Region-Aware Hierarchical Graph Contrastive Learning for Ride-Hailing Driver Profiling

Abstract

Driver profiling, which is the process of extracting driver preferences and behavioral patterns from collected driving data, can be performed on a microscopic or macroscopic scale. Microscopic driver profiling, which uses onboard data, can be incorporated into advanced driver assistance systems or used by insurance companies to implement pay-as-you-drive programs. However, transportation network companies (TNCs) typically cannot access onboard data owing to privacy and cost concerns. Therefore, TNCs typically perform macroscopic driver profiling using the raw GPS trajectories generated by smartphones. Accurate profiling of ride-hailing drivers can enhance the user experience and order dispatching process by allowing the TNCs to accurately predict various downstream tasks such as time of arrival of rides. Notably, most of the approaches that use raw GPS trajectories for driver profiling directly leverage trajectory data without any comprehensive analysis, resulting in neglect of the rich regional semantic information and underlying correlations among trajectories. Therefore, we study the ride-hailing driver profiling problem and propose a Hierarchical Graph Contrastive Learning (HGCL) framework that can automatically learn low-dimensional embeddings encoding driver behaviors from raw GPS data. The HGCL framework consists of a hierarchical graph neural network for capturing the regional features of trajectories and a hierarchical contrastive learning strategy aimed at learning high-quality representations at different levels. The effectiveness of the proposed model is evaluated using driver representation embeddings learned from a real-world large-scale dataset for three downstream tasks. The results of extensive experiments demonstrate the efficacy of the proposed HGCL framework for driver profiling.