Support in Policymaking: A Systematic Exploration of the

Policymaking Process

Daniel Guzman Vargas

1,2

and Sidharta Gautama

1,2

Department of Industrial Systems Engineering and Product Design, Ghent University,

Technologiepark 46, 9052 Gent-Zwijnaarde, Belgium

Flanders Make, B-3920 Lommel, Belgium

Keywords: Decision Support Systems, Urban Vehicle Access Regulations, Wicked Problems, Evidence-based

Policymaking.

Abstract: Nearly all the public policy issues focus on complex social problems (sometimes referred to as ‘wicked’

problems). Failing to address such complexity may result in a weak formulation of the problem at hand and

consequently to policy failure. A decision support system (DSS) appropriate for handling `wicked' problems

in policymaking should help decision-makers cope with the problem's complexity, facilitate the assessing of

multiple alternatives, and favour a discussion towards a common agenda. Making use of the above

requirements, we present in this paper a methodology for a DSS that feeds from a frame representation of

both expert knowledge and policy-related evidence to support decision-makers in the policymaking process.

The application of the methodology in a specific use case suggests the methodology could be applied in a

DSS for the identification of patterns and trends in policy-relevant data, the identification of possible policy

configurations, and the drafting of alternative scenarios based on the possible configurations.

1 INTRODUCTION

The design of policies as effective solutions to social

problems requires, from a policy science perspective,

of sophisticated analysis on the facts with strong

foundations on logic, knowledge, and experience

rather than on political interests or the bargaining of

conflicting interest groups. A weak analysis, and

consequently failure to incorporate complexity, in the

design and formulation of policies may lead to the

failure of such policies (Howlett, 2012; Howlett et al.,

2015; Schneider & Ingram, 2017), or to the ‘creation

of poor, even harmful, policies’ (Cohn, 2004).

Complex social problems, sometimes referred to

as ‘wicked’ problems, lack the sense of clarity that

most of the problems in science or engineering have,

where a problem statement can be clearly defined.

These problems include nearly all of the public policy

issues (Rittel & Webber, 1973), and a general

conclusion seems to be that ‘the methods for problem

handling appropriate to pacified conditions do not

transfer to more turbulent and problematic

environments’ generally ascribed to wicked problems

(Rosenhead, 1996). Rosenhead (1996) suggests that

when dealing with ‘wicked problems’, decision-

makers are more likely to use a method and find it

useful if it (a) accommodates multiple alternative

perspectives, (b) can facilitate negotiating a joint

agenda, (c) functions through interaction and

iteration, and (d) generates ownership of the problem

formulation and its action implications through

transparency of representation. These requirements

outline the specifications of a decision support system

(DSS) appropriate to wicked problems.

According to Rosenhead (1996) the technical

attributes of such a system include, the capability of

representing the problem complexity graphically

rather than algebraically or in tables of numerical

results – to facilitate participation, allow for a

systematic exploration of the solution space – ‘lay

people can generally express their judgments more

meaningfully by choosing between discrete

alternatives rather than across continuous variables’,

focus on the identification of relevant possibilities

rather than estimation numerical probabilities, and the

assessment of alternative scenarios instead of future

forecasts.

In spite of the multitude of studies on policy

analysis, policy failure, and policy transfer (Howlett

et al., 2009), the authors have found no research

120

Guzman Vargas, D. and Gautama, S.

Support in Policymaking: A Systematic Exploration of the Policymaking Process.

DOI: 10.5220/0010641600003064

In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 2: KEOD, pages 120-127

ISBN: 978-989-758-533-3; ISSN: 2184-3228

focused on the development of such DSS. Making use

of the above requirements, we present in this paper a

methodology for a DSS that feeds from a frame

representation of both expert knowledge and policy-

related evidence to support decision-makers in the

policymaking process.

The remainder of this paper consists of 4 parts.

First, we develop a methodology for the systematic

exploration of the solution space. Next, we apply the

methodology to a specific use case; the development

and implementation of Urban Vehicle Access

Regulations (UVARs). We perform a small-scale

experiment of 8 city case implementations, and

finally, we present the conclusions from our findings

and lay down possibilities for future work.

