Walking-friendly cities not only promote health and environmental benefits but also play crucial roles in urban development and local economic revitalization. Generally, pedestrian interviews and surveys used to evaluate walkability, but this is expensive and lack professional insight. To address limitations in current methods for evaluating pedestrian pathways, we propose a novel approach utilizing wearable sensors and deep learning.

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[1] Do-Eun Park, Jong-Hoon Youn, Teuk-Seob Song, "Experimental Source", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/nftc-1w10. Accessed: Mar. 22, 2025.
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doi = {10.21227/nftc-1w10},
url = {http://dx.doi.org/10.21227/nftc-1w10},
author = {Do-Eun Park; Jong-Hoon Youn; Teuk-Seob Song },
publisher = {IEEE Dataport},
title = {Experimental Source},
year = {2024} }
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AU - Do-Eun Park; Jong-Hoon Youn; Teuk-Seob Song
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Do-Eun Park, Jong-Hoon Youn, Teuk-Seob Song. (2024). Experimental Source. IEEE Dataport. http://dx.doi.org/10.21227/nftc-1w10
Do-Eun Park, Jong-Hoon Youn, Teuk-Seob Song, 2024. Experimental Source. Available at: http://dx.doi.org/10.21227/nftc-1w10.
Do-Eun Park, Jong-Hoon Youn, Teuk-Seob Song. (2024). "Experimental Source." Web.
1. Do-Eun Park, Jong-Hoon Youn, Teuk-Seob Song. Experimental Source [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/nftc-1w10
Do-Eun Park, Jong-Hoon Youn, Teuk-Seob Song. "Experimental Source." doi: 10.21227/nftc-1w10