Time series are recorded values of an interesting phenomenon such as stock prices,
household incomes, or patient heart rates over a period of time. Time series data mining …

Bayesian forecasting is a natural product of a Bayesian approach to inference. The
Bayesian approach in general requires explicit formulation of a model, and conditioning on …

Short-term traffic volume prediction, which can assist road users in choosing appropriate
routes and reducing travel time cost, is a significant topic of intelligent transportation system …

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent
transportation systems. On the basis of many existing prediction models, each having good …

Electricity demand forecasting is becoming an essential tool for energy management,
maintenance scheduling and investment decisions in the future liberalized energy markets …

The Bayesian combination method (BCM) proposed by Petridis et al.(2001) is an integrated
method that can effectively improve the predictions of single predictors. However, research …

Short-term prediction of travel time is one of the central topics in current transportation
research and practice. Among the more successful travel time prediction approaches are …

This article addresses the problem of the accuracy of short‐term traffic flow forecasting in the
complex case of urban signalized arterial networks. A new, artificial intelligence (AI)‐based …

This paper proposes a hybrid short-term load forecasting method, which is based on the
fuzzy combination weights as well as the empirical mode decomposition process (FCW …

Carbon emissions from road transport are one of the main issues related to modern
transport planning. To address them adequately, the acquisition of reliable data about traffic …