Traffic flow forecasting, the core element of intelligent transportation system, plays an important role in traffic information services and traffic guidance. Since neural network prediction needs plenty of training samples, it cannot guarantee the real-timeness of traffic flow forecasting. In this paper, a GMDH network was constructed by self-organization, and the network was applied to traffi...

Traffic forecasting from past observed traffic data with small calculation complexity is one of important problems for planning of servers and networks. Focusing on World Wide Web (WWW) traffic as fundamental investigation, this paper would deal with Bayesian forecasting of network traffic on the time varying Poisson model from a viewpoint from statistical decision theory. Under this model, we ...

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simp...

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simp...

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simp...

Abstract A network traffic time series analysis and forecasting method based on wavelet decomposition is proposed in the paper. Firstly, non-stationary network traffic time series are decomposed into multiple stationary components, then various stationary components are modeled using the autoregressive moving average method and at last models of all components are composed to get the prediction...

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges,...

In view of the characteristic that the traffic system is a dynamic and time-varying parameter system, the multi-level recursive forecasting method is proposed, and the multi-level recursive forecasting model of road accidents is established in this thesis. In this method, the forecasting of road accidents is divided into two parts: the forecasting of time-varying parameters and the future forec...

Traffic prediction involves forecasting traffic in terms of Annual Average Daily Traffic (AADT), Design Hour Volumes (DHV) and Directional Design Hour Volumes (DDHV). These forecasts are used for a wide variety of purposes from the planning to the design and operational stages of the highway network. The forecasting needs the historical traffic data as well as the systems characteristics, apart...

Scaling and SVM Zhanquan Sun, Geoffrey Fox a. Key Laboratory for Computer Network of Shandong Province, Shandong Computer Science Center, Jinan, Shandong, 250014, China b. School of Informatics and Computing, Pervasive Technology Institute, Indiana University Bloomington, Bloomington, Indiana, 47408, USA Abstract: Traffic flow forecasting is a popular research topic of Intelligent Transportatio...