Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks

TL;DR

This paper introduces STZINB-GNN, a novel graph neural network model that quantifies uncertainty in sparse, fine-grained origin-destination travel demand predictions by modeling spatial-temporal correlations and zero-inflation.

Contribution

The paper proposes the STZINB-GNN model, which effectively handles sparsity and uncertainty in travel demand prediction using a zero-inflated negative binomial distribution within a graph neural network framework.

Findings

STZINB-GNN outperforms benchmark models in accuracy and confidence interval tightness.

The model provides interpretable parameters with physical meaning.

High-resolution predictions benefit most from the proposed approach.

Abstract

Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy. However, few studies tackled the uncertainty and sparsity issues in fine-grained O-D matrices. This presents a serious problem, because a vast number of zeros deviate from the Gaussian assumption underlying the deterministic deep learning models. To address this issue, we design a Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) to quantify the uncertainty of the sparse travel demand. It analyzes spatial and temporal correlations using diffusion and temporal convolution networks, which are then fused to parameterize the probabilistic distributions of travel demand. The STZINB-GNN is examined using two real-world datasets with various spatial and temporal resolutions. The results demonstrate the superiority of STZINB-GNN over benchmark models, especially under high spatial-temporal resolutions, because of its high accuracy, tight confidence intervals, and interpretable parameters. The sparsity parameter of the STZINB-GNN has physical interpretation for various transportation applications.