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{{Paper
|id=Vol-3698/paper10
|storemode=property
|title=Implementation Framework for Hyperloop Decision-Making Ecosystem
|pdfUrl=https://ceur-ws.org/Vol-3698/paper10.pdf
|volume=Vol-3698
|authors=Aleksejs Vesjolijs
|dblpUrl=https://dblp.org/rec/conf/balt/Vesjolijs24
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==Implementation Framework for Hyperloop Decision-Making Ecosystem==
https://ceur-ws.org/Vol-3698/paper10.pdf
Implementation Framework for Hyperloop
Decision-Making Ecosystem
Aleksejs Vesjolijs1,2*
1 Transport and Telecommunication Institute, Lauvas 2, Riga, LV-1019, Latvia
2 Accenture, 1 Grand Canal Square, Dublin, D04 EE70, Ireland
Abstract
The Hyperloop technology is an ultra-high-speed transportation mode proposed and
envisioned by Elon Musk in 2012 by reimagining vactrains. It leverages pods moving un super-
sonic speed in 3-m vacuum tubes using magnetic levitation for propulsion. Technology
Readiness Level is TRL 5 according to HORIZON 2020 standards. At the end of 2023, the
shutdown of Hyperloop ONE, one of the largest companies in the Hyperloop industry,
emphasized significant challenges in the implementation of Hyperloop technology and revealed
dynamic nature of Hyperloop project. This study identifies a research gap: the absence of digital
decision-making systems specifically designed for Hyperloop projects. To address this gap, the
research presents a six-stage implementation framework for an Hyperloop decision-making
ecosystem. The study involves the creation of a functional block diagram, the design of a
technical solution blueprint, and the proposal and validation of the implementation framework
using a unit testing method on an Hyperloop project case. The proposed technical solution
integrates several advanced technologies, including the Snowflake data warehouse, Streamlit,
Vensim, Microsoft PowerBI, GPT-4, PostgreSQL, and custom Python applications. The
framework's validation process involved iterative testing and refinement, resulting in an
increase in the project maturity model from the level 'Defined' to the 'Capable'.
Keywords
Hyperloop, software ecosystem, data engineering, dataops, decision-making, digital
transformation 1
1. Introduction
Hyperloop marked revolution in transportation since its complex and innovative concept
was introduced by Elon Musk back in 2012 that was later documented in Hyperloop Alpha
Paper publication by Tesla (SpaceX) [1]. Technology initially is envisioned as ultra-high
speed transportation mode which is able to transfer passengers and cargo on supersonic
speed overcoming Kantrowitz limit [2]. According to Hyperloop Alpha Paper, a new
mobility solution proposed to use vacuum tubes of 3.3 m diameter and capsules with
Baltic DB&IS Conference Forum and Doctoral Consortium 2024
∗ Corresponding author.
vesjolijs.a@tsi.lv (A. Vesjolijs)
0009-0008-2845-7498 (A. Vesjolijs)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
frontal area up to 4 m2. These capsules are propelled and guided along a track, it was
designed with distributed acceleration points to minimize the infrastructure, only
requiring energy over a small fraction of the track. The Hyperloop transportation system
(HTS) is considered a promising alternative to short-haul flights, offering reduced travel
times and lower fuel consumption per passenger revenue kilometer [3].
The technology is currently at a low maturity level from engineering, operational, and
cost perspectives [4]. Significant technological, regulatory, planning, financial, and
environmental issues must be resolved before HTS can achieve commercial viability. The
successful deployment of Hyperloop systems requires an integrated approach that
leverages advanced digital tools and technologies to stream-line decision-making
processes.
Research aims to propose a structured approach for integrating various digital tools
and technologies to support decision-making processes in Hyperloop projects. The goal is
to design a framework for implementing Hyperloop decision-making ecosystem.
Research questions.
Q1. What are the key components necessary for developing and deploying a digital
decision-making ecosystem specifically designed for Hyperloop technology projects?
Q2. What strategy(s) can be adopted to implement decision-making ecosystem?
Q3. How does the proposed Hyperloop decision-making ecosystem improve project
maturity levels?
Research tasks and objectives.
O1. Review existing literature on Hyperloop projects, frameworks and best practices
in digital systems, particularly in the context of complex techno-logical projects and define
decision-making ecosystem.
O2. Identify and define key components, technologies, and methodologies that are
relevant to the implementation of digital ecosystems.
