Revolutionizing Mobility:

Big Data Applications in Transport Planning

ANTONELLA FALANGA1,*, ARMANDO CARTENÌ2

1Engineering Department,

University of Campania “Luigi Vanvitelli”,

via Roma 29, 81031 Aversa (Caserta),

ITALY

2Department of Architecture and Industrial Design,

University of Campania “Luigi Vanvitelli”,

via S. Lorenzo 31, 81031 Aversa (Caserta),

ITALY

*Corresponding Author

Abstract: - Today an unprecedented amount of data coming from several sources, including mobile devices,

sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to

the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that,

when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume,

variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse

sources, and potential value addition of this kind of data. Big data's pervasive impact enhances societal aspects,

elevating the quality of life, service efficiency, and problem-solving capacities. However, during this

transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity,

interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the

topic investigated in this research), applications span planning, designing, and managing systems and mobility

services. Among the most common big data applications within the transport sector, there are, for example,

real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety, and all

the autonomous and connected vehicles applications, in addition to the travel demand estimation useful for a

sustainable transportation planning. Emerging technologies, offering substantial big data at lower costs than

traditional methods, play a pivotal role in this context. Starting from these considerations, the present study

explores two recent Italian big-data applications within the transport sector starting from the database of the

Italian Ministry of Infrastructure and Transport and the Ministry of Health. The first one investigates the proper

national demand estimation by transport mode and territorial area of interest, while the second one correlates

the diffusion of the COVID-19 pandemic with the mobility habits in the Country. The lessons learned from

these case studies are: i) the large amount of mobility data is useful for estimating mobility habits as long as

they are adequately treated (e.g. high professional skills are necessary) to certify the quality of the data;

furthermore, also multi-source and multi-format data can significantly contribute to a better knowledge of the

phenomenon, but only if they are adequately archived and processed; ii) the large amount of data made

available to the different (many) operators/institutions has made possible to correlate the spread of the

pandemic with the behavior of citizens; concerning transport sector, was observed that the daily mobility habits

influence infections registered three weeks later and areas with higher transport accessibility are more rapidly

affected by infections.

Key-Words: - Big data, cloud computing, database, decision-making, public transport, sustainable mobility,

transport demand, transportation planning, Covid-19 pandemic, connected vehicles, autonomous

vehicles.

Received: March 27, 2023. Revised: October 23, 2023. Accepted: December 17, 2023. Published: December 31, 2023.

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DOI: 10.37394/232015.2023.19.129

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E-ISSN: 2224-3496

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1 Introduction

In recent years, there has been an extensive

availability of data sourced from entities, sensors,

portable gadgets, the Internet of Things (IoT),

multimedia, social networks, business transactions,

and scientific data, [1], [2]. This copious volume of

data, known as big data, refers to extremely large

and complex data sets that require specific

techniques to be efficiently collected, managed,

processed, and analyzed, [3], [4]. Moreover, they

possess promising market value and have recently

captured substantial attention from scientific circles,

governmental bodies, and industries owing to their

significantly positive influence across various

domains. This surge of information has

revolutionized many sectors, such as business for

improving decision-making processes, healthcare

for clinical record analysis and medical research,

education for enhancing teaching methodologies,

agriculture for optimizing crop management,

finance for risk assessment and fraud detection,

media and entertainment for personalized content

recommendations, emergency for a real-time

response during crisis/events, and also mobility for

the urban planning and for the design/management

of public and private transport services.

From the literature review emerges that there are

three main characteristics associated with big data,

known as the "3Vs", [5], [6], [7]: “volume”, relating

to the enormous amount of generated data, whether

intricately structured or in its raw unstructured form;

“velocity”, referring to the rapid and continuous

generation of data produced and updated in real-

time; “variety”, that indicates the diversity of data

types, spanning from the conventional realms of text

and numbers to the more intricate landscapes of

images and videos. In addition to the “3Vs”, some

experts also include other features like “veracity”,

[8], referring to data quality and reliability - and

“value”, [9], concerning the importance and

potential benefit that can be obtained.

Data sets are so massive that traditional data

processing software cannot handle them. This has

prompted many operators to adopt cloud computing,

[10], [11], [12]. It is a consequence of the ease of

access to web-based remote computing sites. Instead

of installing software for each computer, it only

requires installing a simple application on the local

device, sending processing data to a single computer

in the cloud, [13], [14]. Big data and cloud

computing can be considered as a useful and unique

tool for many practical applications, [15], [16], [17].

