Real-time Intelligent Speed Adaptation in Heterogeneous Road Networks

Big data traffic forecasting is considered as one of the most important traffic management techniques on urban road networks. Big data in intelligent transportation system refers to the large amount of travel information. Various forecast schemes have been proposed to manage the traffic big data. Travel big data has been collected by videos, sensors, and mobile phone services. Videos, sensors and cellular networks are not sufficient for collecting data because of their limited coverage and expensive costs for installation and maintenance. To overcome the limitation of mentioned tools we introduce the GNSS application. Application of GNSS in travel time is proven to be efficient in terms of accuracy. GNSS big data will be managed to reduce traffic congestions and road accidents. This paper introduces a short-time forecast model based on real–time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting the travel speeds using EMA Model. Furthermore it is a significant requirement to introduce suitable control strategy for longitude based on GNSS Application. The GNSS products provide worldwide and real-time services using precise timing information, positioning technologies.