Loop Speed Trap Data Collection Method for an Accurate Short-Term Traffic Flow Forecasting

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.

VANET: A novel service for predicting and disseminating vehicle traffic information

“…16,17 The preventive traffic services, such as vehicle accident detection and route planning, 18,19 use the analyzed and the processed data by the internet clouds to improve the quality of the disseminated data to the vehicles. To support these kinds of services, the big data platforms where it includes several data mining techniques are requested to perform various tasks, such as data selection and aggregation 20,21 to extract the vehicle decisions making.…”

Vehicular-cloud simulation framework for predicting traffic flow data

“…Based on the traffic flow model that was used to evaluate the prediction model of an Algerian highway, and our aggregation method presented in Abdelatif et al (2016), the prediction performance has been tested in two peak hours during the day (case study in highway). Figure 9 shows the performance of the traffic flow prediction at the morning and the evening periods.…”