Congestion pricing policies: Design and assessment for the city of Rome, Italy

Transport Policy xxx (xxxx) xxx–xxx

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Congestion pricing policies: Design and assessment for the city of Rome, Italy Ernesto Cipriani, Livia Mannini, Barbara Montemarani, Marialisa Nigro∗, Marco Petrelli Department of Engineering, "Roma Tre" University, Via Vito Volterra 62, 00146, Rome, Italy

ARTICLE INFO

ABSTRACT

Keywords: Congestion pricing Congestion charging Road pricing Pricing policy Tolling schemes Public transport

Congestion pricing is classified as a transportation demand management measure adopted to reduce impacts modern cities are suffering in terms of traffic congestion, road accidents and air and noise pollution. Such a measure allows linking road transport externalities directly to travelers producing them. The definition of proper tolling schemes enables this measure to act on demand short-term choices, forcing travellers to shift towards low impact road itineries (route diversion) and sustainable transport systems (modal diversion). This paper presents key findings in the design of pricing policies to a specific real size and complex case (city of Rome, Italy) addressing overall transport performances (on the multimodal network) and impacts (affecting the entire community), in a demand elastic context. Main contribution derives from equity matters dealt with in the impact assessment of pricing policies, so filling a gap not extensively studied in this field. Specifically, different tolling schemes have been defined from a quantitative (toll level) and spatial (city zones where pricing is implemented) viewpoint in order to guarantee equity aspects in the application of the measure: affected road users are limited to those travelling in city zones where the mass public transport network (metro and rail) is available; besides, the toll amount is related to the level of accessibility to public transport (whether only in origin/destination of the trip or in both). Results, evaluated adopting a simulation-based approach, are consistent with those obtained in other real world cases, and highlight that the adoption of a proper pricing policy in the city of Rome guarantees a demand diversion towards sustainable transport modes up to 25% for those zones directly involved by the pricing implementation; promising benefits have been observed even in a wider area, entire Province of Rome, not directly affected by the measure, in terms of reduction both of road users (up to 6%) and congestion costs (about 2%).

1. Introduction

the cost of their use of the road better than existing fees and taxes, based also on the emissions produced and/or the traffic flow conditions. The adoption of congestion pricing has to be considered as a policy tool to manage a limited resource, the road space, pushing the road users to modify their behaviours in terms of route choice and mode choice. Congestion pricing schemes can be categorized as a function of their spatial implementation. According to De Palma et al. (2006, 2009) and Gervasoni and Sartori (2007), they are classified into two groups: i) “facility based schemes” where user pays a toll for crossing/traveling one or more road infrastructures; ii) “area-based schemes” where user pays a toll to enter or exit a zone, or to travel within it. The toll can vary depending on the distance travelled, the time of the day, the day of the week, the season, the vehicle type (tolls vary according to number of axles, weight and type of fuel) and level of

Today, cities are suffering in terms of high levels of traffic congestion, implying several externalities including the increase of accidents, air pollutant emissions and noise. Congestion and accidents affect directly road users, while the other ones affect the entire community. The air pollutant limits due to vehicle emissions were widely exceeded across Europe in the last years and the road traffic is the most widespread source of environmental noise, with an estimated 100 million people affected by harmful levels in the EEA-33 member countries1. Adoption of congestion pricing policies help in reducing traffic impacts modern cities are suffering. It is classified as a transportation demand management measures where the road users are charged by specific tolling scheme reflecting

Corresponding author. E-mail address: [email protected] (M. Nigro). 1 https://www.eea.europa.eu/data-and-maps/indicators/exceedances-of-air-quality-objectives-7/assessment; https://www.eea.europa.eu/data-and-maps/indicators/ exposure-to-and-annoyance-by-2/assessment-1. ∗

https://doi.org/10.1016/j.tranpol.2018.10.004 Received 20 July 2017; Received in revised form 20 August 2018; Accepted 11 October 2018 0967-070X/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Cipriani, E., Transport Policy, https://doi.org/10.1016/j.tranpol.2018.10.004

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traffic. While road pricing initiatives, involving Heavy Good Vehicles (HGV) road users, are quite common both in Europe (e.g. the Eurovignette2 involving Denmark, Luxemburg, the Netherlands and Sweden, UK) as well as in other countries (e.g. New Mexico, New Zealand and Oregon3), properly congestion pricing applications has come into limited practice. The main operating schemes are High Occupancy Toll (HOT) lane facilities that can be found especially in the US,4 where the toll is applied to a section of the road in order to access preferential lanes behind charging for road users not meeting occupancy requirements. Other adopted schemes are tolls applied to roads that are part of a corridor (some examples are implemented in Australia or US) or to specific points/links as bridge and tunnel (single link-based tolls, e.g. Dartford Crossing, Humber Bridge, Severn Crossing in the UK). Several recent pilot projects about road pricing are launched in the U.S., but with the objective of developing a funding mechanism that allows drivers to support road maintenance based on the distance they travel or the period of time they use the roads (e.g., the California Road Charge Pilot and OReGO5). There are very few congestion pricing schemes implemented at urban level and, specifically, some of these are Singapore, London (UK), Stockholm and Gothenburg (Sweden) and Milan (Italy). The Singapore's Electronic Road Pricing (ERP) was launched in 1998 and it covers selected expressways and arterial roads as well as three restricted zones in the CBD and the Orchard cordon. Thus, it is a hybrid of facility-based tolls and cordons schemes. The toll is applied to all vehicles (including motorcycles and buses) and it varies by time of day and direction of travel, based on the achievement of a minimum “level of service” (operating speed). The system is going to become even more sophisticated since the future direction is to implement a full distance, time, location and vehicle type based road pricing scheme, using GNSS technologies.6 The London congestion charge was introduced in 2003 (Richardson and Bae, 2008) and it works with a flat daily charge for driving a vehicle within the charging zone. The charging zone was extended to the west in 2007, but it was terminated in January 2011, due to the breakdown of the cost-benefit test (Santos and Fraser, 2006). Stockholm's congestion charge started in 2007. It is designed as a cordon scheme, thus the payment is required for each passage or entry through the cordon and it is dependent by the time of the day. Following the example of Stockholm, Gothenburg introduced a cordon in 2013 with a single charge rule, where a vehicle that passes several payment stations within 60 min is only taxed once. The Milan EcoPass began operating in 2008. Since it is mainly designed to reduce pollution, the toll is differentiated as a function of the vehicle EURO classes following the pollution charge indications of the European Directive 2004/35. Large experiences about the adoption of an urban toll system come

