Analyzing large-scale human mobility data: a survey of machine learning methods and applications

E Toch, B Lerner, E Ben-Zion, I Ben-Gal - Knowledge and Information …, 2019 - Springer
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 …

A survey on spatio-temporal data analytics systems

MM Alam, L Torgo, A Bifet - ACM Computing Surveys, 2022 - dl.acm.org
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 …

Large-scale order dispatch in on-demand ride-hailing platforms: A learning and planning approach

Z Xu, Z Li, Q Guan, D Zhang, Q Li, J Nan, C Liu… - Proceedings of the 24th …, 2018 - dl.acm.org
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 …

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 …

Taxi dispatch with real-time sensing data in metropolitan areas: A receding horizon control approach

F Miao, S Lin, S Munir, JA Stankovic, H Huang… - Proceedings of the …, 2015 - dl.acm.org
Traditional transportation systems in metropolitan areas often suffer from inefficiencies due
to uncoordinated actions as system capacity and traffic demand change. With the pervasive …

Context-aware taxi dispatching at city-scale using deep reinforcement learning

Z Liu, J Li, K Wu - IEEE Transactions on Intelligent …, 2020 - ieeexplore.ieee.org
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 …

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 …

A Markov decision process approach to vacant taxi routing with e-hailing

X Yu, S Gao, X Hu, H Park - Transportation Research Part B …, 2019 - Elsevier
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 …

Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning

S Ji, Z Wang, T Li, Y Zheng - Knowledge-Based Systems, 2020 - Elsevier
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 …

[HTML][HTML] Economic recommender systems–a systematic review

A De Biasio, N Navarin, D Jannach - Electronic Commerce Research and …, 2024 - Elsevier
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 …