Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network

Liu, Menghang; Li, Luning; Li, Qiang; Bai, Yu; Hu, Cheng

doi:10.3390/ijgi10070455

Cited by 9 publications

(4 citation statements)

References 60 publications

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“…Crowd prediction can be simply modeled as a time series forecasting problem to regress an increase in crowd density over time starting from a no-crowd condition or can also use spatial statistics of crowds to predict crowds in spatial regions. Traditionally, it uses moving averaging methods such as autoregressive integrated moving average (ARIMA) (Liu et al 2021). However, these methods fail to capture the complex temporal and spatial dependencies despite several feature engineering techniques.…”

Section: Methods and State-of-the-artmentioning

confidence: 99%

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Visual crowd analysis: Open research problems

Khan,

Menouar,

Hamila

2023

AI Magazine

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Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep‐learning approaches have made it possible to develop fully automated vision‐based crowd‐monitoring applications. However, despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome. In this article, we delve into six major areas of visual crowd analysis, emphasizing the key developments in each of these areas. We outline the crucial unresolved issues that must be tackled in future works, in order to ensure that the field of automated crowd monitoring continues to progress and thrive. Several surveys related to this topic have been conducted in the past. Nonetheless, this article thoroughly examines and presents a more intuitive categorization of works, while also depicting the latest breakthroughs within the field, incorporating more recent studies carried out within the last few years in a concise manner. By carefully choosing prominent works with significant contributions in terms of novelty or performance gains, this paper presents a more comprehensive exposition of advancements in the current state‐of‐the‐art.

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Section: Methods and State-of-the-artmentioning

confidence: 99%

“…Traditionally, it uses moving averaging methods such as autoregressive integrated moving average (ARIMA) (Liu et al. 2021). However, these methods fail to capture the complex temporal and spatial dependencies despite several feature engineering techniques.…”

Section: Crowd Predictionmentioning

confidence: 99%

Visual crowd analysis: Open research problems

Khan,

Menouar,

Hamila

2023

AI Magazine

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“…With their superior capability to characterize spatial and temporal dependencies for time-series predictions, Graph Convolutional Networks (GCNs) characterize networked data with spatial and temporal dependencies for time-series prediction using spatial and temporal convolutions. These models (referred to as spatio-temporal graph convolutional network (STGCN) models) are used for prediction problems such as traffic flow prediction 52,53 , disease diagnosis 54 , bike-demand prediction 55 , point-of-interest (POI) recommendation 53 , pedestrian flow prediction 56 , trajectory prediction 57 , and road network flood inundation prediction 2 . STGCN model architectures have been developed based on the problem characteristics.…”

Section: Related Workmentioning

confidence: 99%

A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

Farahmand

Mostafavi

2023

Sci Rep

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Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial–temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents’ flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents’ activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood.

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“…Compared with CNN, the GCN can process arbitrary graph structure data by using the property that convolution is essentially filtering on the frequency domain (Ni et al., 2021). At present, it has been widely used in traffic flow prediction (Bai et al., 2021; Zhao et al., 2019), pedestrian prediction (Liu et al., 2021), wind speed prediction (Stańczyk & Mehrkanoon, 2021) and so on.…”

Section: Related Workmentioning

confidence: 99%

A Short‐Term Regional Precipitation Prediction Model Based on Wind‐Improved Spatiotemporal Convolutional Network

Qiu

Tang

2022

Earth and Space Science

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Accurate precipitation prediction can help decision makers judge the trend of climate change and formulate more effective measures, and prevent flood and drought disasters. In this paper, we propose a short‐term regional precipitation prediction model based on wind‐improved spatiotemporal convolutional network. Among them, the improved graph convolution network integrates the effects of wind direction and geographic location at past moments to capture the spatial dependence, whilst the gated recurrent unit captures the temporal dependence by learning the dynamic changes of data. The spatio‐temporal memory flow module and attention module are added to capture spatial deformation and temporal variation more accurately, thereby better matching the physical properties of precipitation. The proposed model achieves better prediction results on real data sets. Experiments show that our method is better at extracting the spatio‐temporal information of precipitation data and capturing its time dependence and spatial correlation.

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Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network

Cited by 9 publications

References 60 publications

Visual crowd analysis: Open research problems

Visual crowd analysis: Open research problems

A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

A Short‐Term Regional Precipitation Prediction Model Based on Wind‐Improved Spatiotemporal Convolutional Network

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