also from Oslo and other 8 cities in Norway. In all the cases tolls were introduced along main corridors within the cities with the only purpose of raising revenue. Such experiences do not represent congestion pricing schemes as shown by small changes in traffic volumes but with positive effects derived by the use of renevues. In the last years, the cities started to convert their systems to something similar to congestion pricing, with different fees during the day and the use of the revenues not only for road but also for railway infrastructures projects. Despite the economic benefit resulting from the applications of congestion charging schemes at urban level is also accompanied, in many cases, by the reduction of congestion and related externalities, this policy is usually rejected by public opinion, because it is considered as an additional tax. This has been demonstrated with the case of Edinburgh and Manchester, where cordon tolling schemes were rejected by public referenda in 2005 and 2008, respectively. The same happened in New York City in April 2008 and in Copenhagen in 2012. In order to increase the acceptability of congestion pricing, factors as the evidence of improvements of traffic congestion, public transport and road networks are fundamental (Walker, 2011), together with the vision of the policy as a part of an overall traffic plan with alternative modes and improvements in public transport, all funded by the road pricing revenue. On the basis of the aforementioned considerations, this paper deals with the design of a congestion pricing schemes for the city of Rome, in Italy, and with the assessment of these policies in the short term period. Main contribution derives from equity implications dealt with in the impact assessment of pricing policies recently proposed by the Mobility Agency of Rome to promote sustainable mobility according to current practices usually adopted (Persia et al., 2016). In fact, a new transport Master Plan of the city has been approved in 2014 with the aim of realizing a multimodal and low impact mobility system by 2020. One of the measures proposed by the plan is the adoption of congestion pricing policies, to reduce air pollution and increase demand modal shifts towards public transport network. Equity issues are becoming increasingly important into any transport project definition and evaluation, requiring explicitly criteria for differentiating population groups and their travel behaviour impacts. This is a major challenge in the assessment of congestion charging policies, often not seriously addressed and recognised by an equitable and inclusive perspective. In this context, in the present paper, different tolling schemes are defined from a quantitative (toll level) and spatial (city zones where pricing is implemented) viewpoint in order to guarantee equity aspects in the application of the measure: affected road users are limited to those travelling in city zones where the mass public transport network (metro) is available; besides, the toll amount is related to the level of accessibility to public transport (whether only in origin/destination of the trip or in both). Specifically, two main elements are considered in this paper for a detailed analysis of impacts: the first one is related to the extension of the area-based scheme adopted while the second one involves the public transport accessibility from residence and work locations. These issues are investigated using a simulation approach. Each simulated scenario is evaluated through different indicators represented by a multi variable objective function (OF) taking into account not only traffic emissions and congestion, but also the costs supported by the local authority for implementing the charging scheme, in a demand elastic context (public transport modal shift). A sensitivity analysis on tolling level is also carried out. The rest of the paper is structured as follows: Section 2 deals with a literature review on pricing policy and congestion charging, where the gap between scientific advances and their implementation is underlined. Section 3 defines the methodology adopted for the case of study. Section 4 shows the results of the application. Finally, Section 5 provides policy implications and conclusions.

2 The Eurovignette is a road user charge for HGV (gross vehicle weight of minimum 12 tons) in order to use motorways and toll highways in the Eurovignette countries (https://www.eurovignettes.eu/portal/). Other HGV road user charge systems are adopted in Austria (https://www.asfinag.at/toll), Czech Republic (http://www.mytocz.eu/en/index.html), Germany (https:// www.toll-collect.de/en/), Hungary (https://www.hu-go.hu), Poland (http:// www.viatoll.pl/en), Slovakia (https://www.emyto.sk/web/guest/home) and Slovenia (http://www.dars.si). 3 https://www.gov.uk/government/collections/hgv-road-user-levy; http:// www.newmexicotrucktax.info/weight_distancepermit.html; http://www.nzta. govt.nz/vehicles/licensing-rego/road-user-charges/; http://www.oregon.gov/ ODOT/MCT/Pages/MotorCarrierEducationProgram.aspx#Weight-Mile_Tax. 4 I95 and I495 HOT Lanes project, Virginia; SR167 HOT Lane, Washington State; I-85 HOT lanes, Atlanta; I25 HOT lanes, Colorado. 5 https://www.californiaroadchargepilot.com/; http://www.myorego.org/. 6 http://roadpricing.blogspot.it/2016/03/singapore-will-have-worlds-firstgnss.html.

