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Open AccessArticle

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

1
Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China
2
College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
3
Road Transport Books Center, China Communications Press Co., Ltd., Beijing 100011, China
*
Author to whom correspondence should be addressed.
Sustainability 2017, 9(7), 1148; https://doi.org/10.3390/su9071148
Received: 24 May 2017 / Revised: 23 June 2017 / Accepted: 26 June 2017 / Published: 2 July 2017
(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)
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. View Full-Text
Keywords: sustainability; intersection; multi-source big data fusion; human-scale; information entropy method; image recognition sustainability; intersection; multi-source big data fusion; human-scale; information entropy method; image recognition
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MDPI and ACS Style

Zhang, Y.; Lu, H.; Luo, S.; Sun, Z.; Qu, W. Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data. Sustainability 2017, 9, 1148.

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