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.

模型及预测骑手在接单时的决策序列，提出动态折扣启发式算法（DDS）结合随机激活的神经网络进行模拟实验，并与最先进方法进行对比，显示出改进的性能。

利用随机激活神经网络预测驾驶员疲劳度情况的智能共乘平台

Driver Fatigue Prediction using Randomly Activated Neural Networks for  Smart Ridesharing Platforms

Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies,
can reduce passengers' waiting times by proactively dispatching vehicles to
locations where pickup requests are anticipated in the future. Yet it is
unclear how to best do this: optimal dispatching requires optimizing over
several sources of uncertainty, including vehicles' travel times to their
dispatched locations, as well as coordinating between vehicles so that they do
not attempt to pick up the same passenger. While prior works have developed
models for this uncertainty and used them to optimize dispatch policies, in
this work we introduce a model-free approach. Specifically, we propose MOVI, a
Deep Q-network (DQN)-based framework that directly learns the optimal vehicle
dispatch policy. Since DQNs scale poorly with a large number of possible
dispatches, we streamline our DQN training and suppose that each individual
vehicle independently learns its own optimal policy, ensuring scalability at
the cost of less coordination between vehicles. We then formulate a centralized
receding-horizon control (RHC) policy to compare with our DQN policies. To
compare these policies, we design and build MOVI as a large-scale realistic
simulator based on 15 million taxi trip records that simulates policy-agnostic
responses to dispatch decisions. We show that the DQN dispatch policy reduces
the number of unserviced requests by 76% compared to without dispatch and 20%
compared to the RHC approach, emphasizing the benefits of a model-free approach
and suggesting that there is limited value to coordinating vehicle actions.
This finding may help to explain the success of ridesharing platforms, for
which drivers make individual decisions.

通过一个基于 1500 万次出租车记录的仿真实验，我们展示了一个全新的对于车辆调度问题建模的思路，并且展示了这种思路优于其他传统算法。同时，我们的结果也提示出，在实际场景中，独立决策的分布式策略在实践中更加有效。

MOVI：一种无模型动态船舶管理方法

MOVI: A Model-Free Approach to Dynamic Fleet Management

Ridesharing platforms match drivers and riders to trips, using dynamic prices
to balance supply and demand. A challenge is to set prices that are
appropriately smooth in space and time, so that drivers with the flexibility to
decide how to work will nevertheless choose to accept their dispatched trips,
rather than drive to another area or wait for higher prices or a better trip.
In this work, we propose a complete information model that is simple yet rich
enough to incorporate spatial imbalance and temporal variations in supply and
demand -- conditions that lead to market failures in today's platforms. We
introduce the Spatio-Temporal Pricing (STP) mechanism. The mechanism is
incentive-aligned, in that it is a subgame-perfect equilibrium for drivers to
always accept their trip dispatches. From any history onward, the equilibrium
outcome of the STP mechanism is welfare-optimal, envy-free, individually
rational, budget balanced, and core-selecting. We also prove the impossibility
of achieving the same economic properties in a dominant-strategy equilibrium.
Simulation results show that the STP mechanism can achieve substantially
improved social welfare and earning equity than a myopic mechanism.

该论文提出了一种名为 Spatio-Temporal Pricing（STP）的机制，通过将空间失衡和时间变化纳入考虑，实现了平台合理定价。利用该机制可以实现比传统机制更好的社会福利和收益公平性。