Ride-hailing is a sustainable transportation paradigm where riders access
door-to-door traveling services through a mobile phone application, which has
attracted a colossal amount of usage. There are two major planning tasks in a
ride-hailing system: (1) matching, i.e., assigning available vehicles to pick
up the riders, and (2) repositioning, i.e., proactively relocating vehicles to
certain locations to balance the supply and demand of ride-hailing services.
Recently, many studies of ride-hailing planning that leverage machine learning
techniques have emerged. In this article, we present a comprehensive overview
on latest developments of machine learning-based ride-hailing planning. To
offer a clear and structured review, we introduce a taxonomy into which we
carefully fit the different categories of related works according to the types
of their planning tasks and solution schemes, which include collective
matching, distributed matching, collective repositioning, distributed
repositioning, and joint matching and repositioning. We further shed light on
many real-world datasets and simulators that are indispensable for empirical
studies on machine learning-based ride-hailing planning strategies. At last, we
propose several promising research directions for this rapidly growing research
and practical field.

本文介绍了基于机器学习的打车规划最新发展的全面概述，并提出了一些发展方向。以分类法为基础介绍了各种规划任务和解决方案类型，包括集体匹配、分布式匹配、集体再定位、分布式再定位以及联合匹配和再定位。此外，本文还探讨了许多不可或缺的现实世界数据和模拟器，可供机器学习 - based ride-hailing planning 策略的经验性研究使用。

基于机器学习的打车规划调度调查

A Survey of Machine Learning-Based Ride-Hailing Planning

Ride-hailing service is becoming a leading part in urban transportation. To
improve the efficiency of ride-hailing service, accurate prediction of
transportation demand is a fundamental challenge. In this paper, we tackle this
problem from both aspects of network structure and data-set formulation. For
network design, we propose a spatial-temporal attention multi-graph convolution
network (STA-MGCN). A spatial-temporal layer in STA-MGCN is developed to
capture the temporal correlations by temporal attention mechanism and temporal
gate convolution, and the spatial correlations by multigraph convolution. A
feature cluster layer is introduced to learn latent regional functions and to
reduce the computation burden. For the data-set formulation, we develop a novel
approach which considers the transportation feature of periodicity with offset.
Instead of only using history data during the same time period, the history
order demand in forward and backward neighboring time periods from yesterday
and last week are also included. Extensive experiments on the three real-world
datasets of New-York, Chicago and Chengdu show that the proposed algorithm
achieves the state-of-the-art performance for ride-hailing demand prediction.

本文介绍了一种新的交通工具需求预测方法，采用 STA-MGCN 网络和考虑周期特征的数据集，取得了骑行需求预测的最佳性能，结合纽约、芝加哥和成都三个实际数据集的测试分析。

基于周期性偏移的打车需求预测的时空注意力多图卷积网络

A Spatial-Temporal Attention Multi-Graph Convolution Network for Ride-Hailing Demand Prediction Based on Periodicity with Offset

We present a new practical framework based on deep reinforcement learning and
decision-time planning for real-world vehicle repositioning on ride-hailing (a
type of mobility-on-demand, MoD) platforms. Our approach learns the
spatiotemporal state-value function using a batch training algorithm with deep
value networks. The optimal repositioning action is generated on-demand through
value-based policy search, which combines planning and bootstrapping with the
value networks. For the large-fleet problems, we develop several algorithmic
features that we incorporate into our framework and that we demonstrate to
induce coordination among the algorithmically-guided vehicles. We benchmark our
algorithm with baselines in a ride-hailing simulation environment to
demonstrate its superiority in improving income efficiency meausred by
income-per-hour. We have also designed and run a real-world experiment program
with regular drivers on a major ride-hailing platform. We have observed
significantly positive results on key metrics comparing our method with
experienced drivers who performed idle-time repositioning based on their own
expertise.

通过深度强化学习和决策时间规划，为运营商提供实时的出行调度决策，提高车辆调度效率和服务质量。