Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

With the recent increase in urban drift, which has led to an unprecedented surge in urban
population, the smart city (SC) transportation industry faces a myriad of challenges …

Transportation systems operate in a domain that is anything but simple. Many exhibit both
spatial and temporal characteristics, at varying scales, under varying conditions brought on …

Order dispatching is instrumental to the marketplace engine of a large-scale ride-hailing
platform, such as the DiDi platform, which continuously matches passenger trip requests to …

With the availability of the location information of drivers and passengers, ride-sourcing
platforms can now provide increasingly efficient online matching compared with physical …

With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing
companies have become an important element of the urban mobility system. There are two …

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 …

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 …

Ever since the emergence of ride-sourcing services, the spatial–temporal pricing problem
has been a hot research topic in both the transportation and management fields. The …

This study investigates flexible bus that provides door-to-door service with multiple
passengers sharing the vehicle, which reduces congestion on the urban network. The …