基于改进McMaster算法的高密度停车区域识别及特征分析  

Identification and Characterization of High Density Parking Areas Based on Improved McMaster Algorithm

作　　者：战宇轩 杨勇[1] 王哲[1] 刘畅[1] ZHAN Yuxuan;YANG Yong;WANG Zhe;LIU Chang(China Academy of Transportation Sciences,Beijing 100029,China)

机构地区：[1]交通运输部科学研究院,北京100029

出　　处：《交通工程》2024年第4期84-90,共7页Journal of Transportation Engineering

摘　　要：高密度停车区域的识别是制定停车政策、规划设置停车设施的基础依据。但限于数据收集难度,现有研究无法整体识别城市高密度停车区域。本文通过收集海量OBD数据关联POI数据,采用回归拟合的方法识别出高密度停车区域停车数量阈值。基于改进McMaster算法,整体识别出北京市常发高密度停车区域。结合时间维度与空间维度,总结高密度停车区域特征,发现高密度停车区域部分时段存在大量空置车位现象,不同土地利用性质区域间车辆存在流动现象,车位使用潮汐现象明显。研究结果可为城市规划部门合理制定停车政策,精准规划设置停车设施提供依据。The identification of high-density parking areas is the basic basis for formulating parking policies and planning the installation of parking facilities.However,limited to the difficulty of data collection,the existing research can not identify urban high-density parking areas as a whole.In this paper,by collecting massive OBD data and correlating POI data,the regression fitting method is used to effectively identify the threshold value of the number of parking in high-density parking areas.Based on the improved McMaster algorithm,the high-density parking areas in Beijing are identified as a whole.Combining the time and space dimensions,the study summarizes the characteristics of high-density parking areas,and finds that there are a large number of vacant parking spaces in high-density parking areas in some time periods,and there is a flow phenomenon of vehicles between areas with different land-use properties,and there is an obvious phenomenon of tidal parking space use.The results of the study can provide a basis for urban planning departments to rationally formulate parking policies and accurately plan and set up parking facilities.

关 键 词：城市交通 特征识别 改进McMaster算法 高密度停车区域 

分 类 号：U491[交通运输工程—交通运输规划与管理]