Benjamin G. Ethier , Jeffrey S. Wilson , Sarah M. Camhi , Ling Shi , Philip J. Troped
{"title":"分析日常活动场所的建筑环境特征以及与共享单车使用的关联性","authors":"Benjamin G. Ethier , Jeffrey S. Wilson , Sarah M. Camhi , Ling Shi , Philip J. Troped","doi":"10.1016/j.tbs.2024.100850","DOIUrl":null,"url":null,"abstract":"<div><p>A limited number of studies using static spatial approaches have found that built environment variables are associated with bike share use and fewer have used spatially dynamic activity spaces to examine these relationships. The aim of this pilot study was to examine associations between built environment characteristics of daily activity spaces and bike share using three different geographic information system methods. Thirty-two adult members of Boston’s Blue Bikes bike share wore a GPS unit for up to 7 days. GPS points were used to create buffered track, minimum convex hull (MCH), and standard deviational ellipse (SDE) activity spaces. Multilevel logistic regression was used to estimate associations between docking station density, overall bicycle network density, shared-use trail density, intersection density, land use mix, and greenness, with bike share use. Bike share station density within SDE activity spaces showed a significant positive association with bike share (odds ratio (OR) = 1.19; 95 % confidence interval (CI): 1.02, 1.39). Total bike network and shared-use trail densities within MCH activity spaces were positively associated with bike share (OR = 1.13; 95 % CI: 1.02, 1.26 and OR = 1.75; 95 % CI: 1.06, 2.89, respectively). Intersection density within SDE activity spaces was inversely associated with bike share (OR = 0.91; 95 % CI: 0.83, 0.99). GPS tracking of individuals allowed for spatially and temporally dynamic identification of environmental exposures potentially relevant to bike share use. Overall, the findings are consistent with prior research on the environmental correlates of bike share and reinforce the importance of bicycle infrastructure to support greater bike share use. At the same time larger studies are needed to explore optimal geographic methods to define activity spaces in relation to bike share.</p></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"37 ","pages":"Article 100850"},"PeriodicalIF":5.1000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An analysis of built environment characteristics in daily activity spaces and associations with bike share use\",\"authors\":\"Benjamin G. Ethier , Jeffrey S. Wilson , Sarah M. Camhi , Ling Shi , Philip J. Troped\",\"doi\":\"10.1016/j.tbs.2024.100850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A limited number of studies using static spatial approaches have found that built environment variables are associated with bike share use and fewer have used spatially dynamic activity spaces to examine these relationships. The aim of this pilot study was to examine associations between built environment characteristics of daily activity spaces and bike share using three different geographic information system methods. Thirty-two adult members of Boston’s Blue Bikes bike share wore a GPS unit for up to 7 days. GPS points were used to create buffered track, minimum convex hull (MCH), and standard deviational ellipse (SDE) activity spaces. Multilevel logistic regression was used to estimate associations between docking station density, overall bicycle network density, shared-use trail density, intersection density, land use mix, and greenness, with bike share use. Bike share station density within SDE activity spaces showed a significant positive association with bike share (odds ratio (OR) = 1.19; 95 % confidence interval (CI): 1.02, 1.39). Total bike network and shared-use trail densities within MCH activity spaces were positively associated with bike share (OR = 1.13; 95 % CI: 1.02, 1.26 and OR = 1.75; 95 % CI: 1.06, 2.89, respectively). Intersection density within SDE activity spaces was inversely associated with bike share (OR = 0.91; 95 % CI: 0.83, 0.99). GPS tracking of individuals allowed for spatially and temporally dynamic identification of environmental exposures potentially relevant to bike share use. Overall, the findings are consistent with prior research on the environmental correlates of bike share and reinforce the importance of bicycle infrastructure to support greater bike share use. At the same time larger studies are needed to explore optimal geographic methods to define activity spaces in relation to bike share.</p></div>\",\"PeriodicalId\":51534,\"journal\":{\"name\":\"Travel Behaviour and Society\",\"volume\":\"37 \",\"pages\":\"Article 100850\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2024-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Travel Behaviour and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214367X24001133\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Travel Behaviour and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214367X24001133","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION","Score":null,"Total":0}
An analysis of built environment characteristics in daily activity spaces and associations with bike share use
A limited number of studies using static spatial approaches have found that built environment variables are associated with bike share use and fewer have used spatially dynamic activity spaces to examine these relationships. The aim of this pilot study was to examine associations between built environment characteristics of daily activity spaces and bike share using three different geographic information system methods. Thirty-two adult members of Boston’s Blue Bikes bike share wore a GPS unit for up to 7 days. GPS points were used to create buffered track, minimum convex hull (MCH), and standard deviational ellipse (SDE) activity spaces. Multilevel logistic regression was used to estimate associations between docking station density, overall bicycle network density, shared-use trail density, intersection density, land use mix, and greenness, with bike share use. Bike share station density within SDE activity spaces showed a significant positive association with bike share (odds ratio (OR) = 1.19; 95 % confidence interval (CI): 1.02, 1.39). Total bike network and shared-use trail densities within MCH activity spaces were positively associated with bike share (OR = 1.13; 95 % CI: 1.02, 1.26 and OR = 1.75; 95 % CI: 1.06, 2.89, respectively). Intersection density within SDE activity spaces was inversely associated with bike share (OR = 0.91; 95 % CI: 0.83, 0.99). GPS tracking of individuals allowed for spatially and temporally dynamic identification of environmental exposures potentially relevant to bike share use. Overall, the findings are consistent with prior research on the environmental correlates of bike share and reinforce the importance of bicycle infrastructure to support greater bike share use. At the same time larger studies are needed to explore optimal geographic methods to define activity spaces in relation to bike share.
期刊介绍:
Travel Behaviour and Society is an interdisciplinary journal publishing high-quality original papers which report leading edge research in theories, methodologies and applications concerning transportation issues and challenges which involve the social and spatial dimensions. In particular, it provides a discussion forum for major research in travel behaviour, transportation infrastructure, transportation and environmental issues, mobility and social sustainability, transportation geographic information systems (TGIS), transportation and quality of life, transportation data collection and analysis, etc.