Assessing spatiotemporal bikeability using multi-source geospatial big data: A case study of Xiamen, China
This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we pr...
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| Published in | International journal of applied earth observation and geoinformation Vol. 125; p. 103539 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.12.2023
Elsevier |
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| Online Access | Get full text |
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| Abstract | This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing open-source data, advanced deep neural networks, and GIS spatial analysis, the framework eliminates subjective evaluations and is more efficient and comprehensive than prior methods. The experimental results on Xiamen, China, demonstrate the effectiveness of the framework in identifying areas for improvement and enhancing cycling mobility. The proposed framework provides a structured approach for evaluating bikeability in different geographical contexts, making reproducing bikeability indices easier and more comprehensive to policymakers, transportation planners, and environmental decision-makers.
•A novel spatiotemproal framework is proposed for quantifying the bikeability.•Multi-source geospatial big data fusion is used for the evaluation of bikeability.•Field validation ensures the effectiveness of our bikeability results.•This framework could provide policy implications for government. |
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| AbstractList | This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing open-source data, advanced deep neural networks, and GIS spatial analysis, the framework eliminates subjective evaluations and is more efficient and comprehensive than prior methods. The experimental results on Xiamen, China, demonstrate the effectiveness of the framework in identifying areas for improvement and enhancing cycling mobility. The proposed framework provides a structured approach for evaluating bikeability in different geographical contexts, making reproducing bikeability indices easier and more comprehensive to policymakers, transportation planners, and environmental decision-makers. This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing open-source data, advanced deep neural networks, and GIS spatial analysis, the framework eliminates subjective evaluations and is more efficient and comprehensive than prior methods. The experimental results on Xiamen, China, demonstrate the effectiveness of the framework in identifying areas for improvement and enhancing cycling mobility. The proposed framework provides a structured approach for evaluating bikeability in different geographical contexts, making reproducing bikeability indices easier and more comprehensive to policymakers, transportation planners, and environmental decision-makers. •A novel spatiotemproal framework is proposed for quantifying the bikeability.•Multi-source geospatial big data fusion is used for the evaluation of bikeability.•Field validation ensures the effectiveness of our bikeability results.•This framework could provide policy implications for government. |
| ArticleNumber | 103539 |
| Author | Stein, Alfred Lei, Jinghan Wang, Yanwen Huang, Xiao Chen, Zhidong Jia, Peng Dai, Shaoqing Zhao, Wufan |
| Author_xml | – sequence: 1 givenname: Shaoqing surname: Dai fullname: Dai, Shaoqing organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514AE Enschede, The Netherlands – sequence: 2 givenname: Wufan orcidid: 0000-0002-0265-3465 surname: Zhao fullname: Zhao, Wufan email: wufan.zhao@kuleuven.be organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514AE Enschede, The Netherlands – sequence: 3 givenname: Yanwen surname: Wang fullname: Wang, Yanwen organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514AE Enschede, The Netherlands – sequence: 4 givenname: Xiao surname: Huang fullname: Huang, Xiao organization: Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, United States of America – sequence: 5 givenname: Zhidong surname: Chen fullname: Chen, Zhidong organization: Xiamen Urban Planning and Design Institute, China – sequence: 6 givenname: Jinghan surname: Lei fullname: Lei, Jinghan organization: Xiamen Urban Planning and Design Institute, China – sequence: 7 givenname: Alfred surname: Stein fullname: Stein, Alfred organization: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514AE Enschede, The Netherlands – sequence: 8 givenname: Peng surname: Jia fullname: Jia, Peng organization: School of Resource and Environmental Sciences, Wuhan University, Wuhan, China |
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| Keywords | Bike-sharing Bikeability Spatialtemporal Built environment Multi-source data |
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| Title | Assessing spatiotemporal bikeability using multi-source geospatial big data: A case study of Xiamen, China |
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