TAILIEUCHUNG - Traffic flow prediction model based on neighbouring roads using neural network and multiple regression

This paper discusses the results of experiments we have conducted to determine relationship between roads in a neighbouring area and to determine input factors for our neural network traffic flow prediction model. To choose a particular road as a predicting factor, we calculated the distance between roads in neighbouring area to identify the nearest road. | Journal of ICT, 17, No. 4 (October) 2018, pp: 513–535 How to cite this article: Priambodo, B., & Ahmad, A. (2018). Traffic flow prediction model based on neighbouring roads using neural network and multiple regression. Journal of Information and Communication Technology, 17(4), 513-535. TRAFFIC FLOW PREDICTION MODEL BASED ON NEIGHBOURING ROADS USING NEURAL NETWORK AND MULTIPLE REGRESSION Bagus Priambodo & 2Azlina Ahmad 1 Faculty of Computer Science, Universitas Mercu Buana, Indonesia 2 Institute of Visual Informatic, Universiti Kebangsaan Malaysia, Malaysia 1 ; azlinaivi@ ABSTRACT Monitoring and understanding traffic congestion seems difficult due to its complex nature. This is because the occurrence of traffic congestion is dynamic and interrelated and it depends on many factors. Traffic congestion can also propagate from one road to neighbouring roads. Recent research shows that there is a spatial correlation between neighbouring roads with different traffic flow pattern on weekdays and on weekends. Previously, prediction of traffic flow propagation was based on day and time during weekdays and on weekends. Results obtained from past studies show that further investigation is needed to reduce errors using a more efficient method. We observed from previous research that similarity of traffic condition on weekdays and weekends was not taken into account in predicting traffic flow propagation. Hence, our study is to create and evaluate a new prediction model for traffic flow propagation at neighbouring roads using similarity of traffic flow pattern on weekdays and weekends to achieve more accurate results. We exploit similarity of traffic flow pattern on weekdays and weekends by adding Received: 29 December 2017 Accepted: 3 August 2018 513 Published: 1 October 2018 Journal of ICT, 17, No. 4 (October) 2018, pp: 513–535 time cluster in our proposed model. Thus, our neural network model proposed high correlation road,

• Địa Lý
• Traffic flow prediction model
• Neighbouring roads using neural network and multiple regression
• Neural network and multiple regression
• Neural network traffic flow prediction model
• Multiple regression method
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