2 METHODOLOGY

The list of technical attributes in (Rosenhead, 1996)

frames the design of the framework proposed in this

paper. For this, our framework takes a systematic

approach to define the solution space, guided, and

supported by expert knowledge and policy-relevant

information captured in city case studies.

Case studies can be used to assess a phenomenon

(in this case the policy process) and its contextual

conditions, relying on multiple sources of evidence to

provide ‘rich, thick description and analytic

generalization’ (Vogel & Henstra, 2015).

Case study research applied to policy processes

relies on a large variety of sources such as official

policy documents, meeting transcripts, council

minutes, committee papers, etc. These data sources

are rich in evidence to support the documentation of

the different elements of the policy process (Vogel &

Henstra, 2015), but at the same time pose a challenge

to the researcher: ‘case data is rich in qualitative

detail’. As a result, the presentation of the empirical

evidence in case study research is usually descriptive.

However, as the number of cases increases, making a

contrast between emergent theory, and a complete

unbroken rendering of each case's story becomes

infeasible (Eisenhardt & Graebner, 2007).

Additionally, a larger number of cases may

drastically increase the volume of data which hinders

researchers in the assessment and identification of

important relationships.

These characteristics pose both a challenge and a

motivation for the use of a decision support system.

A DSS that could help policymakers draw relevant

conclusions from the empirical evidence could

support a large collection of multiple case studies.

But how can a DSS best find “patterns” from the

textual description of the empirical evidence? For

this, we take some inspiration from content analysis,

‘a research method for the subjective interpretation of

the content of text data through the systematic

classification process of coding and identifying

themes or patterns.’ Consequently, the successful

identification of patterns and relationships is highly

dependent on the coding process (Hsieh & Shannon,

2005). The novelty of our framework is on

developing a useful representation that could

facilitate the identification of patterns in the

description of empirical evidence, supported by

expert knowledge, and working as a rubric for the

collection of case data.

The methodology for the coding process consists

of two main steps, the parametrization of the solution

space, and the definition of the Policy Life Cycle.

These two steps provide the coding categories, and

their corresponding operational definitions, that

provide non-binding guidance in the collection of

case data, and that allow for a systematic

interpretation of the empirical evidence, facilitating

the identification of patterns, and underlying

relationships, both within and across case analysis.

2.1 Parameterizing the Solution Space

In first instance, the relevant factors that interact in

the development of a policy solution to a specific

policy problem are identified and defined. Here three

types of parameters should be considered: contextual,

control and time.

Contextual parameters reflect the environment in

which the policy problem is framed, the factors that

can influence governmental decisions, and the

elements that policymakers aim to influence with

their decisions – these parameters define the context

in which the policy problems take place. Control

parameters refer to all the relevant factors that

policymakers could control to implement their

policies – these factors shape the overall policy from

a strategic point of view. The third category refers to

a single parameter: time. Social systems are dynamic,

and so are policies (Hom, 2018). Time and timing in

politics are a big deal. Timing can be a strategic tool

(Djourelova & Durante, 2019); it can constraint the

opportunity for the development of a policy – ‘policy

window’ (Kingdon, 2014); and it defines the life-span

of a policy – policy cycle (Howlett et al., 2009; Jann

& Wegrich, 2017). Time helps define the dynamics

of the policy process, providing coherence and logic

to its narrative (Massey, 2017). Policymakers make

decisions that affect and mould social systems, and

consequently, this new state of the affairs demands a

Support in Policymaking: A Systematic Exploration of the Policymaking Process

121

reaction from policymakers. This never-ending series

of discrete events describes both the path taken by the

policymakers in solving a policy problem and its

implications and effects on the system.

The definition of the parameters is achieved

through expert knowledge. A commission of experts

consisting of academics, policy consultants, and other

practitioners immerse in the implementation and

development of such policies is put together to find a

consensus on the parameters relevant to the policy

process. Their input consists of the set of categorical

parameters that best describe the policy context

(contextual parameters) and the elements of the

policy strategy (control parameters). The quality of

the outcome of the methodology is therefore

dependent on the quality of the expert's commission.

