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.

翻译：在网约车平台中，驾驶员在一天内持续接单会导致认知疲劳与倦怠，进而显著影响平台的整体运营效率。与现有文献主要关注建模和学习驾驶员对不同订单的偏好不同，本文提出一种新颖的动态折扣满意（Dynamic Discounted Satisficing, DDS）启发式方法，用于建模和预测驾驶员在给定班次中的序列化接单决策。基于DDS启发式方法，本文设计了一种具有随机激活特性的新型随机神经网络，用于模拟DDS启发式机制并预测特定驾驶员的最终决策。由于网络中引入了随机激活单元，我们开发了一种名为基于采样的时间反向传播（Sampling-Based Back Propagation Through Time, SBPTT）的新型训练算法：该算法通过对激活阈值分布进行采样得到独立的神经网络实例，计算其梯度后聚合更新网络参数。通过仿真实验及芝加哥真实出租车数据集验证，本文提出的方法相较于现有最优方法展现出更优越的性能。