Machine Learning-Based Walkability Modeling In Urban Life Circle
Pixin Gong School of Architecture and Art, North China University of Technology
Xiaoran Huang School of Architecture and Art, North China University of Technology
Chenyu Huang School of Architecture and Art, North China University of Technology
Marcus White Swinburne University of Technology
With China’s fast urbanization, the study of the walkability of residents’ life circles has become critical to improve people’s quality of life. Traditional walkability calculations are based on Lawrence Frank’s theory. However, the weighted calculation method cannot be adapted to ever-changing and complicated scenarios as the scope and topic of research transforming. This study investigated walkability at the community level by combining machine learning techniques with multi-source data. Feature indicators affecting walkability were estimated from multi-source data. Machine learning was used to refine the weighting calculation under the previous indicator framework. We compared the performance of 20 regression models from 6 different machine learning algorithms for estimating the walkability of 14578 communities in downtown Shanghai. It is concluded that the Bagged Tree Model (R2=0.86, RMSE=0.36862) achieves the best performance, which is used to revise the initial walkability index values. The workflow proposed in this paper allows for rapid application of expert empirical consensus to comprehensive urban design and detailed urban governance in the future. References. Vichiensan, V. , & Nakamura, K. . (2021). Walkability perception in asian cities: a comparative study in bangkok and nagoya. Sustainability, 13. Sun, D. , Shan, S. , & WuTinghai. (2012). Life circle theory based county public service distribution:jiangsu pizhou case. Planners. Frank, L. , Devlin, A. , Johnstone, S. , & Loon, J. V. . (2010). Neighbourhood design, travel, and health in Metro Vancouver: Using a walkability index. Frank, L. D., Sallis, J. F., Conway, T. L., Chapman, J. E., Saelens, B. E., & Bachman, W. (2006). Many pathways from land use to health: associations between neighborhood walkability and active transportation, body mass index, and air quality. Journal of the American planning Association, 72(1), 75-87. Ewing, R., & Handy, S. (2009). Measuring the unmeasurable: Urban design qualities related to walkability. Journal of Urban design, 14(1), 65-84. Daniel, P. , & Burns, L. . (2018). How steep is that street?: Mapping ‘real’ pedestrian catchments by adding elevation to street networks. Labdaoui, K. , Mazouz, S. , Moeinaddini, M. , Cools, M. , & Teller, J. . (2021). The street walkability and thermal comfort index (swtci): a new assessment tool combining street design measurements and thermal comfort. Science of The Total Environment, 148663. Wei, Y. D., Xiao, W., Wen, M., & Wei, R. (2016). Walkability, land use and physical activity. Sustainability, 8(1), 65. Zandieh, R., Flacke, J., Martinez, J., Jones, P., & Van Maarseveen, M. (2017). Do inequalities in neighborhood walkability drive disparities in older adults’ outdoor walking?. International journal of environmental research and public health, 14(7), 740. Yamagata, Y., Murakami, D., & Yoshida, T. (2020). Evaluating walkability using mobile GPS data. In Spatial Analysis Using Big Data (pp. 239-257). Academic Press. Yin, L., & Wang, Z. (2016). Measuring visual enclosure for street walkability: Using machine learning algorithms and Google Street View imagery. Applied geography, 76, 147-153. Ewing, R., Handy, S., Brownson, R.C., Clemente, O., Winston, E., 2006. Identifying and measuring urban design qualities related to walkability. J. Phys. Act. Health 3, S223
Keywords: Life Circle, Walkability Indicator, Multi-Source Data, Machine Learning, Refined Urban Design, Sdg 3, Sdg 10, Sdg 11.