The increase in the share of motorization in short-distance trips is a significant contributor to the decline in the share of active travel (AT) and will further pose a challenge to sustainable transport. While many studies have explored the relationship between the built environment (BE) and AT, few have focused on short trips. Additionally, most studies have ignored the important role of the streetscape. To address these gaps, this study utilizes street view big data to quantify street view elements and applies extreme gradient boosting decision trees (XGBoost) to 2020 household travel data in Wuhan. The results indicate that streetscape attributes are more important than land use in predicting short-distance AT, with streetscape being more than 40% relative importance in both models. The contribution of almost all streetscape elements cannot be ignored. Among them, the percentage of driveways showed the highest predictive power. Among land use attributes, population density has the highest relative importance. In addition, most of the independent variables are nonlinearly correlated with the explanatory variables, and this study quantified these association thresholds. These results suggest that optimizing the street built environment has the potential to promote a shift from short-distance driving to AT. The quantification of correlation thresholds provides precise empirical evidence for built environment interventions that promote short-distance AT.

中文翻译：

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为什么选择主动旅行而不是开车？调查街景和土地利用对短途出行主动出行的影响


短途出行中机动化比例的增加是主动出行（AT）比例下降的重要原因，并将进一步对可持续交通构成挑战。虽然许多研究探讨了建筑环境 (BE) 和 AT 之间的关系，但很少有研究关注短途旅行。此外，大多数研究都忽略了街景的重要作用。为了解决这些差距，本研究利用街景大数据来量化街景元素，并将极端梯度提升决策树（XGBoost）应用于2020年武汉家庭出行数据。结果表明，在预测短距离 AT 时，街景属性比土地利用更重要，街景在两个模型中的相对重要性均超过 40%。几乎所有街景元素的贡献都不可忽视。其中，车道的百分比显示出最高的预测能力。在土地利用属性中，人口密度的相对重要性最高。此外，大多数自变量与解释变量呈非线性相关，本研究量化了这些关联阈值。这些结果表明，优化街道建筑环境有可能促进短距离驾驶向自动驾驶的转变。相关阈值的量化为促进短距离 AT 的建筑环境干预措施提供了精确的经验证据。