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LSTM network: a deep learning approach for short‐term traffic forecast
Author(s) -
Zhao Zheng,
Chen Weihai,
Wu Xingming,
Chen Peter C. Y.,
Liu Jingmeng
Publication year - 2017
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2016.0208
Subject(s) - term (time) , computer science , deep learning , artificial intelligence , machine learning , quantum mechanics , physics
Short‐term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short‐term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short‐term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal–spatial correlation in traffic system via a two‐dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

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