PDF(Pubmed)
Abstract：
Travel times between different locations form the basis for most contemporary measures of spatial accessibility. Travel times allow to estimate the potential for interaction between people and places, and is therefore a vital measure for understanding the functioning, sustainability, and equity of cities. Here, we provide an open travel time matrix dataset that describes travel times between the centroids of all cells in a grid (N = 13,132) covering the metropolitan area of Helsinki, Finland. The travel times recorded in the dataset follow a door-to-door approach that provides comparable travel times for walking, cycling, public transport and car journeys, including all legs of each trip by each mode, such as the walk to a bus stop, or the search for a parking spot. We used the r5py Python package, that we developed specifically for this computation. The data are sensitive to diurnal variations and to variations between people (e.g. slow and fast walking speed). We validated the data against the Google Directions API and present use cases from a planning practice. The five key principles that guided the data set design and production - comparability, simplicity, reproducibility, transferability, and sensitivity to temporal and interpersonal variations - ensure that urban and transport planners, business and researchers alike can use the data in a wide range of applications.
摘要：
不同地点之间的旅行时间构成了大多数当代空间可达性衡量标准的基础。旅行时间允许估计人与地方之间互动的潜力，因此是理解功能的重要措施，可持续性和城市的公平。这里,我们提供了一个开放的旅行时间矩阵数据集，该数据集描述了覆盖赫尔辛基都会区的网格（N=13,132）中所有单元格的质心之间的旅行时间，芬兰。数据集中记录的旅行时间遵循门到门的方法，为步行提供可比的旅行时间，骑自行车,公共交通和汽车旅行，包括每种模式的每次行程的所有行程，比如步行到公共汽车站，或者寻找停车位。我们使用了r5pyPython包，我们专门为这种计算开发的。数据对昼夜变化和人之间的变化(例如,缓慢和快速的步行速度)敏感。我们根据GoogleDirectionsAPI验证了数据，并从规划实践中展示了用例。指导数据集设计和生产的五个关键原则-可比性，简单，再现性，可转移性，以及对时间和人际变化的敏感性-确保城市和交通规划者，企业和研究人员都可以在广泛的应用程序中使用这些数据。
