IET Intelligent Transport Systems· 2017Q1
LSTM network: a deep learning approach for short‐term traffic forecast
- 1,802citations
- Q1SCImago
- 2017year
Short summary
A novel deep learning model using a Long Short-Term Memory (LSTM) network has been developed for short-term traffic forecasting, outperforming conventional models.
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Key points
- Introduced a novel traffic forecast model using a Long Short-Term Memory (LSTM) network.
- The LSTM network incorporates a two-dimensional structure with memory units.
- The model is designed to capture temporal-spatial correlations in traffic systems.
- Demonstrated superior performance compared to conventional forecast models.
AI-generated from the title and abstract; the full text is not read.
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 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.
The authors' abstract, as published at the source. IET Intelligent Transport Systems, 2017 · DOI ↗
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Field: Building and Construction
Building and ConstructionEngineering