科研成果
博士生何文文,任福的论文在ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION刊出
发布时间:2021-12-03 15:55:45 发布者:易真 浏览次数:

标题: Predicting the Place Visited of Floating Car: A Three-Layer Framework Using Spatiotemporal Probability

作者: He, WW (He, Wenwen); Ren, F (Ren, Fu)

来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION: 10: 10文献号: 663 DOI: 10.3390/ijgi10100663出版年: OCT 2021

摘要: Human-flow pattern can reflect the urban population mobility and the urban operating state. Understanding the trajectory of urban-population moving patterns can improve the effectiveness of urban-management measures. While most of the existing studies on human moving have placed a huge emphasis on location forecasting through the types of activities humans take part in and urban land-use types, this type of forecasting research is limited to relying on specific activity types and land-use types. The urban-population moving pattern has spatial and temporal characteristics, and this feature greatly affects the prediction of where humans will visit. This study aimed to predict the possible places to visit by using the spatiotemporal model. We analyzed the itinerary characteristics of urban taxis and proposed a model based on the taxi itinerary characteristics to predict the drop-off locations. This model can be used to predict the possible arrival locations of urban taxis. We selected three grids of travel data from each period in another day to test the prediction accuracy of the proposed model. The results show that the model can predict the destination of urban taxis to a certain degree.

文献类型: Article

作者关键词: human flows; taxi trips; spatiotemporal moving patterns; location visited prediction

地址: [He, Wenwen; Ren, Fu] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Ren, Fu] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430074, Peoples R China.

通讯作者地址: Ren, F (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

Ren, F (通讯作者)Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430074, Peoples R China.

影响因子:2.899


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