DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction

Jiang, Renhe; Cai, Zekun; Wang, Zhaonan; Yang, Chuang; Fan, Zipei; Chen, Quanjun; Tsubouchi, Kota; Song, Xuan; Shibasaki, Ryosuke

doi:10.1109/tkde.2021.3077056

Cited by 34 publications

(12 citation statements)

References 50 publications

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“…Human mobility analysis has been an active research area due to its significant implications for urban planning, traffic management, and location-based services [1]. In recent years, an increasing number of studies have focused on human mobility analysis using multi sources data and methodologies [4,20]. These studies have provided insights into the spatial and temporal properties of human mobility, including established an effective human mobility model for prediction and simulation by studying human mobility after natural disasters [21], Fine-grained COVID-19 Propagation Model of human mobility Data [22], and human activities exhibit periodic patterns corresponding to daily and weekly cycles [23].…”

Section: Human Mobility Analysismentioning

confidence: 99%

“…In recent years, with the increasing popularity of devices equipped with Global Positioning System (GPS) has led to an accumulation of extensive human mobility data. Understanding and analyzing human mobility data has become a central focus for researchers [1][2][3][4]. Among which a key aspect is the trip purposes inference, aiming to reveal the latent motivations behind the mobility.…”

Section: Introductionmentioning

confidence: 99%

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A geometry-driven neural topic model for trip purpose inference

Zhang,

Fan,

Song

et al. 2023

Geoinformatica

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Understanding urban human mobility, particularly trip purposes, is essential for optimizing traffic management, personalized recommendations, and urban planning. However, in real-world scenarios, trip purposes cannot be directly extracted from trajectory data. To address this issue, we propose a geometry-driven neural topic model for trip purpose inference. We integrate trajectory data with nearby points of interest (POI) data using a geometry-driven technique to enhance the interpretability of the results. Furthermore, our model captures the semantics and relationships of the data in a high-dimensional space and identifies latent topics representing distinct trip purposes. These learned topics are analyzed using clustering algorithms to group similar trips, enabling trip purpose inference. And we evaluate our model using the trajectory data of Shenzhen and Chengdu, and compare it with baseline models. The results demonstrate that our model performs well. Furthermore, we analyze trajectory data containing trip purpose information to gain insights into human mobility patterns and the influence of trip purposes, paving the way for potential implications and future research directions.

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Section: Human Mobility Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A geometry-driven neural topic model for trip purpose inference

Zhang,

Fan,

Song

et al. 2023

Geoinformatica

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“…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%

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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“…Spatial-temporal prediction plays a crucial role in urban development by providing insights into future trends based on historical spatial and temporal dynamics. Among various spatial-temporal prediction tasks, crowd flow prediction holds significant importance in diverse application scenarios, ranging from emergency management [27,8] and traffic control [26,25] to urban planning [12]. Government agencies rely on crowd flow prediction to devise control measures and prevent potential stampedes during festival celebrations.…”

Section: Introductionmentioning

confidence: 99%

GridFormer: Spatial-Temporal Transformer Network for Citywide Crowd Flow Prediction

Su,

Wu,

Lian

2023

Frontiers in Artificial Intelligence and Applications

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Crowd flow prediction plays a vital role in various fields such as traffic management, public safety, and urban planning. The main challenge in crowd flow prediction lies in effectively modeling the periodic temporal dependency and long-range spatial dependency. In the temporal domain, crowd flow shows a strong periodicity which is exploited by existing works to build multi-time-scale spatial-temporal features. However, these works hardly consider the disturbance of periods, that is, the crowd flow is not strictly periodic. In the spatial domain, existing works mainly utilize CNN to capture spatial dependency, but the small receptive field of the convolution operator limits the ability to capture the long-range dependency between crowd flows in different regions. In this paper, we propose GridFormer, a Transformer network, in which a periodically shifted sampling method and attention mechanism are employed to handle the temporal shifting in the daily and weekly periodicity, and a pyramid 3D Swin Transformers network is designed to capture long-range spatial dependency in a hierarchical manner. Meanwhile, the pyramid 3D Swin Transformers network jointly models spatial-temporal features to enable better interaction between the spatial and temporal domains. Experimental results on three crowd flow datasets demonstrate that our GridFormer outperforms the state-of-the-art crowd flow prediction methods.

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DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction

Cited by 34 publications

References 50 publications

A geometry-driven neural topic model for trip purpose inference

A geometry-driven neural topic model for trip purpose inference

Enhancing crowd flow prediction in various spatial and temporal granularities

GridFormer: Spatial-Temporal Transformer Network for Citywide Crowd Flow Prediction

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