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2. Literature review

Microscopic approaches (Levinson, 2005; Hatem Abou-Senna, 2016), as well as dynamic traffic models (Chen et al., 2016; Joksimovic et al., 2005), were tested for the simulation of road pricing scenarios. However, the calibration effort required for adopting these models, limit their applicability to small-medium size network. Moving from general road pricing to the specific case of congestion pricing in urban contexts, the scientific lierature focuses more on application issues, such as political and social acceptability connected to integrity and equity issues, rather than theoretical ones. Giuliano (1992) and Emmerink et al. (1995) analyzed the barriers against the application of congestion pricing in the USA and the Netherlands. Ison (2000) addressed the same problem for the UK and he concluded that the political class feels more effective in enanching public transport and land use, and citizens prefer the improvement of policies for public transport, as well as for the pedestrian and cycle routes. Thorpe et al. (2000) reached similar results for the cities of Cambridge and Newcastle. Jakobsson et al. (2000) analyzed hostility towards pricing policies by users directly affected by the pricing. Schade and Schlag (2003) also focused on the users' point of view, using interviews in four European cities (Athens, Como, Dresden and Oslo). Ubbels and Verhoef (2005) addressed the acceptability of some pricing and incomes operation measures. Small (1992) proposed to use incomes in various ways, e.g. to reduce taxes, to build new infrastructures, to improve public transport while Parry and Bento (2001) proposed to use incomes in order to decrease taxes. Adler and Cetin (2001) suggested that, in order to overcome the problems of acceptability, the users have to perceive the benefits of road pricing. King et al. (2007) proposed to introduce congestion pricing on freeways and to redistribute incomes to all cities crossed by them. A very detailed analysis of the use of incomes is carried out by Farrell and Saleh (2005). There is a broad consensus among experts that congestion charging is the preferred policy measure to address road congestion. Recent research shows that construction of new roads is not likely to relieve congestion as demand elasticity in the medium run is close to one. It is also important to underline, in this context, that other policies are likely to involve substantial use of public funds while congestion pricing technology is relatively cheap, especially now with the recent advancement in information technologies, and it can be self-financed by the charges (Small, 2007). As shown by the literature review, many aspects about congestion pricing policies have been studied, ranging from theoretical models concerning the definition of the optimal fees to real-world application issues dealing with, for instance, acceptability by the citizens or possible use of revenues. Differently, few analysis has been carried out about the design of pricing measures and their impact assessment, directly involving multimodal network supply performances and equity issues. The aim of the present paper is to provide a contribution in this field, studying the application of pricing policies to the city of Rome, with a global approach addressing overall performances (on the multimodal transport network) and impacts (affecting the entire community), in a demand elastic context. Such observations represent the base of the choice for studying these policy measures with reference to the case of the city of Rome, Italy.

The scientific literature on congestion pricing is rich of contributions, mainly oriented to toll scheme determination and subsequent traffic congestion benefits. The approaches to investigate the problem differ according to many aspects: the way used for representing the user's behaviors, if a mono or a multi modal approach is considered, if stationary or dynamic traffic conditions are simulated, if travel demand is fixed or elastic. Moreover, macroscopic or microscopic traffic simulation approaches can be used, usually according to the dimension of the problem. While in first works on toll calculation started considering only the road network (Dafermos and Sparrow, 1971), recent contributons adopt a multi-modal and multi-user approach in the network design phase in order to guarantee efficiency for the entire transport system (Hamandouch et al., 2007; D'Acierno et al., 2011). Bellei et al. (2002) calculates tolls according to a Network Design Problem, where the tolls are decision variables and the only constraint is the users' equilibrium over the system. This model is known as the Marginal Social Cost Pricing (MSCP). The computation of toll is considered a complex problem, since theoretical drawbacks may appear such as the non-uniqueness of tolls (Dial, 2000), or the non-uniqueness of the value of time (VOT) for road users (Beckmann et al., 1956; Dial, 1999a; 1999b; Arnott and Krauss, 1998; Yang and Huang, 2004; Yang and Zhang, 2008). Verhoef (2000) reported that, in order to reach a first-best condition in the toll calculation problem, i.e. the Pareto's optimum condition characterized by an efficient allocation of available resources, the toll should contain all external costs generated by users. However, the first-best condition is merely a theoretical construct and it can hardly be achieved in a real market (Rouwendal and Verhoef, 2006). As a consequence, many authors studied the toll calculation as a second best problem, obtaining sub-optimal solutions, commonly called second-best solutions. Verhoef (1996b) examined the efficiency of some second-best measurements for a simple network where users can choose between two links: a free and a paid one. Moreover, he analyzed the efficiency of second-best tolls for a network where a user information system is present (Verhoef, 1996a) and the toll calculation for the general case of an elastic network (Verhoef, 2002a; b). Lawphongpanich and Hearn (2004) analyzed the second-best tolls both in the case of fixed demand and elastic one. The issue of second-best road pricing was also addressed by Zhang and Ge (2004), who formulate a mathematical model for scheduling tolls. Ekstrӧm et al. (2009) determine second-best tolls by setting them both in terms of toll level and toll position (choice of infrastructure and positioning of stations). Finally, Koh et al. (2009) presented a model for optimizing both secondbest tolls and road capacity. When the toll changes according to the existing traffic flow conditions, it is defined as responsive toll and it is studied in literature as dynamic road pricing: Kuwahara (2007) analyzed tolls from a dynamic point of view. Dimitriou and Tsekeris (2009) introduced dynamic road pricing to transport networks equipped with traffic monitoring and information systems. However, in practice responsive tolls were applied in a few HOT lane facilities, for instance on I-15 in San Diego and on I394 in Minneapolis, where the tools are updated with a frequency of 6 and 3 min respectively. Also, dynamic road pricing based on forecasted congestion has not yet been implemented in urban areas, since accurate urban congestion prediction is much more difficult to be achieved in urban signalized networks. In the last years, a strong effort is made for the accurate representation of congestion phenomena when studying road pricing. The accurate representation of the network conditions involves a better assessment of the road pricing effects. These are usually quantified in terms of re-routing, changes in the departure time choice, reduction of queuing delay, reduction of travel times, changes in vehicle mileage travelled and toll revenue evaluation.

