In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to decision optimization problems in a typical ridesharing system …

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …

Crowdsourcing is a computing paradigm where humans are actively involved in a
computing task, especially for tasks that are intrinsically easier for humans than for …

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 …

This work explores a novel image geometric abstraction paradigm based on assembly out of
a pool of pre-defined simple parametric primitives (ie, triangle, rectangle, circle and …

Data-driven recommender systems that can help to predict users' preferences are deployed
in many real online service platforms. Several studies show that they are vulnerable to data …

With spatial crowdsourcing applications such as Uber and Waze deeply penetrated into
everyday life, there is a growing concern to protect user privacy in spatial crowdsourcing …

Ride hailing is a widespread shared mobility application where the central issue is to assign
taxi requests to drivers with various objectives. Despite extensive research on task …

This survey gives a selective review of recent development of machine learning (ML) for
combinatorial optimization (CO), especially for graph matching. The synergy of these two …