Cloud computing, at its core, is a paradigm that

entails delivering computing services-including

storage, processing power, and analytics on internet.

It is the digital infrastructure that seamlessly

supports the processing demands and storage

requisites necessitated by the deluge of big data.

This dynamic synergy between big data and cloud

computing has not only streamlined data

management but has also democratized access to

advanced computing resources, enabling

organizations to leverage the full potential of big

data analytics. Therefore, cloud computing not only

provides devices for computing and processing big

data but also works as a service model, [18], [19].

There are numerous joint applications of big data

and cloud computing, for example, in education,

[20], [21], [22], agriculture, [23], [24], [25], [26],

healthcare, [27], [28], [29], business, [30], [31],

[32], [33] and in many other areas, such as

transportation, [34], [35].

Data quality, privacy concerns, cybersecurity

risks caused by increased data volumes, data

interoperability, advanced infrastructure needs, and

the requirement for skilled personnel for effective

big data analysis are among the new difficulties.

Regarding technologies and tools, the Internet of

Things (IoT) comprises devices generating real-time

data interconnected for in-depth data processing,

[36], [37] and automation across sectors.

Additionally, Global Positioning Systems (GPS)

provide real-time location data, enhancing

efficiency, security, and convenience of mobility

and logistics through big data analysis, [38], [39],

[40].

Modern advancements in technology have

revolutionized data acquisition by providing

significant volumes of big data at reduced costs

compared to conventional methods. Within this

framework, these technological innovations play a

crucial role. By leveraging these technological

advancements, this study aims to review the main

applications of big data in transportation and discuss

some noteworthy Italian best practices within the

transport sector.

Starting from these considerations the aim of this

paper is to review some of the main big data

applications (best practices) within the transport

sector, highlighting their strengths with a focus on

innovative changes and directions observed.

Specifically, in the following sections, the

multipronged nature of the big data revolution and

its transformative impact on defining both present

and future trends of the transport sector are

presented. Furthermore, two of the main recent

Italian big-data applications within the transport

sector are discussed, starting from the database of

the Italian Ministry of Infrastructure and Transport

and the Ministry of Health. The first one

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investigates the proper national demand estimation

by transport mode and territorial area of interest,

while the second one correlates the diffusion of the

Covid-19 pandemic with the mobility habits in the

Country.

The paper is organized as follows: Section 2

describes several big data applications in

transportation systems. Section 3 discusses some of

the best practices in Italy. Finally, the conclusions

and research perspectives are reported in Section 4.

2 Big Data Applications in

Transportation Systems

In the vast field of transportation, the integration of

big data applications has emerged as a

transformative force, offering multifaceted solutions

across planning, design, and management domains.

These innovations produce substantial advantages,

ranging from the optimization of travel times and

reduction of road incidents to addressing

environmental concerns. The application of big data

in the transport sector extends beyond basic

optimization, delving into predictive maintenance,

smart transportation systems, and informed

decision-making, [39]. The utilization of advanced

analytics tools and technologies is revolutionizing

the way transportation systems operate, enhancing

their safety, reliability, and sustainability. Their

impact spans various pivotal areas within the

transportation sector as, for example:

 real-time traffic monitoring;

 route optimization for commercial vehicles;

 road safety;

 autonomous and connected vehicles;

 travel demand estimation, mobility habits, and

transportation planning.

Real-time traffic monitoring harnesses the power

of big data analytics by assimilating data from

diverse sources, such as traffic cameras, GPS

devices, and traffic sensors, enabling the swift

identification of traffic congestion and prompt

implementation of corrective measures, [40], [41],

[42], [43].

Big data facilitates route optimization for freight

vehicles, streamlining journeys, curbing costs, and

bolstering operational efficiency. Concurrently, it

facilitates predictive vehicle maintenance,

preempting breakdowns through cloud-based

systems monitoring critical components like

gearbox oil or clutch discs, [44], [45], [46].

The improvement of road safety is a significant

milestone in this path, [47], where big data can be

employed to enhance current systems with data-

driven warning mechanisms, automatic braking

systems, and thorough analysis of accident

causation, [48], [49], [50].

The introduction of self-driving and connected

vehicles and services (e.g. Google car (2015) and

the Robot taxi fleet in California, August 2023),

demonstrates a massive utilization of big data, [51],

[52], [53]. These vehicles count on a constant inflow

of environmental data to drive with safety, achieved

through vehicle-to-vehicle (V2V) and vehicle-to-

infrastructure (V2I) connectivity. The former

enables real-time communication among vehicles,

interchanging pivotal information, such as traffic

status and emergency alerts. By enabling

communication between vehicles and roadway

infrastructure, the latter facilitates interactive

signaling and informative feedback, leading to

enhanced traffic flow.