3. Methodology The assessment of the impacts of congestion pricing policies is investigated using a “what if” approach, where several scenarios have been proposed and evaluated by simulation. The simulated scenarios differ in terms of both the dimension of the congestion pricing area and the tolling schemes considered. Moreover, a sensitivity analysis on tolling level is also carried out. Only short-term effects are taken into account for the evaluation of this pricing policy. This is due to different reasons. First of all, there is a 3

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need of both the local authority and the Mobility Agency of Rome to understand the effects in the short term for its fast implementation. In addiction, the long term impacts on location, land use patterns and urban form are still not completely understood, especially because of the short historical series of empirical data at disposal. This is particularly true about the potential impacts on economic activities in the affected and surrounding areas. Moreover, other specific factors influence the choice of not dealing with long-term effects, such as the inertia in the change of the residential location, typical of the Italian real estate market (Brandi et al., 2014), as well as the inertia in mobility habits due to changes in policies differently from the modifications on supply infrastructures. The simulations are carried out using the traffic model of the city of Rome running on TRANSCAD. This is the strategic model implemented by the Mobility Agency of Rome for planning purposes, dealing with a multimodal transport approach with elastic demand. Specifically, the travel demand submodel is called S.T.I.T. (Cascetta et al., 2000; Nuzzolo and Coppola, 2005), a spatial interaction model, for simulating individual travel choices. Taking into account the focus is on short term analysis, the trip distribution is considered fixed while the modal choice and the route choice can be modified according to the level of service of the transport system. The interaction between private transport demand and supply is solved through a static equilibrium approach, while an optimal strategy assignment model is adopted for the public transport demand. Results of the simulations are mainly analyzed in terms of a) changes in modal split; b) objective function, adopted only for evaluation purposes. The latter is a multi objective function, computed for each scenario, composed by three terms to measure the impacts related to the different interested subjects (local authority and road users). Its formulation is described as follows: O.F. = CA + C.CONG. + E

vehicles. Revenues derived by the adoption of the pricing policy are not included in the multi objective function because, despite they represent a road user's cost, it is assumed that the local administration reinvests them in the improvement of the public transport system. Also the costs supported by the public transport users are not included in the objective function because it is assumed that the performance of the public transport system, even if interested by different level of passengers, remain constant in each scenario. In the following subsections, the case study of Rome is described in terms of its actual transport and land use system as well as in terms of its transport planning vision. Then, the congestion pricing schemes evaluated in this study are described also in terms of their implementation in the simulation process. 3.1. Case of study and design of the congestion charging schemes The city of Rome is characterized by a population of about 3 millions with 1.2 millions employees, contributing to about 552,000 trips in the morning peak hour. About 75% of these trips is made with private vehicles (cars and mopeds). In fact, the mobility system is focused on the use of private vehicles with a road network frequently congested (Gori et al., 2012) and a very high level of automobile ownerships (more than 700 for 1000 persons). With regard to the public transport system, the metro network, characterized by a radial structure, is composed by three lines extending for a total of 60 km. Two metro lines (line A and line B/B1) are connected with a unique interchange in the city centre (Termini rail station). The third line (metro C) is connected with metro A, while the interchange between metro C and metro B is planned for 2021. Other seven rail lines connect the surrounding areas to the city: the union of five of these rail lines creates a half circle inside the GRA known as the “rail ring”. The bus network can be considered as an extensive network with a large service coverage but with low-medium frequency lines. A circular freeway of approximately 68 km of length (known as GRA, Fig. 1) separates the city from its external suburbs. Outside the GRA, in about 900 km2, the density decreases to very low values with respect to the internal GRA area, even if the population is higher than half a million (Table 1). Large part of the downtown area of the city is a restricted access zone (RAZ): the access is allowed to specific road users categories (emergency vehicles, residents, disabled persons, taxi, mopeds and electric vehicles). Heavy Good Vehicles (HGV) with a loaded weight until 3.5 tons have to pay in order to enter and circulate within it. The toll is computed according to the European Emission Standards of the vehicle (EURO category). The RAZ started in 2000 and, in the period

(1)

Where: CA is the costs supported by the local authority in order to implement the charging scheme. It is computed as a function of: - the increase of vehicles per kilometers (VEH-KMpubTransp) and vehicles per hours (VEH-HpubTransp) of the public transport service in order to respond to the increase in public transport demand with the adoption of the congestion pricing policy; - the management costs (Cmanagement) for the charging system operation. thus: CA = κCA(α1VEH-KMpubTransp + VEH-HpubTransp) + α2Cmanagement

(2)

with α1, α2 external weights for CA components, and κCA factor to convert statistics of the public transport service to cost equivalent; C.CONG. are the congestion costs. They are function of the total distance travelled (VEH-KMpriv) and total time spent (VEH-Hpriv) by road users on the network; thus: C.CONG. = κC.CONG.(β1VEH-KMpriv + VEH-Hpriv)

(3)

with β1 external weight for the VEH-KMpriv component of C.CONG., and κC.CONG. factor to convert statistics of the road network to cost equivalent; E are the road emission costs, computed as a function of the total emissions generated by road users (g-VEHpriv); thus: E = κE(g-VEHpriv)

(4)

with κE factor to convert the total emissions by road users to cost equivalent. The total emissions are computed as a function of the distance travelled and the average vehicle speed on the network by road

Fig. 1. City of Rome and circular areas for the congestion pricing. 4

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Table 1 Land use statistics for the city of Rome.

