An Indoor Crowd Movement Trajectory Benchmark Dataset

Zhao, Ying; Xin, Zhao; Chen, Siming; Zhang, Zhuo; Huang, Xin

doi:10.1109/tr.2021.3109122

Cited by 11 publications

(6 citation statements)

References 64 publications

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“…Lastly, people tend to inhale less virus when they are closer to the entrance, approximately 1.5%. According to Zhao et al (2021), who analyzed an indoor trajectory dataset obtained through IoT machines during the 2019 China Intelligence Cybersecurity Conference, it was observed that individuals tend to quickly pass through the entrance and primarily form groups and engage in conversations in the inner areas. However, considering that the inhalation patterns and infection patterns can vary depending on the indoor environment and structure, further studies are warranted to investigate these aspects in greater detail.…”

Section: Discussion and Con Clus I On Smentioning

confidence: 99%

“…In this section, we validate the indoor movement pattern. To achieve this, we utilized indoor trajectory data fromZhao et al (2021), which captures the movement of individuals in an indoor conference venue. This data was collected using smart badges worn by all participants, F I G U R E 6 Droplet spread radius of each behavior for simulation (dot line area: aerosol which is <30 μm, filled by color: >30 μm).…”

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confidence: 99%

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Investigating factors in indoor transmission of respiratory disease through agent‐based modeling

Choi,

Hohl

2023

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The transmission of respiratory diseases such as COVID‐19 is exacerbated in densely populated urban areas and crowded indoor settings. Despite the majority of transmissions occurring in such settings, controlling viral spread through individual‐level contacts indoors remains challenging. Experimental studies have investigated the transmission patterns of respiratory behaviors such as coughing or sneezing in controlled spatial environments. However, the effects of dynamic movement and spatial structures have been ignored, making it difficult to apply findings to urban policy and planning. To address this gap, we developed agent‐based simulations to investigate individual virus inhalation patterns across multiple scenarios in a symmetrical and formulaic indoor space. We conducted sensitivity analysis using regression emulator models to identify significant factors for viral transmission. Our results indicate positive associations with viral transmission in descending order of: (1) stay time; (2) encounter frequency; and (3) initial infected population; while negative associations are: (4) mask wearing; (5) distance to infected people; (6) nearest infected people's mask wearing; and (7) distance to entrance. We also found that narrow passages between obstacles increase virus transmission from breathing. Furthermore, we conducted a case study to investigate the potential of reducing the amount of individually inhaled virus by controlling behaviors and spatial environments. Our findings suggest that mask wearing and reduced stay time can substantially reduce transmission risk, while a large number of contacts and high grouping time result in the growth of the infected population at a certain threshold. These results provide guidance for decision makers to formulate guidelines for curbing the spread of respiratory diseases in indoor spaces.

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Section: Discussion and Con Clus I On Smentioning

confidence: 99%

mentioning

confidence: 99%

Investigating factors in indoor transmission of respiratory disease through agent‐based modeling

Choi,

Hohl

2023

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“…Some other (Zhao et al. 2021b ) work supports analysis from the perspective of mobility dataset construction.…”

Section: Related Workmentioning

confidence: 99%

TriPlan: an interactive visual analytics approach for better tourism route planning

Zhang

Pang

Wen

et al. 2022

J Vis

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Continuous research and development of novel tourism routes is necessary for tourism service providers to improve the tourist experience and industrial competitiveness. However, the route planning is cumbersome due to the time-consuming, extensive, and costly field study. Most of the existing route planning studies focus on recommending tourism routes for users based on attraction characteristics or tourist behavior features, which are generally unexplainable due to the black-box approaches they use. Other solutions allow users to customize itineraries through an interactive interface but often lack guidance from the aspect of route evaluation or destination image perception. In this paper, we thoroughly discuss the requirements and design tasks with domain experts and propose TriPlan, an interactive visual analytics system that provides intuitive planning guidance for tourism product developers. We design and improve multiple coordinated visualizations to facilitate analysis from the perspectives of overall route pattern and individual destination image. We also develop a hierarchical planning view to display the structural information of a plan. In addition, we introduce an automatic route optimization algorithm and multiple interactions to assist users in optimizing and adjusting the itineraries. Finally, we evaluate the usability and effectiveness of our system through three case studies and quantitative and qualitative interviews with the domain experts on real-world datasets. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s12650-022-00861-8) contains supplementary material, which is available to authorized users.

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“…Examples of trajectories of the dataset are shown in Figures 6 and 7a. All these movement patterns and abnormal events are presented in the supplementary materials of Zhao et al (2021). The ICMTD-2019 dataset contains a total number of 10,929 trajectories.…”

Section: Datasetmentioning

confidence: 99%

“…A publicly accessible indoor trajectory dataset (ICMTD‐2019, Zhao et al., 2021) was used to evaluate the proposed method against baselines. The scenario of the dataset was set at a fictional international cyber‐security conference (Figure 6).…”

Section: Evaluationsmentioning

confidence: 99%

A deep pedestrian trajectory generator for complex indoor environments

He,

Zhang,

Wang

et al. 2024

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Pedestrian trajectory data, which can be used to mine pedestrian motion patterns or to model pedestrian dynamics, is crucial for indoor location‐based service studies and applications. However, researchers are faced with the challenges of data shortage and privacy restrictions when using pedestrian trajectory data. We present an Indoor Pedestrian Trajectory Generator (IPTG), which is a novel deep learning model to synthesize pedestrian trajectory data. IPTG first produces feature sequences that encode the spatial–temporal and semantic features of the walking process and then interpolates them into complete trajectories using A* and perturbation algorithms. IPTG has specially designed loss functions that preserve topological constraints and semantic characteristics. Incorporating the prior knowledge of environment constraints and pedestrian walking patterns, the IPTG model is capable of generating topologically and logically sound indoor pedestrian trajectories. We evaluated the synthesized trajectories based on multiple metrics and examined the generated trajectories qualitatively. The results show that IPTG outperforms several baselines, demonstrating its ability to generate semantically meaningful and spatiotemporally coherent trajectories.

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An Indoor Crowd Movement Trajectory Benchmark Dataset

Cited by 11 publications

References 64 publications

Investigating factors in indoor transmission of respiratory disease through agent‐based modeling

Investigating factors in indoor transmission of respiratory disease through agent‐based modeling

TriPlan: an interactive visual analytics approach for better tourism route planning

A deep pedestrian trajectory generator for complex indoor environments

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