Tribby Calvin P, Miller Harvey J, Brown Barbara B, Werner Carol M, Smith Ken R
Department of Geography, The Ohio State University.
Department of Family and Consumer Studies, University of Utah.
Environ Plan B Urban Anal City Sci. 2017 Nov;44(6):1145-1167. doi: 10.1177/0265813516659286. Epub 2016 Jul 20.
Walking is a form of active transportation with numerous benefits, including better health outcomes, lower environmental impacts and stronger communities. Understanding built environmental associations with walking behavior is a key step towards identifying design features that support walking. Human mobility data available through GPS receivers and cell phones, combined with high resolution walkability data, provide a rich source of georeferenced data for analyzing environmental associations with walking behavior. However, traditional techniques such as route choice models have difficulty with highly dimensioned data. This paper develops a novel combination of a data-driven technique with route choice modeling for leveraging walkability audits. Using data from a study in Salt Lake City, Utah, USA, we apply the data-driven technique of random forests to select variables for use in walking route choice models. We estimate data-driven route choice models and theory-driven models based on predefined walkability dimensions. Results indicate that the random forest technique selects variables that dramatically improve goodness of fit of walking route choice models relative to models based on predefined walkability dimensions. We compare the theory-driven and data-driven walking route choice models based on interpretability and policy relevance.
步行是一种主动出行方式，有诸多益处，包括带来更好的健康结果、降低环境影响以及增强社区凝聚力。了解建成环境与步行行为之间的关联是确定支持步行的设计特征的关键一步。通过全球定位系统（GPS）接收器和手机获取的人类移动性数据，结合高分辨率的步行适宜性数据，为分析环境与步行行为之间的关联提供了丰富的地理参考数据源。然而，诸如路线选择模型等传统技术在处理高维度数据时存在困难。本文开发了一种数据驱动技术与路线选择建模的新颖组合，以利用步行适宜性审计。利用美国犹他州盐湖城一项研究的数据，我们应用随机森林的数据驱动技术来选择用于步行路线选择模型的变量。我们基于预定义的步行适宜性维度估计数据驱动的路线选择模型和理论驱动的模型。结果表明，相对于基于预定义步行适宜性维度的模型，随机森林技术选择的变量显著提高了步行路线选择模型的拟合优度。我们基于可解释性和政策相关性比较了理论驱动和数据驱动的步行路线选择模型。
