6,138 research outputs found
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to tradeoff between
immediate and future returns are fundamental questions for a typical
ride-hailing platform. We model ride-hailing as a large-scale parallel ranking
problem and study the joint decision-making task of order dispatching and fleet
management in online ride-hailing platforms. This task brings unique challenges
in the following four aspects. First, to facilitate a huge number of vehicles
to act and learn efficiently and robustly, we treat each region cell as an
agent and build a multi-agent reinforcement learning framework. Second, to
coordinate the agents from different regions to achieve long-term benefits, we
leverage the geographical hierarchy of the region grids to perform hierarchical
reinforcement learning. Third, to deal with the heterogeneous and variant
action space for joint order dispatching and fleet management, we design the
action as the ranking weight vector to rank and select the specific order or
the fleet management destination in a unified formulation. Fourth, to achieve
the multi-scale ride-hailing platform, we conduct the decision-making process
in a hierarchical way where a multi-head attention mechanism is utilized to
incorporate the impacts of neighbor agents and capture the key agent in each
scale. The whole novel framework is named as CoRide. Extensive experiments
based on multiple cities real-world data as well as analytic synthetic data
demonstrate that CoRide provides superior performance in terms of platform
revenue and user experience in the task of city-wide hybrid order dispatching
and fleet management over strong baselines.Comment: CIKM 201
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to trade
off between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a
large-scale parallel ranking problem and study the joint decisionmaking task of order dispatching and fleet management in online
ride-hailing platforms. This task brings unique challenges in the
following four aspects. First, to facilitate a huge number of vehicles
to act and learn efficiently and robustly, we treat each region cell
as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to
achieve long-term benefits, we leverage the geographical hierarchy
of the region grids to perform hierarchical reinforcement learning.
Third, to deal with the heterogeneous and variant action space
for joint order dispatching and fleet management, we design the
action as the ranking weight vector to rank and select the specific
order or the fleet management destination in a unified formulation.
Fourth, to achieve the multi-scale ride-hailing platform, we conduct
the decision-making process in a hierarchical way where a multihead attention mechanism is utilized to incorporate the impacts
of neighbor agents and capture the key agent in each scale. The
whole novel framework is named as CoRide. Extensive experiments
based on multiple cities real-world data as well as analytic synthetic
data demonstrate that CoRide provides superior performance in
terms of platform revenue and user experience in the task of citywide hybrid order dispatching and fleet management over strong
baselines
Book of abstracts of the 24th Euro Working Group on Transportation Meeting
Sem resumo disponível.publishe
- …