Urban growth pattern and commuting efficiency: Empirical evidence from 100 Chinese cities

摘要

With the intensification of urbanization, the improvement of commuting efficiency from an urban form optimization perspective has increasingly attracted scholars & rsquo; attention. This study used the spatial lag model and the spatial error model to investigate China & rsquo;s 100 most congested cities. The main innovation is the introduction of the urban growth pattern (UGP) and how urban form is associated with commuting efficiency from the & ldquo;internal-external, dynamic-static & rdquo; perspective. The relationship between UGP and commuting efficiency was quantified, where several urban form and socio-economic indicators were selected as control variables. The results show that: (1) There is a significant association between UGP and commuting efficiency. Specifically, the urban landscape expansion index increased by one standard deviation, commuting distance increased by 0.162 standard deviation, commuting time increased by 0.114 standard deviation. (2) The empirical analysis of the control variables shows that the polycentric index, city size, population density, road density and car ownership per 100 people are significantly correlated with commuting efficiency. Specifically, when the polycentric index, city size and road density increase by 1 standard deviation, commuting time changes by-0.209, 0.471 and 0.149 standard deviation, respectively. When the city size, population density and car ownership per 100 people increase by 1 standard deviation, the commuting distance changes by 0.473,-0.263 and-0.235 standard deviations, respectively. (3) Under the distinction of different city categories, the relationship between UGP and commuting efficiency is significant in first-tier cities, fourth-tier and below cities. Considering different urban evolution paths between developed and developing countries, this study provides a reference for urban managers to optimize the urban spatial layout to improve commuting efficiency.(c) 2021 Elsevier Ltd. All rights reserved.