Problem statement: Network traffic prediction plays a vital role in the optimal resource allocation and management in computer networks. This study introduces an ARIMA based model augmented by Adaptive Linear Prediction (ALP) for the real time prediction of VBR video traffic. The synergy of the two can successfully address the challenges in traffic prediction such as accuracy in prediction, res...

Internet traffic prediction plays a fundamental role in network design, management, control, and optimization. The self-similar and non-linear nature of network traffic makes highly accurate prediction difficult. In this paper, we proposed a new boosting scheme, namely W-Boost, for traffic prediction from two perspectives: classification and regression. To capture the nonlinearity of the traffi...

Traffic congestion is a major problem in urban areas that has a significant adverse economic impact through deterioration of mobility, safety and air quality. As a result, the importance of better management of the road network to efficiently utilize existing capacity is increasing. To that end, many urban areas build and operate modern Traffic Management Centers (TMCs), which perform several f...

This paper proposes a new route plan on the basis of traffic prediction for finding a fastest route to a given destination in traffic network. So far, lots of traffic prediction systems were introduced to help drivers. Previous works were done mainly on providing restricted route services which depend on only cumulative traffic velocities. For this reason, we consider both real-time and cumulat...

Modeling traffic-accident frequency is a critical issue to better understand the accident trends and effectiveness of current traffic policies practices in different countries. The main objectives this study are model road accidents, fatalities injuries Jordan, using modeling techniques, including regression, artificial neural network (ANN) autoregressive integrated moving average (ARIMA) model...

The prediction of traffic accidents is one of most important issues in our life. In the prediction of traffic accidents, a GIS platform to extract the important features including day, temperature, humidity, weather conditions, and month of occurred traffic accidents has been used. In this study, a decision making system (DMS) based on correlation-based feature selection and classifier algorith...

conclusions applying this information can be useful to policy makers and managers for planning and implementing special interventions to prevent and limit future accidental deaths. background traffic accidents are the main cause of deaths in developing countries. fatalities due to traffic accidents are assessed through a three-year time series forecast. objectives the aim of this study is to us...

Traffic prediction constitutes a hot research topic of network metrology. MultiStep ahead prediction allows to predict more values in the future. Then, the result can be used to act proactively in many prediction applications. In this work, the AutoRegressive Integrated Moving Average (ARIMA) model and the linear minimum mean square error (LMMSE) are used for multiStep predicting. Via experimen...

This paper builds upon our earlier work by applying an optimized version of our non-linear scene prediction method to traffic surveillance video. As previously, a Gabor-filter bank has been selected as a primary detector for any changes in a given image sequence. The detected ROI (region of interest) in arbitrary motion is fed to a non-linear Kalman filter for predicting the next scene in time-...