Creating a cityscape conducive to a safe and efficient pedestrian experience requires a holistic understanding of the relationship between the built structures of a city and the movement of individuals within it. To empower policy makers to design, implement, and successfully deliver measures aimed at reducing footpath congestion and improving pedestrian safety the link between pedestrian volume and different features of the built environment needs to be investigated. Observed pedestrian counts at 50 intersections across the City of Melbourne are used as the input dependent variable of two regression models. A global ordinary least squares regression model and a local geographically weighted regression model are generated and evaluated for best fit of purpose. Spatiotemporal statistical handling is employed to clean the data of contextual anomalies. The output of the regression models identified eight key features as the most statistically significant predictors of pedestrian volume in Melbourne, Australia. These features include distance to schools and train stations and measures of footpath connectivity. This study reveals that due to significant spatial and temporal non-stationarity exhibited between pedestrian count sensors and built environment variables, the geographically weighted regression is the most appropriate modelling technique. This paper presents a methodology for the creation of a robust pedestrian prediction model.

创造一个有利于安全和高效行人体验的城市景观需要全面了解城市的建筑结构与其中的个人活动之间的关系。为了使政策制定者能够设计、实施和成功实施旨在减少人行道拥堵和提高行人安全的措施，需要调查行人流量与建筑环境不同特征之间的联系。在整个墨尔本市的 50 个十字路口观察到的行人计数被用作两个回归模型的输入因变量。生成和评估全局普通最小二乘回归模型和局部地理加权回归模型以达到最佳目的。采用时空统计处理来清理上下文异常的数据。回归模型的输出确定了八个关键特征作为澳大利亚墨尔本行人流量最具有统计意义的预测因子。这些特征包括到学校和火车站的距离以及人行道连通性的测量。这项研究表明，由于行人计数传感器和建筑环境变量之间表现出显着的空间和时间非平稳性，地理加权回归是最合适的建模技术。本文提出了一种创建稳健行人预测模型的方法。这些特征包括到学校和火车站的距离以及人行道连通性的测量。这项研究表明，由于行人计数传感器和建筑环境变量之间表现出显着的空间和时间非平稳性，地理加权回归是最合适的建模技术。本文提出了一种创建稳健行人预测模型的方法。这些特征包括到学校和火车站的距离以及人行道连通性的测量。这项研究表明，由于行人计数传感器和建筑环境变量之间表现出显着的空间和时间非平稳性，地理加权回归是最合适的建模技术。本文提出了一种创建稳健行人预测模型的方法。