RAZ C1-RAZ C2-C1 From rail ring to GRA Outside GRA The whole city

Area [km2]

Inhabitants

Inhabitants [%]

Population density [Inh/km2]

Employees

Employees [%]

Activity density [Emp/km2]

5.5 16.7 17.4 306 939 1285

67,834 135,749 198,600 1,753,145 758,013 2,913,341

2 5 7 60 26 100

12,334 8129 11,414 5729 807 2267

105,052 161,887 142,660 596,142 215,106 1,220,847

9 13 12 49 18 100

19,100 9694 8199 1948 229 950

2010–2015, it brings to a substantial reduction of freight access of old HGVs EURO categories (especially EURO3 and EURO4) and a 54% reduction of CO2 emissions. The new transport Master Plan of the city has been approved in 2014 with the aim of realizing a multimodal and low impact mobility system by 2020. One of the measures proposed by the plan is the adoption of pricing policies, especially to reduce air pollution and increase the adoption of public transport. Among possible pricing policies, the Mobility Agency of Rome is working for the implementation of a congestion pricing area based on the model adopted in Milan. With respect to the actual RAZ, the toll to be paid in order to enter the pricing area is not limited to HGVs, but it is extended to all vehicles (except the special categories and mopeds). To this aim, a credit system is proposed only for the Rome residents, where the number of mobility credits is higher for the citizens located inside the rail ring than for the citizens located outside the rail ring. The reduction of the amount of credits depends by the time window when the vehicle enters in the pricing area, larger if entering during peak hours. Once the credits are finished, the vehicle entering the pricing area pays as a function of its EURO standard. The implementation of such a system will require the development of a control system based on monitoring gates. Previous analyses carried out by the Mobility Agency of Rome identified two possible circular areas of different extension for the application of the congestion pricing policy (Fig. 1). The first one, cordon scheme 1, C1 (Cordon 1 in Figs. 1 and 22 km2) considers the city downtown area, where actually the restricted access zone is located, covering respectively 7% and 22% of the total inhabitants and employees of the city. The second one, cordon scheme 2, C2 (Cordon 2 in Figs. 1 and 40 km2) enclosed C1, expanding up to the “rail ring” and covering respectively 14% and 34% of the total inhabitants and employees of the city.

peak hours (7:00 a.m.–9:00 a.m.). For each cordon scheme, several scenarios have been simulated both reducing and increasing the starting fee (from 0.25 to 4 times the starting fee, Table 2). Finally, considering the cordon C2, explicitly taking into account also equity issues, a penalty is added to the starting fee if the trip can be made using the metro network or the urban rail lines. In particular, the penalty is added if the starting and/or the ending point of the trip has a high accessibility to the mass public transport network. In this way, the penalty is used to balance the existing differences among neighborhoods in terms of performances of the public transport services. The expected impact is an additional way of pushing the people to use public transport if the trips origin and/or the destination have a high accessibility to the public transport. The high accessibility is assigned to the starting zone (O, Table 2) or to both the starting and ending zones (OD, Table 2) if they are within the catchment area of an urban rail or metro station (i.e. in the circle of 500 m radius with respect to the station). The penalty is considered variable from 2 to 4 times the starting fee. 4. Results This section describes the results of the simulations carried out according to the structure reported in Table 2. The results analysis is performed in terms of changes in a) modal split; b) values assumed by the objective function. The assessment of the policy effects is conducted taking into account the changes for any term of objective function and dividing the city into 8 zones (Fig. 2) for an extensive analysis of equity issues:

• Zone 1: it is the actual restricted access zone (RAZ); • Zone 2: it consists of traffic areas outside the RAZ but within C1; • Zone 3: it consists of traffic areas outside C1, but within C2; • Zone 4 and zone 5: outside C2, where the last one (zone 5) extends up to the ring road (GRA) of the city; • Zone 6: outside the ring road of the city, excluding the seaside area; • Zone 7: the seaside area of the city outside the ring road; • Zone 8: outside the municipality (Province of Rome).

3.2. Congestion pricing scenarios With respect to the base year scenario at 2018 (Scenario 0, Table 2), where the congestion pricing is not implemented, several scenarios have been simulated considering both the two alternative cordon schemes C1 and C2 proposed by the Mobility Agency of Rome, as well as several fee levels with respect to the tolling scheme reported in the previous section. The tolling scheme is based on the EURO vehicle's standard and on a system of mobility credits depending on the residence location (inside or outside the rail ring). The starting fee fixed by the Mobility Agency of Rome is 2.5 € for EURO 5 and 6, 3.0 € for EURO 4, 3.5 € for EURO 3 (no access is permitted for EURO 2-1-0). The credits are equal to 180 if the residence is inside the rail ring, otherwise 120. Thus, to implement the scheme in the simulation approach, an average fee is computed taking into account the cars fleet composition based on emission standards of the privately-owned cars registered to Rome residents (ACI statistics, http://www.aci.it), as well as the inhabitants located inside or outside the rail ring. The average fee for road vehicles entering (or exiting in order to consider the return trip) in the congestion pricing area is equal to 2.90 €. For origin-destination trips crossing the pricing area, the fee is added in the simulation as an additional link cost defined on each entry/exit link of the cordon. All the simulations refer to the morning

Starting from the analysis in terms of OD pairs and without considering the penalties in the case of high public transport accessibility, results of the simulation show that the pricing policy can reduce the private demand especially for those OD trips directly affected by the application of the tolling scheme (for instance, trip destination inside the congestion pricing area); instead, some trips involving external OD pairs, not directly affected by the toll (for instance, origin and destination both outside the congestion pricing area), are characterized by an increase of the private demand as they take advantage of additional capacity provided along specific routes of the road network. However, these two different and opposed effects, i.e. the reduction of the private demand for ODs directly affected by the pricing policy and the increase of private demand for other ODs, can imply an overall benefit (i.e. the reduction of private demand) for the whole metropolitan area. This is true in case of C2, thus if the congestion pricing involves a larger area. In fact, if C2 is adopted, when the fee is low 5

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Table 2 Simulated congestion pricing scenarios.

Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario a

0 C1_0 C1_0.25 C1_0.5 C1_2 C1_3 C1_4 C2_0 C2_0.25 C2_0.5 C2_2 C2_3 C2_4 C2_OD_P2 C2_OD_P3 C2_OD_P4 C2_O_P2 C2_O_P3 C2_O_P4

DATE

CORDON SCHEME

FEE

PENALTIES

AVERAGE FEE [€]

2018 2018

No Pricing Pricing for C1

– –

2018

Pricing for C2

2018

Pricing for C2 + Penalties for ODs with high accessibility

– 2.90 0.73 1.45 5.80 8.70 11.60 2.90 0.73 1.45 5.80 8.70 11.60

2018

Pricing for C2 + Penalties for O with high accessibility

– Starting_FEEa Starting_FEE/4 Starting_FEE/2 2a Starting_FEE 3a Starting_FEE 4a Starting_FEE Starting_FEEa Starting_FEE/4 Starting_FEE/2 2a Starting_FEE 3a Starting_FEE 4a Starting_FEE Starting_FEEa Starting_FEE Starting_FEE Starting_FEEa Starting_FEE Starting_FEE

–

2a 3a 4a 2a 3a 4a

Starting_FEE Starting_FEE Starting_FEE Starting_FEE Starting_FEE Starting_FEE

Starting fee: 2.5 € EURO 5–6; 3.0 € EURO 4; 3.5 € EURO 3; No access permitted for EURO 2-1-0.

private mode itself, is detected especially for trips between neighboring zones (short trips). This is due to the lack of the toll for mopeds in the considered pricing policy. Focusing on the generated trips by car, Table 3 shows that, in addition to the zones subject to the pricing scheme, also the external zones next to the cordons are affected by the adoption of the policy: in the case of C1, zone 3 is subject to a maximum reduction of generated trips by car of about 2% with respect to the no pricing scenario, while this reduction can be equal to a maximum value of about 12% in zone 4 for C2. Moving away from the surrounding of the cordon, these effects are reduced until reaching the zones outside the ring road (zones 6 and 7), where the reductions are almost zero. This is due from both the reduction of the number of trips involved by the congestion pricing, as well as the no convenience in moving with the public transport for longer routes, which implies a higher number of transfers between public transport lines. In terms of attracted trips, the reduction of trips by car is less evident than for generated trips, since the egress phase is more binding than the access phase in the mode choice. Next results focus on the analysis of the values of the multi variable objective function that permit to evaluate the scenarios in terms of impacts related to the local authority and the road users. In the case of application of pricing on C2 (Fig. 3, C2), the scenario where the starting fee is doubled with respect to the starting fee (Scenario C2_2, average fee of 5.80€) can be considered as the best compromise for the different interested subjects. In fact, the objective function assumes a value close to the actual minimum point (which would be the one with a quadruple of the starting fee, 11.60€) without penalizing the road users with an excessive toll. Adopting the average fee of 5.80€ in C2 results in a reduction of 0.7% of the road emission costs and 2% of the congestion costs with respect to the no-pricing scenario, requiring an increase of the local authority costs of only 0.7%. In the case of C1, when the fee increases, the function has a growing shape (Fig. 3, C1); this occurs because of the OD trips crossing the cordon area, who prefer a longer route with respect to the use of public transport. This increase of the distance travelled causes higher congestion and emission costs with respect to fix the fee to an average value of 5.80€. In the case of C2, similar effects on the objective function are negligible because the pricing area is larger than C1; therefore, the amount of the fee should be further increased with respect to the maximum simulated value (higher than 11.60€) in order to detect a similar shape of. An interesting result for C1 is that, with an average fee of 1.45€, it is possible to obtain approximately the same total costs as in the case of

Fig. 2. Zoning system with metro lines and urban rail network.

(until a fee of 2.90€), the overall effect is low (reduction of 1% of the private demand with respect to the no pricing scenario). A substantial change in modal shift can be appreciated when the fee increases to three-four times the starting fee (Scenario C2_3 and Scenario C2_4 in Table 2), obtaining respectively a reduction of 5 and 6% of the private demand with respect to the no pricing scenario. Important consideration can be made about the impact of the congestion pricing area extention. In the case of C1, the effects on modal shift are negligible mainly due to the limited area. In fact, the OD pairs directly involved by the toll are few and the other trips can avoid the congestion pricing area with a minimum increase of the distance travelled, not producing a change towards public transport. Only increasing the fee to the maximum value considered (Scenario C1_4 in Table 2), the change in modal shift can be substantial (−3% of the private demand), but still not comparable with the results of C2. This result shows that the extension of the congestion pricing area has to be not only defined by the analysis of the level of trips generated or attracted in this area but also evaluating the presence of easy alternative routes for the road users. Results in terms of modal shift on attracted/generated trips by car highlight similar results (Table 3). The attracted trips by car are reduced essentially for those traffic zones located within the pricing area (zone 2 for C1, zones 2 and 3 for C2). However, for a low value of the fee, i.e. until an average fee of 1.45€, an inertness is detected in changing mode of transport also for these zones. When moving to a detailed analysis of these results in comparison with the alternative modes, a shift from the car to the mopeds, thus a change inside the 6

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Table 3 Results for C1 and C2 in terms of modal shift for the different traffic zones.

Table 4 Results for C2 in terms of modal shift in case of penalties.

MODAL SHIFT (%) for ATTRACTED TRIPS - CARS (C1)

0.00 € 0.73 € 1.45 € 2.90 € 5.80 € 8.70 € 11.60 €

Zone 1 (RAZ)

Zone 2 (C1RAZ)

Zone 3 (C2-C1)

Zone 4

21.74% 21.76% 21.75% 21.76% 21.77% 21.79% 21.79%

20.76% 20.01% 18.97% 17.24% 14.12% 11.54% 9.40%

21.54% 21.82% 21.62% 21.74% 22.09% 22.05% 22.06%

50.70% 51.16% 51.02% 50.98% 51.06% 51.02% 50.63%

MODAL SHIFT (%) for ATTRACTED TRIPS inside C2 Zone 5

66.50% 66.91% 66.91% 66.86% 66.73% 66.63% 66.31%

Zone 6

72.40% 72.64% 72.70% 72.63% 72.58% 72.49% 72.26%

No penalties

Zone 7

64.04% 64.09% 64.16% 64.12% 64.01% 64.08% 63.94%

0.00 € 0.73 € 1.45 € 2.90 € 5.80 € 8.70 € 11.60 €

Zone 2 (C1RAZ)

