Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.landurbplan.2018.08.018 |
Quantifying place: Analyzing the drivers of pedestrian activity in dense urban environments | |
Lai, Yuan; Kontokosta, Constantine E. | |
2018-12-01 | |
发表期刊 | LANDSCAPE AND URBAN PLANNING |
ISSN | 0169-2046 |
EISSN | 1872-6062 |
出版年 | 2018 |
卷号 | 180页码:166-178 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Understanding pedestrian behavior is critical for many aspects of city planning, design, and management, including transportation, public health, emergency response, and economic development. This study bridges in situ observations of pedestrian activity and urban computing by integrating high-resolution, large-scale, and heterogeneous urban datasets and analyzing both fixed attributes of the urban landscape (e.g. physical and transit infrastructure) with dynamic environmental and socio-psychological factors, such as weather, air quality, and perceived crime risk. We use local pedestrian count data collected by the New York City (NYC) Department of Transportation (DOT) and an extensive array of open datasets from NYC to test how pedestrian volumes relate to land use, building density, streetscape quality, transportation infrastructure, and other factors typically associated with urban walkability. We quantify, classify, and analyze place dynamics, including contextual and situational factors that influence pedestrian activity at high spatial-temporal resolution. The quantification process measures the urban context by extracting rich, yet initially fragmented and siloed, urban data for individual geolocations. Based on these features, we then construct contextual indicators by selecting and combining features relevant to pedestrian activity, and develop a typology of place to support the generalizability of our analysis. Finally, we use multivariate regression models with panel-corrected standard errors to estimate how specific contextual features and time-varying situational indicators impact pedestrian activity across time of day, day of the week, season, and year. The results provide insights into the key drivers of local pedestrian activity and highlight the importance accounting for the immediate urban environment and socio-spatial dynamics in pedestrian behavior modeling. |
英文关键词 | Pedestrian mobility Walkability Urban planning Urban computing Machine learning Urban data |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000449896300018 |
WOS关键词 | GEOGRAPHIC INFORMATION-SYSTEMS ; NEIGHBORHOOD WALKABILITY ; WALKING BEHAVIOR ; LAND-USE ; HEALTH ; IMPACT ; TRAVEL |
WOS类目 | Ecology ; Environmental Studies ; Geography ; Geography, Physical ; Regional & Urban Planning ; Urban Studies |
WOS研究方向 | Environmental Sciences & Ecology ; Geography ; Physical Geography ; Public Administration ; Urban Studies |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/24897 |
专题 | 资源环境科学 |
作者单位 | 1.NYU, Dept Civil & Urban Engn, New York, NY 10003 USA; 2.NYU, Ctr Urban Sci & Progress, New York, NY 10003 USA |
推荐引用方式 GB/T 7714 | Lai, Yuan,Kontokosta, Constantine E.. Quantifying place: Analyzing the drivers of pedestrian activity in dense urban environments[J]. LANDSCAPE AND URBAN PLANNING,2018,180:166-178. |
APA | Lai, Yuan,&Kontokosta, Constantine E..(2018).Quantifying place: Analyzing the drivers of pedestrian activity in dense urban environments.LANDSCAPE AND URBAN PLANNING,180,166-178. |
MLA | Lai, Yuan,et al."Quantifying place: Analyzing the drivers of pedestrian activity in dense urban environments".LANDSCAPE AND URBAN PLANNING 180(2018):166-178. |
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