Development of GIS-Based Simulations for Evaluating Interventions

in Latvia's Transport System

Justina Hudenko

1,2 a

, Igors Kukjans

and Inguna Jurgelane Kaldava

JSC “LatRailNet”, Latvia

Riga Technical University, Latvia

Keywords: User Interface, Online Simulation, State Intervention Outcomes, Transport Systems.

Abstract: This paper presents the development and testing of a user interface (UI) for the Transport Interventions

outcomes simulation Model (TIM) as part of Latvia's efforts to comprehensively assess state interventions in

its transport system. Following the design thinking process, the paper outlines stakeholder needs and proposes

a centralized website for accessing transport system data and outcomes. The TIM UI facilitates data

exploration, submission of proposals, and initial impact assessment. It integrates GIS technology for enhanced

visualization and decision-making. Testing results inform improvements in navigation, clarity, visual design,

and performance. The TIM UI contributes to sustainable transportation planning and policy formulation in

Latvia by engaging stakeholders and enabling informed decision-making.

1 INTRODUCTION

This research is motivated by the requirement to

comprehensively assess the outcomes of state

interventions within Latvia's transport system. These

interventions, extending beyond mere infrastructural

enhancements, are linked to broader environmental

(ESG) and regional development objectives.

Consequently, the transport system assumes a pivotal

role as an agent facilitating progress toward these

multifaceted goals.

This publication serves as a follow-up to the initial

phase of the design thinking process, which focuses

on prototyping and understanding the needs of

stakeholders. The previous paper (Hudenko et al.,

2022) outlines the first two steps Scheer et al. (2012)

based design thinking process: (1) Observation and

(2) Synthesis. The observations of railway

stakeholders were conducted through focus group

discussions, involving representatives from railway

policy makers, professionals (railway undertakers),

and implementers of railway policy (the

infrastructure manager and the capacity allocation

body). The synthesis of stakeholder insights revealed

varying levels of development and involvement in

https://orcid.org/0000-0002-2347-8539

https://orcid.org/0009-0003-5166-6347

https://orcid.org/0000-0001-6756-7675

ESG processes among different stakeholder groups.

While some stakeholders demonstrated a high level

of engagement and accountability, others indicated a

more passive approach, primarily driven by

regulatory requirements. The results also highlighted

stakeholders' perceptions and motivations regarding

ESG activities, revealing areas of alignment and

divergence among different groups. These findings

underscore the importance of addressing

stakeholders' varying levels of awareness and

engagement in ESG activities within the railway

sector, particularly in fostering effective

communication and collaboration among

stakeholders to advance sustainable strategies in

transportation planning and execution.

This research bridges this gap and discusses the

subsequent phases of the design thinking process: (3)

Ideate; (4) Prototype; and (5) Testing. These phases

involve the development and testing of an open-

access Geographic Information System (GIS) based

data and data simulation tool among stakeholders, so

that they can understand broader objectives of

transportation planning.

GIS technology coordinates a wide range of

applications across different industries including

258

Hudenko, J., Kukjans, I. and Kaldava, I.

Development of GIS-Based Simulations for Evaluating Inter ventions in Latvia’s Transport System.

DOI: 10.5220/0012748700003758

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2024), pages 258-265

ISBN: 978-989-758-708-5; ISSN: 2184-2841

regional planning, environmental management,

transportation and allows users to understand

patterns, relationships, and trends that are not always

apparent in tabular data, thus enabling users to make

informed decisions.

By leveraging GIS technology, this research seeks

to empower policymakers, transport and regional

development planners, and transportation

stakeholders with actionable insights into the

potential impacts of various interventions on Latvia's

transport system. Furthermore, we seek to address

challenges in this domain to both SIMULTECH

society and practitioners.

In the following sections, this paper will delve

into the methodological details of developing GIS-

based simulations for interventions in Latvia's

transport system. It will explore the underlying data

sources, modelling techniques, and simulation

methodologies employed, highlighting key insights

and implications for urban planning and policy

formulation.

