Exploring a quantitative assessment approach for car dependence: A case study in Munich

Abstract

While discussions are ongoing about the exact meaning of car dependence, its assessment has been primarily qualitative. The few quantitative approaches adopted so far have tended to analyze either high car use and ownership or a lack of public transport accessibility as indicators of car dependence. This study aims to quantitatively evaluate car dependence in Munich after merging these three aspects—car use, ownership, and lack of public transportation—and identify its associated potential spatial predictors. The exploratory approach is applied to traffic zones in the transit service area around Munich, Germany, which includes calculating an indicator for car dependence and its linkage with socio-spatial factors using multiple linear regression. For this purpose, traffic data from 2017 and census data from 2011 are used, which are the most recent available. It was found that car dependence is higher in suburban areas with low local numbers of employees, low land costs, and high average income tax payments. Identifying areas with higher car dependence and associated factors can help decision makers focus on or prioritize these areas in providing better access to alternative transportation and basic opportunities. Future research could focus on application in additional regions, using recent and aligned data, and further combinations with qualitative research.