摘要
现有的城市居民出行调查周期较长,交通小区划分粒度粗糙,导致调查不能及时准确地获取居民出行信息。针对该问题,提出了一种基于手机信令数据的城市居民动态OD矩阵提取方法。首先,针对信令数据中的两种复杂噪声:乒乓切换和漂移数据,提出了基于窗口阈值的检测与等效位置替换方法,以及复杂漂移点的检测和标记处理方法;然后,提出一种改进的ST-DBSCAN聚类方法,引入一种等时化方法将时间信息与空间信息相结合,识别出行过程中的驻留点;最后,基于地理信息系统构建含有道路关键节点的路网,将居民出行OD与路网节点相匹配,有效推导出城市居民动态OD矩阵。实验结果表明:与ST-DBSCAN算法相比,所提改进的ST-DBSCAN算法在聚类效果和识别速度上分别提升了6.10%和5.26%;与统计方法和二阶统计量方法相比,基于改进的ST-DBSCAN算法的动态OD矩阵提取方法在均方误差(MSE)上分别降低了16.98%和21.55%。以北京市为例,运用提出的动态OD矩阵提取方法,能够及时有效地分析城市居民日常与高峰时段的出行特征。
Previous surveys of urban resident travel were hindered by prolonged durations and insufficient granularity in traffic zone divisions,which impeded the timely and accurate acquisition of travel data.To address this issue,this study proposed a method for extracting the dynamic origin-destination(OD)matrix of urban residents based on mobile phone signaling data.Firstly,methods to address two complex types of noise inherent in the signaling data:ping-pong switching data and drifting data were proposed.Specifically,a window thresholdbased detection and equivalent location replacement method for ping-pong switching data was proposed,as well as a complex drift point detection and marking method for drifting data.Secondly,an enhanced ST-DBSCAN clustering algorithm was proposed,which incorporated a temporal isochronization method to integrate temporal and spatial information,enabling the identification of dwell points during travel.Finally,a road network with key nodes was established using geographic information system(GIS),aligning resident travel OD with the network nodes to effectively derive the dynamic OD matrix of urban residents.Experimental results showed that the enhanced STDBSCAN clustering algorithm outperformed the traditional ST-DBSCAN,improving clustering efficiency by 6.10%and identification speed by 5.26%.Furthermore,the dynamic OD matrix extraction method based on the enhanced ST-DBSCAN clustering algorithm achieved approximately 16.98%and 21.55%reductions in mean squared error compared to the conventional statistical methods and the second-order statistical methods,respectively.By applying the proposed dynamic OD matrix extraction method to the case of Beijing,this study was able to conduct timely and effective analyses of daily and peak travel patterns of urban residents.
作者
田钊
张乾钟
赵轩
陈斌
佘维
杨艳芳
TIAN Zhao;ZHANG Qianzhong;ZHAO Xuan;CHEN Bin;SHE Wei;YANG Yanfang(School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450001,China;School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China;China Academy of Transportation Sciences,Beijing 100029,China;Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing 100029,China)
出处
《郑州大学学报（工学版）》
CAS
北大核心
2024年第3期46-54,共9页
Journal of Zhengzhou University（Engineering Science）
基金
河南省重点研发与推广专项基金资助项目(212102310039)
河南省高校科技创新人才支持计划基金资助项目(21HASTIT031)
综合交通运输大数据应用技术交通运输行业重点实验室开放课题(2022B1201)。
关键词
城市出行
智能交通系统
手机信令数据
动态OD矩阵
驻留点识别
时空特征分析
city travel
intelligent traffic system
cell phone signaling data
dynamic origin-destination matrix
dwell point identification
spatial-temporal characteristics analysis