2 THE IDEATION

In the ideation phase of our project, we propose the

development of a comprehensive website aimed at

providing a centralized platform for stakeholders to

access information on existing transport system

developments, planned interventions, and their

outcomes (Fig. 1).

Figure 1: Conceptual Framework of Stakeholder

Involvement in the Green Deal Website.

Figure 1 illustrates the ideation proposal

regarding the involvement of various stakeholders in

the Green Deal via the website. The website is

structured into four main sections: transport

(transports operators), infrastructure (policy makers),

equipment (infrastructure undertakers), and

behaviour (transport users).

Each section is tailored to focus on achieving the

goals of the Green Deal within their respective

domains by addressing these questions:

 Transport operators: "How might we make

sustainability desirable for rail stakeholders?"

 Transport users: "How might we encourage

green purchase habits of the final consumer

market?"

 Policy makers: "How might we make railways

as a role model of sustainable transportation?”,

“How might we involve competing modes of

transport in the creation of co-modal transport,

using the advantages of each mode of

transport?" and "How might we guide global

decisions at the individual stakeholder's level?"

 Infrastructure undertakers: "How might we

calculate the consequences of the Green Deal

on final market?" and "How might we raise

awareness of ESG processes to all three scopes

of emissions?"

If implemented, the proposed website would

serve as a valuable tool for stakeholders by

consolidating data and insights on ongoing and

proposed transport interventions, providing

accessible and up-to-date information. This platform

would enable policymakers, urban planners,

transportation engineers, and other stakeholders to

gain valuable insights into the current state of the

transport system, identify improvement areas, and

explore potential intervention strategies.

The first testing of prototyped website asked to

develop an intuitive interface that allows users to

navigate through different sections, including a

comprehensive database of existing transport system

developments, detailed profiles of planned

interventions, and comprehensive reports on the

outcomes of previous interventions. Additionally,

interactive maps and visualizations were asked to be

incorporated to enhance the user experience and

facilitate data exploration:

 The list of accepted infrastructure projects,

those location, and details (Figure 2)

 Use studies (optional – not discussed)

 Possibilities to submit proposals (Figure 3).

Development of GIS-Based Simulations for Evaluating Interventions in Latvia’s Transport System

259

Figure 2: Latvia Infrastructure Project Data Visualization.

Figure 2 depicts a visualization an accepted

projects’ explanation tool. It includes a map of Latvia

infrastructure, divided into layers. Each layer has

information about the project and associated

investments as well as its outcomes such as

infrastructure quality, regional connectivity, and

incidents.

Figure 3: Project Proposal Submission Tool with

Interactive Map Display.

Figure 3 illustrates the ideated proposal of

submission interactive map, with multiple survey of

information, including required interventions as well

as affected infrastructure, traffic, equipment, and

behaviour aspects. At the end there is a result section

where stakeholders require an initial impact of their

proposal where they can assess feasibility of the

submitted proposal.

In summary, the ideation step of the prototyped

website focused on developing an intuitive interface

to allow users to navigate through various sections,

including a comprehensive database of existing

transport system developments, detailed profiles of

planned interventions, and comprehensive reports on

the outcomes of previous interventions. Additionally,

interactive maps and visualizations were proposed to

enhance the user experience and facilitate data

exploration, allowing stakeholders to submit

proposals and to make those initial assess. The next

step discusses the prototype of the transport

interventions initial assessment tool.

3 THE PROTOTYPE

This section will discuss the Transport Interventions

outcomes simulation Model (TIM), which enables

researchers, interested stakeholders, and policy

analysts to calculate, in a comparable manner, the

effects of taxes, investments, and other transport

interventions on transport system outcomes for

specific countries or groups of countries.

3.1 TIM Model

Figure 4 show a representation of the TIM model

inputs, interactions, and outcomes.

