Knowledge Engineering and Data Science


Traffic Density Prediction using IoT-based Double Exponential Smoothing

Asmara, Rosa Andrie (Unknown)
Noprianto, Noprianto (Unknown)
Ilmy, Muhammad Ainur, State Polytechnic of Malang (Unknown)
Arai, Kohei (Unknown)



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

publication:keds

Publisher

Subject

Computer Science & IT Engineering

Description

The journal welcomes experimental and theoretical findings on data science and knowledge engineering along with their applications to real-life ...