Zone 3 (C2-C1)

Zone 4

43.03% 43.38% 43.38% 43.49% 43.60% 44.00% 44.09%

42.13% 41.38% 40.18% 37.98% 33.81% 29.75% 25.78%

44.88% 45.03% 44.76% 44.33% 43.85% 43.43% 42.95%

45.07% 45.22% 44.97% 44.78% 44.46% 44.14% 43.82%

Zone 5

Zone 6

52.13% 52.28% 52.18% 51.95% 51.74% 51.52% 51.23%

51.61% 51.82% 51.83% 51.73% 51.79% 51.74% 51.58%

48.36% 49.23% 49.24% 49.47% 49.31% 49.20% 48.35%

Zone 1 (RAZ)

Zone 2+3 (C2RAZ)

Zone 4

Zone 5

Zone 6

Zone 7

Cars

Scenario

Cars

Scenario

Cars

0.00 € 0.73 € 1.45 € 2.90 € 5.80 € 8.70 € 11.60 €

21.10% 20.33% 19.61% 17.92% 15.17% 12.78% 10.83%

C2_OD_P2 C2_OD_P3 C2_OD_P4

17.43% 17.16% 16.79%

C2_O_P2 C2_O_P3 C2_O_P4

17.12% 16.17% 15.50%

21.74% 21.75% 21.76% 21.77% 21.79% 21.82% 21.83%

21.10% 20.33% 19.61% 17.92% 15.17% 12.78% 10.83%

50.70% 50.98% 51.01% 50.82% 50.71% 50.28% 49.79%

66.50% 66.85% 66.81% 66.59% 66.38% 66.00% 65.50%

72.40% 72.63% 72.64% 72.44% 72.33% 72.12% 71.80%

64.04% 64.16% 64.09% 64.03% 64.04% 63.92% 63.78%

Zone 1 (RAZ)

Zone 2+3 (C2RAZ)

Zone 4

Zone 5

Zone 6

Zone 7

43.03% 43.50% 43.44% 43.69% 43.83% 44.16% 44.52%

43.55% 43.08% 42.22% 40.61% 37.31% 33.97% 30.69%

44.88% 44.44% 43.62% 42.10% 38.93% 35.72% 32.46%

45.07% 44.96% 44.74% 44.20% 43.44% 42.69% 42.11%

52.13% 52.14% 51.97% 51.53% 50.85% 50.24% 49.67%

51.61% 51.76% 51.76% 51.56% 51.39% 51.17% 50.94%

Introduction of penalties

Introduction of penalties for O

Average fee

Cars

Scenario

Cars

Scenario

Cars

0.00 € 0.73 € 1.45 € 2.90 € 5.80 € 8.70 € 11.60 €

43.55% 43.08% 42.22% 40.61% 37.31% 33.97% 30.69%

C2_OD_P2 C2_OD_P3 C2_OD_P4

39.81% 39.11% 38.32%

C2_O_P2 C2_O_P3 C2_O_P4

40.60% 40.79% 40.85%

5.80€. However, as already analyzed in terms of modal shift, the policy applied in this small area is not able to catch a sufficient demand and this turns into no improvement in terms of total congestion and road emission costs with respect to the no-pricing scenario. Moving to the simulated scenarios with the introduction of a penalty for trips with a high accessibility to the public transport (Table 4), it can be noticed that doubling the penalty both in the origin as well as in the origin/destination points (P2, Table 4), a modal split similar to the one obtained in the case of an average fee of 2.90€ applied to all the road users can be reached. If the penalty is applied to users with high accessibility only at their starting point (origin point O), the results show that when the fee increases, the attracted trips by car decrease up to the same level corresponding to the fee of 5.80€ (but applied to all the road users). However, in this case, the policy can be considered fair, since penalized users are only those who are actually well connected by the public transport system. Reduction of the trips by car increasing the penalty (for values of the penalty higher than P2) is low. Instead, the modified tolling scheme affects a low additional value of the trips (i.e. only those who are in the catchement areas of the metro/rail stations are penalized). Moreover, road users whose trips have destinations far from the metro and urban rail stations and not adequately served by bus lines may decide to accept the payment of the toll with the added penalty, even if their origin point is well connected by public transport.

MODAL SHIFT (%) for GENERATED TRIPS - CARS (C2)

0.00 € 0.73 € 1.45 € 2.90 € 5.80 € 8.70 € 11.60 €

Average fee

No penalties

Zone 7

MODAL SHIFT (%) for ATTRACTED TRIPS - CARS (C2)

0.00 € 0.73 € 1.45 € 2.90 € 5.80 € 8.70 € 11.60 €

Introduction of penalties for O

MODAL SHIFT (%) for GENERATED TRIPS inside C2

MODAL SHIFT (%) for GENERATED TRIPS - CARS (C1) Zone 1 (RAZ)

Introduction of penalties for OD

The following figures move to the analysis of the values assumed by the multi variable objective function in the case of penalties. If the penalty is applied to road users with high accessibility only in their starting point (Fig. 4), the objective function is characterized by a high elasticity as the penalty increases. This elasticity is due to the fact that the policy affects only some specific demand components, i.e. those whose trips are directly affected by the pricing area, having the origin or the destination inside C2, and that have a good connection with the

Fig. 3. OF trend for pricing policies on C1 and C2 without penalties.

