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

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

Автор: Kasliono, Suprapto, Faizal Makhrus

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 3 vol.13, 2021 года.

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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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Forecasting, Correlation, Traffic, Vehicle queue, Extreme leaning machine (ELM), Neural network, Correlation coefficient

Короткий адрес: https://sciup.org/15017739

IDR: 15017739   |   DOI: 10.5815/ijisa.2021.03.02

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