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

Léonard Moulin, Valeria Maria Urbano

► To cite this version:

Léonard Moulin, Valeria Maria Urbano. Evaluating the effectiveness of two congestion limitation policies in Milan: Charge increase and vehicle type. Transport Policy, 2025, 165 (May 2025), pp.17-27. 10.1016/j.tranpol.2025.02.006. hal-04952736

HAL Id: hal-04952736

https://hal.science/hal-04952736v1

Submitted on 17 Feb 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

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

Léonard Moulin<sup>a,●,*</sup>, Valeria Maria Urbano<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Congestion charge

Road pricing

Mobile phone data

Traffic congestion

## 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.

## 1. Introduction

In the last decade, from 2012 to 2022, the average number of passenger cars per 1000 inhabitants in the European Union increased significantly (by 14.3%), reaching 560.<fnref n="1" /> In the Milan area, this figure is even higher, at 622 cars per 1000 inhabitants, according to the Milan transport agency (AMAT). Although this rate may be lower than in other Italian cities, it is relatively high compared to similar charges in other European countries. Although vehicular traffic has become synonymous with urban development in major cities, the rise in car ownership has not come without challenges. Particularly in densely populated areas, increasing traffic has given rise to various issues,

with pollution and congestion in particular emerging as significant challenges.

To address these challenges, cities around the world have turned to innovative solutions, with road pricing systems in particular seen as a promising solution. These systems, aimed at managing demand for private car use, seek to reduce congestion and mitigate environmental impacts by ensuring that costs associated with vehicle usage are internalized by the users themselves.

Cities such as Singapore, London (Givoni, 2012), and Stockholm (Börjesson et al., 2012; Börjesson and Kristoffersson, 2018) have implemented such systems, and demonstrated their efficacy in reducing traffic congestion and emissions. Following suit, Milan, one of Europe's

<footnote><sup>a</sup> Institut national d'études démographiques (INED), 9 cours des Humanités, 93300, Aubervilliers, France</footnote>

<footnote><sup>b</sup> Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Raffaele Lambruschini, 20156, Milan, Italy</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: leonard.moulin@ined.fr (L. Moulin).</footnote>

<footnote n="1">¹ Eurostat. (2024, January 17). Passenger cars per 1000 inhabitants reached 560 in 2022 [News article]. Retrieved from the link https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20240117-1 on 05/14/2024.</footnote>

<footnote n="2">² EU Urban Mobility Observatory. (2024, March 8). Milan to see temporary vehicle bans from high levels of air pollution [News article]. retrieved from https://urban-mobility-observatory.transport.ec.europa.eu/news-events/news/milan-see-temporary-vehicle-bans-high-levels-air-pollution-2024-03-08_enon14/05/2024.</footnote>
most congested and polluted cities,² embarked on an experiment of its
own with a congestion pricing scheme. In January 2008, the municipal-
ity of Milan introduced the Ecopass scheme—a pioneering effort aimed
at improving air quality within the city center.

This initial intervention, modeled after London's congestion charge, consisted of an area-wide charge for vehicles entering the 8 km² city center zone during peak hours on weekdays. In January 2012, building on the success and lessons learned from the Ecopass initiative, Milan expanded its efforts with the implementation of the Area C charging zone. This expansion aimed to address the shortcomings of the Ecopass scheme while leveraging its public acceptance and popularity. More recently, Milan's road pricing scheme has undergone a series of policy adjustments aimed at further optimizing its effectiveness. In 2022 and 2023, these included restrictions on more polluting vehicle types and an increase in rates respectively.

The Milan road charging scheme has been studied in the academic literature from various points of view, including its direct effects on travel behavior (Beria, 2016; Lehe, 2019), as well as its economic and environmental impacts (Beria, 2016; Gibson and Carnovale, 2015; Percoco, 2013, 2014; Croci, 2016). While empirical studies have underscored the efficacy of Milan's road pricing scheme in reducing vehicular traffic in terms of the absolute number of vehicles accessing the area, these studies have not considered changes in overall mobility flows within the area (i.e. the number of unique individuals entering Area C). They thus provide limited insights into overall shifts in transportation mode shares among individuals within the area. This study seeks to fill this gap by evaluating the effectiveness of the recent regulatory changes in terms of the percentage of vehicles within overall mobility flows within Area C.

The main contributions of the present study are twofold. First, it introduces a novel approach to evaluating congestion policies, focusing on the percentage of vehicles within overall mobility flows in Area C. To the best of our knowledge, previous studies on the effects of congestion reduction policies have primarily relied on survey data (e.g. Tarriño-Ortiz et al., 2022; Guensler et al., 2020; González-Aliste et al., 2023) or the absolute number of vehicles accessing the area (e.g. Percoco, 2013; Croci, 2016; Gibson and Carnovale, 2015). Survey data, while informative for collecting in-depth information about the drivers of mobility habit changes, lack scalability, whereas vehicle count data fail to account for overall shifts in mobility patterns. By integrating vehicle access data from the municipality's open data portal with mobile phone data, used for the first time in this context to capture comprehensive mobility patterns, this study provides a new perspective on policy effectiveness. Second, it exploits the temporal proximity of two policy changes implemented in Milan to assess and compare their respective impacts on car usage, providing insights into the effectiveness of different congestion policy configurations. This unique natural experiment leverages the consecutive implementation of two distinct policies, allowing for a controlled comparison of their effects on car usage.

Although it is difficult to draw general and robust conclusions
from a single case, it is important to note that, given the numerous
confounding factors influencing congestion pricing systems, focusing
on one system is both reasonable and common in studies on congestion
policies (Veitch and Rhodes, 2024; Rotaris et al., 2010). Furthermore,
Milan's significant congestion levels, along with the fact that it was the
first city in Italy to implement such a policy, make it a particularly
valuable case study, as highlighted in previous studies (Percoco, 2013;
Gibson and Carnovale, 2015; Rotaris et al., 2010; Beria, 2016).

This paper is structured as follows. Section 2 sets out the general
theoretical background on congestion pricing systems, describes Mi-
lan's Area C scheme and gives an overview of previous evaluations of
its efficacy. In Section 3, we outline our data sources and methodology,
and we present some descriptive statistics. In Section 4, we present
our results and discuss them. Finally, Section 5 concludes the study,
addressing its limitations and suggesting avenues for future research.

2. Theoretical background

This section provides the theoretical background for the study and is organized into three subsections. The first subsection offers an overview of congestion pricing systems, including their characteristics and classifications. The second subsection focuses on Milan's Area C, describing its features as a congestion pricing system and its operational mechanisms. The third subsection reviews the literature on the effectiveness of Area C, analyzing empirical studies across three dimensions: direct effects on travel behavior, economic impacts, and environmental outcomes.

