Abstract: Existing dynamic vehicle routing systems for microtransit services either provide prompt confirmation (without continually optimizing vehicle routes, which reduces system efficiency) or continually optimize vehicle routes (without providing immediate service guarantees, which hinders user satisfaction and adoption). The disclosed system overcomes that tradeoff by integrating a prompt confirmation module (that provides near-instantaneous acceptance or rejection) with an anytime optimization module (that continuously improves vehicle manifests during the idle periods between request arrivals).

Abstract: A system for forecasting energy consumption by vehicles in a mixed-vehicle fleet. The system includes neural network(s) that generate a predictive function for vehicles in each of a number of classes (e.g., electric vehicles, hybrid vehicles, internal combustion vehicles, vehicle models, model years, etc.). To capture both the generalizable patterns that govern energy consumption across all vehicle classes and the features and relationships that are specific to each class, the neural network(s) include a multi-task learning model that includes shared layers for all of the classes of vehicles and a set of vehicle-specific layers for each class. In some of those embodiments, for example to predict to energy consumption of an additional class with limited data, the neural networks further include an inductive transfer learning model that includes the shared layers transferred from the multi-task learning model and vehicle-specific layers for the additional class.