publications

@article{han_understanding_2024,

title = {Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensors},

author = {Chaeyeon Han and Pavan Seshadri and Yiwei Ding and Noah Posner and Bon Woo Koo and Animesh Agrawal and Alexander Lerch and Subhrajit Guhathakurta},

url = {https://doi.org/10.1007/s44212-024-00053-9},

doi = {10.1007/s44212-024-00053-9},

issn = {2731-6963},

year  = {2024},

date = {2024-07-01},

urldate = {2024-07-10},

journal = {Urban Informatics},

volume = {3},

number = {1},

pages = {22},

abstract = {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.},

keywords = {Active mobility, Audio-based, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Multimedia, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Pedestrian, Sensors},

pubstate = {published},

tppubtype = {article}

}