2.1. *Congestion pricing systems*

Congestion pricing, also referred to as road pricing, functions as a Pigouvian tax aimed at constraining or reducing vehicular traffic within a specific area by requiring individual travelers to internalize the adverse externalities generated by their transportation behavior (Pigou, 1920). The term “Pigouvian” stems from the work of Pigou (1920), in which he specifically advocated a tax on congestion (Pigou, 1920). This taxation model aims at dissuading drivers from using private cars by mandating a fee for their use for transportation purposes (Ecola and Light, 2009). The final goal is to drive changes that run across various aspects of mobility behavior, including trip frequency, destinations, modes of transportation, timing, routes, and even long-term decisions such as residential and occupational choices. The academic debate on congestion pricing remains ongoing. While most researchers support the implementation of congestion pricing systems, various questions arise around their design and practical application, such as determining the appropriate toll levels, covering operational costs, and managing surplus revenue (Lindsey, 2006).

De Palma and Lindsey (2011), in their overview paper on traffic congestion pricing methodologies, defined four dimensions along which congestion pricing schemes can be classified, including the type of scheme, degree of time differentiation (time of day or day of the week), other dimensions of toll differentiation, and technology. Given the subordinate nature of technology to choices on the other three dimensions, this section focuses on the first three dimensions of classification.

The first and most prominent dimension is the type of congestion pricing system. Three general types of schemes have been implemented: facility-based, area-based, and distance-based. Facility-based systems, the most common type of pricing scheme, is a congestion charge applied to specific facilities such as roads, bridges, and tunnels, levying tolls either on the entire facility, a single lane, or the vehicle at specific points based on distance traveled (De Palma and Lindsey, 2011). An example of facility-based congestion pricing is the adoption of high-occupancy toll (HOT) lanes in the United States. Area-based systems, as their name indicates, involve charging vehicles upon entering and/or exiting a designated area, and using checkpoints and video surveillance technology to regulate traffic. These systems can be designed as cordon or zonal systems: cordon systems delineate man-made boundaries around an area, such as concentric circles around the city center, and charge tolls upon entering or exiting. Examples of these systems are the Stockholm cordon zone and the Milan Area C. Zonal schemes are designed to charge fees for entering or exiting the zone, but also for traveling within the zone without crossing its perimeter, with boundaries defined by natural features or elements of the built environment (De Palma and Lindsey, 2011). Examples of zonal schemes include the Paris and London low-emission zones. Lastly, distance-based schemes impose charges that vary (in linear or non-linear fashion) with distance traveled. While no pure distance-based system for managing private vehicle congestion has been implemented to date, there are hybrid distance- and facilities-based schemes (Veitch and Rhodes, 2024).

Moving on to the dimension of time differentiation, a distinction
is generally drawn between “static pricing” and “dynamic charg-
ing” (Cottingham and Greenwood, 2007). The former refers to fixed
schedules of charges announced well in advance of travel decisions, while the latter involves charges contingent upon real-time congestion levels. Expanding upon this classification, De Palma and Lindsey (2011) classify tolls as flat, scheduled, or responsive (dynamic) tolls. Flat tolls are fixed and independent of the time of day, day of the week, or season. These systems are historically prevalent due to technological constraints and public acceptance due to the simplicity of the scheme (Selmoune et al., 2020; Gu et al., 2018). Scheduled tolls are also considered static pricing strategies (Cottingham and Greenwood, 2007), but introduce predetermined variations that can be based on the time of the day, the day of the week, and the season. Dynamic pricing strategies, in contrast, are designed to adapt to near-real-time traffic conditions (De Palma and Lindsey, 2011). Compared to static charges, dynamic pricing is likely to be more effective at managing traffic demand by aligning tolls with congestion levels (Zhao et al., 2024), particularly in scenarios involving high adoption rates of autonomous vehicles (Shatanawi et al., 2022). Recent studies have indicated that real-time traffic optimization could improve congestion management and urban traffic conditions (Lee et al., 2019).

Additional dimensions of differentiation can be employed in designing and implementing congestion pricing systems. These include distinctions based on vehicle attributes such as vehicle type, axle count, and weight, features of driving such as speed and other indicators of dangerous driving, and the categorization of vehicles and drivers (De Palma and Lindsey, 2011). Variations implemented in London, such as exemptions for disabled drivers, a 90% discount for residents and a 12.5% discount for fleets, are examples of the last of these.

## 2.2. Milan Area C

The Area C charging zone was implemented in Milan on January 16, 2012, with the aim of revising and rebranding the Ecopass system, introduced in 2008, in response to identified shortcomings and to leverage existing public support (Hensher and Li, 2013). Prior to the implementation of Area C, a public consultation held in 2011 indicated strong approval for continuing the system, with a 79% voter endorsement for the congestion pricing program (Percoco, 2013). Area C retained the same geographical boundaries and entry points as its predecessor, along with its technological infrastructure, while introducing modifications to the fee structure (Hensher and Li, 2013). Area C, which is a fixed rate system, simplified the fee mechanism compared to Ecopass, which applied variable rates based on vehicle engine and fuel type (Rotaris et al., 2010; Beria, 2016). Vehicles crossing the cordon on weekdays are subject to a flat fee, at a rate based on their categorization as general, resident, or commercial users, thereby reducing the complexity of the payment system (Hensher and Li, 2013; Lehe, 2019). The introduction of exemptions for specific vehicle types, including motorcycles, and restrictions on certain vehicles further enhanced perceived fairness, contributing to public acceptance (Veitch and Rhodes, 2024). The Area C charging zone consists of the Cerchia dei Bastioni (CB) (see Fig. 1), an 8 km² area in central Milan with more than 99,000 residents as of December 31, 2019.<fnref n="3" />

The cordon includes 43 access points equipped with cameras to capture vehicle number plates upon entry, with charges remaining constant throughout the day and requiring only one payment per day. The charging hours are from 7:30 AM to 7:30 PM on weekdays. Policy adjustments were enacted in 2022 and 2023, including restrictions on more polluting vehicle types and an increase in toll fees. On October 1, 2022, access to Area C was restricted for certain categories of vehicles,<fnref n="4" /> And on October 30, 2023, the entry fee for the transportation of people within Area C was increased from €5 to €7.5, with fees for service vehicles increasing from €3 to €4.50, representing a total increase of 50%.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Map of Milan Area C, with entrance points as blue and red dots. Entrance points in red are dedicated to public transport. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

## 2.3. Effectiveness of the Area C policy

Studies have investigated the effectiveness of the Area C policy across various impact dimensions, including direct effects on travel behavior as well as economic and environmental impacts. Table 1 summarizes the key findings on the effectiveness of the Area C policy by impact dimensions.

Examining the direct impact of Area C, Beria (2016) analyzed the distribution of entries per vehicle category in 2012 after its implementation, revealing an overall 31.1% reduction in traffic within the area compared to the previous year, 2011, when the earlier Ecopass pricing scheme was in place. Another comparison between Ecopass and Area C, as reported by Lehe (2019), citing data from a report published by the Agenzia Mobilità, Ambiente e Territorio (AMAT, the municipal agency for transport, environment and land use) in 2018, underscored the increased effectiveness of Area C: they found that Area C decreased the number of vehicle entries while charging a greater number of vehicles, with the proportion paying the charge surging to 56%. According to a more recent estimate, published by AMAT in 2022,<fnref n="5" /> the number of vehicle entries decreased by 38.5% over the period 2011–2021. The figure for 2020 (–46.8%) clearly reflects the impact of the COVID-19 pandemic on mobility. Its effects persisted in 2021, leading to an increase in private transportation due to the fear of using public transport.

