The current and expected future proliferation of mobile and embedded technology provides
unique opportunities for crowdsourcing platforms to gather more user data for making data …

Shared mobility has emerged as a sustainable alternative to both private transportation and
traditional public transport, promising to reduce the number of private vehicles on roads …

The imbalance of vehicle supply and demand is a common phenomenon that influences the
efficiency of online ride-hailing systems greatly. To address this problem, a three-level …

Prediction of taxi service demand and supply is essential for improving customer experience
and provider's profit. Recently, graph neural networks (GNNs), modeling city areas as nodes …

Current urbanization trends are leading to heightened demand of smarter technologies to
facilitate a variety of applications in intelligent transportation systems. Automated …

Short-term demand forecasting for on-demand ride-hailing services is a fundamental issue
in intelligent transportation systems. However, previous research predominantly focused on …

In the literature, much effort has been devoted to providing taxi drivers with
recommendations on how to find passengers efficiently. However, the load balancing …

Owing to a need for low latency data accesses, emerging IoT and mobile applications
commonly require distributed data stores (eg, key-value or KV stores) to operate entirely at …

In recent years, ride-hailing services have emerged as a popular means of transportation for
the residents of urban areas. There is an inequality in the spatio-temporal distribution of …

The prediction of pick‐up regions for online ride‐hailing can reduce the number of vacant
vehicles on the streets, which will optimize the transportation efficiency of cities, reduce …