摘要
城市大数据为探索城市内部居民出行的行为特征提供数据支撑。本文将以兰州市出租车GPS轨迹数据为基础,结合数据挖掘和可视化技术,研究兰州市城市居民出行规律和城市空间交互特征。首先,分析4个城区居民出行特征和城区间空间交互特征;然后,采用城市栅格方法,统计分析城市栅格空间之间的交通出行量,并采用CLARA聚类算法识别工作日和周末的城市交通热点区域;最后,建立有向加权复杂网络模型,分析城市交通热点区域之间的空间交互强度。研究结果表明,在工作日和周末兰州市居民出行行为时空特征和城市空间交互特征都存在明显差异,相比于周末,工作日出行更加紧凑密集且具有较强目的性,出行量的聚类结构总体呈现与兰州市河谷型地形相匹配的“哑铃”状分布形状,接近城市中心的相邻聚类区域之间空间交互强度较强。该研究结果可为城市交通管理和居民出行提供决策服务。
Urban big data provides data support for exploring the behavior characteristics of urban residents’travel.Combining data mining with visualization technology,the resident travel law and urban space interaction characteristics of Lanzhou are studied based on the taxi GPS trajectory.Firstly,the travel characteristics and the inter-district spatial interaction characteristics of the four districts are analyzed.Then,the traffic trips between the urban grid and urban traffic hotspots of weekday and weekend are studied by the CLARA clustering algorithm.Finally,a directed-weighted complex network model is established to analyze the space interaction characteristics between urban traffic hotspots.The results show that there are significant differences in the spatial and temporal characteristics of urban travel behaviors and urban space interaction characteristics in the weekday and weekend.Compared with weekend,urban travel in the weekday are more compact and purposeful.The cluster structure of travel topology presents a"dumbbell"distribution shape matching the valley topology of Lanzhou.The spatial interaction between adjacent clustering areas close to the city center is strong.The results of this study can provide decision-making services for urban traffic management and residents’travel.
作者
杨文亮
冯慧芳
YANG Wen-liang;FENG Hui-fang(College of Mathematics & Statistics, Northwest Normal University, Lanzhou 730070, China)
出处
《计算机与现代化》
2021年第1期87-93,共7页
Computer and Modernization
基金
国家自然科学基金资助项目(71761031)。
关键词
居民出行
交互特征
交通热点
CLARA聚类算法
GPS轨迹
resident travel
interaction characteristics
traffic hotspots
CLARA clustering algorithm
GPS trajectory
