Distributional Effects of Distance-Based Road Pricing: A Behavioral Microsimulation Study for the Brussels-Capital Region

At the intersection of transport economics and public finance, this research contributes to the empirical literature on transport pricing as a policy tool for addressing the externalities of vehicle use. In line with recent technological developments and ongoing policy debates, it provides an ex-ante evaluation of a distance-based road pricing scheme that varies by time (peak and off-peak), location (congested and non-congested zones), and vehicle characteristics. While such systems are widely recognized as efficient, as they better align driving costs with externalities, their implementation in passenger transport remains limited. This absence is largely driven by concerns about distributional effects, highlighting the need for robust empirical evidence on equity implications as a key input for policy feasibility.

In this context, this research assesses the distributional impacts of a potential distance-based tax in the Brussels-Capital Region, one of the most congested urban areas in Europe. The analysis adopts a behavioral microsimulation approach to examine longer-term effects. By explicitly incorporating behavioral responses, the study addresses an important gap in the literature, which has largely relied on static and aggregate analyses that overlook how individuals adjust their travel behavior in response to pricing policies. The central research question is: what are the distributional impacts of a distance-based road pricing scheme, differentiated by time, location, and vehicle characteristics, considering both static (immediate) and dynamic (post-adaptation) effects?

Methodologically, the approach is structured in two complementary layers. The first layer estimates individual behavioral responses using a stated preference survey based on a discrete choice experiment administered to a representative sample of car users. Respondents are asked to evaluate a recent trip and choose between maintaining it or selecting alternatives that vary in cost, travel time, mode, and timing. These choices are used to estimate individual-level price elasticities through a Mixed Multinomial Logit model. The resulting behavioral parameters are then incorporated into the second layer.

The second layer consists of a behavioral microsimulation model built on a synthetic microdataset. This dataset is built by enriching the Brussels Travel Behavior Survey (OVG, 2024) with administrative fiscal data and the estimated behavioral responses. Individuals in the OVG (receiver dataset) are matched with similar profiles from the two complementary sources (donor datasets) using machine learning techniques, including kernel canonical correlation analysis, which captures nonlinear relationships and projects observations into a common latent space. Variables of interested are then imputed using similarity-based weighting. The resulting dataset is aligned with aggregate statistics to ensure consistency with real-world distributions. This integrated framework enables a detailed assessment of policy impacts across the income distribution and supports the simulation of compensation mechanisms.

Preliminary results suggest that, in static terms, a distance-based tax is not more regressive on average than the current vehicle ownership tax, largely due to lower car ownership among low-income households. However, conditional on being a driver, some regressive effects emerge, driven not only by uniform tariffs but also by the higher prevalence of less efficient vehicles among lower-income groups, which are expected to pay higher per-kilometer charges. Dynamic results indicate stronger behavioral responses among lower-income individuals, raising important policy considerations regarding accessibility and fairness. Overall, the findings aim to provide new evidence on the extent to which distance-based road pricing can better reconcile efficiency and equity objectives.