2.2 Definition of the Policy Life Cycle

In the process of defining the parameters of the

solution space, the group of experts should be asked

to think about the different stages that comprise the

development of the policy strategy from its

conception until after its implementation. For this, we

refer the participants to the policy cycle for

inspiration. The policy cycle intends to simplify the

policy process by deconstructing it into discrete

stages that describe the chronology of the policy

process, starting with the Agenda Setting, where a

problem is defined and recognized, and the need for

intervention is expressed, passing onto Policy

Formulation, where the objectives of a subsequent

policy are defined and alternatives for action are

considered. This is followed by the Decision-making

stage, where the final adoption (final course of action)

is formally set. Next, the Implementation phase,

where the adopted policy is executed or enforced, and

finally, the Evaluation stage, where the effects

(intended and unintended) of the implemented policy

are assessed in relation with the objectives previously

set and the current problem perception (agenda). The

outcome may lead to policy continuation,

termination, or re-design (Howlett, 2019; Jann &

Wegrich, 2017).

The policy cycle helps dive into the complexity of

policymaking, providing guidance to action, and

although it has been recognized that its application is

by no means universal (‘practice varies from problem

to problem’), the policy cycle is a good heuristic in

policymaking for the answer to the question ‘what to

do now?’ (Bridgman & Davis, 2003).

In our Framework, we rely on the policy cycle as

a source of inspiration. Participants should be allowed

to re-think and re-shape the stages in the way their

experience find it more suitable. This new,

“customized” definition of the policy cycle

(hereinafter policy life cycle) is another output of our

framework. Participants can make use of the policy

life cycle as a tool to trigger the discussion on the

parameters, both contextual and of control, that may

play a role at each policy stage. Participants should be

asked to describe all the factors of utmost relevance

at each of the policy stages.

As proponents of the policy cycle propose, the

fragmentation of the policy into stages allows for a

more detailed view of the process. The policy life

cycle is then used as a “custom” tool in the

parametrization process that contributes to the

definition of detailed elements that are part of a policy

strategy and that may have been difficult to foresee at

a higher level. Additionally, the linear temporality of

the process provides streamlined thinking, and a

conception of the parameters as changing elements in

a policy as a timeline, i.e., as a sequence of discrete

events describing the change of state of a (set of)

parameter(s) as the policy matures. During this

process, relationships between the parameters may

arise and their use in finding new parameters not yet

defined should be encouraged. However, participants

should be asked to focus on the identification of the

parameters only: the magnitude and characteristics of

these relationships should not be discussed here.

3 THE UVAR CASE

To better illustrate the potential of the methodology,

we apply it to the case of UVARs in Europe.

UVARs, in the broad sense, are measures to

regulate the access of urban vehicles to urban

infrastructure in order to cope with societal

challenges that markets alone cannot. In general, such

policies intend to deal with the negative externalities

generated by traffic: pollution, congestion, traffic-

related accident rates, etc. (Carnovale & Gibson,

2013; Elbert & Friedrich, 2019; Lopez, 2018). Given

the range of objectives, magnitude of the problems,

urban contexts, and political landscapes, UVARs may

take many forms. Low emission zones (LEZs), for

example, are designated areas where the access of

polluting vehicles is restricted or penalized (Lopez,

2018). Congestion charging (CC) refers to the

imposition of a fee to access congested areas during a

specified time frame (Morton et al., 2017). Partial or

total vehicle access bans such as a limited traffic zone

(LTZ) (in Italian: Zona a traffico limitato) or a

pedestrian zone. Traffic cell architecture where traffic

between cells is limited by design (e.g., Barcelona

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Superblock scheme). Or hybrid designs, e.g., CC with

differential “emission” fees (Goddard, 1997).

With over 200 LEZs in place across Europe

(Mudway et al., 2019), CC schemes present in major

cities such as London, Stockholm, Milan, Superblock

schemes in Barcelona and Vitoria-Gasteiz, and the

LTZs predominant in Italian cities, the widespread of

UVARs highlights the intention of decision-makers

across Europe in dealing with negative traffic

externalities through UVARs.

However, the implementation of restrictive

policies, such as UVARs, is generally accompanied

by strong public and political controversy, with

public acceptability of a proposed UVAR playing a

major role in determining, whether or not the

proposed UVAR survives the policy process (Lopez,

2018; Morton et al., 2017). All in all, the extensive

application of UVARs in urban areas across Europe

poses a motivation for data-driven approaches that

could satisfy the need for new and better

implementations of such policies.

