From the point of view of the information supplied by an ATIS to the motorists entering a freeway of one of the most relevant is the Forecasted Travel Time, that is the expected travel time that they will experience when traverse a freeway segment. From the point of view of ATMS the dynamic estimates of time dependencies in OD matrices is a major input to dynamic traffic models used for estimat...

Short term traffic forecasting has been a very important consideration in many areas of transportation research for more than 3 decades. Short-term traffic forecasting based on data driven methods is one of the most dynamic and developing research arenas with enormous published literature. In order to improve forecasting model accuracy of wavelet neural network, an adaptive particle swarm optim...

The main objective of this paper is to develop and test an approach for the spatio-temporal analysis of traffic flow variation on two lane highways. Traffic count devices located in several points of two lane highway links give the aggregate time series of data used in this approach, which is based on the Principal Components Analysis (PCA). The statistical analysis of main sources of temporal ...

The existing well-known short-term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related inf...

Traffic forecasting is one of the most popular spatio-temporal tasks in field machine learning. A prevalent approach to combine graph convolutional networks and recurrent neural for processing. There has been fierce competition many novel methods have proposed. In this paper, we present method rough differential equation (STG-NRDE). Neural equations (NRDEs) are a breakthrough concept processing...

The paper addresses intelligent data processing and mining in distributed multi-agent networks. We consider a distributed traffic network represented as a multi-agent system, where each agent-vehicle autonomously forecasts its travelling time. We propose a structure for such agents and their corresponding distributed data processing and mining modules. We use distributed linear and kernelbased ...