  Drivers in ridesharing platforms exhibit cognitive atrophy and fatigue as
they accept ride offers along the day, which can have a significant impact on
the overall efficiency of the ridesharing platform. In contrast to the current
literature which focuses primarily on modeling and learning driver's
preferences across different ride offers, this paper proposes a novel Dynamic
Discounted Satisficing (DDS) heuristic to model and predict driver's sequential
ride decisions during a given shift. Based on DDS heuristic, a novel stochastic
neural network with random activations is proposed to model DDS heuristic and
predict the final decision made by a given driver. The presence of random
activations in the network necessitated the development of a novel training
algorithm called Sampling-Based Back Propagation Through Time (SBPTT), where
gradients are computed for independent instances of neural networks (obtained
via sampling the distribution of activation threshold) and aggregated to update
the network parameters. Using both simulation experiments as well as on real
Chicago taxi dataset, this paper demonstrates the improved performance of the
proposed approach, when compared to state-of-the-art methods.