With this goal in mind, an expert commission

consisting of academics, practitioners, and

consultants with ample European experience in urban

planning, transport planning, quality assurance, and

development of urban mobility projects was set to

follow our methodology. As a first result, the policy

(UVAR) life cycle was defined by the experts’

commission as consisting of four phases.

The Ideation phase covers the time span in which

problems come to the attention of governments and a

set of (conceptual) solutions emerges in response.

The Design phase covers a time span in which UVAR

measure’s designs are developed in more detail.

Multiple designs may be considered and assessed

resulting in a proposal for the technical and strategic

design of the UVAR measure. The Implementation

phase involves executing the selected policy option.

Involves all the necessary action to put the UVAR

measure into practice. Finally, in the Operation phase

all the activities following the launching of the full

scale UVAR measure take place.

Additionally, during the definition of the policy

(UVAR) life cycle, three “policy gates” were

identified. Each “gate” refers to specific event(s) that

determine the end of a phase (or the beginning of a

new one). Together, the 4 phases and the 3 gates

define the policy life cycle of an UVAR (

Figure 1).

In the Decision-making gate the actual decision

on a particular course of action to follow is made –

selection of UVAR measure at a conceptual level. In

the Adoption gate the final design is approved for

implementation. Finally, the final decision needed for

the full-scale operation of the UVAR is made in the

Commissioning gate. Making use of the policy life

cycle (Figure 1), participants can continue with the

parametrization of the solution space.

Figure 1: Policy (UVAR) life cycle as depicted by the

experts’ commission.

For the definition of the contextual parameters,

participants were inspired by the work of (Gillis et al.,

2016) on monitoring of sustainable mobility in cities.

Through a review of relevant and scientifically sound

indicators applicable in different social and economic

contexts, (Gillis et al., 2016) identified a set of

indicators that could be applied for the evaluation of

a city’s mobility system, monitoring, bench-marking

assessment, and back-casting. However, instead of

relying on the specific set of indicators proposed in

(Gillis et al., 2016), the group of experts decided to

focus instead on the set of parameters used for the

calculation of such indicators. This to avoid further

assessment and discussions on the validity of each

indicator, or methodological issues in their

determination. By doing so, the data collected allows

a DSS to calculate this or any other set of indicators

when performing an analysis, or to work with the

granular information if appropriate.

Subsequently, participants reached a consensus

on a total of 35 contextual parameters. These

parameters can be grouped into 5 clusters: general

information parameters such as GDP, surface,

distribution of direct and indirect land use for

mobility, and availability of functional activities in

the target area. Demographic information parameters

account for the number of inhabitants, and population

distributions by gender, age, employment status,

income, and household size. Transport information

parameters cover total length and distribution of road

network by use, total number of passenger trips per

year per transport or per shared mobility type, and

vehicle fleet distribution per fuel type. General

mobility information parameters map the availability

of public transport (PT) and shared mobility modes,

ticket prices for PT, availability of ticketing machines

and offices, size and distribution of PT vehicle fleet,

number of PT stops, and distribution of accessibility

to and user satisfaction towards PT. Finally, effects on

inhabitants parameters capture satisfaction levels

towards noise level, quality of air, and public spaces,

traffic accident rate, average distance, time and main

Support in Policymaking: A Systematic Exploration of the Policymaking Process

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transport modes for work-home/home-work trips.

For the control parameters, the process yielded a

total of 23 parameters that reflect the experience of

the participants gained through their academic and

research background. The set of parameters can be

grouped into 4 thematic clusters: system

design/technology, governance, user needs, and

mobility services and concepts.

System design/technology parameters focus on the

availability, functionality, and status of UVAR-

related systems. Covering aspects related to UVAR

operation: technology options for enforcement,

monitoring and evaluation, and communication-

dissemination of UVAR-related information.

Governance parameters relate to the availability

and types of legal frameworks, and political and

planning instruments that can support the

development of UVARs. Additionally, some of the

parameters in this cluster intend to capture the actors

and/or institutions that shape, influence and/or make

decisions, as well as details on participatory and

transparency issues.

User needs parameters focus on whether the

different relevant user groups and stakeholders have

been identified, and whether user needs have been

included/considered. Additionally, some of the

parameters here intend to monitor the tone of the

general opinion, the level of acceptability towards the

measure, and the main arguments for or against it.

