Work place: Master of Computer Science, Universitas Gadjah Mada, Yogyakarta, Indonesia

E-mail: kasliono@gmail.com


Research Interests: Artificial Intelligence


Kasliono, S.Mat., M.Cs. is a Master Student in Computer Science, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada. The research interest is artificial intelligence.

Author Articles
Point Based Forecasting Model of Vehicle Queue with Extreme Learning Machine Method and Correlation Analysis

By Kasliono Suprapto Faizal Makhrus

DOI: https://doi.org/10.5815/ijisa.2021.03.02, Pub. Date: 8 Jun. 2021

Traffic is a medium to move from one point to another. Therefore, the role of traffic is very important to support vehicle mobility. If congestion occurs, mobility will be hampered so that it gives influence to other sectors such as financial, air pollution and traffic violations. This study aims to create a model to predict vehicle queue at the traffic lights when its status is red. The prediction is conducted by using Neural Network with Extreme Learning Machine method to predict the length of the vehicle queue, and Correlation Analysis was used to measure the correlation between the connected roads. The conducted experiments use data of the length of the vehicle queue at the traffic lights which was obtained from DISHUB (Transportation Bureau) DI Yogyakarta. Several experiments were carried out to determine the optimum prediction model of vehicle queue length. The experiments found that the optimum model had an average MAPE value of 15.5882% and an average Running Time of 5.2226 seconds.

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