Figure 4: The TIM Model Overall Scheme.

Figure 4 summarizes the framework of the TIM

and its core components:

 Interventions. Presently, our focus primarily

encompasses investment strategies.

Nevertheless, a supplemental investigation

incorporates additional potential interventions,

such as legal frameworks and taxation policies.

 Model Core ("Black Box"). This component

serves as the central processing unit, where raw

data undergoes correlation analysis, forecasting,

weighting, and integration processes.

 Alignment with Decision Makers. This stage

involves the identification of outcome priorities

to ensure congruence with the preferences and

objectives of decision-making entities.

 Outcomes. The model simulates the

comparative advantages inherent in various

intervention scenarios.

Subsequent sections will elucidate the constituent

blocks of the model in reverse order.

3.1.1 The Outcomes and Capabilities

TIM can be used in many ways. For online users:

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 Standard estimation of environment impact,

mobility redistribution and productivity

statistics under actual conditions.

 Budgetary effects.

 Illustrative changes to traffic redistribution on

transport modes and public/private source of

transport financing.

More advanced capabilities (outside of online

version):

 Ex-ante and ex-post assessment of complex

policy reforms e.g., Transport Development

Master plan.

 Policy transfer’s assessment e.g., how adopting

a policy measure currently effective in country

A would have effect in country B.

 Assessment of other "what if" questions effects

and costs of EU-wide policy reforms.

In summary, we expect that TIM's comprehensive

functionalities make it a valuable tool for

policymakers, researchers, and stakeholders involved

in transportation planning and policy analysis at both

national and international levels.

3.1.2 Policy Priorities

The TIM model either reflects the intervention-

benefit outcomes for a specific period (ex-post

analysis) or the outcomes for an actual (planned) or

imaginary intervention (ex-ante) scenario.

This is a fact that decision-making is not always

as simple as the value of a specific parameter.

Therefore, the primary output of the TIM model is an

aggregated weighted of scenario comparable

advantages of key performance indicators (KPIs) for

various scenarios. This outcome is attained through

the application of the Analytic Network Process

(ANP) methodology. The output consists of two lists:

evaluations of comparable advantages and those

weights (policies).

Technically an evaluation of comparable

advantages is the aggregate of input data correlations

and forecasts as described below. These components

in rare cases fractions can be either added or

subtracted to build the unknown aggregate.

TIM policies are functions, which typically

represent an eligibility for the intervention (e.g. better

connectivity indicators leads "best match" to

investments). The purpose of using policy functions

as building blocks of the model is to provide a general

structure, which can be seen as using a standardised

language to describe transport policy instruments.

As a default TIM produces a table with forecasted

KPIs, those weights and aggregated value. The KPI

are divided into three groups:

 Productivity: Nominal labor productivity per

hour in the transportation and storage sector;

GDP per capita at purchasing power parity;

Transport infrastructure index;

 Cohesion: Regional GDP disparity; Attraction

of private investment in planning regions; Wage

in planning regions;

 Sustainability: Share of energy generated from

RES in transport; Share of zero-emission

vehicles in the total number of vehicles;

Reduction in the number of pedestrian fatalities

in road traffic accidents.

The concept of "best match" refers to achieving

an optimal system-dataset combination where no

further improvements can be made in one aspect

without sacrificing performance in another aspect,

e.g. Pareto optimality.

Achieving a "best match" suggests that there may

be multiple combinations of interventions that could

be considered optimal, as it is not possible to improve

one aspect (e.g. productivity) without compromising

another aspect (e.g. sustainability). We use the

concept of Pareto optimality for selecting the "best

match" intervention/priorities combination by

considering the trade-offs between different criteria

and striving for an outcome where no further

improvements can be made without sacrificing

performance in other areas.

3.1.3 Data Input

TIM is based on two inputs: raw data and correlations

coefficients.

