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p-Index From 2020 - 2025
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All Journal Knowledge Engineering and Data Science
Muhammad Ainur Ilmy
Politeknik Negeri Malang
Computer Science & IT Engineering

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Traffic Density Prediction using IoT-based Double Exponential Smoothing Rosa Andrie Asmara; Noprianto Noprianto; Muhammad Ainur Ilmy; Kohei Arai
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p168-178

Abstract

The number of vehicles and currents that tend to increase causes traffic density. A system is proposed to calculate the number of vehicles and predict real-time traffic density. This research uses Haar Cascade to detect the number of cars and motorcycles and the Double Exponential Smoothing (DES) for forecasting the number of vehicles on the road. MAPE describes forecasting accuracy as a base for selecting the best smoothing constant (Alpha). The best test results from June 13 to 20, 2020, are cars on June 14, 2020 (alpha 0.5, MAPE 0%) and Motorcylecycles on June 18, 2020 (alpha 0.5, MAPE 0.1134% ). The most significant MAPE results of the car were on June 15, 2020, with alpha 0.5 and MAPE 2.1073%. The 3 minutes haar cascade detects 72.58% of cars and 81.90% of motorcycles.
Co-Authors Kohei Arai Noprianto, Noprianto Rosa Andrie Asmara