Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data

To evaluate the sustainability of an enormous number of urban intersections, a novel assessment model is proposed, along with an indicator system and corresponding methods to determine the indicators. Considering mainly the demands and feelings of the urban residents, the three aspects of safety, fu...

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Main Authors:	Yuhuan Zhang, Huapu Lu, Shengxi Luo, Zhiyuan Sun, Wencong Qu
Format:	Article
Language:	English
Published:	MDPI AG 2017-07-01
Series:	Sustainability
Subjects:	sustainability intersection multi-source big data fusion human-scale information entropy method image recognition
Online Access:	https://www.mdpi.com/2071-1050/9/7/1148

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spelling	doaj-080c2ad8fec5446891262e2bc5a652042020-11-24T21:23:14ZengMDPI AGSustainability2071-10502017-07-0197114810.3390/su9071148su9071148Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big DataYuhuan Zhang0Huapu Lu1Shengxi Luo2Zhiyuan Sun3Wencong Qu4Institute of Transportation Engineering, Tsinghua University, Beijing 100084, ChinaInstitute of Transportation Engineering, Tsinghua University, Beijing 100084, ChinaInstitute of Transportation Engineering, Tsinghua University, Beijing 100084, ChinaCollege of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, ChinaRoad Transport Books Center, China Communications Press Co., Ltd., Beijing 100011, ChinaTo evaluate the sustainability of an enormous number of urban intersections, a novel assessment model is proposed, along with an indicator system and corresponding methods to determine the indicators. Considering mainly the demands and feelings of the urban residents, the three aspects of safety, functionality, and image perception are taken into account in the indicator system. Based on technologies such as street view picture crawling, image segmentation, and edge detection, GIS spatial data analysis, a rapid automated assessment method, and a corresponding multi-source database are built up to determine the indicators. The improved information entropy method is applied to obtain the entropy weights of each indicator. A case study shows the efficiency and applicability of the proposed assessment model, indicator system and algorithm.https://www.mdpi.com/2071-1050/9/7/1148sustainabilityintersectionmulti-source big data fusionhuman-scaleinformation entropy methodimage recognition
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yuhuan Zhang Huapu Lu Shengxi Luo Zhiyuan Sun Wencong Qu
spellingShingle	Yuhuan Zhang Huapu Lu Shengxi Luo Zhiyuan Sun Wencong Qu Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data Sustainability sustainability intersection multi-source big data fusion human-scale information entropy method image recognition
author_facet	Yuhuan Zhang Huapu Lu Shengxi Luo Zhiyuan Sun Wencong Qu
author_sort	Yuhuan Zhang
title	Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data
title_short	Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data
title_full	Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data
title_fullStr	Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data
title_full_unstemmed	Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data
title_sort	human-scale sustainability assessment of urban intersections based upon multi-source big data
publisher	MDPI AG
series	Sustainability
issn	2071-1050
publishDate	2017-07-01
description	To evaluate the sustainability of an enormous number of urban intersections, a novel assessment model is proposed, along with an indicator system and corresponding methods to determine the indicators. Considering mainly the demands and feelings of the urban residents, the three aspects of safety, functionality, and image perception are taken into account in the indicator system. Based on technologies such as street view picture crawling, image segmentation, and edge detection, GIS spatial data analysis, a rapid automated assessment method, and a corresponding multi-source database are built up to determine the indicators. The improved information entropy method is applied to obtain the entropy weights of each indicator. A case study shows the efficiency and applicability of the proposed assessment model, indicator system and algorithm.
topic	sustainability intersection multi-source big data fusion human-scale information entropy method image recognition
url	https://www.mdpi.com/2071-1050/9/7/1148
work_keys_str_mv	AT yuhuanzhang humanscalesustainabilityassessmentofurbanintersectionsbaseduponmultisourcebigdata AT huapulu humanscalesustainabilityassessmentofurbanintersectionsbaseduponmultisourcebigdata AT shengxiluo humanscalesustainabilityassessmentofurbanintersectionsbaseduponmultisourcebigdata AT zhiyuansun humanscalesustainabilityassessmentofurbanintersectionsbaseduponmultisourcebigdata AT wencongqu humanscalesustainabilityassessmentofurbanintersectionsbaseduponmultisourcebigdata
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Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data

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