Raw data or parameters contain the information

the model needs to produce its output. These are the

initial, unprocessed data inputs from transport sector

stakeholders that serve as the foundation for analysis

within TIM. They are stored in XML files and consist

of factual information or observations collected from

sensors, databases, or official statistics. Raw data

consists of assessment unit, such as: Number of

transport units on a road or rail section (for traffic

dimension); Maximum transport unit available (for

infrastructure dimension); Revenue generated by one

transported unit (for behaviour dimension); Number

of incidents per infrastructure unit (for equipment

dimension). The assessment units consist of meta data

e.g., definitions of e.g., what is a transport unit, when

and how it is collected, etc. The meta data is also a

part of the input data and requires extensional

communication among data users.

Coefficients are numerical values or parameters

used to quantify the relationships or correlations

between different factors within the transportation

Development of GIS-Based Simulations for Evaluating Interventions in Latvia’s Transport System

261

system’s row data. In the context of TIM, coefficients

include regression coefficients, correlation

coefficients, elasticity coefficients, and other

coefficients derived from econometric models or

empirical studies. These coefficients play a crucial

role in estimating the effects of policy interventions,

predicting outcomes, and assessing the impacts of

various factors on transportation system performance.

Coefficients mostly derived from statistical analyses

or previous research and are used to represent the

strength and direction of the relationships between

variables.

Furthermore, while in an ideal research setting,

assessing the outcomes of interventions for the year

YYYY would involve the modification of data

relevant to the same year, it is essential to

acknowledge that in the real-world corresponding

data is not consistently guaranteed. Therefore,

datasets used for simulating several intervention

years, are compiled by forecasting technics and

alongside with user’s adjustments develop baseline or

base scenario.

These forecasting parameters reflect the

dynamics of the transport system and represent the

probable outcome in the absence of interventions.

Additional parameters can be included by

stakeholders to improve forecasting accuracy by

users based on their knowledge and the specific

context of the time series being modelled. Some

examples of additional parameters mentioned during

ideation were:

• Seasonal and cyclical patterns that repeat

over fixed periods.

• Known cases or events (e.g., legal changes,

end of related projects etc) that lead to

gradual changes in the data over time.

• Influence of the external variable such as

demography economic indicators, weather

data, or any other relevant factors.

• System dynamics components such as

saturation and delays in feedback.

The involvement of stakeholders provides a

powerful framework for understanding and managing

complex transportation systems, allowing

policymakers, managers, and researchers to explore

the dynamic behaviour of the system and design more

effective strategies.

3.1.4 Interventions

The name of the model, reflects its core function of

simulating outcomes resulting from interventions,

including taxes, investments, and legal boundaries.

Technically, the intervention file in the TIM model

consists of multiple variables that aggregate a set of

key performance indicators (KPIs) relative to a

baseline scenario. The comparison of different

intervention scenario allows to find the "best much"

as described in section 3.1.2. The intervention file

also consists of name, type, amount and starting year

of intervention, affected row data sets and coefficient

used for updating those values to the corresponding

year.

For the online user the interventions are

standardised (standard interventions), that means that

online users can propose to change only specified

data sets, which were used over the ex-ante

assessment, as described in section 3.2.

Advanced users can introduce special intervention

function, defining any variables and data sets as well

as develop a list of interventions worked off in

sequence when the TIM model performs its

calculations called "policy". These is a file that define

variables that aggregates the effect of each

intervention to the sequent intervention and on the

final output. The variables in the file capture both

direct impacts and indirect influences, considering the

spatial relationships and interactions among

interventions.

Technically there is no difference between

standard interventions and special interventions, both

use ANP functions for their implementation and both

need to be listed in the "policy" to be performed.

Thus, calling some of them "special" is just a matter

of better comprehensibility, that shows that user can

influence the outcomes by defining of relationships

and interconnections.

Through this approach, the TIM model refers to

prevalent users’ inquiries regarding the omission of

certain transport interventions from simulation due to

datasets typically lacking information on past

contributions of the progressing transport system.

