a transit application. Although reports from passengers have received attention from researchers, ensuring a sufficient volume of reports is challenging due to passenger's reluctance. The limited number of reports results in the sparsity of the congestion label, which can be an issue in building a stable prediction model. To address this issue, we propose a semi-supervised method for congestion forecasting for trains, or SURCONFORT. Our key idea is twofold: firstly, we adopt semi-supervised learning to leverage sparsely labeled data and many unlabeled data. Secondly, in order to complement the unlabeled data from nearby stations, we design a railway network-oriented graph and apply the graph to semi-supervised graph regularization. Empirical experiments with actual reporting data show that SURCONFORT improved the forecasting performance by 14.9% over state-of-the-art methods under the label sparsity.