Ride-hailing platforms have been facing the challenge of balancing demand and
supply. Existing vehicle reposition techniques often treat drivers as
homogeneous agents and relocate them deterministically, assuming compliance
with the reposition. In this paper, we consider a more realistic and
driver-centric scenario where drivers have unique cruising preferences and can
decide whether to take the recommendation or not on their own. We propose
i-Rebalance, a personalized vehicle reposition technique with deep
reinforcement learning (DRL). i-Rebalance estimates drivers' decisions on
accepting reposition recommendations through an on-field user study involving
99 real drivers. To optimize supply-demand balance and enhance preference
satisfaction simultaneously, i-Rebalance has a sequential reposition strategy
with dual DRL agents: Grid Agent to determine the reposition order of idle
vehicles, and Vehicle Agent to provide personalized recommendations to each
vehicle in the pre-defined order. This sequential learning strategy facilitates
more effective policy training within a smaller action space compared to
traditional joint-action methods. Evaluation of real-world trajectory data
shows that i-Rebalance improves driver acceptance rate by 38.07% and total
driver income by 9.97%.

通过实地用户研究，我们提出了一种个性化车辆重新定位技术，使用深度强化学习来估计司机对重新定位建议的决策，以提高供需平衡和司机接受率。实际轨迹数据的评估结果显示，这种技术能够提高司机接受率 38.07% 和总司机收入 9.97%。

i-Rebalance：供需平衡的个性化车辆重新定位

i-Rebalance: Personalized Vehicle Repositioning for Supply Demand  Balance

Large ride-hailing platforms, such as DiDi, Uber and Lyft, connect tens of
thousands of vehicles in a city to millions of ride demands throughout the day,
providing great promises for improving transportation efficiency through the
tasks of order dispatching and vehicle repositioning. Existing studies,
however, usually consider the two tasks in simplified settings that hardly
address the complex interactions between the two, the real-time fluctuations
between supply and demand, and the necessary coordinations due to the
large-scale nature of the problem. In this paper we propose a unified
value-based dynamic learning framework (V1D3) for tackling both tasks. At the
center of the framework is a globally shared value function that is updated
continuously using online experiences generated from real-time platform
transactions. To improve the sample-efficiency and the robustness, we further
propose a novel periodic ensemble method combining the fast online learning
with a large-scale offline training scheme that leverages the abundant
historical driver trajectory data. This allows the proposed framework to adapt
quickly to the highly dynamic environment, to generalize robustly to recurrent
patterns and to drive implicit coordinations among the population of managed
vehicles. Extensive experiments based on real-world datasets show considerably
improvements over other recently proposed methods on both tasks. Particularly,
V1D3 outperforms the first prize winners of both dispatching and repositioning
tracks in the KDD Cup 2020 RL competition, achieving state-of-the-art results
on improving both total driver income and user experience related metrics.

本文提出了一个名为 V1D3 的 value-based 动态学习框架，它可以同时处理车辆分配和重新定位问题，并结合在线体验和历史轨迹数据进行周期性合成，取得了大幅提升，成为 KDD Cup 2020 RL 竞赛中车辆调度和定位的冠军。

价值函数是你所需的一切：适用于出行平台的统一学习框架

Value Function is All You Need: A Unified Learning Framework for Ride  Hailing Platforms

Ride-hailing platforms generally provide various service options to
customers, such as solo ride services, shared ride services, etc. It is
generally expected that demands for different service modes are correlated, and
the prediction of demand for one service mode can benefit from historical
observations of demands for other service modes. Moreover, an accurate joint
prediction of demands for multiple service modes can help the platforms better
allocate and dispatch vehicle resources. Although there is a large stream of
literature on ride-hailing demand predictions for one specific service mode,
little efforts have been paid towards joint predictions of ride-hailing demands
for multiple service modes. To address this issue, we propose a deep multi-task
multi-graph learning approach, which combines two components: (1) multiple
multi-graph convolutional (MGC) networks for predicting demands for different
service modes, and (2) multi-task learning modules that enable knowledge
sharing across multiple MGC networks. More specifically, two multi-task
learning structures are established. The first one is the regularized
cross-task learning, which builds cross-task connections among the inputs and
outputs of multiple MGC networks. The second one is the multi-linear
relationship learning, which imposes a prior tensor normal distribution on the
weights of various MGC networks. Although there are no concrete bridges between
different MGC networks, the weights of these networks are constrained by each
other and subject to a common prior distribution. Evaluated with the
for-hire-vehicle datasets in Manhattan, we show that our propose approach
outperforms the benchmark algorithms in prediction accuracy for different
ride-hailing modes.

本研究提出了一种深度多任务多图卷积学习方法，通过多个多图卷积神经网络对不同服务模式的需求进行预测，并建立多任务学习模块来实现跨网络知识共享，从而更好地实现车辆资源的分配和调度。在曼哈顿的出租车数据集上进行评估，结果表明我们的方法在不同出行方式的需求预测准确性方面优于基准算法。