Framework for real-time traffic forecasting methodology under exogenous parameters
Ensuing from the deteriorating conditions of road networks, traffic forecasting techniques with mathematical and computer-theory methods have been employed to address the real-time prediction of traffic conditions and their dynamic control, with the optimum use of the current infrastructure. The prediction of traffic conditions, acknowledging multiple regimes, its transitions and driver’s behaviour parameters, is a highly desired attribute in intelligent transportation systems (ITS), as it could increase operational performance. The study in question aims to provide a framework to define the reasons that might preserve the conditions of recurrent and non-recurrent congestion occurrence in highways, so as to develop in long term an efficient dynamic traffic prediction model that would mitigate congestion emergence, before being triggered by interactive exogenous parameters, such as weather conditions, traffic composition, incident occurrence, traffic direction and seasonality. Contrary to currently existent prediction models, the challenge of the model to be developed is to capture traffic dynamics and enhance predictability upon multiregime (congestion, near-congestion, free flow) and transitional traffic behaviour, combining exogenous multi-dimensional determinants with real-time or near-real-time data, resulting to dynamic prediction models for highway traffic. Apart from the dynamic aspect, the transferability issue will be attempted to be addressed. Furthermore, active traffic management (ATM) highway management schemes will be formed in microscopic scale, with principal goal to control traffic by optimising the operation of an emergency lane as additional traffic lane.