This study proposes a multiple sources and multiple measures based traffic flow prediction
algorithm using the chaos theory and support vector regression method. In particular, first …

Considering the chaotic characteristics of traffic flow, this study proposes a Bayesian theory-
based multiple measures chaotic time series prediction algorithm. In particular, a time series …

Traffic flow prediction has become a kernel study in intelligent transportation system. A
prediction model of short-time traffic flow is presented based on chaotic time series analysis …

This paper proposes an optimized prediction algorithm of radial basis function neural
network based on an improved artificial bee colony (ABC) algorithm in the big data …

In this paper, we applied non-linear time series modeling techniques to analyze the traffic
data collected from the Beijing Xizhimen. The results indicated that chaotic characteristics …

Based on the previous literature review, this paper builds a short-term traffic speed
forecasting model using Support Vector Machine (SVM) regression theory (referred as SVM …

With the rapid increase in car ownership, urban transport systems are challenged by the
overwhelming traffic demand and congestion. Dynamic prediction of traffic flows is of …

Traffic flow decomposition is an alternative method to explore the composition of traffic flow
and improve prediction accuracy. However, most of them suffer from the inability to fully …

Sažetak This paper takes the time series of short-term traffic flow as research object. The
delay time and embedding dimension are calculated by CC algorithm, and the chaotic …

To deal well with the normally distributed random error existed in the traffic flow series, this
paper introduces the ν-Support Vector Regression (ν-GSVR) model with the Gaussian loss …