Concerning the pollution reduction effect, Beria (2016) showed that the traffic decline in 2012 led to an 18% reduction in particulate matter and a 35% decrease in CO2 emissions within the charging area. Moreover, a two-month suspension of charging in 2012 prompted by protests from parking garage owners served as a natural experiment

<footnote n="3">³ This data is sourced from the report “Milano e i suoi quartieri 2020”, retrieved from the link https://www.comune.milano.it/documents/20126/2313917/MILANO+QUARTIERI+2020.pdf on January 5, 2024.</footnote>

<footnote n="4">⁴ Euro 2 gasoline passenger transport vehicles, Euro 3 and 4 diesel with factory-installed particulate filters (FAP), Euro 0, 1, 2, 3, and 4 diesel with aftermarket-installed FAP by December 2018 and with compliance class (reported on the registration document) for particulate mass of at least Euro 4, and Euro 5 diesel.</footnote>

<footnote n="5">⁵ Data retrieved from https://www.comune.milano.it/-/mobilita.-area-c-compie-10-anni-l-analisi-dell-andamento-del-provvedimento-nei-dati-di-amat accessed on January 5, 2024.</footnote>
<table><caption>**Table 1**
Summary of key findings on the effectiveness of the Area C policy by impact dimensions.</caption><thead><tr><th>Impact dimension</th><th>Study</th><th>Key findings</th></tr></thead><tbody><tr><td rowspan="2">Traffic reduction</td><td>Beria (2016)</td><td>31.1% reduction in traffic in 2012 compared to 2011 (Ecopass scheme).</td></tr><tr><td>Lehe (2019)</td><td>Area C reduced vehicle entries while increasing the number of vehicles paying the charge (56%).</td></tr><tr><td rowspan="4">Pollution reduction</td><td>Beria (2016)</td><td>18% reduction in particulate matter and 35% reduction in CO2 emissions in 2012.</td></tr><tr><td>Gibson and Carnovale (2015)</td><td>Significant reduction in traffic and pollution, confirming policy's effectiveness.</td></tr><tr><td>Percoco (2013)</td><td>Short-term reductions in some pollutants, with levels returning to pre-treatment levels shortly after.</td></tr><tr><td>Percoco (2014)</td><td>Same conclusion as Percoco (2013), suggesting the exemption for motorcycles may limit effectiveness.</td></tr><tr><td rowspan="3">Revenue generation</td><td>Beria (2016)</td><td>Net annual revenue of €13 million in 2012, reinvested into public transport and bicycle-sharing initiatives.</td></tr><tr><td>Veitch and Rhodes (2024)</td><td>Revenue recycling enhances public support for congestion pricing systems.</td></tr><tr><td>Croci (2016), Lehe (2019)</td><td>Gross revenues consistently 2–3 times greater than operating costs, confirming financial viability.</td></tr></tbody></table>

for assessing the policy's effects on pollution, although various studies have drawn differing conclusions (Gibson and Carnovale, 2015; Percoco, 2013, 2014). Gibson and Carnovale (2015) found that the policy significantly reduced traffic and pollution, providing evidence that congestion pricing systems also influenced traffic in untolled areas, as demonstrated more recently in other cities (Herzog, 2024). On the contrary, Percoco (2013) found that the charges only significantly decreased the concentration of certain pollutants, particularly carbon monoxide and particulates, in the short term—with pollution levels returning to pre-treatment levels one week after implementation. They suggest that the policy's underperformance may be due to the exemption for motorcycles.

From a revenue standpoint, Beria (2016) estimated that the Area C system yielded a net annual revenue of approximately €13 million in 2012 after accounting for operating costs, with funds reinvested in enhancing public transport and expanding bicycle-sharing initiatives. This revenue recycling fosters a positive rapport between the public and government, enhancing the acceptability of congestion pricing systems Veitch and Rhodes (2024). Fines also contribute to revenues, with penalties from 2012–2014 totaling roughly €20–25 million per year.<fnref n="6" /> More recent studies have indicated that the system consistently generates gross revenues that are double to triple its operating costs, confirming its financial viability (Croci, 2016; Lehe, 2019).

While previous empirical studies have demonstrated the effectiveness of Milan's road pricing scheme in reducing traffic volumes by focusing on the number of vehicles entering the area, they have not considered the changes in overall mobility patterns over time. As a result, these studies offer limited insights into broader shifts in transportation mode shares within the area. This study aims to address this gap by assessing the impact of recent regulatory changes on the proportion of vehicles within the overall mobility flows in Area C.

## 3. Data, methodology and descriptive statistics

This section provides an overview of the data sources, analytical methods, and descriptive statistics used in this study. It is structured into three subsections. The first subsection describes the data collection process and key characteristics of the dataset. The second subsection outlines the methodological framework employed for analyzing the data. Finally, the third subsection presents descriptive statistics to offer initial insights on the effectiveness of the two policy change. Fig. 2 illustrates the research flowchart, outlining the sequential steps of the study. These steps range from the literature review, which shaped the research question, to data collection, the application of analytical models, and the interpretation of results.

### 3.1. Data collection

Two primary data sources were used to gather information on the percentage of vehicles within overall mobility flows in Area C. Data on the number of vehicles were drawn from the open data portal of the municipality of Milan.<fnref n="7" /> This dataset includes the daily number of [vehicle entries] to Area C from January 2019 to December 2023. The overall flow of mobility in the area was computed by using data provided by a telecommunications provider (i.e.a company that provides mobile phone services in Italy). These data provide an estimate of the number of individuals present each day in the area based on collected mobile phone data. The reference period for these data spans from January 2022 to December 2023. Consequently, the dataset built on these two sources contains the percentage of vehicles out of the total number of individuals present in the area for the period January 2022 to December 2023. This percentage represents our dependent variable.

To estimate the influence of two policy changes on the percentage of vehicles within overall mobility flows in Area C, additional information was collected on the occurrence of strikes and holidays, the prices of diesel and gasoline, the cost of public transportation,<fnref n="8" /> and weather conditions in terms of rainfall. Furthermore, to control for the type of population present in the area (as transportation mode preferences may vary among individuals), the dataset also includes the percentages of women and of elderly individuals (aged 65 or over) within the total mobility flow in the area. These percentages are derived from the mobile phone data used to calculate the total mobility flows, and they are provided in an aggregated format (as daily percentages for each area) rather than at the level of individual users. Sources and data descriptions for these datasets are provided in the subsequent paragraph.

