Analyzing large-scale human mobility data: a survey of machine learning methods and applications
Human mobility patterns reflect many aspects of life, from the global spread of infectious
diseases to urban planning and daily commute patterns. In recent years, the prevalence of …
diseases to urban planning and daily commute patterns. In recent years, the prevalence of …
A survey on spatio-temporal data analytics systems
Due to the surge of spatio-temporal data volume, the popularity of location-based services
and applications, and the importance of extracted knowledge from spatio-temporal data to …
and applications, and the importance of extracted knowledge from spatio-temporal data to …
Large-scale order dispatch in on-demand ride-hailing platforms: A learning and planning approach
We present a novel order dispatch algorithm in large-scale on-demand ride-hailing
platforms. While traditional order dispatch approaches usually focus on immediate customer …
platforms. While traditional order dispatch approaches usually focus on immediate customer …
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 …
Taxi dispatch with real-time sensing data in metropolitan areas: A receding horizon control approach
Traditional transportation systems in metropolitan areas often suffer from inefficiencies due
to uncoordinated actions as system capacity and traffic demand change. With the pervasive …
to uncoordinated actions as system capacity and traffic demand change. With the pervasive …
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 …
MOVI: A model-free approach to dynamic fleet management
T Oda, C Joe-Wong - IEEE INFOCOM 2018-IEEE Conference …, 2018 - ieeexplore.ieee.org
Modern vehicle fleets, eg, for ridesharing platforms and taxi companies, can reduce
passengers' waiting times by proactively dispatching vehicles to locations where pickup …
passengers' waiting times by proactively dispatching vehicles to locations where pickup …
A Markov decision process approach to vacant taxi routing with e-hailing
The optimal routing of a vacant taxi is formulated as a Markov Decision Process (MDP)
problem to account for long-term profit over the full working period. The state is defined by …
problem to account for long-term profit over the full working period. The state is defined by …
Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning
Dynamic taxi route recommendation aims at recommending cruising routes to vacant taxis
such that they can quickly find and pick up new passengers. Given citizens' giant but …
such that they can quickly find and pick up new passengers. Given citizens' giant but …
[HTML][HTML] Economic recommender systems–a systematic review
Many of today's online services provide personalized recommendations to their users. Such
recommendations are typically designed to serve certain user needs, eg, to quickly find …
recommendations are typically designed to serve certain user needs, eg, to quickly find …