Abstract Machine learning (ML) plays the core function to intellectualize the transportation
systems. Recent years have witnessed the advent and prevalence of deep learning which …

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 …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

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 …

Short-term passenger demand forecasting is of great importance to the on-demand ride
service platform, which can incentivize vacant cars moving from over-supply regions to over …

Taxi demand prediction has recently attracted increasing research interest due to its huge
potential application in large-scale intelligent transportation systems. However, most of the …

The accurate short-term passenger flow prediction is of great significance for real-time public
transit management, timely emergency response as well as systematical medium and long …

With the rapid development of urbanization, the boom of vehicle numbers has resulted in
serious traffic accidents, which led to casualties and huge economic losses. The ability to …

Short-term passenger flow prediction and passenger flow control are essential for managing
congestion in metros. This paper proposes a new dynamic radial basis function (RBF) …

Accurate short-term forecasting of public transport demand is essential for the operation of
on-demand public transport. Knowing where and when future demands for travel are …