The transportation sector has been able to unlock

unprecedented opportunities to forecast and

understand mobility demand by integrating big data

analytics, [54], [55]. Authorities can make informed

decisions and strategically plan thanks to this

transformative approach, by examining the

challenging dynamics of transportation needs. These

improvements in data collection are not restricted to

standard methods, as they draw insights from

diverse sources (e.g. traffic sensors, GPS systems,

public transportation transactions, mobile apps, and

social media; [56], [57], [58], [59]. Data sets are

combined to provide a complete understanding of

travel patterns, vehicle positions, trip durations, and

the reasons for individual trips.

A thorough analysis process occurs after data

collection, which uncovers intricate mobility

patterns and highlights significant trends. The

crucial points of travel behavior are revealed during

this phase, which includes peak travel hours, routes

that are frequently used, and recurring congestion

points, [60], [61]. Demand models are created by

utilizing this wealth of analyzed data. These models,

which are complex in their implementation,

consider several aspects that affect travel behavior,

such as socioeconomic conditions, geographic

locations, mode choices, and available paths. These

demand models become very significant in

forecasting future mobility needs, [62], [63]. For

instance, predictive capabilities derived from big

data analytics can help authorities accurately

anticipate forthcoming demands, [64], [65], [66].

This foresight allows transportation agencies to plan

infrastructures and services more effectively.

Examples of application in this sense are, that

transport agencies can adapt the timetables of public

transport services to meet expected demand at

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certain times, thereby reducing waiting times but

also optimizing resources, thus enhancing the

overall efficiency of the transportation ecosystem,

[67], [68].

Finally, the integration of big data analytics in

transportation planning contributes to sustainable

mobility initiatives and revolutionizing the way

transportation services are envisioned, planned, and

executed.

3 Some Recent Big Data Best

Practices Applications in Italy

In the upcoming subsections, the focus will be on

presenting successful case studies within the Italian

transportation sector, drawing reference from the

periodic report “Mobility Trends Observatory” of

the Italian Ministry of Infrastructure and Transport,

[69], [70], [71] that summarizes the main insights of

the big data database on the Italian mobility habits.

Additionally, the main findings of a research

study carried out by the Italian University of

Campania “Luigi Vanvitelli” and performed by

some of the authors of this paper, examining the role

of mobility in pandemic diffusion, [72], [73], will

also be discussed.

3.1 The Mobility Trends of the Italian

Observatory of the Ministry of

Infrastructure and Transport

In the spring of 2020, a Mobility Habits

Observatory was established by the Ministry of

Infrastructures and Transport based on Big Data

available from many sources (e.g. transport modes,

and distance bands). Its mission also includes

disseminating mobility data and statistics within the

scientific and scholarly community to bolster the

Country's scientific production and deepen

knowledge within the mobility and transportation

sector. The data and analyses presented are sourced

from transport multimodal operators and the

Ministry of Infrastructure and Transport’s General

Directorates, coordinated by the Ministry of

Infrastructure and Transport’s Technical Mission

Structure:

 National Multimodal Operators:

- ANAS S.p.A.;

- Motorway Concessionaire Companies

- Trenitalia S.p.A.;

- Italo - Nuovo Trasporto Viaggiatori S.p.A.

- Port Authority;

- Assaeroporti - Italian Airport Operators

Association;

- FS Research Centre of Ferrovie dello Stato

Italiane Group.

 Ministry of Infrastructure and Transport’s

General Directorates:

- General Directorate for Roads and

Motorways and Oversight and Safety in

Road Infrastructures;

- General Directorate for Oversight of

Motorway Concessionaires;

- General Directorate for Railway

Infrastructures and Railway Interoperability;

- General Directorate for Transport and

Railway Infrastructures;

- General Directorate for Oversight of Port

Authorities, Port Infrastructures, Maritime

Transport, and Inland Waterways;

- General Directorate for Airports and Air

Transport.

The last published report of the Observatory is

the second quarter of 2023, [71]), and Fig. 1 and

Fig. 2 report some examples of trend

representations, showing, for each mode of

transport, the evolution of demand for 2019, 2020,

2021, 2022 and until the second quarter of 2023

(where available). In particular, Fig. 1 reveals that in

2020, during the first wave of the COVID-19

pandemic, a radical decline in mobility demand

from February to March was observed. Afterwards,

there was a period of recovery, up to August,

followed by a reduction until November during the

second main wave of COVID-19 diffusion in Italy.

