Privacy-Preserving Taxi-Demand Prediction Using Federated Learning
This addresses privacy concerns for taxi service providers and city planners, though it is incremental as it applies an existing method to a new domain.
The paper tackled taxi-demand prediction while preserving privacy by using federated learning, achieving accurate predictions within 1% error compared to a model trained on integrated data.
Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportation infrastructure and services. However, the use of sensitive data in these systems raises concerns about privacy and security. In this paper, we propose the use of federated learning for taxi-demand prediction that allows multiple parties to train a machine learning model on their own data while keeping the data private and secure. This can enable organizations to build models on data they otherwise would not be able to access. Evaluation with real-world data collected from 16 taxi service providers in Japan over a period of six months showed that the proposed system can predict the demand level accurately within 1\% error compared to a single model trained with integrated data.