Abstract
A large number of articles have documented that as population density of cities increases, car use declines and public transit use rises. These articles had a significant impact of promoting high-density compact urban development to mitigate traffic congestion. Another approach followed by other researchers used the urban scaling model to indicate that traffic congestion increases as population size of cities increases, thus generating a possible contradictory result. Therefore, this study examines the role of both density and population size on traffic congestion in 164 global cities by the use of Stochastic Impacts by Regression on Population, Affluence and Technology model. We divide 164 cities into the two subgroups of 66 low density cities and 98 high density cities for analysis. The findings from the subgroups analysis indicated a clear-cut difference on the critical role of density in low-density cities and the exclusive role of population size in high-density cities. Furthermore, using threshold regression model, 164 cities are divided into the two regions of large and small population cities to determine population scale advantage of traffic congestion. Our findings highlight the importance of including analysis of subgroups based on density and/or population size in future studies of traffic congestion.
Highlights
Traffic congestion in large global cities continues to worsen resulting in high economic cost, lost time, accidents, pollution, and many other negative effects for billions of urban inhabitants [1,2,3,4]
The IPAT model was extended into the stochastic impacts by regression on population, affluence and technology (STIRPAT) model which enabled to estimate the proportional change in environmental impact per given proportional change in population
The reason is that traffic index (TI) scores such as CO2 emission or other environmental and ecological measures may be greatly influenced by underlying elements such as population size, income level, and technology
Summary
Traffic congestion in large global cities continues to worsen resulting in high economic cost, lost time, accidents, pollution, and many other negative effects for billions of urban inhabitants [1,2,3,4]. Using an aggregate sample allows the researcher to analyze the overall pattern of traffic congestion for all cities in one sample without differentiating heterogeneous characteristics such as density, population, and income. This research attempts to resolve these contradictory results by using both aggregate and disaggregate samples of cities In other words, both population density and population size are used simultaneously as the key determinants of traffic density. The TI includes 164 large global cities with complete data on the traffic congestion index, population, income per capita, and population density. The threshold regression model was used to divide 164 cities into 46 cities whose population size was larger than the threshold value and 118 cities whose population size was less than the threshold value This remainder of this paper is organized into four sections.
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