despite the growing trend in intelligent transportation systems applications. Besides, there still many problems waiting for an accurate solution such as traffic flow forecasting. In this paper, based on real-time data provided by dual loop speed traps detectors at given slot of time; we propose a cloud data collection method aimed to improve prediction accuracy. To reach this accuracy, two traffic parameters was introduced: average speed and foreseen arrival time between two vehicles. By adopting Choquet integral operator, these parameters can subsequently aggregated to busiest parameters. Afterwards, a simple linear regression is applied for a dual purpose: forecasting and proving that there is a relationship between derived busiest arrival time and the traffic flow (q). Moreover, simulation flowcharts results illustrate that the forecasts by the Choquet operator ensure an accurate results to the real-time data. In contrast, weighted average operator results weak accuracy forecast compared to the real-time data.