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Loop speed trap data collection method for an accurate short-term traffic flow forecasting

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Abstract

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.
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Dates and versions

hal-02437024 , version 1 (13-01-2020)

Identifiers

  • HAL Id : hal-02437024 , version 1

Cite

Sahraoui Abdelatif, Derdour Makhlouf, Philippe Roose, Djamel Becktache. Loop speed trap data collection method for an accurate short-term traffic flow forecasting. International Conference on Mobile Web and Intelligent Information Systems, 2016, Vienna, Austria. pp.56-64. ⟨hal-02437024⟩

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