Modeling and Forecasting Passenger Car Ownership Based on Symbolic Regression

Lian Lian,Menglan Zheng,Hongfeng Xu,Wen Tian

doi:10.3390/su10072275

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.

Highlights

The worldwide increase in urban mobility since the 1960s has directly resulted in increasing motor vehicles, especially in many low-income populous countries, such as China and India [1]
Car ownership is an important variable in car travel behavior research [2]
Several improved Gompertz functions have already been proposed for better forecasting vehicle ownership growth [12,13,14], where the corresponding parameters were estimated by statistics-based regression methods

Summary

Introduction

The worldwide increase in urban mobility since the 1960s has directly resulted in increasing motor vehicles, especially in many low-income populous countries, such as China and India [1]. Car ownership is an important variable in car travel behavior research [2]. It is important for academic researchers, environmentalists and policymakers to accurately forecast the development trend of vehicle ownership. The ownership level of various vehicles, e.g., cars [4] and hybrid electric vehicles [5], can be analyzed on the basis of product life cycle and diffusion model which contains several sigmoid-shaped functions, e.g., the logistic, the Richards and Gompertz function [6,7]. Several improved Gompertz functions have already been proposed for better forecasting vehicle ownership growth [12,13,14], where the corresponding parameters were estimated by statistics-based regression methods

Objectives

Methods

Results