The independent variables are summarized in Table 2. Holiday and strike day are boolean variables that are equal to 1 if the day is a holiday or if the day was marked by a public transport strike, and 0 otherwise. These two variables were collected manually. The diesel and gasoline prices represent the average quarterly prices of diesel and gasoline, calculated based on daily values obtained from the Ministry of Enterprises and Made in Italy. The rainfall variable refers to daily precipitation measured in millimeters for the area of Milan (city center, sensor in Brera area. The data is sourced from the Lombardy region's environmental protection agency (ARPA Lombardia). Public transportation price indicates the price of a single-ride ticket for public transportation usage, collected manually. Finally, women and elderly variables represent the daily percentages of women and elderly people (aged 65+) present in the area each day, respectively, according to the data provided by a telecommunications provider. These variables contribute information on various socioeconomic and contextual factors that may have influenced the effects of the charge increase and the vehicle type restriction in Area C.

<footnote n="6">⁶ Data retrieved from https://milano.repubblica.it/cronaca/2015/09/25/news/area_c_bottino_d_oro_2_3_milioni_di_verbali_magenta_varco_killer-123613594/ on January 5, 2024.</footnote>

<footnote n="7">⁷ https://dati.comune.milano.it/.</footnote>

<footnote n="8">⁸ Diesel and gasoline prices in Italy can change at the discretion of distributors: the figures presented in our analysis are derived from government data on quarterly averages. There was also an upward trend in public transportation prices during the study period.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 2. Research flowchart.</figcaption></figure>

<table><caption>**Table 2**

Definition of independent variables.</caption><thead><tr><td>Name</td><td>Description</td><td>Source</td></tr></thead><tbody><tr><td>Festive day</td><td>Boolean variable equals 1 if the day is a holiday, and 0 otherwise</td><td>Manual collection</td></tr><tr><td>Strike day</td><td>Boolean variable equals 1 if the day was a public transport strike day, and 0 otherwise</td><td>Manual collection</td></tr><tr><td>Diesel price</td><td>Average price of diesel per trimester</td><td>Ministero delle Imprese e del Made in Italy</td></tr><tr><td>Gasoline price</td><td>Average price of gasoline per trimester</td><td>Ministero delle Imprese e del Made in Italy</td></tr><tr><td>Rainfall precipitation</td><td>Daily rainfall precipitation in millimeters</td><td>ARPA Lombardia</td></tr><tr><td>Public transportation price</td><td>Single-ride ticket price for public transportation</td><td>Manual collection</td></tr><tr><td>Percentage women</td><td>Daily percentage of women present in the area</td><td>Telecommunication provider</td></tr><tr><td>Percentage elderly</td><td>Daily percentage of elderly people (65+) present in the area</td><td>Telecommunication provider</td></tr></tbody></table>

## 3.2. Methodology

The goal of this study is to assess the effectiveness of two different restriction policies included in the overall Area C congestion policy, based on traffic data from the city of Milan and data on the presence of individuals in the charging area from a telecommunications operator. We estimate the effects of these two policies using fixed effects regressions. Our dependent variable represents the ratio of the number of vehicles entering Area C to the number of people present in the same area on the same day, as captured through mobile phone data. Given that the data was collected on a daily basis over a two-year period (2022 and 2023), the expected total number of observations is 730. However, due to the unavailability of vehicle entry data for 7 days in December 2022, the actual number of observations is 723. To interpret the coefficient in percentage terms, we apply a logarithmic transformation to this dependent variable.<fnref n="9" /> In this study, we employ fixed effects regressions to account for unobserved heterogeneity that could bias our estimates. This method allows us to control for time-invariant characteristics by including fixed effects for specific temporal dimensions. Specifically, we estimate the following model:

$$ \ln Y_t = \alpha + \beta_1 R1_t + \beta_2 R2_t + X'_t\gamma + Z'_t\delta + \kappa_t + \lambda_t + \mu_t + v_t + \epsilon_t \quad (1) $$

where $Y_t$ is the outcome of interest, i.e. the vehicle share of mobility flows in Area C in day $t$. $R1_t$ and $R2_t$ are the variables of interest, respectively for Policy 1, i.e. the vehicle type restriction, and for Policy 2, the charge increase. $X'_t$ includes a set of covariates, including a binary variable to indicate whether the day is a holiday, a binary variable for strike days, and variables for diesel, gasoline and public transport

prices and daily precipitation. We also include two sociodemographic variables, $Z'_t$: the proportions of women and people aged over 65 in daily total mobility flow. $\epsilon_{i,t}$ is an unobserved error term.

Fixed effects for the year ($\kappa_t$), the month ($\lambda_t$), the week ($\mu_t$), and the day ($v_t$) are included to control for time-related factors, such as seasonality and temporal variations (Lecouteux and Moulin, 2024; Percoco, 2013). These fixed effects eliminate the influence of unobserved, time-invariant factors that could bias the estimated coefficients of the variables of interest. Specifically, year fixed effects ($\kappa_t$) capture broad annual trends, such as economic growth, policy changes, or long-term shifts in mobility patterns. Month fixed effects ($\lambda_t$) account for seasonal fluctuations, such as weather-related changes. Week fixed effects ($\mu_t$) address recurring weekly patterns and capture specific events tied to certain weeks, such as school holidays. Finally, day fixed effects ($v_t$) control for systematic variations on specific days, such as differences between weekdays and weekends, as well as one-time events or anomalies occurring on particular days (e.g., national holidays, strikes, or weather extremes).

We estimate two models, one for the total sample of users in the Area C zone, and another focusing specifically on Italian nationals. The decision to conduct a distinct analysis for nationals is motivated by their status as the predominant resident group in the area. Given their direct exposure to mobility policies and pricing changes, their behavior plays a pivotal role in determining the effectiveness of these measures. Furthermore, focusing on Italian nationals may offer a more nuanced understanding of local dynamics, as their transport preferences and socioeconomic conditions may differ from those of non-citizen residents, who often face specific economic or logistical challenges. This dual modeling approach ensures a comprehensive assessment of the impact of the regulations on both the general urban population and its most affected subgroup.

<footnote n="9">⁹ This is a common approach in the literature (see Boggio and Beria, 2019; Lin et al., 2022).</footnote>
<table><caption>**Table 3**
Sociodemographic characteristics of the population present in Area C each day.</caption><caption>Source: Telecommunications provider.</caption><thead><tr><th>Variable</th><th>Statistic</th><th>Mean</th><th>St. Dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td rowspan="4">Gender</td><td>Female</td><td>0.38</td><td>0.04</td><td>0.22</td><td>0.44</td></tr><tr><td>Male</td><td>0.45</td><td>0.05</td><td>0.27</td><td>0.51</td></tr><tr><td>Masked gender</td><td>0.001</td><td>0.001</td><td>0.00</td><td>0.01</td></tr><tr><td>Undefined gender</td><td>0.17</td><td>0.08</td><td>0.07</td><td>0.51</td></tr><tr><td rowspan="3">Nationality</td><td>Italian</td><td>0.83</td><td>0.08</td><td>0.49</td><td>0.93</td></tr><tr><td>Other</td><td>0.17</td><td>0.08</td><td>0.07</td><td>0.51</td></tr><tr><td>Masked nationality</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.001</td></tr><tr><td rowspan="6">Age</td><td>18 or under</td><td>0.04</td><td>0.01</td><td>0.01</td><td>0.07</td></tr><tr><td>19–30</td><td>0.14</td><td>0.02</td><td>0.07</td><td>0.17</td></tr><tr><td>31–45</td><td>0.17</td><td>0.02</td><td>0.10</td><td>0.21</td></tr><tr><td>46–64</td><td>0.29</td><td>0.03</td><td>0.17</td><td>0.34</td></tr><tr><td>Over 65</td><td>0.19</td><td>0.02</td><td>0.14</td><td>0.24</td></tr><tr><td>Undefined age</td><td>0.17</td><td>0.08</td><td>0.07</td><td>0.51</td></tr></tbody></table>

Note: The Table shows Area C presence data by sociodemographic characteristics in 2022 and 2023. Mean, standard deviation, minimum and maximum values over the observed period are shown.