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remains approximately constant. In both the policies taking into account the penalty (Figs. 4 and 5), the higher reductions in terms of congestion costs and road emission costs can be obtained with the higher penalty (P4). These consist of about −0.8% for road emission costs and −2% for congestion costs with respect to the no-pricing scenario. If these reductions are computed with respect to the starting fee applied to all the road users (C2_0), thus without introducing additional penalties, their values move to −0.5% and −0.7% respectively, while there is no substantial reduction if C2_0 is compared with the lower penalty P2. 5. Conclusions and discussion Efficient transportation is a key element of any economy and its sustainable growth. Neverethless, modern cities are facing severe traffic congestion, road accidents and air and nise pollution resulting from the rapid urban growth, mainly in terms of population, as detected worldwide. In this context, demand management policies can be adopted for relieving transport systems suffering from low level of service and mitigate mobility related issues. Among these policies, congestion pricing is a measure that allows to link road transport externalities directly to travelers producing them. The definition of proper tolling schemes enables this measure to act on demand short-term choices, forcing travellers to shift towards low impact road itineries (route diversion) and sustainable transport systems (modal diversion). While currently this policy has not been used yet in Rome, its adoption has been planned in the last urban master plan, as it has shown to be effective in several cities around the globe (London, Stockholm, Singapore, Gothenburg, and Milan). Besides, it does not require substantial budget funds to be implemented, as it is expected to be self-financing. Current methods for managing road traffic in Rome do not work very well: the city experiences frequent and growing congestion implying high generalized travel costs and low social welfare. This has motivated both local authority and researchers to study innovative policy measures that have benefited of recent advancement in information technologies when applied to manage traffic demand: congestion pricing. On the basis of the aforementioned considerations, this paper presents key findings in the design of pricing policies to the large size and complex case of the city of Rome (Italy) addressing overall transport performances (on the multimodal network) and impacts (affecting the entire community), in a demand elastic context. Main contribution derives from equity matters dealt with in the impact assessment of pricing policies, so filling a gap not extensively studied in this field. Specifically, different tolling schemes have been defined from a quantitative (toll level) and spatial (city zones where pricing is implemented) viewpoint in order to guarantee equity aspects in the application of the measure: affected road users are limited to those travelling in city zones where the mass public transport network (metro and rail) is available; besides, the toll amount is related to the level of accessibility to public transport (whether only in origin/destination of the trip or in both); in fact, it is expected that if the trip destination and/ or the origin are accessible by public transport, then the road user will choose to shift towards public transport, against an increase in his/her transport cost. A “what if” approach has been adopted to evaluate the effects of congestion pricing in the short term, where several scenarios have been simulated considering, alternatively, two different zones for pricing located in the inner city area, as well as different toll levels for vehicles entering the zones. All the simulated scenarios have been evaluated both in terms of changes both in modal split and in externalities (congestion and road emissions) and local authority costs for implementing the congestion pricing measure. Results, evaluated adopting a simulation-based approach, are consistent with those obtained in other real world cases, and highlight that

Fig. 4. OF trend (global and single component) for pricing policies on C2 with penalties for high accessibility in the starting point.

public transport in their starting point. Such OD trips are inclined to change their transport mode (from private to public transport) as the fee becomes higher, thus increasing the residual capacity of the road network. Instead, those demand components that move outside the area concerned by the policy, taking advantage of highest residual capacity of the road network, increase the overall travelled distances (and in some cases also the overall travelled times), although the overall balance of congestion costs has a decreasing trend. The scenario with lower total costs is the starting fee without any penalty (Scenario_C2_0), thus demonstrating the weakness of the policy. This is due to the costs supported by the local authority (CA) within the objective function that has a growing trend with the increase of the penalty. Since the assumption that the metro and rail services have a residual capacity able to meet the increase of passenger on their lines, the increase of CA is linked to the effort of the public transport operator in order to ensure an adequate number of buses to connect the last metro and the rail stations with the final destinations (increase of the bus frequencies for the “last-mile” of the trip). In the case of penalty for road users with high accessibility to metro and urban rail stations (in both the origin and the destination points, Fig. 5), the same elasticity of the objective function with respect to the increase of the penalty is observed. Overall, the function assumes a downward trend: this is due to the influence of the costs supported by the local authority in order to respond to the increase in public transport demand, which changes in a different way with respect to the previous case. As in the case where the penalty is applied only in the origin point of the trips, also here it has been assumed that no increase of metro and rail service's capacity is considered with the increase of public transport demand. However, since the ODs that really move to the public transport are only those who are already well connected both in the origin and in the destination point, the effort that the public transport operator has to support with the increase of the penalty

Fig. 5. OF trend (global and single component) for pricing policies on C2 with penalties for high accessibility on both the starting and ending point. 8

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the adoption of a proper pricing policy in the city of Rome guarantees a demand diversion towards sustainable transport modes up to 25% (with respect to no pricing scenario) for those zones directly involved by the pricing implementation; specifically, the reduction is equal to 13% inside the pricing area and about 12% in its surroundings, with the adoption of a toll level equal to about four times the initial level proposed by the Rome Mobility Agency; in the same way, halving the toll level (so that it is well balanced between local authority and road users), nearly halves the modal diversion (about 12%). Such variation levels are comparable with those detected in the implementation of congestion pricing both in the London and in the Stockholm Congestion Charging schemes, where the car traffic entering the cordon declined by about 20% in their short term application (Richardson and Bae, 2008). The adoption of the latter toll level guarantees moderate improvement of road emission and congestion costs (respectively 1% and 2% with respect to the no pricing scenario). Promising benefits have been observed even in a wider area, entire Province of Rome, not directly affected by the measure, in terms of reduction both of road users (up to 6%) and congestion costs (about 2%). This occurs when adopting a toll level equal to about three times the level initially proposed by the Mobility Agency of Rome. Differently, the adoption of the latter toll level lowers modal diversion to about 1%. Such results are affected by the no-charging policy of mopeds adopoted in the simulated scenarios (as suggested by Rome Mobility Agency), thus resulting in modal split shift towards mopeds rather than towards public transport. Finally, simulations carried out have highilighted how the current structure of metro and rail network in Rome should be improved in order to guarantee an adequate coverage level, thus making the congestion pricing policy more effective, even with moderate toll levels. Future developments will require to investigate the effects of pricing policy in long term scenarios where future development of new metro lines in Rome are forecasted, as well as the upgrade of the urban rail lines to metro service.

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