A Clustering-Based Multi-Agent Reinforcement Learning Framework for Finer-Grained Taxi Dispatching
The rapid growth of Internet services dramatically drives the development of various
intelligent technologies. As an important composition, modern ride-hailing platforms allow …
intelligent technologies. As an important composition, modern ride-hailing platforms allow …
Supply-demand-aware deep reinforcement learning for dynamic fleet management
Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are
idle and that passengers spend on waiting. As a key component of these platforms, the fleet …
idle and that passengers spend on waiting. As a key component of these platforms, the fleet …
A robust deep reinforcement learning approach to driverless taxi dispatching under uncertain demand
With the progressive technological advancement of autonomous vehicles, taxi service
providers are expected to offer driverless taxi systems that alleviate traffic congestion and …
providers are expected to offer driverless taxi systems that alleviate traffic congestion and …
Context-aware taxi dispatching at city-scale using deep reinforcement learning
Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
A Deep Reinforcement Learning Approach for Online Taxi Dispatching
YB Wang, TY Zhang, ZC Wei - 2023 International Conference …, 2023 - ieeexplore.ieee.org
With the development of smart city transportation systems, developing reasonable
dispatching strategies for idle ride-hailing vehicles has become an urgent research problem …
dispatching strategies for idle ride-hailing vehicles has become an urgent research problem …
META: A city-wide taxi repositioning framework based on multi-agent reinforcement learning
The popularity of online ride-hailing platforms has made people travel smarter than ever
before. But people still frequently encounter the dilemma of “taxi drivers hunt passengers …
before. But people still frequently encounter the dilemma of “taxi drivers hunt passengers …
Combinatorial optimization meets reinforcement learning: Effective taxi order dispatching at large-scale
Ride hailing has become prevailing. Central in ride hailing platforms is taxi order
dispatching which involves recommending a suitable driver for each order. Previous works …
dispatching which involves recommending a suitable driver for each order. Previous works …
H-TD2: Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatch
We present H-TD 2: Hybrid Temporal Difference Learning for Taxi Dispatch, a model-free,
adaptive decision-making algorithm to coordinate a large fleet of automated taxis in a …
adaptive decision-making algorithm to coordinate a large fleet of automated taxis in a …
Multiagent Reinforcement Learning‐Based Taxi Predispatching Model to Balance Taxi Supply and Demand
With the improvement of people's living standards, people's demand of traveling by taxi is
increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their …
increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their …
An integrated reinforcement learning and centralized programming approach for online taxi dispatching
Balancing the supply and demand for ride-sourcing companies is a challenging issue,
especially with real-time requests and stochastic traffic conditions of large-scale congested …
especially with real-time requests and stochastic traffic conditions of large-scale congested …