Recognizing temporal patterns in traffic flow has been an important consideration in short‐
term traffic forecasting research. However, little work has been conducted on identifying and …

Short-term traffic volume data are characterized by rapid and intense fluctuations with
frequent shifts to congestion. Currently, research in short-term traffic forecasting deals with …

This article proposes a prototype of an urban traffic control system based on a prediction‐
after‐classification approach. In an off‐line phase, a repository of traffic control strategies for …

A spatiotemporal approach that simultaneously utilises both spatial and temporal
relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate …

We propose a novel approach for network-wide traffic state prediction where the statistical
time series model ARIMA is used to postprocess the residuals out of the fundamental …

In this paper, we discuss three problems that occur within short-term traffic prediction when
the information from only a single point loop detector is used. First, we analyze the retrieval …

Analyses on the dynamics of traffic flow, ranging from intersection flows to network‐wide flow
propagation, require accurate information on time‐varying local traffic flows. To effectively …

The characterization of the dynamics of traffic states remains fundamental to seeking for the
solutions of diverse traffic problems. To gain more insights in traffic dynamics in the temporal …

This paper discusses the trend modeling for traffic time series. First, we recount two types of
definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic …

In this paper, we evaluate the performance of a dynamic approach to classifying flow
patterns reconstructed by a switching-mode macroscopic flow model considering a …