基于出租车GPS轨迹的城市区域时空交互特征分析

计算机与现代化 ›› 2021, Vol. 0 ›› Issue (01): 87-93.

基于出租车GPS轨迹的城市区域时空交互特征分析

(西北师范大学数学与统计学院,甘肃兰州730070)

出版日期:2021-01-28 发布日期:2021-01-29
作者简介:杨文亮(1995—),男,甘肃天水人，硕士研究生,研究方向:统计学习及大数据分析,E-mail: 1528467910@qq.com; 通信作者:冯慧芳(1971—)，女,教授,博士,CCF会员,研究方向:车载自组织网络,统计学习及大数据分析,E-mail: hffeng@nwnu.edu.cn。
基金资助:
国家自然科学基金资助项目(71761031)

Analysis of Spatio-temporal Interaction Characteristics of Urban Area Based on Taxi GPS Trajectory

(College of Mathematics & Statistics, Northwest Normal University, Lanzhou 730070, China)

Online:2021-01-28 Published:2021-01-29

摘要/Abstract

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。

关键词: 居民出行, 交互特征, 交通热点, CLARA聚类算法, GPS轨迹

Abstract: 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.

Key words: resident travel, interaction characteristics, traffic hotspots, CLARA clustering algorithm, GPS trajectory

杨文亮, 冯慧芳. 基于出租车GPS轨迹的城市区域时空交互特征分析[J]. 计算机与现代化, 2021, 0(01): 87-93.

YANG Wen-liang, FENG Hui-fang. Analysis of Spatio-temporal Interaction Characteristics of Urban Area Based on Taxi GPS Trajectory[J]. Computer and Modernization, 2021, 0(01): 87-93.

参考文献［23］

［1］	MA X L, WU Y J, WANG Y H, et al. Mining smart card data for transit riders’ travel patterns［J］. Transportation Research Part C: Emerging Technologies, 2013,36:1-12.
［2］	ZHANG Y P, MARTENS K, LONG Y. Revealing group travel behavior patterns with public transit smart card data［J］. Travel Behaviour and Society, 2018,10:42-52.
［3］	ZHANG Y P, LIU L. Understanding temporal pattern of human activities using temporal areas of interest［J］. Applied Geography, 2018,94:95-106.
［4］	ZHANG Y P, LIU L, WANG H. A new perspective on the temporal pattern of human activities in cities: The case of Shanghai［J］. Cities, 2019,87:196-204.
［5］	刘瑜,肖昱,高松,等. 基于位置感知设备的人类移动研究综述［J］. 地理与地理信息科学, 2011,27(4):8-13.
［6］	杨喜平,方志祥. 移动定位大数据视角下的人群移动模式及城市空间结构研究进展［J］. 地理科学进展, 2018,37(7):880-889.
［7］	SAGL G, DELMELLE E, DELMELLE E. Mapping collective human activity in an urban environment based on mobile phone data［J］. Cartography and Geographic Information Science, 2014,41(3):272-285.
［8］	徐金垒,方志祥,萧世伦,等. 城市海量手机用户停留时空分异分析:以深圳市为例［J］. 地球信息科学学报, 2015,17(2):197-205.
［9］	曹劲舟,涂伟,李清泉,等. 基于大规模手机定位数据的群体活动时空特征分析［J］. 地球信息科学学报, 2017,19(4):467-474.
［10］	ZHAO Z L, SHAW S L, XU Y, et al. Understanding the bias of call detail records in human mobility research［J］. International Journal of Geographical Information Science, 2016,30(9):1738-1762.
［11］	STEIGER E, WESTERHOLT R, RESCH B, et al. Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data［J］. Computers, Environment and Urban Systems, 2015,54:255-265.
［12］	HU W S, JIN P J. An adaptive hawkes process formulation for estimating time-of-day zonal trip arrivals with location-based social networking check-in data［J］. Transportation Research Part C: Emerging Technologies, 2017,79:136-155.
［13］	李丛敏,李杰,张康,等. 面向签到日志的用户行为模式交互探索［J］. 软件学报, 2019,30(6):1819-1834.
［14］	ZHANG J Q, CHEN Z H, LIU Y Q, et al. Space-time visualization analysis of bus passenger big data in Beijing［J］. Cluster Computing, 2018,21(1):813-825.
［15］	张欣环,吴一昊,吴金洪,等. 基于IC卡和GPS信息的城市常规公交客流分析［J］. 浙江师范大学学报(自然科学版), 2019,42(2):208-214.
［16］	YU W, BAI H, CHEN J, et al. Analysis of space-time variation of passenger flow and commuting characteristics of residents using smart card data of Nanjing metro［J］. Sustainability, 2019,11(18):4989-5007.
［17］	张超,潘海啸. 市郊轨道交通站点周边居民购物出行研究:以上海市为例［J］. 城市规划, 2019,43(3):96-103.
［18］	张俊涛,武芳,张浩. 利用出租车轨迹数据挖掘城市居民出行特征［J］. 地理与地理信息科, 2015,31(5):104-108.
［19］	程静,刘家骏,高勇. 基于时间序列聚类方法分析北京出租车出行量的时空特征［J］. 地球信息科学学报, 2016,18(9):1227-1239.
［20］	ZHANG H, SHI B Y, ZHUGE C X, et al. Detecting taxi travel patterns using GPS trajectory data: A case study of Beijing［J］. KSCE Journal of Civil Engineering, 2019,23(4):1797-1805.
［21］	ZHANG S, LIU X, TANG J J, et al. Urban spatial structure and travel patterns: Analysis of weekday and holiday travel using inhomogeneous Poisson point process models［J］. Computers, Environment and Urban Systems, 2019,73:68-84.
［22］	FAN P L, XU L L, YUE W Z, et al. Accessibility of public urban green space in an urban periphery: The case of Shanghai［J］. Landscape & Urban Planning, 2017,165:177-192.
［23］	KAUFMAN L, ROUSSEEUW P J. Finding Groups in Data: An Introduction to Cluster Analysis［M］. Hoboken, New Jersey, USA: John Wiley & Sons, 1990.

[1]	王盼红, 朱昌明. 融合CNN与交互特征的多标签图像分类方法[J]. 计算机与现代化, 2022, 0(09): 85-92.
[2]	张小芳, 冯慧芳. 基于轨迹大数据的动态最优路径规划[J]. 计算机与现代化, 2021, 0(11): 82-88.