2018
DOI: 10.3390/su10072275
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Modeling and Forecasting Passenger Car Ownership Based on Symbolic Regression

Abstract: Numerous functions,

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Cited by 14 publications
(7 citation statements)
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“…Lu et al [31] improved the stochastic differential equation related to the Gompertz curve, so that the model can present the remaining slow increase when the S-shaped curve has reached its saturation level and can better fit the real data when there are fluctuations in it. Lian et al [32] utilizes data-driven symbolic regression to automatically find a generalized function by symbolic regression (NE-SR) for passenger car ownership, and then uses the new proposed function to forecast passenger car ownership in China. Dargay et al [26] builds a model that explicitly models the vehicle saturation level as a function of a country's urbanization and population density characteristics based on the Gompertz model.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Lu et al [31] improved the stochastic differential equation related to the Gompertz curve, so that the model can present the remaining slow increase when the S-shaped curve has reached its saturation level and can better fit the real data when there are fluctuations in it. Lian et al [32] utilizes data-driven symbolic regression to automatically find a generalized function by symbolic regression (NE-SR) for passenger car ownership, and then uses the new proposed function to forecast passenger car ownership in China. Dargay et al [26] builds a model that explicitly models the vehicle saturation level as a function of a country's urbanization and population density characteristics based on the Gompertz model.…”
Section: Literature Reviewmentioning
confidence: 99%
“…They suggested that a framework consisting of distinct models would be beneficial for making long-term macroeconomic predictions. Lian et al (2018) develop a new method for modeling and forecasting passenger car ownership using symbolic regression, a machine-learning technique that can automatically generate mathematical models from data. The study by Mena-Oreja and Gozalvez, (2020) aimed to evaluate the effectiveness of various deep-learning techniques for predicting traffic using data from different sources such as sensors, social media, and weather reports.…”
Section: Introductionmentioning
confidence: 99%
“…slow growth during low income levels, rapid increase as income levels rise quickly, and finally a saturation period. The Gompertz statistical model has been found to fit the historical relationship between car ownership and income levels best, although other functions have also been used in previous studies (Lian et al, 2018). The basic Gompertz function is shown in Eq.…”
Section: Future Transport Demand Projectionsmentioning
confidence: 99%