Starting from 2021 there was then a slow recovery

of demand levels (with different seasonal

fluctuations), which led up to 2022 when the

mobility levels observed in the pre-pandemic (2019)

were (approximately) recovered.

The analyses have been carried out avoiding,

where not strictly necessary, any form of data

processing, also to minimize possible subjective

interpretations of the results.

From these analyses emerges that the large

amount of mobility data is useful for estimating

mobility habits if they are adequately treated (high

professional skills are necessary) to certify the

quality of these data. Furthermore, even multi-

source and multi-format data can contribute to a

better knowledge of the phenomenon, but only if

they are adequately archived and processed.

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Fig. 1: Car vehicles*km on highways (January 2019-June 2023)

Source: [71]

Fig. 2: Monthly demand and supply of high-speed rail passengers (January 2019-June 2023).

Base 100 = value January 2020

Source: [71]

3.2 The Contrast to COVID-19: The Role of

Mobility in the Spread of the Pandemic

in Italy

The Engineering Department of the University of

Campania “Luigi Vanvitelli” conducted a research

study to explore the role of mobility in pandemic

dissemination and the utilization of big data of the

Italian Ministry of Infrastructure and Transport and

the Italian Ministry of Health for this aim. This

study produced two scientific results published in

high-impact international journals, [72], [73].

The COVID-19 pandemic has led to a high

number of deaths, economic hardship, and the

disruption of daily life. Consequently, the pandemic

significantly altered mobility habits and mode

choices, leading to a preference among users for

private vehicles over public transport, [74], [75],

[76].

The findings of the analysis of the first study

carried out by [72], probing the impact of urban

mobility on COVID-19 spread in Italy revealed that

mobility habits constitute one of the variables

contributing to COVID-19 infections, along with the

daily number of tests conducted and several

environmental factors (such as PM particle pollution

and temperature), [76]. Notably, regions near the

epicenter exhibited a higher infection risk,

especially during the initial phase of the outbreak

(temporal decay phenomena).

The study, utilizing data from the Official

National Monitoring Observatory “Audimob”, [77],

examines mobility rates in Italy through periodic

phone and computer interviews, covering mobility

habits pre and post the national lockdown. 2,175

interviews were conducted between January and

February (pre-COVID-19) and 1,398 interviews

immediately after the lockdown in March 2020.

Precisely, the authors, [72], estimate a multiple

linear regression model linking daily certified

COVID-19 cases to socio-economic, environmental,

100

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million Car vehicles*km/week

Car vehicles 2023 Car vehicles 2022 Car vehicles 2021 Car vehicles 2020 Car vehicles 2019

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health care, and mobility habit variables. The study

considered daily new positive COVID-19 cases as

dependent variables and tested various independent

variables at the regional level:

𝑌

𝑟,𝑖 = 𝛽1∙ 𝑃𝑂𝑃𝑑,𝑟 + 𝛽2∙𝑃𝑀𝑟+ 𝛽3∙ 𝑁𝑇𝐸𝑆𝑇𝑆𝑟,𝑖 + 𝛽4∙

𝑇𝑇𝑟,𝑖 + 𝛽5∙ 𝑇𝑅𝐼𝑃𝑆𝑟,𝑖−𝑥 + 𝛽6∙ 𝑇𝐸𝑀𝑟,𝑖−𝑥 + 𝐶𝑜𝑛𝑠𝑡

(1)

where:

𝑌

𝑟,𝑖 represents the daily count of new positive

COVID-19 cases in the r-th region on the i-th day

(source: Italian Ministry of Health, 2020);

𝑃𝑂𝑃𝑑,𝑟 indicates the population density [10 ∗

inhabitants/km2] in the provincial capital of the r-th

region, [78].

𝑃𝑀𝑟 is the Particulate Matter (PM) pollutant

variable [number of days], measuring the days in

2019 when the national PM10 daily limit exceeded

50 μg/m3, [79].

𝑁𝑇𝐸𝑆𝑇𝑆𝑟,𝑖 is the health care variable estimating the

number of COVID-19 tests performed on the i-th

day per 1000 population in the r-th region, [70].

𝑇𝑇𝑟,𝑖 is the weighted average travel time [hours]

from the r-th region to the initial COVID-19 cluster

in Codogno (Lombardy) on the i-th day.

