Traffic Density Prediction using IoT-based Double Exponential Smoothing

Rosa Andrie Asmara; Noprianto Noprianto; Muhammad Ainur Ilmy; Kohei Arai

doi:10.17977/um018v5i22022p168-178

Traffic Density Prediction using IoT-based Double Exponential Smoothing

Rosa Andrie Asmara, Noprianto Noprianto, Muhammad Ainur Ilmy, Kohei Arai

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.

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DOI: http://dx.doi.org/10.17977/um018v5i22022p168-178