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http://dx.doi.org/10.5762/KAIS.2021.22.3.58

A Study on the traffic flow prediction through Catboost algorithm  

Cheon, Min Jong (Division of Information System, Hanyang University)
Choi, Hye Jin (Division of Information System, Hanyang University)
Park, Ji Woong (Division of Information System, Hanyang University)
Choi, HaYoung (Division of Information System, Hanyang University)
Lee, Dong Hee (Division of Information System, Hanyang University)
Lee, Ook (Division of Information System, Hanyang University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.3, 2021 , pp. 58-64 More about this Journal
Abstract
As the number of registered vehicles increases, traffic congestion will worsen worse, which may act as an inhibitory factor for urban social and economic development. Through accurate traffic flow prediction, various AI techniques have been used to prevent traffic congestion. This paper uses the data from a VDS (Vehicle Detection System) as input variables. This study predicted traffic flow in five levels (free flow, somewhat delayed, delayed, somewhat congested, and congested), rather than predicting traffic flow in two levels (free flow and congested). The Catboost model, which is a machine-learning algorithm, was used in this study. This model predicts traffic flow in five levels and compares and analyzes the accuracy of the prediction with other algorithms. In addition, the preprocessed model that went through RandomizedSerachCv and One-Hot Encoding was compared with the naive one. As a result, the Catboost model without any hyper-parameter showed the highest accuracy of 93%. Overall, the Catboost model analyzes and predicts a large number of categorical traffic data better than any other machine learning and deep learning models, and the initial set parameters are optimized for Catboost.
Keywords
Machine Learning; Artificial Intelligence; Catboost; Deep Learning; LSTM;
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