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 …
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 …
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 …
Coordinating ride-sourcing and public transport services with a reinforcement learning approach
Combining ride-sourcing and public transit services (with ride-sourcing service to address
the first/last-mile issues) can bring many benefits, such as saving passengers' trip fares …
the first/last-mile issues) can bring many benefits, such as saving passengers' trip fares …
Learning to delay in ride-sourcing systems: A multi-agent deep reinforcement learning framework
Ride-sourcing services are now reshaping the way people travel by effectively connecting
drivers and passengers through mobile internets. Online matching between idle drivers and …
drivers and passengers through mobile internets. Online matching between idle drivers and …
A distributed model-free ride-sharing approach for joint matching, pricing, and dispatching using deep reinforcement learning
M Haliem, G Mani, V Aggarwal… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Significant development of ride-sharing services presents a plethora of opportunities to
transform urban mobility by providing personalized and convenient transportation while …
transform urban mobility by providing personalized and convenient transportation while …
Deeppool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning
AO Al-Abbasi, A Ghosh… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The success of modern ride-sharing platforms crucially depends on the profit of the ride-
sharing fleet operating companies, and how efficiently the resources are managed. Further …
sharing fleet operating companies, and how efficiently the resources are managed. Further …
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 …
AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning
Taxi cruising route planning has attracted considerable attention, and relevant studies can
be broadly categorized into three main streams: recommending one or multiple areas …
be broadly categorized into three main streams: recommending one or multiple areas …