Origin-Destination Demand Prediction for Ride-sharing with Deep Bayesian Learning

With the rise of ride-sharing services, accurately predicting origin-destination demand has become a critical task for ensuring efficient allocation of resources and enhancing the overall user experience. Traditional demand prediction methods often rely on deterministic models that fail to capture the inherent uncertainty and variability in travel patterns. In this paper, we propose a novel approach for origin-destination demand prediction in ride-sharing using deep Bayesian learning techniques. By leveraging the power of deep neural networks and the flexibility of Bayesian modeling, our method not only provides accurate demand estimates but also quantifies the associated uncertainties. We demonstrate the effectiveness of our approach through extensive experiments on a real-world ride-sharing dataset, comparing it with several baseline methods commonly used in the field. The experimental results showcase the superior performance of our proposed method in accurately predicting the origin-destination demand in ride-sharing. Our approach achieves significantly lower mean absolute error (MAE) and root mean square error (RMSE) compared to the baseline methods, indicating its improved accuracy. Moreover, our method demonstrates a higher coverage probability, highlighting its better reliability and uncertainty estimation capabilities. The key contributions of our paper include the development of a deep Bayesian neural network for demand prediction, the introduction of probabilistic layers to capture uncertainties, and the utilization of dropout regularization for improved uncertainty estimation. Our proposed approach offers a robust and scalable solution for accurate origin-destination demand prediction in ride-sharing, providing valuable insights for decision-making and resource allocation.