In this paper, we study a one-to-one matching ride-sharing problem to save the travellers' total travel time considering travel time uncertainty. Unlike the existing work where the uncertainty set is assumed to be known or roughly estimated, in this work, we propose a learning-based robust optimization framework to handle the issue properly. Specifically, we assume the travel time varies in an uncertainty set which is predicted by a machine learning approach- ARIMA using travel time historical data, the predicted uncertainty set then serves as the input parameter for the robust optimization model. To evaluate the proposed approach, we conduct a group of numerical experiments based on New York taxi trip record data sets. The results show that our proposed data-driven robust optimization approach outperforms the robust optimization model with a given uncertainty set in terms of total travel time savings. Further, the proposed approach can improve the travel time savings up to 112.8%, and 34% by average. Most importantly, our proposed approach is capable of handling the uncertainty in a more effective way when the uncertainty degrees become high.

To reduce the vehicle relocation rate considering relieving disequilibrium of the supply-demand ratios across regions for car-sharing systems, in this paper, we propose a data-driven optimization framework by integrating the non-parametric learning algorithm and two-stage stochastic programming modeling technique to address the one-way station-based car-sharing relocation problem. In contrast with the most existing work that deals with demand uncertainty using predefined probability distributions, the learning-based framework is capable of handling demand uncertainty by learning the intrinsic pattern from large-scale historical data and computing high quality solutions. To validate the performance of our proposed approach, we conduct a group of numerical experiments based on New York taxicab trip record data set. The experimental results show that our proposed data-driven approach outperforms the parametric approaches and deterministic model in terms of business profit, relocation rate, and value of stochastic solution (VSS). Most significantly, compared with the deterministic approach, the vehicle relocation rates are reduced by approximate 80%, 70% and 40% under small fleet size, medium fleet size and large fleet size, respectively. In addition, the VSS of our approach is more than 3 times higher than the one of Poisson distribution by average.

We propose a data-driven optimization model to reduce riders' wait time for vehicle guidance and rebalancing operations, considering the rider demands are under uncertainty. Instead of assuming a pre-defined rider demand distribution, we propose a data-driven framework that integrates Mixture Density Networks (MDNs) and a two-stage stochastic programming model. The integrated framework can compute high-quality guidance and rebalancing solutions that benefit drivers and riders in the ride-hailing system by leveraging the time-series historical data from real data sets. To prove the performance and effectiveness of our approach, we conduct a group of simulations based on the New York High Volume For-Hire Vehicle (HVFHV) trip records. The validation results show that the proposed method outperforms the data-driven deterministic models using GRU and moving average methods. Most significantly, the riders' average wait time using our proposed approach can be reduced by 75.9% compared to the batched matching mechanism.

In this paper, we propose a data-driven robust optimization model to reduce total travel cost in ride-sharing systems under travel time uncertainty. Instead of using a pre-defined uncertainty set, we study a data-driven robust optimization approach that integrates gated recurrent units (GRUs) predictions with a one-stage robust optimization model. The proposed approach has the ability to compute high quality solutions by leveraging the large-scale historical data to derive the uncertainty set for the designed robust optimization model. To evaluate the proposed approach, we conduct a group of simulations based on the New York taxi trip record data sets. The validation results show that our data-driven robust optimization approach outperforms the robust optimization approach with a pre-defined uncertainty set in terms of travellers' total travel cost. Most importantly, the total travel cost under the proposed approach is reduced by up to 31.7%, and by 26.7% on average compared with the robust model with a pre-defined uncertainty set.