Evaluating the effectiveness of two congestion limitation policies in Milan: Charge increase and vehicle type

Abstract

Congestion pricing, also referred to as road pricing, is a form of Pigouvian taxation designed to limit or reduce vehicular traffic within a specific area. These systems aim to encourage changes in driving behavior and the choice of transportation mode. An example of a congestion pricing system is the Milan Area C charging zone, which operates on a fixed-rate basis. In recent years, two changes to the system have been introduced: (i) restrictions on the vehicle types allowed to enter the zone, and (ii) a 50 percent increase in the congestion charge. This study introduces a novel and replicable approach to evaluate the effectiveness of these policy changes, by studying the ratio of vehicles entering Area C to the total number of unique individuals within it, leveraging mobile phone data on user presence. Using fixed-effects models to control for unobserved heterogeneity across time, this study analyzes the impact of these two policy changes in the Area C congestion pricing system. The findings indicate that both policy changes influenced individuals’ choice of transportation mode, with vehicle type restrictions having a greater impact than price increases. This shows the effectiveness of the two types of measures, offering insights for policymakers on how to enhance congestion charging system effectiveness through refined pricing strategies and vehicle limitations. Additionally, demographic characteristics of users present in the area, as captured through mobile phone data, such as the proportion of women and elderly individuals, significantly influence transportation choices. Recognizing these factors is essential for policymakers, as it highlights the need for equitable policies that improve acceptance and effectiveness among vulnerable groups. Additionally, demographic characteristics of users present in the area, as captured through mobile phone data—such as the proportion of women and elderly individuals—significantly influence transportation choices.