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- W3007926275 abstract "Short-term traffic prediction consists a crucial component in intelligent transportation systems. With the explosion of automated traffic monitoring sensors and the flourishing of deep learning techniques, a growing body of deep neural network models have been employed to tackle this problem. In particular, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks have demonstrated their advantages in modeling and predicting the spatiotemporal evolution of traffic flows. In this paper, we propose a novel Convolutional LSTM neural network architecture for multi-lane short-term traffic prediction. Compared to existing methods, we highlight the importance of (1) applying multiple features to characterize traffic conditions; (2) explicitly considering the routing between neighbouring lanes and downstream/upstream traffics; and (3) predicting multiple time-step traffic in a rolling-prediction manner. Experiments on 10 months 5-minute interval observations of the US I-101 Northern freeway at California Bay Area verify the proposed model. The results show that our model has considerable advantages in predicting multi-lane short-term traffic flow." @default.
- W3007926275 created "2020-03-06" @default.
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- W3007926275 date "2020-01-01" @default.
- W3007926275 modified "2023-10-12" @default.
- W3007926275 title "Multi-Lane Short-Term Traffic Forecasting With Convolutional LSTM Network" @default.
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- W3007926275 doi "https://doi.org/10.1109/access.2020.2974575" @default.
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