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Spatial heterogeneity of urban illegal parking behavior: A geographically weighted Poisson regression approach

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  • Zhou, Xizhen
  • Ding, Xueqi
  • Yan, Jie
  • Ji, Yanjie

Abstract

Understanding the spatial changes in urban illegal parking behavior is of great significance to sustainable urban parking management. At present, the imbalance between the supply and demand of urban parking in China has resulted in more and more illegal parking. Meanwhile, few studies have been conducted on the influencing factors of such behavior. Taking the city of Nanjing as an example, a geographically weighted Poisson regression model using multi-source data was constructed to reveal the spatial geographical impact of the built environment, traffic facilities and different types of parking lots on illegal parking. The results show that there is heterogeneity in the spatial distribution of those explanatory variables' effects. From an overall perspective, home-work attributes, government institutions, health care services, and schools are positively related to illegal parking, and are likely to be the key control objects in daily parking management. Variables such as scenic spots, leisure sports, public parking lots, and curb parking facilities are negatively correlated with illegal parking. It is worth noting that the increase in dedicated parking lots has not prevented illegal parking. Meanwhile, public parking and curb parking facilities have an inhibitory effect on illegal parking with the effect of the latter being significantly higher than that of the former. The outcomes of this research provide comprehensive guidance on urban traffic management, policy making, and sustainable urban development.

Suggested Citation

  • Zhou, Xizhen & Ding, Xueqi & Yan, Jie & Ji, Yanjie, 2023. "Spatial heterogeneity of urban illegal parking behavior: A geographically weighted Poisson regression approach," Journal of Transport Geography, Elsevier, vol. 110(C).
    • Handle: RePEc:eee:jotrge:v:110:y:2023:i:c:s0966692323001084
    DOI: 10.1016/j.jtrangeo.2023.103636
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