𝑇𝑅𝐼𝑃𝑆𝑟,𝑖−𝑥 is the average number of people aged

14–80 who made at least one trip (“mobility habits”)

“x” days before the i-th day in the r-th region

[100,000 ∗ people/day]. This variable investigates

the correlation between daily certified coronavirus

cases and mobility habits made “x” days earlier.

𝑇𝐸𝑀𝑟,𝑖−𝑥 is the average daily temperature “x” days

before the i-th day in the r-th region [°C], [80].

𝐶𝑜𝑛𝑠𝑡 is a constant variable that was estimated to

account for attributes not explicitly covered in the

model.

For more details about the model formulation

and the hypothesis performed refer to the paper

[72].

The daily count of new cases demonstrated a

correlation with travels done three weeks earlier,

suggesting a potential 21-day period for detecting

positivity (Fig. 3). This timeframe implies that the

commonly implemented 14-day quarantine based

solely on incubation-based epidemiological

considerations might underestimate the virus

containment approach due to potential delays

between infection and detection.

The second study conducted by [73], discussed

in this paper aimed to support policymakers and

decision-makers in formulating optimal strategies to

address the COVID-19 crisis, both from a

transportation and security standpoint.

Specifically, the study investigated the

correlation between positive COVID-19 cases and

the accessibility of transportation within a specific

area.

Fig. 3: Delta new COVID-19 cases/day, observed daily 14–80 years old population mobility habits and daily

mobility habits shifted 21 days forward (positivity detection time); estimation starting from the big data

database of both the Italian Ministry of Health and the Italian Ministry of Infrastructure and Transport

Source: [72]

0,0

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4/23/2020

4/25/2020

4/27/2020

Day

Daily new COVID-19 cases (thousands)

Daily mobility habits, Daily mobility

habits 21 days forward (millions of

people/day)

Daily mobility habits Daily mobility habits 21 days forward Daily new COVID-19 cases

Shifted 21 days forward

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Estimates were derived through provincial

(zonal) aggregation. The data considered

encompassed daily reports on new Coronavirus

cases (from February 21 to May 5, 2020, sourced

from the Italian Ministry of Health), Italian National

census data for 2019 (ISTAT), Particulate Matter

measurements in 2019 (Italian Regional

Environmental Protection Agency - ARPA), daily

rail service characteristics from February to May

2020 (Trenitalia, NTV), and an ad-hoc survey in

five major rail stations nationwide, conducted from

October to November 2019, capturing key mobility

habits of rail passengers.

The authors estimated, [73] a multiple linear

regression model, linking Italy's total COVID-19

cases to socioeconomic, territorial, and transport

accessibility variables:

𝑌

𝑝,𝑖 = 𝛽1∙ 𝑃𝑂𝑃

𝑝+ 𝛽2∙ 𝑃𝑂𝑃𝑑,𝑝 + 𝛽3∙ 𝑆𝑂𝑈𝑝+

+ 𝛽4∙𝑃𝑀𝑝+ 𝛽5∙𝐴𝐶𝐶𝑝+ 𝐶𝑜𝑛𝑠𝑡 (2)

where:

𝑌

𝑝,𝑖 is the dependent variable representing the total

COVID-19 positive cases in the p-th province from

February 21 to April 20, 2020, [70];

𝑃𝑂𝑃

𝑝 is the population in the p-th province, [78];

𝑃𝑂𝑃𝑑,𝑝 is the population density in the p-th

province, [78].

𝑆𝑂𝑈𝑝 is a dummy variable, equal to 1 for provinces

in southern Italy (including Sicily), characterized by

warmer weather and extensive coastlines, and 0

otherwise [1; 0];

𝑃𝑀𝑝 measures particulate matter in the p-th

province, indicating the number of days in 2019

when the national PM10 daily limit exceeded 50

μg/m3, [79], [100*number of days];

𝐴𝐶𝐶𝑝 is the proposed rail-based accessibility

measure for the p-th province towards all study area

zones, calculated using Equation (2) [number/100];

𝐶𝑜𝑛𝑠𝑡 is a variable encompassing attributes not

otherwise explained in the model [number].

For more details about the model formulation and

the hypothesis performed refer to the paper [73].

