Musakwa Walter, Selala Kadibetso M
University of Johanesburg, Johannesburg, Gauteng, South Africa.
University of Johannesburg, Department of Quality and Operations Management, South Africa.
Data Brief. 2016 Nov 9;9:898-905. doi: 10.1016/j.dib.2016.11.002. eCollection 2016 Dec.
Plans for smart mobility through cycling are often hampered by lack of information on cycling patterns and trends, particularly in cities of the developing world such as Johannesburg. Similarly, traditional methods of data collection such as bicycle counts are often expensive, cover a limited spatial extent and not up-to-date. Consequently, the dataset presented in this paper illustrates the spatial and temporal coverage of cycling patterns and trends in Johannesburg for the year 2014 derived from the geolocation based mobile application Strava. To the best knowledge of the authors, there is little or no comprehensive dataset that describes cycling patterns in Johannesburg. Perhaps this dataset is a tool that will support evidence based transportation planning and smart mobility.
通过骑行实现智能出行的计划往往因缺乏有关骑行模式和趋势的信息而受阻，尤其是在约翰内斯堡等发展中世界城市。同样，传统的数据收集方法，如自行车计数，往往成本高昂，空间覆盖范围有限且不及时。因此，本文展示的数据集说明了2014年约翰内斯堡基于地理位置的移动应用程序Strava得出的骑行模式和趋势的时空覆盖情况。据作者所知，几乎没有全面描述约翰内斯堡骑行模式的数据集。也许这个数据集是一个支持基于证据的交通规划和智能出行的工具。
