Kálmán Kandó Faculty of Electrical Engineering, Department of Automation, University of Óbuda, Budapest, Hungary.
John von Neumann Faculty of Informatics, Biomatics and Applied Artificial Institution, Óbuda University, Budapest, Hungary.
PLoS One. 2022 Oct 6;17(10):e0274779. doi: 10.1371/journal.pone.0274779. eCollection 2022.
The discovery of human mobility patterns of cities provides invaluable information for decision-makers who are responsible for redesign of community spaces, traffic, and public transportation systems and building more sustainable cities. The present article proposes a possibilistic fuzzy c-medoid clustering algorithm to study human mobility. The proposed medoid-based clustering approach groups the typical mobility patterns within walking distance to the stations of the public transportation system. The departure times of the clustered trips are also taken into account to obtain recommendations for the scheduling of the designed public transportation lines. The effectiveness of the proposed methodology is revealed in an illustrative case study based on the analysis of the GPS data of Taxicabs recorded during nights over a one-year-long period in Budapest.
城市人类移动模式的发现为决策者提供了宝贵的信息，他们负责重新设计社区空间、交通和公共交通系统，并建设更可持续的城市。本文提出了一种可能性模糊 c-质心聚类算法来研究人类移动性。所提出的基于质心的聚类方法将步行距离内的典型移动模式分组到公共交通系统的车站。还考虑了聚类行程的出发时间，以获得对设计的公共交通线路调度的建议。该方法的有效性在基于布达佩斯一年内夜间记录的出租车 GPS 数据的实例研究中得到了验证。
