Attention based simplified deep residual network for citywide crowd flows prediction

Dai, Genan; Hu, Xiaoyang; Ge, Youming; Ning, Zhiqing; Liu, Yubao

doi:10.1007/s11704-020-9194-x

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…The usage of a graph-based model provides many advantages for policymakers. For instance, our solution allows policymakers to use spatial-tessellation (e.g., make predictions at street level, block-level, 4 The results fixing others time intervals or tile sizes are similar. and others).…”

Section: Discussionmentioning

confidence: 99%

“…Most of the solutions leverage convolutional neural networks (CNNs) and recurrent networks (RNNs) to capture spatio-temporal patterns and dependencies. Examples are [4,6,13,19,20,27,29,32,35,38]. Some other solutions also rely on attention mechanisms.…”

Section: Related Workmentioning

confidence: 99%

“…Some other solutions also rely on attention mechanisms. Examples are [4,13,29,32]. In what follows, we introduce additional details of the models we will use as baselines in this study.…”

Section: Related Workmentioning

confidence: 99%

“…To investigate the robustness of CrowdNet, for the flow prediction problem, to variation in the spatial and temporal aggregation, we investigate how the performance of CrowdNet changes varying time intervals (fixing a tile size of 1000𝑚) or tile sizes (fixing time intervals to 60𝑚𝑖𝑛). 4 CrowdNet is robust with respect to the temporal aggregation. As shown by Table 5 the RMSE increases as the time interval increases.…”

Section: Figure 3b Makes the Comparison For A Larger Time Interval (6...mentioning

confidence: 99%

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Enhancing crowd flow prediction in various spatial and temporal granularities

Cardia¹,

Luca²,

Pappalardo³

2022

Preprint

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Thanks to the diffusion of the Internet of Things, nowadays it is possible to sense human mobility almost in real time using unconventional methods (e.g., number of bikes in a bike station). Due to the diffusion of such technologies, the last years have witnessed a significant growth of human mobility studies, motivated by their importance in a wide range of applications, from traffic management to public security and computational epidemiology. A mobility task that is becoming prominent is crowd flow prediction, i.e., forecasting aggregated incoming and outgoing flows in the locations of a geographic region. Although several deep learning approaches have been proposed to solve this problem, their usage is limited to specific types of spatial tessellations and cannot provide sufficient explanations of their predictions. We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks. Compared with state-of-the-art solutions, CrowdNet can be used with regions of irregular shapes and provide meaningful explanations of the predicted crowd flows. We conduct experiments on public data varying the spatio-temporal granularity of crowd flows to show the superiority of our model with respect to existing methods, and we investigate CrowdNet's reliability to missing or noisy input data. Our model is a step forward in the design of reliable deep learning models to predict and explain human displacements in urban environments.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Some other solutions also rely on attention mechanisms. Examples are [4,13,29,32]. In what follows, we introduce additional details of the models we will use as baselines in this study.…”

Section: Related Workmentioning

confidence: 99%

Section: Figure 3b Makes the Comparison For A Larger Time Interval (6...mentioning

confidence: 99%

See 2 more Smart Citations

Enhancing crowd flow prediction in various spatial and temporal granularities

Cardia¹,

Luca²,

Pappalardo³

2022

Preprint

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show abstract

“…In this respect, traffic flow prediction has received special attention in the last two decades [1]. Using spatio-temporal data [2] obtained from a range of sensors, a variety of real-world problems can be solved, such as the demand for taxis [3][4][5], urban traffic control and congestion avoidance [6,7], abnormal event detection [8,9], and travel time estimation or route planning [10][11][12], amongst others.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks

et al. 2021

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Due to the need to predict traffic congestion during the morning or evening rush hours in large cities, a model that is capable of predicting traffic flow in the short term is needed. This model would enable transport authorities to better manage the situation during peak hours and would allow users to choose the best routes for reaching their destinations. The aim of this study was to perform a short-term prediction of traffic flow in Madrid, using different types of neural network architectures with a focus on convolutional residual neural networks, and it compared them with a classical time series analysis. The proposed convolutional residual neural network is superior in all of the metrics studied, and the predictions are adapted to various situations, such as holidays or possible sensor failures.

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AreaTransfer: A Cross-City Crowd Flow Prediction Framework Based on Transfer Learning

Wei

Guo

et al. 2022

Lecture Notes in Computer Science

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Attention based simplified deep residual network for citywide crowd flows prediction

Cited by 16 publications

References 31 publications

Enhancing crowd flow prediction in various spatial and temporal granularities

Enhancing crowd flow prediction in various spatial and temporal granularities

Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks

AreaTransfer: A Cross-City Crowd Flow Prediction Framework Based on Transfer Learning

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