traffic accidents are one of the most important challenges  of public hygiene that needs global attention and effort. the recent research was conducted in order to determine the crash trend and develop the accident prediction model that leads to death in ilam from 2010 to 2014. the recent study based on descriptive analysis was performed by using the  statistical data related to death caused by...

This paper assesses the predictability of network traffic by considering two metrics: 1) how far into the future a traffic rate process can be predicted for a given error constraint; 2) what the minimum prediction error is over a specified prediction time interval. The assessment is based on two stationary traffic models: the Auto-Regressive Moving Average (ARMA) model and the Markov-Modulated ...

Network traffic prediction has been an important problem in the field of network designing and planning. Several prediction approaches are developed to predict the network traffic as well as its generalization promotion ability. By analyzing the limitation of previous approaches, we propose a network traffic prediction approach based on the Chaos theory and Regression-SMKL (RRSMKL) algorithm. T...

background : the recognition of road traffic noise as one of the main sources of environmental pollution has led to develop models that enable to predict noise level from fundamental variables. traffic noise prediction models are required as aids for designing roads and highways. in addition, sometimes are used in the assessment of existing or envisaged changes in traffic noise conditions. in t...

AbstrAct The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that ...

The accurate traffic flow time series prediction is the prerequisite for achieving traffic flow inducible system. Aiming at the issue about multi-step prediction traffic flow chaotic time series, the traffic flow Volterra Neural Network (VNN) rapid learning algorithm is proposed. Combing with the chaos theory and the Volterra functional analysis, method of the truncation order and the truncatio...

In recent years, the time variation of Internet traffic has increased due to the growth of streaming and cloud services. Backbone networks must accommodate such traffic without congestion. Traffic engineering with traffic prediction is one approach to stably accommodating time-varying traffic. In this approach, routes are calculated from predicted traffic to avoid congestion, but predictions ma...

The presence of the complex scaling behavior in network traffic makes accurate traffic prediction a challenging task. Some conventional prediction tools such as the recursive least square method are not appropriate for network traffic prediction. In this paper we propose a timescale decomposition approach to real time traffic prediction. The raw traffic data is first decomposed into multiple ti...