Journal ArticleDOI
LSTM network: a deep learning approach for short-term traffic forecast
TLDR
A novel traffic forecast model based on long short-term memory (LSTM) network is proposed, which considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units.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.read more
Citations
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Journal ArticleDOI
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
TL;DR: A novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state and shows that the proposed model outperforms baseline methods on two real-world traffic state datasets.
Journal ArticleDOI
Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM
Xiangyun Qing,Yugang Niu +1 more
TL;DR: A novel solar prediction scheme for hourly day-ahead solar irradiance prediction by using the weather forecasting data is proposed and it is demonstrated that the proposed algorithm outperforms these competitive algorithms for single output prediction.
Journal ArticleDOI
Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
TL;DR: This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations and the FCL-Net achieves the better predictive performance than traditional approaches.
Journal ArticleDOI
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.
TL;DR: Wang et al. as mentioned in this paper proposed a spatiotemporal recurrent convolutional networks (SRCNs) for traffic forecasting, which inherit the advantages of deep CNNs and LSTM neural networks.
Posted Content
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
TL;DR: A network grid representation method that can retain the fine-scale structure of a transportation network and outperform other deep learning-based algorithms in both short-term and long-term traffic prediction is proposed.
References
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Speech Recognition with Deep Recurrent Neural Networks
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Proceedings Article
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