Abstract
Urban open spaces provides various benefits to citizens, but the thermal environment under this space is being affected by the accelerated urbanization and global warming. Based on this, this paper is dedicated to conducting research on improving the attractiveness of outdoor environmental spaces and improving outdoor thermal comfort. The main work of this paper is first to propose a street comfort model by considering both environmental and climatic factors, which is trained to learn using indirect data. Secondly, the comfort level of each street is combined with the frequency of non-motorized trips on that street to obtain the urgency index of rectification for that street and to achieve accurate recommendations for urban planning. Considering the public accessibility of the data in the paper in cities across China, this study can be easily deployed to other cities to support urban planning and provide useful recommendations for improvement of urban open spaces.
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**e, L., Yu, Z., Huang, F., Zhu, D. (2023). Recommendations for Urban Planning Based on Non-motorized Travel Data and Street Comfort. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_3
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DOI: https://doi.org/10.1007/978-3-031-32910-4_3
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