Abstract
Despite the growing trend in intelligent transportation systems applications, there are 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-based data collection method which is aimed to improve prediction accuracy. To reach this level of accuracy, two traffic parameters were introduced, the average speed and the foreseen arrival time between two vehicles. By adopting Choquet integral operator, these parameters can subsequently aggregate to busiest traffic parameters. Afterwards, a simple linear regression is applied for a dual purpose, the first to predict the traffic flow, then prove that there is a relationship between derived busiest arrival time and the traffic flow. Moreover, the simulation charts demonstrates that the forecasts by the Choquet operator ensure an accurate results to the real-time data. In contrast, the forecasts using weighted average operator lead to low accuracy compared with real-time data.
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Acknowledgments
This work is subscribed in the context of the thematic research project entitled “Integrated Road Traffic in Algeria”, our objective is to develop VANETs applications that are “suspected” of spatiotemporal context, so to generalize the exchange of V2V information’s (Vehicle-to-Vehicle), V2I information’s (Vehicle-to-Infrastructure) and even V2X, and support all communication types (radio, internet, etc.) in their activities.
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Abdelatif, S., Makhlouf, D., Roose, P., Becktache, D. (2016). Loop Speed Trap Data Collection Method for an Accurate Short-Term Traffic Flow Forecasting. In: Younas, M., Awan, I., Kryvinska, N., Strauss, C., Thanh, D. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2016. Lecture Notes in Computer Science(), vol 9847. Springer, Cham. https://doi.org/10.1007/978-3-319-44215-0_5
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DOI: https://doi.org/10.1007/978-3-319-44215-0_5
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