Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services

Abstract

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions.

On the Quality Requirements of Demand Prediction for Dynamic Public Transport

“…For future work, we plan to extend the current study in several respects, as follows. We plan to construct and perturb a joint distribution on all OD pairs [31], and then compare the subsequent optimization stability vs. the marginal distributions in this work. We also plan to quantify the change in performance when optimization utilizes the full predictive distributions instead of the currently common predictive means [32].…”