Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph

TL;DR

This paper introduces Gallat, a novel graph attention network that effectively models dynamic, directed, and weighted graphs for accurate passenger demand prediction in ride-hailing services.

Contribution

Gallat is the first model to simultaneously incorporate dynamic, directed, and weighted aspects of graphs for passenger demand prediction, improving over existing methods.

Findings

Gallat outperforms state-of-the-art models on real-world datasets.

The model effectively captures complex spatiotemporal dependencies.

Pretraining accelerates the model's convergence and accuracy.

Abstract

In recent years, ride-hailing services have been increasingly prevalent as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in different regions is vital for effective traffic flow control and route planning. As both spatial and temporal patterns are indispensable passenger demand prediction, relevant research has evolved from pure time series to graph-structured data for modeling historical passenger demand data, where a snapshot graph is constructed for each time slot by connecting region nodes via different relational edges (e.g., origin-destination relationship, geographical distance, etc.). Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i.e., weights) of passenger demands between two connected regions. However, existing graph-based solutions fail to simultaneously consider those three crucial aspects of dynamic, directed, and weighted (DDW) graphs, leading to limited expressiveness when learning graph representations for passenger demand prediction. Therefore, we propose a novel spatiotemporal graph attention network, namely Gallat (Graph prediction with all attention) as a solution. In Gallat, by comprehensively incorporating those three intrinsic properties of DDW graphs, we build three attention layers to fully capture the spatiotemporal dependencies among different regions across all historical time slots. Moreover, the model employs a subtask to conduct pretraining so that it can obtain accurate results more quickly. We evaluate the proposed model on real-world datasets, and our experimental results demonstrate that Gallat outperforms the state-of-the-art approaches.