Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
This addresses the problem of efficient demand-responsive transit services for urban or campus mobility, but it is incremental as it builds on existing methods like quantile regression and linear programming.
The study tackled the problem of dynamically operating transit services in areas with crowd movements by developing an online predictive optimization framework that integrates demand prediction and supply optimization, resulting in the framework often obtaining the ground truth optimal solution and outperforming conventional methods that do not use full predictive distributions.
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.