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.
LSTM network: a deep learning approach for short-term traffic forecast
IET Intelligent Transport Systems ; 11 , 2 ; 68-75
2017-03-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
traffic data analysis , two-dimensional network , intelligent transportation systems , travel modes , learning (artificial intelligence) , traffic management , road traffic control , LSTM deep-learning approach , departure time , temporal-spatial correlation , memory units , short-term traffic forecasting , LSTM network , travel routes , long-short-term memory network , intelligent transportation system , computation power , recurrent neural nets
Metadata by IET is licensed under CC BY 3.0
LSTM network: a deep learning approach for short‐term traffic forecast
Wiley | 2017
|DOAJ | 2019
|