Finally, mobility services and concepts

parameters cover the types and status of

developments related to improvements in PT, soft

mobility, parking systems, shared mobility, urban

logistics, etc.

To visualize the possibilities of our approach, we

make use of a set of 8 different case studies covering

8 European cities that have implemented different

UVAR measures (Table 1). Case study researchers

have been given the set of parameters and the UVAR

life cycle as a rubric for the collection of information.

In this way, researchers were asked to use the

definition of the different stages of the UVAR life

cycle and the different parameter categories to focus

and direct their research.

Table 1: List of city case studies and respective UVAR

measure with correspondent planning instrument.

City UVAR Measure Planning Instrument

Milan, IT CC General urban traffic plan

Barcelona, ES Superblock scheme Urban mobility plan

Bologna, IT LTZ Traffic plan*

La Rochelle, FR Delivery regulations Urban

ravel plan

Ghent, BE

Traffic circulation

lan (2017)

Mobility plan

Gr. London, GB Pollution charge Air quality strategy

Mechelen, BE Cycling zone Mobility plan

Amsterdam, NL LEZ Traffic plan for clean ai

* The LTZ was initially conceived in the Traffic Plan (1972), at that moment it was not

called LTZ, and later included in the General urban traffic

lan (1996).

Before the beginning of the documentation, case

study researchers were presented with the third

parameter: time. Researchers were asked to focus on

the chronology of the events that describe the process,

making note of the time of occurrence of each event.

Accounting for time as one of the relevant parameters

means that the outcome of the data collection process

will yield a timeline of data points containing the

main events that describe the process (hereinafter

process timeline), alongside changes in contextual

parameters. Furthermore, the identification of the

events in the process timeline that correspond to the

“policy gates” helps us define the policy life cycle.

Finally, researchers had the task of reporting for

each event the source of their information, e.g.,

academic papers, interviews, emails, etc. Thus, each

event should be backed entirely by one (or a

combination) of these sources and be independent of

the researcher's understanding of the process.

4 DISCUSSION AND RESULTS

The process timeline in Figure 2 shows the policy life

cycle for each of the city cases. Here we can see, for

instance, that a constant throughout all the cases is the

short span of the implementation phase (followed

behind by the design phase) with respect to the

Figure 2: Policy life cycle of city case studies.

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Figure 3: Parameterized instance - Case study Ghent (BE).

ideation phase. This could be explained due to how

controversial UVAR measures are, and the political

debate and discussions they trigger. Aiming for short

implementations seems to be a strategic decision.

Speeding up to materialize the measure, thus steering

the political and public debate away from mere

assumptions on the impacts they may have once

launched. This strategic choice allows decision-

makers to redirect the focus of users and opposition

towards the real impacts and perceptions of a

“tangible” measure. The speed of the process is again

a motivation for the support of policymakers in the

quick design of robust policy strategies.

Additionally, the coding of the information

described in the process timelines using the

parameters defined and identified by the commission

of experts, allow us to represent the problem's

complexity graphically. Which facilitates the

visualization and identification of patterns in the data.

Figure 3 illustrates the parameterized instance of the

case study for the city of Ghent, i.e., a frame

representation of the case of Ghent (BE). Here, each

row marks the beginning of an UVAR-related event,

and each black box corresponds to the “activation” of

a control parameter triggered in each event. This

could be, for instance, the renovation of PT

infrastructure in the ideation phase (Figure 3 event

(a)), a call for a referendum from part of citizen

groups opposing the UVAR measure in the design

phase (Figure 3 event (b)), the allocation of Park and

Ride (P+R) locations to complement the UVAR

measure in the implementation phase (Figure 3 event

(c)), or the beginning of participatory workshops to

gather citizen feedback on the measure in the

operation phase (Figure 3 event (d)). Making use of

the parameterized instances, we can easily identify

common features in the UVAR process across cities.

From a between city analysis of the parameterized

instances we can see that, for example, monitoring

activities appear in the ideation phase in all 8 city

cases (

Figure 4). Furthermore, in all the cases, its

occurrence is directly linked to the problem

identification and definition.

