Knowledge Engineering and Data Science
Vol 5, No 2 (2022)

Traffic Density Prediction using IoT-based Double Exponential Smoothing

Rosa Andrie Asmara (Politeknik Negeri Malang)
Noprianto Noprianto (Politeknik Negeri Malang)
Muhammad Ainur Ilmy (Politeknik Negeri Malang)
Kohei Arai (Saga University)



Article Info

Publish Date
30 Dec 2022

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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Journal Info

Abbrev

keds

Publisher

Subject

Computer Science & IT Engineering

Description

Knowledge Engineering and Data Science (2597-4637), KEDS, brings together researchers, industry practitioners, and potential users, to promote collaborations, exchange ideas and practices, discuss new opportunities, and investigate analytics frameworks on data-driven and knowledge base systems. ...