[1]焦萍,王李轩,袁长伟,等.基于GWR模型的出租载客时空分异分析[J].长安大学学报(自然科学版),2020,40(5):77-86.
 JIAO Ping,WANG Li xuan,YUAN Chang wei,et al.A GWRbased study on spatialtemporal differentiation oftaxi pickup activities[J].Journal of Chang’an University (Natural Science Edition),2020,40(5):77-86.
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基于GWR模型的出租载客时空分异分析()
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长安大学学报(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
第40卷
期数:
2020年5期
页码:
77-86
栏目:
交通工程
出版日期:
2020-09-15

文章信息/Info

Title:
A GWRbased study on spatialtemporal differentiation oftaxi pickup activities
作者:
焦萍王李轩袁长伟耿新瑞
(1. 西安航空学院 经济管理学院,陕西 西安 710077; 2. 长安大学 经济与管理学院,陕西 西安 710064)
Author(s):
JIAO Ping WANG Lixuan YUAN Changwei GENG Xinrui
(1. School of Economics and Management, Xian Aeronautical University, Xian 710077, Shaanxi, China;2. School of Economics and Management, Changan University, Xian 710064, Shaanxi, China)
关键词:
交通工程时空特征地理加权回归模型核密度估计出租车轨迹GPS
Keywords:
traffic engineering spatialtemporal characteristic GWR model kernel density estimation taxi trajectory GPS
文献标志码:
A
摘要:
为充分挖掘出租车轨迹数据反映的运营规律,将城市电子地图兴趣点(point of interest,POI)数据作为影响参数纳入出租车轨迹数据研究中,探讨出租车载客运营的时空分异现象。通过处理出租车轨迹数据提取并统计各时段出租车上客量,证实出租车载客具有时间非平稳性;基于核密度估计方法对各载客高峰时段载客点聚类,分析出租车载客的空间非平稳性;构建载客热点提取模型,提取载客热点并定义热度值;从城市POI数据中筛选出显著影响载客热点分布及热度值的设施,计算其与各时段产生的载客热点的最短距离,分时段建立出租车载客地理加权回归(geographical weighted regression,GWR)模型,探讨载客热点与各类城市设施的时空关系,并以西安市出租车GPS数据为例展开实证分析,对提出的模型进行验证。结果表明:与普通线性回归模型(OLS)相比,地理加权回归模型拟合效果显著提高,拟合优度均值从0.29提高到0.57;在研究区域不同时间段,各类设施点回归系数均值存在明显时间差异,同类设施点回归系数分布存在明显空间差异;研究区域内出租车载客热点的时空分异现象大体符合城市居民出行规律,在工作日各高峰时段出租车发挥公共交通系统替补作用这一现象更为显著。研究结果可为出租车空间需求预测、出租车载客调度及价格调控精细化管理提供参考。
Abstract:
In order to mine the taxi operation law that reflected by the taxi trajectory data as much as possible, the POI data of the city was taken as the influence parameter into the data research of the taxi trajectories when the phenomenon of timespace difference of the taxi operation was discussed. The times of the pickup in each time interval were extracted and counted to confirm that the taxicab pickup events have time nonstationary, and the pickup points in each peak period were clustered by using the kernel density estimation, to confirm that the taxicab pickup events have the spatial nonstationarity. The hot spot extraction model was established to extract hot spots and defined the heat value, facilities that significantly affect the distribution and heat value of hot spots were picked from the urban POI data. In order to study the temporal and spatial characteristics of hot spots and various facilities of urban, the geographical weighted regression (GWR) model was established in different time periods by calculating the shortest distance between facilities and the hot spots. Additionally, a case study was conducted based on GPS data of taxies in Xian. The results show that comparing to the ordinary least squares(OLS)model,GWR provided a much better fit(with the average value of goodness of fit increasing from 0.29 to 0.57). At different time periods in the study area, the mean values of regression coefficients of various facilities have obvious temporal differences, in addition, the distribution of regression coefficients of similar facilities has obvious spatial differences. The spatialtemporal differentiation of hot spots in the study area generally conforms to the travel regularities of urban residents, and taxis play a more important role as a substitute for public transport system at the peak hours of working days. 〖JP2〗The research results can provide implications to the prediction of taxi spatial demand and fine management of taxi dispatch and price control. 3 tabs, 9 figs, 21 refs.

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更新日期/Last Update: 2020-10-12