Vehicle Traffic Flow Forecasting on Caltrans PeMS Dataset Using Machine Learning Algorithms and LSTM Networks

Authors: 1 Jiarui Chang, 2 Jingwen Du, 3 Hojin Chung
Affiliation: 1 Rice University (Houston, USA), 2 Cornell University (Ithaca, USA) , 3 Gyeonggi Suwon International School (Suwon, Korea)


Keywords: Deep learning model, Traffic flow prediction, Caltrans PeMs dataset, LSTM model
ABSTRACT. In Intelligent transportation systems, accurate traffic flow prediction is fundamental in transportation modeling and management.Previous studies have classified prediction approaches into three categories including a time series approach with ARIMA model for finding traffic flow patterns and using those patterns for prediction, a probabilistic approach for modeling and forecasting from a probabilistic perspective, and nonparametric approaches that can perform better by handling un-deterministic and complex time series traffic datasets.This paper analyzes historical timeseries traffic data from sensors using machine learning algorithms as baseline models and designs a deep learning LSTM model to train using the historical dataset to forecast traffic flow using the trained model. The paper also compares the performance of machine learning algorithms and the deep learning model. The results show the deep learning LSTM model to outperform machine learning models.


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