Predicting Driver Fatigue and Stopping Decisions in Smart Ridesharing Platforms using Stochastic Neural Networks

This paper proposes a novel Dynamic Discounted Satisficing (DDS) heuristic to model and predict driver's sequential ride decisions during a given shift, and develops a stochastic neural network with random activations to implement the DDS model in a data-driven manner.