Big data from smartphone applications are enabling travel behavior studies at an unprecedented scale. In this paper, we examine pedestrian route choice preferences in San Francisco, California using a large, anonymized dataset of walking trajectories collected from an activity-based smartphone application. We study the impact of various street attributes known to affect pedestrian route choice from prior literature. Unlike most studies, where data has been constrained to a particular destination type (e.g. walking to transit stations) or limited in volume, a large number of actual trajectories presented here include a wide diversity of destinations and geographies, allowing us to describing typical pedestrians’ preferences in San Francisco as a whole. Other innovations presented in the paper include using a novel technique for generating alternative paths for route choice estimation and gathering previously hard-to-get route attribute information by computationally processing a large set of Google Street View images. We also demonstrate how the estimated coefficients can be operationalized for policy and planning to describe pedestrian accessibility to BART stations in San Francisco using ‘perceived distance’ as opposed to traversed distance.

来自智能手机应用程序的大数据正在以前所未有的规模支持旅行行为研究。在本文中，我们使用从基于活动的智能手机应用程序收集的步行轨迹的大型匿名数据集来研究加利福尼亚州旧金山的行人路线选择偏好。我们研究了已知影响先前文献中行人路线选择的各种街道属性的影响。与大多数研究不同，数据被限制在特定的目的地类型（例如步行到公交站）或数量有限，这里呈现的大量实际轨迹包括广泛多样的目的地和地理位置，使我们能够描述典型的行人整个旧金山的偏好。论文中提出的其他创新包括使用一种新技术为路线选择估计生成替代路径，并通过计算处理大量 Google 街景图像来收集以前难以获得的路线属性信息。我们还展示了如何将估计系数用于政策和规划，以使用“感知距离”而不是穿越距离来描述旧金山 BART 车站的行人可达性。