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Identification of low-carbon travel block based on GIS hotspot analysis using spatial distribution learning algorithm

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
  • Published:
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Abstract

In the future, big data will become an efficient and useful means for improving urban planning, and machine learning can take city as a simplified and efficient system. We take full advantage of the benefits of new technology, but also clarify that city is not a machine, also cannot fully mechanically control the urban development. This study presents a methodology for identifying low-carbon travel block, which can be used to identify the built environment conducive to residents’ low-carbon travel. We chose the four elements of traffic survey—travel mode, travel time, travel purpose and travel frequency—as the framework to evaluate travel carbon emissions. Using the index data collected from “WeChat,” a popular social-media platform in China and questionnaire surveys, we conducted hotspot analysis of the spatial distribution of travel carbon emissions in GIS. We obtained a comprehensive carbon emissions and its spatial distribution through the superposition of hotspot density surface of different indexes. The results show that E block within the research area has the lowest travel carbon emissions. These results suggest some planning implications from three aspects—land use mode, road network and public service facilities: In the old urban district of Pucheng, the ratio of residential building area and other types’ building area should be “4:1–3:1”; and we should develop the travel model of bicycle, and the interval of bicycle lanes should be 350–450 m; The ratio of walking road to total road area should be 15–20%, and the width of road should be restricted. Coverage of transit site buffered for the radius of 150 m is 40–50%, coverage of shopping services buffered for the radius of 50 m is 45–60%, and coverage of recreational facilities buffered for the radius of 100 m is 50–70%. The results confirm that “functional mixing” and “dense road network” are beneficial to residents’ low-carbon travel proposed by the predecessors. At the same time, we found that not the higher volume rate is, the more favorable for low-carbon travel. Small cities have limited number of population and scattered distribution of professional posts, which are not suitable for the traditional mode of improving the volume ratio and the bus system. It is not that the higher the bus station coverage is, the better for residents to travel as low-carbon, and the high popularity of public transportation in small cities will increase the carbon emission of residents. The study provides a new way to evaluate the carbon emission assessment of blocks and provides a basis for block planning with low-carbon concept.

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Acknowledgements

The content of this paper is based on the National Science & Technology Pillar Program during the 12th Five-year Plan Period (2015BAL01B02), Social Development and Scientific Research Projects in Shaanxi Province (2015SF294), Special Project of Education Teaching Reform in Central University (2016) (jgy16085).

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Authors and Affiliations

  1. School of Architecture, Chang’an University, Chang’an Middle Road No. 75, Xi’an, 710061, China

    Quanhua Hou, Xuan Zhang, Bo Li & Xiaoqing Zhang

  2. College of Civil and Architectural Engineering, North China University of Science and Technology, Tangshan, 063000, China

    Wenhui Wang

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  1. Quanhua Hou
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  2. Xuan Zhang
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Correspondence to Quanhua Hou.

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Hou, Q., Zhang, X., Li, B. et al. Identification of low-carbon travel block based on GIS hotspot analysis using spatial distribution learning algorithm. Neural Comput & Applic 31, 4703–4713 (2019). https://doi.org/10.1007/s00521-018-3447-8

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  • DOI: https://doi.org/10.1007/s00521-018-3447-8

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