LSTM network: a deep learning approach for short‐term traffic forecast.

Zhao, Zheng; Chen, Weihai; Wu, Xingming; Chen, Peter C. Y.; Liu, Jingmeng

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.

INTELLIGENT transportation systems; TRAFFIC estimation; TRAFFIC engineering; ARTIFICIAL intelligence; CHOICE of transportation; DEEP learning

IET Intelligent Transport Systems (Wiley-Blackwell), 2017, Vol 11, Issue 2, p68

1751-956X

Academic Journal

10.1049/iet-its.2016.0208