GNN-based Passenger Request Prediction

TL;DR

This paper introduces a Graph Neural Network framework with attention mechanisms to accurately predict passenger origin-destination flows in ride-sharing, capturing complex dependencies and patterns.

Contribution

It develops a novel GNN-based model with attention for OD flow prediction, addressing limitations of previous demand forecasting methods.

Findings

The proposed model outperforms existing baselines in prediction accuracy.

Optimal grid cell size is identified to balance complexity and performance.

Extensive simulations validate the effectiveness of the approach.

Abstract

Passenger request prediction is essential for operations planning, control, and management in ride-sharing platforms. While the demand prediction problem has been studied extensively, the Origin-Destination (OD) flow prediction of passengers has received less attention from the research community. This paper develops a Graph Neural Network framework along with the Attention Mechanism to predict the OD flow of passengers. The proposed framework exploits various linear and non-linear dependencies that arise among requests originating from different locations and captures the repetition pattern and the contextual data of that place. Moreover, the optimal size of the grid cell that covers the road network and preserves the complexity and accuracy of the model is determined. Extensive simulations are conducted to examine the characteristics of our proposed approach and its various components. The results show the superior performance of our proposed model compared to the existing baselines.