Abstract Many studies have identified links between the built environment (BE) and transit use. However, little is known about whether the BE predictors of bus, train, tram and other transit modes are different. Studies to date typically analyze modes in combination; or analyze one mode at a time. A major barrier to comparing BE impacts on modes is the difference in the types of locations that tend to be serviced by each mode. A method is needed to account for this ‘mode location bias’ in order to draw robust comparison of the predictors of each mode. This study addresses this gap using data from Melbourne, Australia where three types of public transport modes (train, tram, bus) operate in tandem. Two approaches are applied to mitigate mode location bias: a) Co-located sampling – estimating ridership of different modes that are located in the same place; and b) Stratified BE sampling – observations are sampled from subcategories with similar BE characteristics. Regression analyses using both methods show that the BE variables impacting ridership vary by mode. Results from both samples suggest there are two common BE factors between tram and train, and between tram and bus; and three common BE factors between train and bus. The remaining BE predictors – three for train and tram and one for bus - are unique to each mode. The study's design makes it possible to confirm this finding is valid irrespective of the type of locations serviced by modes. This suggests planning and forecasting should consider the specific associations of different modes to their surrounding land use to accurately predict and match transit supply and demand. The Stratified sampling approach is recommended for treating location bias in future mode comparison, because it explains more ridership variability and offers a transferrable approach to generating representative samples.

中文翻译：

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解决建筑环境交通模式使用研究中的交通模式位置偏差

摘要 许多研究已经确定了建筑环境 (BE) 和交通使用之间的联系。然而，关于公共汽车、火车、电车和其他交通方式的 BE 预测因子是否不同，我们知之甚少。迄今为止的研究通常结合分析模式；或一次分析一种模式。比较 BE 对模式的影响的一个主要障碍是每种模式倾向于服务的位置类型的差异。需要一种方法来解释这种“模式位置偏差”，以便对每种模式的预测变量进行可靠的比较。本研究使用来自澳大利亚墨尔本的数据来解决这一差距，其中三种公共交通方式（火车、电车、公共汽车）同时运行。应用两种方法来减轻模式位置偏差：a) 同地抽样——估计位于同一地点的不同模式的乘客人数；b) 分层 BE 抽样——从具有相似 BE 特征的子类别中抽样观察。使用这两种方法的回归分析表明，影响乘客量的 BE 变量因模式而异。两个样本的结果表明电车和火车之间以及电车和公共汽车之间有两个常见的 BE 因素；以及火车和公共汽车之间的三个常见 BE 因素。其余的 BE 预测器——三个用于火车和电车，一个用于公共汽车——对于每种模式都是独一无二的。该研究的设计使得可以确认这一发现是有效的，而不管模式所服务的位置类型如何。这表明规划和预测应考虑不同模式与其周围土地利用的具体关联，以准确预测和匹配公交供需。建议使用分层抽样方法来处理未来模式比较中的位置偏差，因为它解释了更多的乘客变化并提供了一种可转移的方法来生成代表性样本。