期刊
IET INTELLIGENT TRANSPORT SYSTEMS
卷 11, 期 2, 页码 68-75出版社
WILEY
DOI: 10.1049/iet-its.2016.0208
关键词
learning (artificial intelligence); intelligent transportation systems; road traffic control; recurrent neural nets; LSTM network; LSTM deep-learning approach; short-term traffic forecasting; intelligent transportation system; travel modes; travel routes; departure time; traffic management; traffic data analysis; computation power; long-short-term memory network; temporal-spatial correlation; two-dimensional network; memory units
资金
- International Scientific and Technological Cooperation Projects of China [2015DFG12650]
- National Science Foundation of China [61573048, 61620106012]
- Beijing Municipal Natural Science Foundation [3152018]
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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