DNN-based prediction model for spatio-temporal data

Zhang, Junbo; Zheng, Yu; Qi, Dekang; Li, Ruiyuan; Yi, Xiuwen

doi:10.1145/2996913.2997016

Cited by 573 publications

(357 citation statements)

References 3 publications

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“…In this section, we first conduct experiments on two public benchmarks (e.g., TaxiBJ [36] and BikeNYC [36]) to evaluate the performance of our model on citywide crowd flow prediction. We further…”

Section: Methodsmentioning

confidence: 99%

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Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

Zhang

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

220

143

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Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow changes. In this paper, we propose a unified neural network module to address this problem, called Attentive Crowd Flow Machine (ACFM), which is able to infer the evolution of the crowd flow by learning dynamic representations of temporally-varying data with an attention mechanism. Specifically, the ACFM is composed of two progressive ConvL-STM units connected with a convolutional layer for spatial weight prediction. The first LSTM takes the sequential flow density representation as input and generates a hidden state at each time-step for attention map inference, while the second LSTM aims at learning the effective spatial-temporal feature expression from attentionally weighted crowd flow features. Based on the ACFM, we further build a deep architecture with the application to citywide crowd flow prediction, which naturally incorporates the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks (i.e., crowd flow in Beijing and New York City) show that the proposed method achieves significant improvements over the state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

“…Fouladgar et al [9] introduced a scalable decentralized deep neural networks for urban short-term traffic congestion prediction. In [36], a deep learning based framework was proposed to leverage the temporal information of various scales (i.e. temporal closeness, period and seasonal) for crowd flow prediction.…”

Section: Related Workmentioning

confidence: 99%

Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

Zhang

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

220

143

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show abstract

“…Region [2]. There are many definitions of Region in terms of different scales and semantic meanings.…”

Section: Crowd Flow Predictionmentioning

confidence: 99%

Urban flow prediction from spatiotemporal data using machine learning: A survey

et al. 2020

Self Cite

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“…Zhang et al. () designed a prediction model for spatio‐temporal data based on deep learning. The model consists of a spatio‐temporal component and a global component to combine the information related to spatial dependencies, temporal closeness, and other global factors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zhang, Zheng, Qi, Li, and Yi () designed a prediction model for spatio‐temporal data based on deep learning. However, they focused on a single model, which is regarded as the base learner in our traffic state prediction module.…”

Section: Introductionmentioning

confidence: 99%

A spatio‐temporal ensemble method for large‐scale traffic state prediction

Liu

et al. 2019

Computer aided Civil Eng

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How to effectively ensemble multiple models while leveraging the spatio‐temporal information is a challenging but practical problem. However, there is no existing ensemble method explicitly designed for spatio‐temporal data. In this paper, a fully convolutional model based on semantic segmentation technology is proposed, termed as spatio‐temporal ensemble net. The proposed method is suitable for grid‐based spatio‐temporal prediction in dense urban areas. Experiments demonstrate that through spatio‐temporal ensemble net, multiple traffic state prediction base models can be combined to improve the prediction accuracy.

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DNN-based prediction model for spatio-temporal data

Cited by 573 publications

References 3 publications

Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

Urban flow prediction from spatiotemporal data using machine learning: A survey

A spatio‐temporal ensemble method for large‐scale traffic state prediction

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