Similarly, we can observe that improvements in

PT are a constant across all city cases. This is

expected, as these improvements may mitigate some

of the negative externalities of road traffic, thus

falling in line with the objectives of the UVAR

measure and acting as a complement to it. For to an

increase of supply and/or economic incentives for the

use of PT, appear mainly in Bologna, Amsterdam,

Milan, and London. This finding aligns instance, we

can see how improvements in PT linked with the

notion that this kind of interventions are of special

Support in Policymaking: A Systematic Exploration of the Policymaking Process

125

Figure 4: Emergence of monitoring activities in city case studies (in red). Events in green belong to the ideation phase.

importance in restrictive measures such as LEZs,

LTZs, or CCs in order to provide an alternative to

private vehicles (Croci, 2016).

Furthermore, just as the improvements in PT can

be conceived as a complement to the UVAR strategy,

an analysis of the parameterized instances allows us

to see that in all the city cases the UVAR measure is

formally developed as part of a broader strategy that

represents the vision of the city in relation to air

quality, mobility and/or sustainability (Table 1). The

conception of an UVAR as one of the components of

a major political instrument, instead of a stand-alone

measure, seems to provide context and purpose to the

measure, aligning it with the goals of the city and

showcasing the UVAR measure as a “consistent” step

towards the city’s goals.

Another key point of the methodology relates

directly to the data collection process. Making use of

the outcome of the coding process as a rubric that

highlights the important factors case study

researchers should consider during the data collection

process, facilitates the allocation of resources. Case

study researchers can in this way find a major

proportion of the information through desktop

research, before going into the field to corroborate

and complement their findings, e.g., through

interviews or field visits. For the 8 city cases, on

average 53 events were registered in the process

timelines for each city case. From here, we could see

that on average 62 different sources were cited per

city case. The different data sources cover press

releases, official (policy) documents, reports of

special-purpose bodies, interviews, and academic

articles. Of the total number of data sources, field

interviews account for only 19% of the data sources.

Meaning that the remainder 81% of the data sources

could be collected without the need for a field study,

thus reducing the number of resources needed in the

documentation of each city case.

We can see then, how our framework could be

used to identify patterns and common trends in the

policy process that can give light into crucial aspects

of the policy strategy, and thus support policymakers

in the implementation and development of policies.

Additionally, the analysis of the city cases

showcases possible system configurations in a

graphic manner (Figures 3-4) which facilitates the

identification and formulation of alternative scenarios

based on the “patterns” observed in the relevant case

studies. The small number of case studies, however,

restraint us from drawing statistically significant

results. Furthermore, the limited sample size in

combination with the heterogeneity of the sample (in

terms of their urban landscapes), hinders the

identification of useful patterns among the contextual

parameters that could help us support the validity of

these parameters or assess the findings in (Gillis et al.,

2016). Despite all this, we believe the findings

summarized in this section can illustrate the potential

of the methodology in the assessment of policy

processes and inspire future research on the matter.

5 CONCLUSIONS AND FUTURE

WORK

The methodology presented could be used to identify

patterns and trends in the policy process that can give

light into crucial aspects of the policy strategy, and

thus support policymakers in policymaking. The

methodology is based upon a systematic exploration

of the problem space supported by expert knowledge

and case study research.

To illustrate the Framework’s potential, we have

used the case of UVARs in a set of 8 city cases studies

(

Table 1). Despite the small sample size, experiments

suggest that the methodology could be used in a DSS

for the identification of patterns and trends in the data

(in spite of the large amount of data and variables),

and the identification of possible scenarios (policy

configurations). Furthermore, the proposed

methodology seems to facilitate the data collection

process, supporting desktop research and reducing the

time and effort needed for field research.

Finally, the ease of use of the methodology, and

the features of the graphic representation (Figures 3

and 4) suggest that integration with advanced data

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126

analysis techniques could facilitate the identification

of patterns and trends. Furthermore, the methodology

could make use of Big Data to, e.g., monitor public

opinion through the collection and analysis of social

network data, or perform a continuous evaluation and

monitoring of contextual parameters.

ACKNOWLEDGEMENTS

The work reported in this paper is funded under the

European Commission H2020 project ReVeAL –

Regulating Vehicle Access for improved Liveability

(grant agreement No. 815008). ReVeAL is a R&I

project aiming to enable cities to optimize urban

space and transport network usage through new and

integrated packages of urban vehicle access policies

and technologies.

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