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 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 …
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 …
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 …
Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach
In this paper, we define and investigate a novel model-free deep reinforcement learning
framework to solve the taxi dispatch problem. The framework can be used to redistribute …
framework to solve the taxi dispatch problem. The framework can be used to redistribute …
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 …
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 …
Real-time dispatching of large-scale ride-sharing systems: Integrating optimization, machine learning, and model predictive control
This paper considers the dispatching of large-scale real-time ride-sharing systems to
address congestion issues faced by many cities. The goal is to serve all customers (service …
address congestion issues faced by many cities. The goal is to serve all customers (service …
A taxi order dispatch model based on combinatorial optimization
L Zhang, T Hu, Y Min, G Wu, J Zhang, P Feng… - Proceedings of the 23rd …, 2017 - dl.acm.org
Taxi-booking apps have been very popular all over the world as they provide convenience
such as fast response time to the users. The key component of a taxi-booking app is the …
such as fast response time to the users. The key component of a taxi-booking app is the …
Deep reinforcement learning with knowledge transfer for online rides order dispatching
Ride dispatching is a central operation task on a ride-sharing platform to continuously match
drivers to trip-requesting passengers. In this work, we model the ride dispatching problem as …
drivers to trip-requesting passengers. In this work, we model the ride dispatching problem as …
相关搜索
- taxi dispatching reinforcement learning
- city scale reinforcement learning
- deep reinforcement supply demand
- deep reinforcement fleet management
- deep reinforcement knowledge transfer
- ride sharing reinforcement learning
- combinatorial optimization reinforcement learning
- large scale machine learning
- large scale reinforcement learning