Real time traffic flow is often difficult to predict precisely because of the nonlinear and stochastic characteristics of the traffic flow data. Intelligent prediction methods such as artificial neural network (ANN), support vector machine (SVM), etc. have been proven effective to discover the nonlinear information hidden in the traffic flow data. Nevertheless, their efficiency limits in the lo...

Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the potential of deep learning methods in traffic forecasting has not yet fully been exploited in terms of the depth of the model architecture, the spatial scale of the prediction area, and the predictive power of spatial-...

This literature review paper focuses on identifying practices and scholarly literature related to tourist traffic including choice of destinations and travel within destinations. Specific attention was given to application of forecasting approaches, factors included as significant contributors, and choice models. An implementation strategy for interpreting traffic counts in high tourism areas i...

Network-level multi-step road condition forecasting is an important step in accurate road maintenance planning, where correct maintenance activities are defined in place and time of road networks. Forecasting methods have developed from engineering models to non-linear machine learning methods that make use of the collected condition and traffic data of the road network. Least Squares Support V...

Internet Traffic has grown exponentially over last few years due to provision of multiple class services through Internet backbone. With the explosive use of Internet, contemporary Internet routers are susceptible to overloads and their services deteriorate drastically and often cause denial of services. In this paper, an analysis is made how forecasting technique, routing algorithm and Genetic...

The performance of many components in intelligent transportation systems depends heavily on the quality of short-term traffic forecasts. We propose a new method for forecasting traffic based on type-2 fuzzy logic. Type-2 fuzzy logic is powerful in handling uncertainties, including uncertainties in measurements and data used to calibrate the parameters. In our formulation, the value of a members...