To successfully deploy an intelligent transportation system, it is essential to construct an effective method of traffic speed prediction. Recently, due to the advancements in sensor technology, traffic data have experienced explosive growth. It is therefore a challenge to construct an efficient model with highly accurate predictions. To improve the accuracy and the efficiency of short-term traffic predictions, we propose a prediction model based on deep learning approaches. We use a long short-term memory (LSTM) network to analyze sequential sensor data to predict the car speed of the next time interval on the freeway. Unlike the traditional model that only considers the changes in traffic speed which is used to derive the temporal and spatial features from the prediction road section, we mainly consider the features of the number of the most representative car types and the traffic speed variation of the front road segment that is ahead of the prediction road segment in addition to the number of cars, the road occupancy, and the traffic speed latency to successfully learn and capture the hidden patterns from the sensor data so as to improve the prediction accuracy. To the best of our knowledge, very few investigations have been conducted to consider the correlation between car speed and car type for a prediction model. Moreover, our extensive experiments demonstrate that the proposed method for traffic speed prediction has achieved high accuracy.
A Short-term Traffic Speed Prediction Model Based on LSTM Networks
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 19 , 3 ; 510-524
2021-09-01
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Short-term traffic prediction , Long short-term memory network , Deep learning , Big data analysis , Data mining Engineering , Electrical Engineering , Automotive Engineering , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics , Civil Engineering , User Interfaces and Human Computer Interaction
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