Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

Universitas Mercu Buana, Faculty of Computer Science, Jakarta, Indonesia

^* Corresponding author: bagus.priambodo@mercubuana.ac.id, yuwan.jumaryadi@mercubuana.ac.id

Abstract

During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.