### 3.3. Descriptive statistics

In this section, we present descriptive statistics on the variables used in the study to analyze the impact of congestion pricing policy restrictions in Area C in Milan on mobility flows, overall and for specific population groups. Descriptive statistics concerning the sociodemographic characteristics of our study population are presented in Table 3. These statistics are drawn from mobile phone data on daily numbers of individuals present in the congestion charging area over the period 2022–2023.

The mean proportion of identified women in the area on a given day in the study period is 0.38 (SD: 0.04), ranging from 0.22 to 0.44. Identified men constitute an average of 0.45 of the daily population (SD: 0.05), ranging from 0.27 to 0.51. The data thus show a slightly higher average proportion of men than women in the area. The total does not sum to 100% due to the inclusion of masked and undefined gender categories. Individuals who chose to mask their gender represent a negligible proportion of the population, but there is a significant portion of the population whose gender was not identified in the data for some other reason, which could impact the accuracy of gender-related analyses.

Italian nationals make up an average of 83 percent of the daily population in Area C (SD: 8 percent), while non-nationals represent 17 percent of the population (SD: 8 percent). The proportion of individuals who chose to mask their nationality are negligible. The overwhelming majority of the population present in Area C each day are Italian nationals, with a significant minority of non-nationals. The congestion pricing policy implemented in 2012 is thus mainly applied to Italian nationals.

The average age distribution of the population of individuals present each day in Area C is as follows: 4 percent aged 18 or under, 14 percent aged 19–30, 17 percent aged 31–45, 29 percent aged 46–64, and 19 percent aged 65 or over. The substantial proportion of individuals whose age is undefined (17 percent) may reflect data identification challenges for the telecommunications provider. The two age ranges that are most prevalent, 31–45 and 46–64, correspond to working age, a demographic that is thus targeted by policies aimed at reducing commuter traffic by private car in Milan’s city center.

The small proportion of individuals whose gender, nationality, and/or age is masked in the data implies that the data collection process is quite transparent and reliable.

Figs. 3 and 4 show the monthly distribution of mobility flows within Area C (i.e. the number of unique individuals present in Area C) and monthly distribution of vehicle flows in Area C (i.e. the number of vehicles entering Area C from outside) in the years 2022 and 2023. The combined view of these two factors provides an initial descriptive

overview of how different restrictions, such as vehicle type limitations and congestion charge increases, influence the choice to use a private car to access the study area.

Thus, we can see that while the average number of people accessing Area C increased between September 2022 and October 2022 (i.e. yellow boxes, months 9 and 10 in Fig. 3), the number of vehicles (i.e. yellow boxes, months 9 and 10 in Fig. 4) did not, seemingly indicating that the vehicle limitation policy launched on October 1, 2022 was somewhat effective. This observation repeats throughout the year 2023: increases in the number of people present each day in Area C are not matched by equivalent increases in the number of monthly average number of vehicles entering the area. While more people accessed Area C each day in 2023 than in 2022, the average number of vehicles each month was lower than the value for the same month a year earlier. The pattern following the increase in the congestion charge on October 30, 2023, seems to be similar, with the average number of people present in Area C in November and December 2023 higher than the values for the same months in 2022, but the number of vehicles accessing Area C almost identical to the values for the same months in 2022.

The variations in traffic on different days reported in Fig. 5 show that on weekdays, traffic in Area C in 2023 was lower than in 2022. This suggests that the two limitation policies, increased charges and vehicle restrictions, primarily affected commuters.

In summary, our descriptive analysis provides an initial overview of mobility patterns and the potential impacts of congestion pricing policies on vehicle circulation in the Area C congestion charging zone. We now assess the reliability of these initial insights using models to obtain quantitative estimates of the effects of the two limitation policies.

### 4. Results and discussion

We estimate Eq. (1) by running different types of models. First, we estimate the effects of the charge increase and vehicle type limitation with a simple OLS model, without fixed effects (Model 1). In a second step, we include fixed effects in the model (Model 2). Third, we also include covariates for transport conditions and costs (Model 3), before including sociodemographic characteristics in a fourth step (Model 4). Table 4 shows the results for the entire population of interest, whereas Table 5 is restricted to individuals identified as Italian (identified by using nationality variable) in the mobile phone data.

Regardless of the specification used (from (1) to (4)), both policy changes appear to have had a significant negative effect on vehicle share of mobility flows in the Area C zone, with Policy 1 (vehicle type limitation) having a greater impact than Policy 2 (charge increase). While Policy 1 had an immediate effect, its impact may change over time. In particular, it may prompt individuals to replace their vehicles with models that comply with the policy requirements, allowing them to continue driving into the area with their private cars. Hence, the policy’s overall effectiveness could diminish or even become negligible in the medium to long term.e (Rotaris et al., 2010).

In the models for the entire population, the intensity of the negative effect of the first restriction, i.e. the restriction of the types of vehicles allowed to enter the charging area, diminishes as more variables are added to the model, while that of the second restriction, the charge increase, increases as more variables are added. Whether for the entire population or for Italian nationals, the vehicle type restriction seems to have had a slightly stronger effect than the charge increase on vehicle share in central Milan. While limiting the type of vehicles logically has an effect on people’s access to Area C by car, as it affects the number of cars that can enter the area, our results also indicate that increases in charges tend to discourage people from coming by car to the city center. According to Model 4, the increase in charges reduced the vehicle share of mobility flows in Area C by 5.2% (4.8% when only Italians are considered), while the restriction on the types of vehicles allowed in the zone reduced it by 8.4% (10.7% for Italians).
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Mean daily mobility flows (number of unique individuals present) in Area C (mln).</figcaption></figure>

*Note:* The figure presents a boxplot showing the distribution of the daily number of individuals present in Area C for each month (from January to December) across the years 2022 (represented by yellow boxes) and 2023 (represented by green boxes), according to mobile phone data. In the boxplot, the central box represents the interquartile range (IQR), which spans from the first quartile (Q1) to the third quartile (Q3). The top of the box marks the 25th percentile of the data. The median (Q2), indicating the midpoint of the distribution, is represented by a line inside the box. The bottom of the box marks the 75th percentile. The ‘whiskers’ extend from the edges of the box to the minimum and maximum values that fall within 1.5 times the interquartile range, illustrating the overall range of the data. Any outliers beyond this range are indicated as individual points. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

*Source:* Telecommunications provider.

