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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.09998 |
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| _version_ | 1866913979411988480 |
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| author | Han, Chaeyeon Seshadri, Pavan Ding, Yiwei Posner, Noah Koo, Bon Woo Agrawal, Animesh Lerch, Alexander Guhathakurta, Subhrajit |
| author_facet | Han, Chaeyeon Seshadri, Pavan Ding, Yiwei Posner, Noah Koo, Bon Woo Agrawal, Animesh Lerch, Alexander Guhathakurta, Subhrajit |
| contents | While various sensors have been deployed to monitor vehicular flows, sensing pedestrian movement is still nascent. Yet walking is a significant mode of travel in many cities, especially those in Europe, Africa, and Asia. Understanding pedestrian volumes and flows is essential for designing safer and more attractive pedestrian infrastructure and for controlling periodic overcrowding. This study discusses a new approach to scale up urban sensing of people with the help of novel audio-based technology. It assesses the benefits and limitations of microphone-based sensors as compared to other forms of pedestrian sensing. A large-scale dataset called ASPED is presented, which includes high-quality audio recordings along with video recordings used for labeling the pedestrian count data. The baseline analyses highlight the promise of using audio sensors for pedestrian tracking, although algorithmic and technological improvements to make the sensors practically usable continue. This study also demonstrates how the data can be leveraged to predict pedestrian trajectories. Finally, it discusses the use cases and scenarios where audio-based pedestrian sensing can support better urban and transportation planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensors Han, Chaeyeon Seshadri, Pavan Ding, Yiwei Posner, Noah Koo, Bon Woo Agrawal, Animesh Lerch, Alexander Guhathakurta, Subhrajit Audio and Speech Processing Artificial Intelligence Machine Learning Multimedia Sound While various sensors have been deployed to monitor vehicular flows, sensing pedestrian movement is still nascent. Yet walking is a significant mode of travel in many cities, especially those in Europe, Africa, and Asia. Understanding pedestrian volumes and flows is essential for designing safer and more attractive pedestrian infrastructure and for controlling periodic overcrowding. This study discusses a new approach to scale up urban sensing of people with the help of novel audio-based technology. It assesses the benefits and limitations of microphone-based sensors as compared to other forms of pedestrian sensing. A large-scale dataset called ASPED is presented, which includes high-quality audio recordings along with video recordings used for labeling the pedestrian count data. The baseline analyses highlight the promise of using audio sensors for pedestrian tracking, although algorithmic and technological improvements to make the sensors practically usable continue. This study also demonstrates how the data can be leveraged to predict pedestrian trajectories. Finally, it discusses the use cases and scenarios where audio-based pedestrian sensing can support better urban and transportation planning. |
| title | Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensors |
| topic | Audio and Speech Processing Artificial Intelligence Machine Learning Multimedia Sound |
| url | https://arxiv.org/abs/2406.09998 |