Traffic forecasting is attracting considerable interest due to its widespread application in intelligent transportation systems. Given the complex and dynamic traffic data, many methods focus on how establish a spatial-temporal model express non-stationary patterns. Recently, latest Graph Convolution Network (GCN) has been introduced learn spatial features while time neural networks are used te...

Recent advances in time series, where deterministic and stochastic modelings as well as the storage and analysis of big data are useless, permit a new approach to short-term traffic flow forecasting and to its reliability, i.e., to the traffic volatility. Several convincing computer simulations, which utilize concrete data, are presented and discussed.

With accurate visitor traffic forecasts, retail businesses can optimise their staff schedule and stock distribution for increased profits. Existing models such as Linear Regression, ARIMA and other statistical methods are being employed for traffic predictions, but the accuracy of these models leaves room for improvement. Neural Network models show promising results on similar data. Therefore, ...

Intelligent Transportation Systems (ITS) is an emerging concept which has been utilised to improve efficiency and sustainability of existing transportation systems. Short term traffic flow forecasting, the process of predicting future traffic conditions based on historical and realtime observations is an essential aspect of ITS. The existing well-known algorithms used for short-term traffic for...