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 …
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 …
Augmenting decisions of taxi drivers through reinforcement learning for improving revenues
Taxis (which include cars working with car aggregation systems such as Uber, Grab, Lyft
etc.) have become a critical component in the urban transportation. While most research and …
etc.) have become a critical component in the urban transportation. While most research and …
Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services
The popularity of ride-hailing platforms has significantly improved travel efficiency by
providing convenient and personalized transportation services. Designing an effective ride …
providing convenient and personalized transportation services. Designing an effective ride …
Using reinforcement learning to minimize taxi idle times
K O'Keeffe, S Anklesaria, P Santi… - Journal of Intelligent …, 2022 - Taylor & Francis
Taxis spend a large amount of time idle, searching for passengers. The routes vacant taxis
should follow in order to minimize their idle times are hard to calculate; they depend on …
should follow in order to minimize their idle times are hard to calculate; they depend on …
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 …
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 …
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 …
Reward design for driver repositioning using multi-agent reinforcement learning
A large portion of passenger requests is reportedly unserviced, partially due to vacant for-
hire drivers' cruising behavior during the passenger seeking process. This paper aims to …
hire drivers' cruising behavior during the passenger seeking process. This paper aims to …