In this paper, we evaluate the capability to detect traffic anomalieswith Shewhart, CUSUM, andEWMA control charts. In order to cope with seasonal variation and serial correlation, control charts are not applied to traffic measurement time-series directly, but to the prediction errors of exponential smoothing and Holt-Winters forecasting. The evaluation relies on flow data collected in an ISP ba...

This is the preprint version of our paper on Advances in Engineering Software. With several characteristics, such as large scale, diverse predictability and timeliness, the city traffic data falls in the range of definition of Big Data. A Virtual Reality GIS based traffic analysis and visualization system is proposed as a promising and inspiring approach to manage and develop traffic big data. ...

The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a Neural Network Ensemble (NNE) for the prediction of TCP/IP traffic using a Time Series Forecast...

Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two non-parametric techniques are selected because...

Accurate forecasting of future performance of hotels is needed so hospitality constituencies in specific destinations can benchmark their properties and better optimize operations. As competition increases, hotel managers have urgent need for accurate short-term forecasts. In this study, time series models including several tourism big data sources, including search engine queries, website traf...

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-a...

The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode d...

Transportation system has time-varying, coupling and nonlinear dynamic characteristics. Traffic flow forecast is one of the key technologies of traffic guidance. It is very difficult to accurately forecast them effectively. This paper has analyzed the complexity and the evaluation index of urban transportation network and has proposed the forecasting model of the hybrid algorithm based on chaos...

The Traffic demand of the communication has strong relations to the gross domestic product (GDP). Some statistical models are well known for the demand forecast. As such models, there are the Linear regression Model (LM) and the Auto Regression model (AR). However the LM cannot apply analyzing a traffic demand, because its relations between a GDP and a traffic demand have the non linear shape. ...