Currently, deep learning has been successfully applied in many fields and achieved
amazing results. Meanwhile, big data has revolutionized the transportation industry over the …

To improve the prediction accuracy of traffic flow, a travel time prediction model based on
gradient boosting decision tree (GBDT) is proposed. In order to test the applicability of …

In recent years, the rapid economic development of China, the increase of the urban
population, the continuous growth of private car ownership, the uneven distribution of traffic …

Rapid and accurate short-term passenger flow prediction plays an important and far-
reaching role in passenger flow control and early warning. In fact, the short-term passenger …

Traffic parameter forecasting is critical to effective traffic management but is a challenging
task due to the stochasticity of traffic flow characteristics, especially in urban road networks …

Finding fastest driving routes is significant for the intelligent transportation system. While
predicting the online traffic conditions of road segments entails a variety of challenges, it …

Road transportation is the backbone of modern economies, albeit it annually costs 1.25
million deaths and trillions of dollars to the global economy, and damages public health and …

In this paper, we propose a detrending based and deep learning based many-to-many traffic
prediction model called DeepTrend 2.0 that accepts information collected from multiple …

Tree-based and deep learning methods can automatically generate useful features. Not only
can it enhance the original feature representation, but it can also learn to generate new …

The urban road traffic flow condition prediction is a fundamental issue in the intelligent
transportation management system. While extracting the high-dimensional, nonlinear and …