This will lead us to the next problem of "How

might we guide global decisions at the individual

stakeholder's level?", which on an ideational level is

addressed through the introduction of a challenging

user interface.

3.2 TIM User Interface

The user interface prototype was introduced in 2023

as a step forward in improving the user experience

within simulation modelling and GIS applications,

particularly in addressing the complex transport

system dynamics of guiding "strategical level"

decisions at the individual stakeholder's level. This

milestone sets the foundation for continued research

and development aimed at refining the interface's

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functionality and effectiveness in facilitating

informed decision-making processes.

In the subsequent text, we will present the results

from the testing phase of the developed prototype.

3.2.1 Explanatory Pages

By distinguishing between raw data inputs and

coefficients primarily assessing data correlations,

users can gain a clearer understanding of the various

types of information integrated within TIM and their

respective contributions to the analysis and decision-

making process. Therefore, the key elements of the

model are interpreted to users through the "About"

window in the user interface.

Figure 5: Clarifying Raw data Utilization in TIM: ˝About˝

window.

The "About" window sections presents

aggregated data in four model dimensions:

• "Traffic" encompasses two distinct groups of

indicators: (1) traffic flow intensity, reflecting

the tangible load on the transportation

infrastructure in terms of vehicles, and (2)

turnover of transported units (passengers or

freight tonnes). Traffic indicators are delineated

by three dimensions: passenger and freight

transport, intensity of public and commercial

transport, and road transport and railway

movement. Furthermore, efforts are made to

delineate the regional distribution of traffic for

GIS representation.

• "Infrastructure" distinct two groups of

indicators: (1) throughput, delineating the

maximum volume of vehicles passing through a

designated section of the infrastructure per day,

and (2) throughput capacity, representing the

projected maximum capacity for both linear

infrastructure and terminals. To ensure data

comparability, the same dimensions employed

for "Traffic" indicators are adopted."

• "Equipment" is characterized by three primary

groups of indicators: (1) environmentally

friendly equipment associated with the

availability of environmentally safer modes of

transport as electrified railway lines, cycling

paths, alternative fuel filling stations etc., (2)

safety (for passengers) and security (for freight)

of transported units, as well as (3)

Competitiveness and time efficiency that are

influenced by infrastructure quality, with

improved infrastructure correlating to reduced

time losses.

• "Behaviour" assessed through three groups of

indicators: (1) changes in behaviour patterns,

like modal shift, management shift and spatial

shift; (2) revenue, that pertains to the ability of

individuals and businesses to meet the costs

associated with transportation and make vehicle

choices based on price considerations; (3) costs,

where assessments consider separately allocated

capital and operational costs, along with

evaluating the cost burden on service users.

Figure 6: Clarifying Data Correlations in TIM: ˝Shift2Rail˝

window.

This section of the user interface displays all

submitted use cases and statistics clarifying

regression, correlation, and elasticity coefficients,

derived from econometric models or empirical

studies utilized within the TIM model. Additionally,

we aim to present unexplained aspects of the model

for future evaluations by scientists and students.

The "Existing plans" window explain in GIS and

excel representation those plans that already

approved and aimed to explain those outcomes and

restrictions that appears in traffic during

implementation (see Figure 7)

We aimed to provide enhancements across

various categories of lanes to facilitate easier

navigation. But in fact, we were engaged in extensive

discussions with stakeholders regarding the level of

detail in mapping, as well as the implementation of

notifications for announced project changes.

Additionally, considerations such as data accuracy (in

terms of understanding of definitions), usability for

final users, and stakeholder feedback were also

prominent in our deliberations.

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263

Figure 7: Clarifying Approved Interventions in the

Transport System with GIS instruments.

3.2.2 TIM Public Access

In recent years, the development of user interfaces

(UIs) for complex systems has garnered significant

attention due to their crucial role in facilitating user

interactions and improving overall user experience.

