Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Data

TL;DR

This paper introduces CC-Net, a privacy-preserving collaborative learning system using hierarchical federated learning and contrastive learning to improve taxi demand prediction accuracy without compromising customer data privacy.

Contribution

The paper presents a novel hierarchical federated learning approach with clustering and contrastive learning for enhanced privacy-preserving taxi demand prediction.

Findings

CC-Net improves prediction accuracy by at least 2.2% over existing methods.

The approach maintains data privacy while enabling collaborative model training.

Evaluation on real-world data from Japanese taxi providers demonstrates effectiveness.

Abstract

Accurate taxi-demand prediction is essential for optimizing taxi operations and enhancing urban transportation services. However, using customers' data in these systems raises significant privacy and security concerns. Traditional federated learning addresses some privacy issues by enabling model training without direct data exchange but often struggles with accuracy due to varying data distributions across different regions or service providers. In this paper, we propose CC-Net: a novel approach using collaborative learning enhanced with contrastive learning for taxi-demand prediction. Our method ensures high performance by enabling multiple parties to collaboratively train a demand-prediction model through hierarchical federated learning. In this approach, similar parties are clustered together, and federated learning is applied within each cluster. The similarity is defined without data exchange, ensuring privacy and security. We evaluated our approach using real-world data from five taxi service providers in Japan over fourteen months. The results demonstrate that CC-Net maintains the privacy of customers' data while improving prediction accuracy by at least 2.2% compared to existing techniques.