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Daily number of vehicles entering Area C (thousands).</figcaption></figure>

*Note:* The figure presents a boxplot showing the distribution of daily numbers of vehicles entering Area C for each month (from January to December) in the years 2022 (represented by yellow boxes) and 2023 (represented by green boxes). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

*Source:* Open data portal of the city of Milan.

The relatively limited effect of the charge increase, especially compared to the reduction in absolute vehicular traffic reported by Beria (2016) following the initial introduction of Milan's congestion charge,

is consistent with previous research. Studies suggest that increases in charge levels typically have a much milder effect than the initial implementation of the charges themselves (Lehe, 2019). The initial
<figure><img src="image_7.png" /><figcaption>Fig. 5. Day type distribution of daily number of vehicle accesses in Area C.</figcaption></figure>

Note: The figure shows the first quartile, the median, the third quartile and the minimum and maximum values for the number of vehicles accessing Area C per day over the years 2022 and 2023, as described above for Fig. 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Source: Open data portal of the city of Milan.

<caption>Table 4</caption>

Results of fixed-effects regression for the two congestion limitation policies.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Policy 1</td><td>-0.104***<br>(0.008)</td><td>-0.118***<br>(0.010)</td><td>-0.094***<br>(0.011)</td><td>-0.084***<br>(0.014)</td></tr><tr><td>Policy 2</td><td>-0.014<br>(0.011)</td><td>-0.047***<br>(0.014)</td><td>-0.047***<br>(0.013)</td><td>-0.052***<br>(0.013)</td></tr><tr><td>Holidays</td><td></td><td></td><td>-0.075**<br>(0.029)</td><td>-0.113***<br>(0.031)</td></tr><tr><td>Strikes</td><td></td><td></td><td>0.042***<br>(0.010)</td><td>0.036***<br>(0.011)</td></tr><tr><td>Diesel price</td><td></td><td></td><td>-0.642***<br>(0.062)</td><td>-0.583***<br>(0.058)</td></tr><tr><td>Gasoline price</td><td></td><td></td><td>0.411***<br>(0.058)</td><td>-0.078<br>(0.119)</td></tr><tr><td>Public transport price</td><td></td><td></td><td>-0.102<br>(0.147)</td><td>-0.328*<br>(0.156)</td></tr><tr><td>Rainfall</td><td></td><td></td><td>0.000<br>(0.000)</td><td>-0.000<br>(0.000)</td></tr><tr><td>Women</td><td></td><td></td><td></td><td>-2.035***<br>(0.556)</td></tr><tr><td>People over 65 years old</td><td></td><td></td><td></td><td>5.378***<br>(1.021)</td></tr><tr><td>Constant</td><td>-2.729***<br>(0.007)</td><td>-2.713***<br>(0.021)</td><td>-2.054***<br>(0.335)</td><td>-1.160**<br>(0.385)</td></tr><tr><td>Year fixed effects</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Month fixed effects</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Week fixed effects</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Day fixed effects</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Robust standard errors</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>N</td><td>723</td><td>723</td><td>723</td><td>723</td></tr><tr><td>AIC</td><td>-1406.956</td><td>-1760.425</td><td>-1939.574</td><td>-2014.533</td></tr><tr><td>RMSE</td><td>0.091</td><td>0.068</td><td>0.060</td><td>0.057</td></tr><tr><td>Adjusted R2</td><td>0.241</td><td>0.574</td><td>0.671</td><td>0.703</td></tr></tbody></table>

Note: This table reports estimates the effects of two changes in Milan's congestion limitation policies (a charge increase and a restriction on the types of vehicles allowed to enter the charging zone), along with covariates for transport conditions and costs as well as sociodemographic covariates, on vehicle entries into the Area C zone. Column 1 presents the results of the OLS estimation without covariates. Column 2 includes fixed effects. Column 3 includes covariates for transport conditions and costs. Column 4 includes sociodemographic characteristics. Robust standard errors are in parentheses. Asterisks indicate traditional levels of statistical significance (***, **, *, for p < 0.001, 0.01, 0.05, respectively).
<table><caption>**Table 5**
Results of fixed-effects regression for the two congestion limitation policies, Italian nationals only.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Policy 1</td><td>-0.072***<br>(0.012)</td><td>-0.082***<br>(0.011)</td><td>-0.075***<br>(0.012)</td><td>-0.107***<br>(0.014)</td></tr><tr><td>Policy 2</td><td>-0.050**<br>(0.016)</td><td>-0.060***<br>(0.013)</td><td>-0.061***<br>(0.013)</td><td>-0.048***<br>(0.013)</td></tr><tr><td>Holidays</td><td></td><td></td><td>-0.019<br>(0.034)</td><td>-0.096**<br>(0.033)</td></tr><tr><td>Strikes</td><td></td><td></td><td>0.042***<br>(0.011)</td><td>0.034**<br>(0.011)</td></tr><tr><td>Diesel price</td><td></td><td></td><td>-0.740***<br>(0.073)</td><td>-0.640***<br>(0.061)</td></tr><tr><td>Gasoline price</td><td></td><td></td><td>0.334***<br>(0.066)</td><td>0.163<br>(0.121)</td></tr><tr><td>Public transport price</td><td></td><td></td><td>-0.230<br>(0.164)</td><td>-0.455**<br>(0.175)</td></tr><tr><td>Rainfall</td><td></td><td></td><td>-0.000<br>(0.000)</td><td>-0.000<br>(0.000)</td></tr><tr><td>Women</td><td></td><td></td><td></td><td>-4.579***<br>(0.592)</td></tr><tr><td>People over 65 years old</td><td></td><td></td><td></td><td>5.496***<br>(1.088)</td></tr><tr><td>Constant</td><td>-2.556***<br>(0.009)</td><td>-2.465***<br>(0.022)</td><td>-1.281**<br>(0.390)</td><td>-0.051<br>(0.415)</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Month fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Week fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Day fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Robust standard errors</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>N</td><td>723</td><td>723</td><td>723</td><td>723</td></tr><tr><td>AIC</td><td>-735.022</td><td>-1634.584</td><td>-1768.769</td><td>-1935.300</td></tr><tr><td>RMSE</td><td>0.145</td><td>0.075</td><td>0.068</td><td>0.060</td></tr><tr><td>Adjusted R2</td><td>0.070</td><td>0.754</td><td>0.798</td><td>0.840</td></tr></tbody></table>

Note: This table reports estimates of the effects of two changes in Milan's congestion limitation policies (a charge increase and a restriction on the types of vehicles allowed to enter the charging zone), along with covariates for transport conditions and costs as well as sociodemographic covariates, on vehicle entries into the Area C zone, using mobile phone data only for Italian nationals. Column 1 presents the results of the OLS estimation without covariates. Column 2 includes fixed effects. Column 3 includes covariates for transport conditions and costs. Column 4 includes sociodemographic characteristics. Robust standard errors are in parentheses. Asterisks indicate traditional levels of statistical significance (***, **, *, for p < 0.001, 0.01, 0.05, respectively).

implementation of the Ecopass congestion policy had an impact: the share of charged vehicles decreased from 22% in 2008 to 14% in 2011 (Lehe, 2019). In our study, we observed a mere 5.2% decrease in the share of vehicles within overall mobility in the zone when the charge was increased.