The TIM UI, serves as a central access point for

public usage, providing access to feedback to existing

transport policy through the implementation of the

"Submit Proposal" feature (see Figure 8)

Figure 8: TIM User Interface’s prototype.

By integrating the "Submit Proposal" feature into

the TIM interface, we aim to address the question

raised in the ideation part of the research: "How might

we encourage green purchase habits of the final

consumer market?" Our goal is to encourage users of

the infrastructure to actively participate in social

dialog. This approach fosters better public

participation and allows us to demonstrate the logic

behind intervention assessments. Users will be driven

through the set of questions ensuring reliability and

viability of the submitted proposals. This initial

assessment will be shared with the final users,

fostering transparency and trust in the decision-

making process.

After the registration process, the "Submit

Proposal" feature on the user interface's main window

displays the following components:

• The "Type" ribbon allows users to access

parameters related to the specific state

intervention and switch between TIM

parameters for customized calculations.

• Additional ribbons are available for specifying

details such as prescription, location, duration,

and description of the proposal. These details

are stored and listed in the "Delivered" window

of the user interface for easy reference and

tracking.

• The delivered proposals are also shown on the

map.

Once user click the "Next" button located at the

bottom of the user interface, a run dialog is initiated

to support the manipulation of intervention

parameters. These dialogues enable the configuration

of adjustments in traffic, infrastructure, equipment,

and other input datasets, all of which are essential for

simulating transportation system outcomes.

The TIM UI offers initial calculations based on

built-in regression, correlation, and elasticity

coefficients. These calculations not only facilitate

standard computations but also aid in error

identification and the assessment of consistency in

various considerations. Users are encouraged to

provide remarks and evidence to refine decision-

making processes in line with real-world scenarios.

Each TIM model category is presented within its

own window, and users can navigate between these

windows by clicking on the respective flags. The

"Results" window compiles all delivered proposals

for policymakers, serving as a foundational resource

for discussions and public involvement.

4 CONCLUSIONS

The paper introduces a proposed user interface to

centralize information on transport system

developments and intervention outcomes,

particularly focusing on broader environmental and

regional development objectives aligned with the

European Green Deal. It emphasizes the importance

of effective communication and collaboration among

stakeholders to advance sustainable strategies in

transportation planning and execution.

The TIM user interface facilitates user interaction

and enhances the overall user experience, featuring a

"Submit Proposal" feature to encourage public

participation and demonstrate intervention

assessment logic. It allows users to customize

calculations, provide input data, and access detailed

information on delivered proposals.

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The users’ feedback allowed us to improve some

parts of the ideation process:

• Changing some parts and adding some features

(such as buttons, ribbons, and dropdown menus)

for intuitive navigation between different

sections and windows within the interface.

• Wordings improvement for clarity of interface

elements.

• Users appreciate better visual layout and design

of the interface.

Further involvement of users in submission

process would improve the functionality:

• We are aiming to increase the accuracy of

calculations, ensuring that the results align with

users’ expectations.

• We are looking for feedback that the interface

functions properly across different devices and

screen sizes, including desktop computers,

laptops, tablets, and smartphones.

• We are continuously improving performance

under different load conditions to ensure that it

can handle a high volume of user interactions

without performance degradation.

In conclusion, the research contributes to

advancing sustainable transportation strategies in

Latvia by providing a comprehensive framework for

evaluating state interventions, promoting stakeholder

engagement, and facilitating informed decision-

making processes in transportation planning and

policy formulation.

REFERENCES

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(2022). Latvian Rail Transport Sector Stakeholders'

Perception of Green Deal Policy Measures. Economics

and Business, 36, 120–133. https://doi.org/10.2478/eb-

2022-0008

Scheer, A., Noweski, C. & Meinel, C. (2012). Transforming

constructivist learning into action: design thinking in

education. Design and Technology Education: An

International Journal, 17, 8-19.

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