Urban planners often seek to establish land use patterns around transit stations that encourage non-auto travel. However, the willingness of travelers to use different modes in the vicinity of transit remains understudied, in part because of the lack of spatially-precise data on destination and mode choices. Using transportation content extracted from Yelp, a location-based social network (LBSN), we investigate how travel mode to non-work destinations is influenced by proximity to transit. We use textual analysis to analyze travel for non-work activities in seven cities across North America and Europe. Mixed-effect and binomial logistic models show how reported travel by mode varies by distance to rail transit stations. We find that for most non-work activity purposes, reported rail use is highly sensitive to proximity to stations, but some purposes are more amenable to rail use overall. Additionally, compared to non-US cities, US cities are far more parking-dependent near rail stations. The results suggest that not all activities elicit the same levels of non-auto travel, and transit-oriented planning should account for specific activities and regional factors that may modify willingness to travel by different modes. While subject to limitations, LBSNs can illuminate local travel with greater spatial specificity than traditional surveys.

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研究邻近铁路对非工作目的地出行的影响：来自北美和欧洲城市Yelp数据的证据

城市规划者经常寻求在公交车站周围建立土地使用模式，以鼓励非汽车出行。但是，旅行者在过境附近使用不同模式的意愿仍未得到充分研究，部分原因是缺乏关于目的地和模式选择的空间精确数据。使用从Yelp（基于位置的社交网络（LBSN））中提取的交通内容，我们研究到非工作目的地的出行方式如何受到交通便利程度的影响。我们使用文本分析来分析北美和欧洲七个城市的非工作旅行。混合效应和二项式物流模型显示了按模式报告的出行如何随距铁路车站的距离而变化。我们发现，出于大多数非工作活动的目的，报告的铁路使用对车站附近非常敏感，但某些目的更适合铁路使用。此外，与非美国城市相比，美国城市在火车站附近对停车的依赖程度更高。结果表明，并非所有活动都会引起相同水平的非汽车出行，而面向公交的规划应考虑到特定的活动和可能会改变不同出行意愿的区域性因素。尽管受到限制，但LBSN可以比传统调查以更大的空间特异性来阐明本地旅行。以过境为导向的规划应考虑到特定的活动和区域因素，这些因素可能会改变人们以不同方式出行的意愿。尽管受到限制，但LBSN可以比传统调查以更大的空间特异性来阐明本地旅行。以过境为导向的规划应考虑到特定的活动和区域因素，这些因素可能会改变人们以不同方式出行的意愿。尽管受到限制，但LBSN可以比传统调查以更大的空间特异性来阐明本地旅行。