In 2018, Börjesson and Kristoffersson (2018) analyzed the effects of charge increases in Sweden, finding similar results. They offered two explanations for the differential effects of initial implementation and charge increases. Firstly, the initial charge deters drivers with more elastic demand, meaning that those remaining are less sensitive to subsequent price increases. Secondly, from a behavioral economics perspective, Shampanier et al. (2007) highlight that consumers are disproportionately sensitive to any positive price—however small— due to psychological factors, a phenomenon known as the “zero price effect”. In other contexts, such as London, the 2005 charge increase from £5 to £8 revealed an elasticity of demand that was only one third of the original effect observed when the charge was first introduced (Evans, 2008). Although further increases raised the charge to £15, no comprehensive studies have been conducted to assess the effects of these later adjustments. It is worth noting that while increasing charges can effectively reduce traffic, recent studies conducted in Switzerland have shown that public support diminishes when the policy design entails high costs. Although improved travel speeds can enhance public acceptance of such policies, support tends to decline beyond a certain price threshold, even for designs that provide significant speed gains (Lichtin et al., 2024).

Covariates for transport conditions and costs have similar effects for the whole sample and for the sub-sample consisting only of Italian nationals. Holidays have a significant negative effect on the vehicle share

of mobility flows, suggesting that they reduce the number of vehicles in the zone. Unsurprisingly, strikes in the public transportation sector seem to have a significant positive effect on the vehicle proportion of mobility flows. When there are strikes in public transportation, people are likely to turn to their cars as an alternative. Diesel prices have a significant effect on the vehicle share of mobility flows, a large effect (–0.583), especially for Italians (–0.640). On the other hand, public transportation prices have a significant negative effect (–0.328 overall and –0.455 for Italians). Rainfall does not appear to have any effect on the vehicle share of mobility flows.

We are interested not only in the effect of these policy changes on vehicle traffic, but also in the demographic characteristics of people who come to the area by car before and after the policy. As demonstrated by Franklin (2012), demographic characteristics, such as age and gender, modulate travel responses to congestion pricing. In our study, we found that the percentage of women within the total population present in the charging area has a significant negative association with the proportion of vehicles in mobility flows, indicating that days with a higher percentage of women are associated with lower vehicle use. This result contrasts with the findings of Franklin (2012) and Transek (2006), which suggest that women reduce their automobile trips significantly less than men. In contrast, the percentage of people over the age of 65 is positively associated with the vehicle share of mobility flows, suggesting they might be inclined to use their car. Unlike previous studies on the effect of age, which did not find any direct influence on vehicle use (Franklin, 2012), our study reveals a relationship between age and mobility choices. Adding these two variables to the model renders the effect of gasoline prices insignificant, suggesting that this influence is absorbed by demographic factors. This
may be explained by the varying usage patterns of different vehicle types across demographic groups.

Overall, the results suggest that these traffic-limiting measures, especially vehicles type restrictions, have had a significant impact on vehicle share of mobility flows in the Area C zone in Milan. Other factors such as fuel prices, public transportation prices, and holidays also play a role in individuals’ transportation choices. Restrictions and covariates, such as the proportion of women, appear to have stronger and more significant effects on the vehicle share of mobility flows in Milan’s Area C in the subsample of Italian nationals.

## 5. Conclusion

The Milan road charging scheme has been extensively studied, for its direct effects on travel behavior (Beria, 2016; Lehe, 2019) as well as its wider economic and environmental impacts. While empirical studies have demonstrated the scheme's effectiveness in reducing the number of vehicles entering Area C, these studies have often overlooked the question of shifts in transportation mode preferences and the composition of overall mobility flows within the area.

This paper addresses this gap through an assessment of the impact of recent regulatory changes on the proportion of vehicles within Area C's overall mobility flows. To do so, it uses mobile phone data to capture broader mobility flows, for the first time in this context, combined with vehicle access data from the municipality's open data portal. More specifically, the two recent adjustments to Milan's road pricing scheme studied here, implemented in 2022 and 2023, consist of new entry restrictions on more polluting vehicle types and an increase in the congestion charge. We estimate the effects of these two policy changes separately using fixed-effects models.

Our findings indicate that both measures had a significant negative impact on vehicle entries into Area C. Specifically, Policy 1, vehicle type restrictions, appears to have had a greater impact than Policy 2, an increase in charges. Nevertheless, other factors such as fuel prices, public transport costs, and special events also influence individual transportation choices. Additionally, demographic factors such as the proportion of women and the percentage of elderly people significantly affect vehicle usage in Milan's Area C, particularly among Italian nationals.

This study is subject to two key limitations. First, while we leverage detailed vehicle access and mobility flow data, it is not individual-level data. As such, we are unable to precisely analyze the effects of individual characteristics—such as income, household size, or specific sociodemographic factors—on car usage and transportation mode shifts. We rely on aggregate data for certain covariates (e.g., gender, age distribution), which, although useful, may obscure individual-level variability in responses to the policy changes. Second, the analysis would benefit from more granular transportation data, particularly at the hourly level and regarding specific vehicle types. Access to such detailed data would enable a more nuanced examination of mobility changes resulting from the two congestion policies implemented. By capturing variations in vehicle usage patterns throughout the day and across different vehicle categories, we could better understand the effectiveness of these policies and their impact on overall mobility behavior. Moreover, while we can assess the immediate effects of the policies, their longer-term impacts and potential behavioral adaptations remain outside the scope of this analysis. This highlights an important avenue for future research, where a more comprehensive examination of these long-term effects could provide valuable insights into the sustainability and efficacy of these policies.

The findings of this study have practical implications for policymakers in several areas, and are also of interest for other cities. First, the proposed approach based on the analysis of the proportion of vehicles within the overall mobility flow is a replicable approach. It can be used to estimate the effect of congestion policies in other cities, examining the numbers of individuals present, and not only the absolute number

of vehicles accessing the area.

Secondly, the evidence supporting the effectiveness of vehicle type restrictions and increased charge level could justify the enhancement of pricing policies and vehicle limitations in Milan and similar cities experiencing congestion issues. Specifically, the study highlights the more pronounced impact of vehicle type restrictions compared to a charge increase. However, while this is the immediate effect of this policy change, further research is needed to examine its potential dynamic effects over time. Vehicle restrictions may accelerate the rate of vehicle substitution, and in the medium to long term, the impact may dwindle or even become negligible (Rotaris et al., 2010). The approach proposed here can be used to assess such longer-term effects as well. By analyzing this percentage over a longer time period, the approach can provide insights into the vehicle substitution rate, capturing dynamics that cannot be effectively understood by considering only the absolute number of vehicles.

