The rapid development in machine learning and in the emergence of new data sources
makes it possible to examine and predict traffic conditions in smart cities more accurately …

In the last two decades, the growing need for short‐term prediction of traffic parameters
embedded in a real‐time intelligent transportation systems environment has led to the …

Federated learning (FL) is a machine learning paradigm where a shared central model is
learned across distributed devices while the training data remains on these devices …

Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as
subway/bus stations, is a practical application and of great significance for urban traffic …

PM2. 5 is one of the most important pollutants related to air quality, and the increase of its
concentration will aggravate the threat to people's health. Therefore, the prediction of …

This study aims to combine the modeling skills of deep learning and the domain knowledge
in transportation into prediction of metro passenger flow. We present an end-to-end deep …

Predicting large-scale transportation network traffic has become an important and
challenging topic in recent decades. Inspired by the domain knowledge of motion prediction …

Accurate and timely traffic flow information is important for the successful deployment of
intelligent transportation systems. Over the last few years, traffic data have been exploding …

Urban spatiotemporal flow prediction is of great importance to traffic management, land use,
public safety. This prediction task is affected by several complex and dynamic factors, such …

Tree based ensemble methods have reached a celebrity status in prediction field. By
combining simple regression trees with 'poor'performance, they usually produce high …