{"title":"基于时空加权回归的专车需求时空影响因素研究","authors":"Zilin Chen, Haiyang Yu, Runkun Liu","doi":"10.1109/TOCS53301.2021.9688587","DOIUrl":null,"url":null,"abstract":"Ridesplitting is an effective mode of sustainable transportation and has rapidly spread in China. So, it is necessary to study the influencing factors of ridesplitting. However, as an important dimension, time has not been incorporated into the traditional model. In this paper, the GTWR model is be used to explore the spatiotemporal factors of ridesplitting demand, and compared with the traditional GWR model, the results show that GTWR model has stronger explanatory capabilities. Furthermore, we visualize the temporal and spatial heterogeneity of model coefficients to analyze the spatiotemporal correlation between ridesplitting demand and influencing factors. Based on this, we find that the impact of built environmental factors on ridesplitting demand will change over time.","PeriodicalId":360004,"journal":{"name":"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Exploring the Spatiotemporal Factors of Ridesplitting Demand Based on the Geographically and Temporally Weighted Regression\",\"authors\":\"Zilin Chen, Haiyang Yu, Runkun Liu\",\"doi\":\"10.1109/TOCS53301.2021.9688587\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ridesplitting is an effective mode of sustainable transportation and has rapidly spread in China. So, it is necessary to study the influencing factors of ridesplitting. However, as an important dimension, time has not been incorporated into the traditional model. In this paper, the GTWR model is be used to explore the spatiotemporal factors of ridesplitting demand, and compared with the traditional GWR model, the results show that GTWR model has stronger explanatory capabilities. Furthermore, we visualize the temporal and spatial heterogeneity of model coefficients to analyze the spatiotemporal correlation between ridesplitting demand and influencing factors. Based on this, we find that the impact of built environmental factors on ridesplitting demand will change over time.\",\"PeriodicalId\":360004,\"journal\":{\"name\":\"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TOCS53301.2021.9688587\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TOCS53301.2021.9688587","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exploring the Spatiotemporal Factors of Ridesplitting Demand Based on the Geographically and Temporally Weighted Regression
Ridesplitting is an effective mode of sustainable transportation and has rapidly spread in China. So, it is necessary to study the influencing factors of ridesplitting. However, as an important dimension, time has not been incorporated into the traditional model. In this paper, the GTWR model is be used to explore the spatiotemporal factors of ridesplitting demand, and compared with the traditional GWR model, the results show that GTWR model has stronger explanatory capabilities. Furthermore, we visualize the temporal and spatial heterogeneity of model coefficients to analyze the spatiotemporal correlation between ridesplitting demand and influencing factors. Based on this, we find that the impact of built environmental factors on ridesplitting demand will change over time.