LSTM network: a deep learning approach for short‐term traffic forecast
Beihang University · National University of Singapore
Abstract
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…
Citation impact
- FWCI
- 127.10
- Percentile
- 100%
- References
- 49
Authors
5Topics & keywords
- Computer science
- Term (time)
- Long short term memory
- Intelligent transportation system
- Computation
- Deep learning
- Traffic generation model
- Network traffic simulation