Macroscopic interval-split free-flow model for vehicular cloud computing

Modeling and simulation have shown essential for forecasting load and resource availability in large-scale complex scenarios. The growth of urban environments, as well as the use of ICT in enabling applications and services, has encouraged several works on the modeling of transportation. High mobility of vehicles in such a context consists of a significant challenge in modeling traffic. Several microscopic and macroscopic models have been designed aiming to represent the movement of vehicles accurately in road segments, involving different levels of complexity, precision, and realism. Out of these models, Free-flow models have shown useful due to being light and reasonably accurate for estimating load in short-time predictions. A recent free-flow traffic flow modeled using queues assumed constant vehicle speed along the road segment; this assumption may lead to a lack of realism and accuracy. Therefore, we propose a free-flow model based on this previous work where the road segment is split into several intervals, representing the oscillations of the speed of vehicles. The proposed model has shown correctness comparable to the previous free-flow model, considering that it has included speed varying behavior of vehicles.