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

  • Quanhua Hou
  • Xuan Zhang
  • Bo Li
  • Xiaoqing Zhang
  • Wenhui Wang
S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
  • 56 Downloads

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.

Keywords

Low-carbon travel Built environment GIS hotspot analysis Block 

Notes

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).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

References

  1. 1.
    Huang B, Lv B (2013) Study on optimization path of urban spatial morphology with low-carbon perspective. China Market 32(4):51–56Google Scholar
  2. 2.
    Xinhua News Agency (2016) China will no longer build gated residential districts to promote block system. http://news.ifeng.com/a/20160221/47521435_0.shtml
  3. 3.
    Wang Z, Li L, Li Y (2015) From super block to small block: urban form transformation and its road network impacts in Chenggong, China. Mitig Adapt Strateg Glob Change 20(5):683–699MathSciNetCrossRefGoogle Scholar
  4. 4.
    Liu C, Qing S (2011) An empirical analysis of the influence of urban form on household travel and enemy consumption computers. Environ Urban Syst (0198-9715) 35:347–357CrossRefGoogle Scholar
  5. 5.
    Xinyu Cao, Handy SL, Mokhtarian Patricia L, Davis UC (2006) The influences of the built environment and residential self-selection on pedestrian behavior: evidence from Austin, TX. Transportation 2006(33):1–20.  https://doi.org/10.1016/j.trd.2017.02.003 Google Scholar
  6. 6.
    Qin B, Tian H (2013) The impact of community space form on residents’ carbon emissions. In: Urban Planning Society of China. Urban era, collaborative planning—proceedings of 2013 annual meeting of Chinese urban planning. Tsingtao, Shandong, pp 613–623 (Chinese) Google Scholar
  7. 7.
    Yan-wei CHAI, Zuo-peng XIAO, Zhi-lin LIU (2011) Comparison analysis on carbon emissions of family daily travel of Beijing residents based on spatial behavior constraints. Sci Geogr Sin 31(07):843–849 (Chinese) Google Scholar
  8. 8.
    Jiang Y, Dong-quan He, Zegras C (2011) The influence of block form on the energy consumption of residents. Urban Transp China 9(4):21–29 (Chinese) Google Scholar
  9. 9.
    Moilanen M (2010) Matching and settlement patterns: the case of Norway. Pap Region Sci 89(3):607–623.  https://doi.org/10.1111/j.1435-5957.2009.00264.x CrossRefGoogle Scholar
  10. 10.
    Yang J, Shen Q, Shen J, He C (2012) Transport impacts of clustered development in Beijing: compact development versus overconcentration. Urban Stud 49(6):1315–1331.  https://doi.org/10.1177/0042098011410336 CrossRefGoogle Scholar
  11. 11.
    Vivier J (2002) Public transport for sustainable mobility. Int Assoc Public Transp (UITP) 5Google Scholar
  12. 12.
    Schwanen T, Mokhtarian PL (2007) Attitudes toward travel and land use and choice of residential block type: evidence from the San Francisco bay area. Hous Policy Debate 18(1):171–207CrossRefGoogle Scholar
  13. 13.
    Brown MA, Southworth F, Stovall TK et al (2005) Towards a climate-friendly built environment. Pew Center on Global Climate Change, ArlingtonGoogle Scholar
  14. 14.
    Cao J, Mokhtarian PL, Handy SL (2009) Examining the impacts of residential self-selection on travel behaviour: a focus on empirical findings. Transp Rev 29(3):359–395CrossRefGoogle Scholar
  15. 15.
    PengyuZhu SongnianZhao, Wang Liping, Yammahi Salem Al (2017) Residential segregation and commuting patterns of migrant workers in China. Transp Res Part D.  https://doi.org/10.1016/j.trd.2016.11.010 Google Scholar
  16. 16.
    Badland HM, Schofield GM, Garrett N (2008) Travel behavior and objectively measured urban design variables associations for adults traveling to work. Health Place 14(1):85–95.  https://doi.org/10.1016/j.healthplace.2007.05.002 CrossRefGoogle Scholar
  17. 17.
    Karathodorou N, Graham DJ, Noland RB (2009) Estimating the effect of urban density on fuel demand. In: Transportation Research Board 88th Annual Meeting. Washington DC, United StatesGoogle Scholar
  18. 18.
    Ding C, Wang Y, Xie B (2014) Understanding the role of built environment in reducing vehicle miles traveled accounting for spatial heterogeneity. Sustainability 6:589–601.  https://doi.org/10.3390/su6020589 CrossRefGoogle Scholar
  19. 19.
    Ding C, Lin Y, Liu C (2014) Exploring the influence of built environment on tour-based commuter mode choice: a cross-classified multilevel modeling approach. Transp Res Part D Transp Environ.  https://doi.org/10.1016/j.trd.2014.08.001 Google Scholar
  20. 20.
    Hai-xiao PAN (2010) Urban spatial structure towards low carbon: new urban transport and land use model. Urban Stud 17(01):40–45Google Scholar
  21. 21.
    Qin B, Sun S (2013) Planning parameters and household carbon emission: evidence from high- and low-carbon blocks in Beijing. Habitat Int 37:52–60CrossRefGoogle Scholar
  22. 22.
    Liao C (2010) Trip characteristics and residential planning strategy research based on low-carbon transportation. Harbin Institute of Technology (Chinese) Google Scholar
  23. 23.
    Sun B, Ermagun A, Dan B (2017) Built environmental impacts on commuting mode choice and distance: evidence from Shanghai. Transp Res Part D Transp Environ.  https://doi.org/10.1016/j.trd.2016.06.001 Google Scholar
  24. 24.
    Guo L, Huang B (2014) Study on the impact of urban neighborhood environment elements on low-carbon travel mode: the case study of Wuhan in Hubei Province. Huazhong Archit 32(11):134–139Google Scholar
  25. 25.
    Lin-cheng XIAO (2012) Influence mechanism research on community family daily carbon emissions-taking Nanjing for example. Nanjing University, Nanjing (Chinese) Google Scholar
  26. 26.
    Wang D, Lin T (2014) Residential self-selection, built environment, and travel behavior in the Chinese context. J Transp Land Use 7(3):5–14CrossRefGoogle Scholar
  27. 27.
    Cao Jason (2015) Examining the relationship between block built environment and travel behavior: a review from the US perspective. Urban Plann Int 37(04):46–52Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Quanhua Hou
    • 1
  • Xuan Zhang
    • 1
  • Bo Li
    • 1
  • Xiaoqing Zhang
    • 1
  • Wenhui Wang
    • 2
  1. 1.School of ArchitectureChang’an UniversityXi’anChina
  2. 2.College of Civil and Architectural EngineeringNorth China University of Science and TechnologyTangshanChina

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