Fayyaz S S Kiavash, Liu Xiaoyue Cathy, Zhang Guohui
Department of Civil & Environmental Engineering, University of Utah, Salt Lake City, UT, United States of America.
Department of Civil & Environmental Engineering, University of Hawaii at Manoa, Holmes, Honolulu, HI, United States of America.
PLoS One. 2017 Oct 5;12(10):e0185333. doi: 10.1371/journal.pone.0185333. eCollection 2017.
The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George's transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis.
城市化地区的社会功能高度依赖于公共交通系统的便捷可达性，并得到其支持，特别是对于那些汽车拥有量受限的弱势群体而言。为了准确评估公共交通可达性，捕捉公交服务的时空变化至关重要。这可以通过测量每天每个时刻（例如每分钟）起讫点（OD）对之间的最短路径或最短出行时间来实现。近年来，通用公交数据规范（GTFS）数据因其在时空分析中的互操作性，在站点间出行时间估计方面越来越受欢迎。许多软件包，如ArcGIS，已经开发了工具箱来实现基于GTFS的出行时间估计。它们在计算特定时刻（如上午8:00）OD对之间的出行时间时表现良好，但随着数据维度的增加（如所有时刻和大型网络），可能会变得计算效率低下且不实用。在本文中，我们引入了一种计算优雅且数学高效的新算法来解决这个问题。开发了一个用C++编写的开源工具箱来实现该算法。我们在圣乔治市的公交网络上实现了该算法，以展示该工具箱实现的可达性分析。实验证据表明计算时间显著减少。所提出的算法和工具箱很容易转移到其他公交网络，以便公交机构和研究人员进行高分辨率的公交性能分析。
