Car Dependency in Urban Accessibility

Abstract

To achieve net-zero emissions, cities must transition away from reliance on private vehicles. However, car-centric urban growth has transformed the automobile from a convenience tool into a necessity for accessing essential services, creating significant "car dependency". This study introduces a novel Car Dependency Index (CDI) that quantifies the accessibility gap between private and public transport across 18 cities in Europe and North America. Utilising high-resolution geospatial data and numerical simulations, we reveal pronounced spatial inequalities, showing that car dependency remains a primary driver of car ownership even when accounting for income. A ``what-if" simulation of the planned metro expansion in Rome predicts a reduction of approximately 60,000 commuting vehicles, yet highlights that isolated interventions have localised impacts. We conclude that systemic, network-level transit expansions are essential to dismantle car-based systems and foster equitable, sustainable urban mobility. Our framework provides policymakers with an objective, scalable tool to identify viable areas for car-free zones and target infrastructure investments effectively.

Figures (7)

• Figure 1: Schematic representation of POIs accessibility by different transport modes. The agent at the centre of the figure can access points of interest (POIs), indicated by pins, using either public transport or a private car. Areas of the city, represented on a hexagonal grid, are shown in red if reachable by car within a normal-length journey, in blue if reachable by public transport within a normal-length journey, and in purple if reachable by both modes.
• Figure 2: Opportunity scores by public transport and car. Panels (a) and (b) present Opportunity Scores in Rome by public transport and by car, respectively. Panel (c) shows the population-averaged opportunity scores of various cities.
• Figure 3: Maps of Car Dependency Index. In the blue areas, public transport is estimated to be more efficient than cars, while the opposite is true in the red areas. Peripheries are generally more car-dependent, but the presence of urban train stations and metro stops has a positive impact—evident in the white spots where a car is not strictly necessary.
• Figure 4: Car Dependency Index distribution among city residents. In Panel (a), we show the cumulative distribution of car dependency scores for the resident population of different cities. In Panel (b), as an example, we show the spatial distribution of the Car Dependency Index values in the Paris metropolitan area, highlighting the smaller administrative boundary.
• Figure 5: Share of car use in commuting vs median car dependency index of the cities considered.