The findings from estimations revealed that

transportation accessibility was the variable that best

explained the number of COVID-19 infections

(approximately 40% in weight) (Fig. 4). This

implies that the higher the accessibility within a

specific geographical area, the more easily the virus

reaches its population. Furthermore, other

contextual factors, such as socioeconomic,

territorial, and pollution-related variables were

found to be significant. The results suggest that

accessibility, typically an indicator of an area's

prosperity, becomes a primary conduit for contagion

during a pandemic. The quantitative assessments

conducted suggest that a potentially more

sustainable approach to curbing social interactions

could involve tailoring lockdown measures in

proportion to the transportation accessibility of the

respective areas. This approach proposes that areas

with higher accessibility warrant stricter mobility

restriction policies, thereby potentially mitigating

the spread of the virus more effectively.

Parameters’ estimation results of multiple

regression models (Model 1, [72] and Model 2,

[73]) are shown in Table 1.

The original findings of these two studies have

been useful in shaping actions and mobility

restriction policies to counter the pandemic.

Furthermore, they pave the way for defining

potential policies or best practices to enhance the

management of mobility restrictions.

4 Conclusion

The emergence of big data has sparked a paradigm

shift across various sectors, including transportation,

offering a transformative impact on decision-

making processes and service delivery. The

integration of big data analytics in transportation

systems has brought forth a myriad of applications,

ranging from real-time traffic monitoring to route

optimization, road safety, and even shaping the

trajectory of autonomous and connected vehicles.

These applications have substantially contributed to

optimizing travel times, reducing road incidents, and

fostering sustainable mobility initiatives.

In the context of Italy, the Mobility Trends

Observatory of the Ministry of Infrastructure and

Transport, [71], has played a crucial role in

monitoring national mobility demand, contributing

valuable insights for infrastructure investments and

transportation planning. Additionally, the research

conducted by the University of Campania “Luigi

Vanvitelli” investigated the role of mobility in the

spread of COVID-19. The findings highlighted the

significant influence of mobility habits on infection

rates and the correlation between transportation

accessibility and the rapid spread of the virus. The

study findings significantly influenced mobility

restriction policies, proposing customized lockdown

measures and more effective strategies for managing

mobility.

The two case studies discussed in this paper

should be considered as possible examples of big-

data applications in the transport sector (the topic of

this paper). Their applicability and transferability to

other case studies (e.g. regions, nations) must be

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verified case by case and will be the subject of

future research.

As we navigate the future, leveraging big data in

transportation systems remains pivotal, not only for

informed decision-making but also for optimizing

service delivery and addressing future challenges in

the ever-evolving landscape of mobility and

transportation.

Fig. 4: Total number of COVID-19 cases in Italy (left side - estimation starting from the big data database of

the [70]) and rail-based transport accessibility model (2) estimation results (right side - estimation starting from

the big data database of the Italian Ministry of Infrastructure and Transport)

Source: [73]

Table 1. Multiple regression models (Model 1 and Model 2): parameters’ estimation results

Model 1 (source: [72])

Model 2 (source: [73])

Variable

Std. error

t-value

p-value

Std. error

t-value

p-value

𝑃𝑂𝑃

𝑗∗

0.040

5.057

0.419

𝑃𝑂𝑃𝑑,𝑗∗

0,069

2.299

0.022

0.403

1.850

0.067

𝑃𝑀

𝑗∗

0.291

2.944

0.003

0.107

2.648

0.009

𝑁𝑇𝐸𝑆𝑇𝑆𝑟,𝑗∗

0.254

7.495

<0.0001

𝑇𝑇

𝑗∗,𝑖

2.119

-2.336

0.020

𝑇𝑅𝐼𝑃𝑆

𝑗∗,𝑖−𝑥

0.531

18.460

<0.0001

𝑇𝐸𝑀

𝑗∗,𝑖−𝑥

1.388

-4.724

<0.0001

𝑆𝑂𝑈

𝑗∗

-401.445

1.840

0.069

𝐴𝐶𝐶

𝑗∗

0.197

2.304

0.023

𝐶𝑜𝑛𝑠𝑡 [number]

9.270

1.979

0.048

-1788.444

2.111

0.037

Number of observations

1200

0.427

0.424

148.359

1.30E–140

105

0.571

0.549

25.568

2.58E–16

R-squared

Adj. R-squared

F-statistic (6, 1193)

P-value (F)

*The subscript “j” denotes the region for Model 1, while it refers to the province territorial aggregation when referencing

Model 2, where it designates the province.

Source: [72] and [73]

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors contributed to the present research at all

stages from the formulation of the problem to the

final findings and solution. In particular, the author

- Antonella Falanga contributed to formal analysis,

resources, methodology, data curation,

elaboration, validation, writing, review, and

editing.

- Armando Cartenì contributed to the

conceptualization of the paper, supervision, and

review.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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