Third, by incorporating demographic factors into the analysis, this study opens up new avenues for equity research in the field. Ensuring equity in the implementation of pricing policies, particularly for vulnerable demographic groups, is essential. Monitoring the impact of policy changes on different population segments, and adopting measures to mitigate negative effects on those with specific mobility needs, such as women or the elderly, could improve both policy acceptance and effectiveness. Although the study lacks individual-level data to directly assess these effects, analyzing policy impacts on the proportion of these groups within the overall daily mobility flow in the city provides initial evidence of differing responses to the policies and their influence on car usage.

## CRediT authorship contribution statement

Léonard Moulin: Writing – original draft, Methodology, Formal analysis, Conceptualization. Valeria Maria Urbano: Writing – original draft, Methodology, Funding acquisition, Formal analysis, Conceptualization.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used ChatGPT to improve readability and language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This study was funded by the European Union - NextGenerationEU, Mission 4, Component 2, under the aegis of the GRINS (Growing Resilient, Inclusive and Sustainable) project (GRINS PE00000018 - CUP: D43C22003110001, Spoke 7 "Territorial sustainability"). The views and opinions expressed are solely those of the authors, and do not necessarily reflect those of the European Union, nor can the European Union be held responsible for them.

## Data availability

The data that has been used is confidential.
References

Beria, P., 2016. Effectiveness and monetary impact of Milan's road charge, one year after implementation. Int. J. Sustain. Transp. 10 (7), 657–669.

Boggio, M., Beria, P., 2019. The role of transport supply in the acceptability of pollution charge extension. The case of Milan. Transp. Res. A 129, 92–106.

Börjesson, M., Eliasson, J., Hugosson, M.B., Brundell-Freij, K., 2012. The Stockholm congestion charges—5 years on. Effects, acceptability and lessons learnt. Transp. Policy 20, 1–12.

Börjesson, M., Kristoffersson, I., 2018. The Swedish congestion charges: Ten years on. Transp. Res. A 107, 35–51.

Cottingham, W.N., Greenwood, D.A., 2007. An Introduction to the Standard Model of Particle Physics. Cambridge University Press.

Croci, E., 2016. Urban road pricing: A comparative study on the experiences of London, Stockholm and Milan. Transp. Res. Procedia 14, 253–262, Transport Research Arena TRA2016.

De Palma, A., Lindsey, R., 2011. Traffic congestion pricing methodologies and technologies. Transp. Res. C 19 (6), 1377–1399.

Ecola, L., Light, T., 2009. Equity and congestion pricing a review of the evidence sponsored by the environmental defense fund.

Evans, R., 2008. Demand elasticities for car trips to central London as revealed by the central London congestion charge. Transp. Lond. Policy Anal. Div. 4.

Franklin, J.P., 2012. Role of context in equity effects of congestion pricing. Transp. Res. Rec. 2297 (1), 29–37.

Gibson, M., Carnovale, M., 2015. The effects of road pricing on driver behavior and air pollution. J. Urban Econ. 89, 62–73.

Givoni, M., 2012. Re-assessing the results of the London congestion charging scheme. Urban Stud. 49 (5), 1089–1105.

González-Aliste, P., Derpich, I., López, M., 2023. Reducing urban traffic congestion via charging price. Sustain. 15 (3), 2086.

Gu, Z., Liu, Z., Cheng, Q., Saberi, M., 2018. Congestion pricing practices and public acceptance: A review of evidence. Case Stud. Transp. Policy 6 (1), 94–101.

Guensler, R., Ko, J., Kim, D., Khoeini, S., Sheikh, A., Xu, Y., 2020. Factors affecting Atlanta commuters' high occupancy toll lane and carpool choices. Int. J. Sustain. Transp. 14 (12), 932–943.

Hensher, D.A., Li, Z., 2013. Referendum voting in road pricing reform: A review of the evidence. Transp. Policy 25, 186–197.

Herzog, I., 2024. The city-wide effects of tolling downtown drivers: Evidence from London's congestion charge. J. Urban Econ. 144, 103714.

Lecouteux, G., Moulin, L., 2024. Cycling in the aftermath of COVID-19: An empirical estimation of the social dynamics of bicycle adoption in Paris. Transp. Res. Interdiscip. Perspect. 25, 101115.

Lee, S., Younis, M., Murali, A., Lee, M., 2019. Dynamic local vehicular flow optimization using real-time traffic conditions at multiple road intersections. IEEE Access PP, 1–1.

Lehe, L., 2019. Downtown congestion pricing in practice. Transp. Res. C 100, 200–223.

Lichtin, F., Smith, E.K., Axhausen, K.W., Bernauer, T., 2024. How to design publicly acceptable road pricing? Experimental insights from Switzerland. Ecol. Econom. 218, 108102.

Lin, S., Zhu, S., Li, X., Li, R., 2022. Effects of strict vehicle restrictions on various travel modes: A case study of Zhengzhou, China. Transp. Res. A 164, 310–323.

Lindsey, R., 2006. Do economists reach a conclusion on road pricing? The intellectual history of an idea. Econ J. Watch. 3 (2), 292–379.

Percoco, M., 2013. Is road pricing effective in abating pollution? Evidence from Milan. Transp. Res. D 25, 112–118.

Percoco, M., 2014. The impact of road pricing on housing prices: Preliminary evidence from Milan. Transp. Res. A 67, 188–194.

Pigou, A., 1920. The Economics of Welfare. Macmillan.

Rotaris, L., Danielis, R., Marcucci, E., Massiani, J., 2010. The urban road pricing scheme to curb pollution in Milan, Italy: Description, impacts and preliminary cost-benefit analysis assessment. Transp. Res. A 44 (5), 359–375.

Selmoune, A., Cheng, Q., Wang, L., Liu, Z., 2020. Influencing factors in congestion pricing acceptability: A literature review. J. Adv. Transp. 2020, 1–11.

Shampanier, K., Mazar, N., Ariely, D., 2007. Zero as a special price: The true value of free products. Mark. Sci. 26 (6), 742–757.

Shatanawi, M., Alatawneh, A., Mészáros, F., 2022. Implications of static and dynamic road pricing strategies in the era of autonomous and shared autonomous vehicles using simulation-based dynamic traffic assignment: The case of Budapest. Res. Transp. Econ. 95, 101231.

Tarriño-Ortiz, J., Gómez, J., Soria-Lara, J.A., Vassallo, J.M., 2022. Analyzing the impact of low emission zones on modal shift. Sustain. Cities Soc. 77, 103562.

Transek, A., Equity effects of congestion fees: an investigation method and case study in Stockholm. Publication, 36.

Veitch, E., Rhodes, E., 2024. A cross-country comparative analysis of congestion pricing systems: Lessons for decarbonizing transportation. Case Stud. Transp. Policy 15, 101128.

Zhao, C., Tang, J., Hu, L., Hu, C., Dai, G., 2024. Impact of carbon charge policy on mode shifts in public transportation. Transp. Res. D 137, 104491.