Modeling and Forecasting Passenger Car Ownership Based on Symbolic Regression

Lian Lian, Wen Tian, Hongfeng Xu and Menglan Zheng
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Lian Lian: School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China
Wen Tian: School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China
Hongfeng Xu: School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China
Menglan Zheng: School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China

Sustainability, 2018, vol. 10, issue 7, 1-16

Abstract: Numerous functions, especially the Gompertz function, have been predetermined to analyze the growth in vehicle ownership. This study utilizes the data-driven symbolic regression to automatically find a generalized function, named as new equation by symbolic regression (NE-SR), for passenger car ownership in six representative countries including Japan, England, USA, Finland, Poland and Australia. Then the new proposed function is applied for forecasting the passenger car ownership in China up to the year 2060. The experimental results indicate that the NE-SR, as an extension of the Gompertz function, fits better than the classical Gompertz function for car ownership growth. In NE-SR function, three scenarios can be realized by the variation of parameter signs, which are represented by the patterns of Japan, USA and Australia, respectively. The predicted results based on the NE-SR also show that the Chinese car ownership still has a potential to increase after 2060 in the pattern of Japan and Australia, but grows until around 2057 in the pattern of USA. The results can be used to further predict the energy demand and carbon emissions of passenger cars, which can provide a basis for the policymaker to propose transportation and environmental strategies.

Keywords: vehicle ownership; Gompertz function; per capita GDP; symbolic regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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