DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction

@article{Wang2018DeepSTCLAD,
  title={DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction},
  author={Dongjie Wang and Yan Yang and Shangming Ning},
  journal={2018 International Joint Conference on Neural Networks (IJCNN)},
  year={2018},
  pages={1-8}
}
Urban resource scheduling is an important part of the development of a smart city, and transportation resources are the main components of urban resources. [] Key Method In order to improve the performance, deep learning is used to assist prediction. But most of the deep learning methods only utilize temporal dependence or spatial dependence of data in the forecasting process. To address these limitations, a novel deep learning traffic demand forecasting framework which based on Deep Spatio-Temporal ConvLSTM…

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References

SHOWING 1-10 OF 23 REFERENCES

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

TLDR
This work designs an end-to-end structure of ST-ResNet, a deep-learning-based approach to collectively forecast the inflow and outflow of crowds in each and every region of a city, based on unique properties of spatio-temporal data.

Traffic Flow Prediction With Big Data: A Deep Learning Approach

TLDR
A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently and is applied for the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction.

DNN-based prediction model for spatio-temporal data

TLDR
A Deep-learning-based prediction model for Spatio-Temporal data (DeepST), which is comprised of two components: spatio-temporal and global, and built on a real-time crowd flow forecasting system called UrbanFlow1.

DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction

TLDR
This study proposes a deep learning based prediction algorithm, DeepTFP, to collectively predict the traffic flow on each and every traffic road of a city using three deep residual neural networks to model temporal closeness, period, and trend properties of traffic flow.

Poster: DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction

TLDR
A deep learning based prediction algorithm, DeepTFP, is proposed to collectively predict the traffic flow on each and every traffic road of a city using three deep residual neural networks to model temporal closeness, period, and trend properties of traffic flow.

DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data

TLDR
This study introduces a brand-new data-driven precipitation prediction model called DeepRain, which predicts the amount of rainfall from weather radar data, which is three-dimensional and four-channel data, using convolutional LSTM (ConvLSTM).

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

TLDR
This paper proposes the convolutional LSTM (ConvLSTM) and uses it to build an end-to-end trainable model for the precipitation nowcasting problem and shows that it captures spatiotemporal correlations better and consistently outperforms FC-L STM and the state-of-the-art operational ROVER algorithm.

Multi-task learning of time series and its application to the travel demand

TLDR
A support vector regression model is developed for joint learning of mutually dependent time series and it is the regularization-based multi-task learning previously developed for the classification case and extended to time series.

A multi-level clustering approach for forecasting taxi travel demand

TLDR
This paper uses time-series modeling to forecast taxi travel demand, in the context of a mobile application-based taxi hailing service, and employs a multi-level clustering technique where demand is aggregated over neighboring cells/geohashes to improve the model performance.