Trends in Swiss Passenger Vehicles Based on Machine Learning Segmentation

Road transport represents a major contributor to air pollution, energy consumption, and carbon dioxide emissions in Switzerland. In response, stringent emission regulations, penalties for non-compliance, and incentives for electric vehicles have been introduced. This study investigates how these policies, along with shifting consumer preferences and vehicle design advancements, have influenced the composition of the Swiss new passenger car fleet. Using machine learning techniques, we segment passenger vehicles to analyze trends over time. Our findings reveal a decline in micro and small vehicles, alongside an increase in lower- and upper-middle-class vehicles, sport utility vehicles, and alternative powertrains across all segments. Additionally, steady increases in vehicle width, length, and weight are observed in all classes since 1995. While technological advancements led to reductions in energy consumption and carbon dioxide emissions until 2016, an increase has since been observed, driven by higher engine power, greater vehicle weight, and changes in certification schemes.

Publication Details

Journal
Repository for Publications and Research Data (ETH Zurich)
Published
2025-04-02
DOI
https://doi.org/10.3929/ethz-b-000734062
Primary Topic
Energy, Environment, and Transportation Policies
Type
article
Field-Weighted Citation Impact
0.00
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article

Trends in Swiss Passenger Vehicles Based on Machine Learning Segmentation

Repository for Publications and Research Data (ETH Zurich)
Energy, Environment, and Transportation Policies
article

Trends in Swiss Passenger Vehicles Based on Machine Learning Segmentation

article en

Abstract

Road transport represents a major contributor to air pollution, energy consumption, and carbon dioxide emissions in Switzerland. In response, stringent emission regulations, penalties for non-compliance, and incentives for electric vehicles have been introduced. This study investigates how these policies, along with shifting consumer preferences and vehicle design advancements, have influenced the composition of the Swiss new passenger car fleet. Using machine learning techniques, we segment passenger vehicles to analyze trends over time. Our findings reveal a decline in micro and small vehicles, alongside an increase in lower- and upper-middle-class vehicles, sport utility vehicles, and alternative powertrains across all segments. Additionally, steady increases in vehicle width, length, and weight are observed in all classes since 1995. While technological advancements led to reductions in energy consumption and carbon dioxide emissions until 2016, an increase has since been observed, driven by higher engine power, greater vehicle weight, and changes in certification schemes.

Repository for Publications and Research Data (ETH Zurich)
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Energy, Environment, and Transportation Policies
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Trends in Swiss Passenger Vehicles Based on Machine Learning Segmentation · Repository for Publications and Research Data (ETH Zurich) (2025) | TGRS Research Map | TGRS