O3. Establish the relationships and interactions between Hyperloop decision-making
ecosystem components within the framework.
O4. Develop an implementation framework.
O5. Conduct framework approbation.
2. Research Methodology
The research methodology for the development of implementation framework for
Hyperloop decision-making ecosystem is shown in Figure 1. The state-of-the-art contains
a problem definition, literature analysis to define research gap, ecosystem definition.
Figure 1: Research methodology overview.
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The Functional Block Diagram (FBD) method is selected to map underlying ecosystem’s
processes in structured approach. It serves as a basis for the technical solution blueprint.
The design step involves identifying the key components and technologies necessary for
development and deployment of a digital decision-making ecosystem. Step 3 is the
development of implementation framework based on the conceptual model. It contains
framework design, integration and deployment strategy, and discussion on potential
challenges. Step 4 is framework approbation and conceptional adoption using Python
programming language unit testing method. The proposed research methodology is aimed
to achieve research goals, addressing the complex and multidisciplinary nature of
implementing digital decision-making ecosystems for Hyperloop projects.
3. State-of-the-Art
In this Section, research state-of-the-art is presented. Hyperloop decision-making
ecosystem is described, concept is discussed, and Functional Block diagram is de-signed. It
contributes to completing Research Objective 1 (O1).
3.1. Problem Definition
Due to technology complexity [3], dynamic nature of Hyperloop project [5], challenged
decisions [6], the Hyperloop is still in TRL 5 according to Horizon 2020 standards [7]. The
implementation of Hyperloop systems present complex decision-making challenges that
underlines necessitate a robust ecosystem for effective management that answers the
following success factors: namely reliability, scalability, technical feasibility, quantum
factor, safety, regulatory approval, social acceptance, environmental sustainability,
infrastructure integration and usability factors [8]. Study has filtered out and analysed 95
papers in Scopus DB (keywords – hyperloop, hyperloop system(s), hyperloop project(s),
hyperloop decision-making, hyperloop challenges, et al) and identified research gap, that
currently there is no hyperloop decision-making ecosystem software that can
improve decision-making process for Hyperloop project, by assessing success factors of
Hyperloop implementation for specific case.
3.2. Hyperloop Decision-Making Ecosystem
To address the research gap, the study proposes the concept of the Hyperloop Decision-
Making Ecosystem and implementation framework for future product deployment. The
Hyperloop Decision-Making Ecosystem is a technology platform that applies the enhanced
ETL process with use of Generative AI. Research proposed refined ecosystem definition
for ecosystem specific for Hyperloop project based on literature analysis: a networked
community of actors (organisations, software products, individuals) supported by an
underpinning technological platform in the same environment that enables actors to process
multivariate data, produce knowledge, extract insights, foster innovation, create value, and
support decision-making.
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Ecosystem’s functional block diagram (FBD) is shown on Figure 2. Initial INPUT 1-7
correlates to multivariate input sources (1 – Generative AI model, 2 – external database(s),
3 – internal database(s), 4 - sensors, 5 – satellite data, 6 – industry reports, 7 – user input).
The Hyperloop decision-making ecosystem is a software framework designed to
leverage big data for the efficient planning and implementation of Hyperloop project. As a
big data product, this ecosystem integrates diverse datasets, employing advanced
analytics, machine learning, and predictive modeling to facilitate informed decision-
making. The ecosystem is structured to handle the complexity and scale of data generated
by various actors (see INPUTS 1-7 above). By transforming raw data into actionable
insights, the ecosystem enables stakeholders to make data-driven decisions that enhance
safety, efficiency, and cost-effectiveness. Advanced analytics tools process data to identify
patterns, predict potential issues, and optimize performance. Ecosystem finite state is data
visualization and system modelling which accessed by end users of the system.
Figure 2: Hyperloop decision-making ecosystem functional block diagram.
4. Design
In this Section Hyperloop decision-making ecosystem technical solution blueprint is
prosed, and components are identified and defined. It contributes to the completion of
Research Objectives 2 and 3 (O2 and O3).
As a big data product, Hyperloop decision-making ecosystem utilizes Extract-
Transform-Load approach [9], enhanced with Generative AI (GenAI), namely Extract-
Generate-Transform-Load. The ecosystem’s adaptive structure promotes scalability,
flexibility, enabling continuous improvement and development in response to the evolving
needs of the Hyperloop industry.
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The technical solution presented in Figure 3 is a sophisticated system designed to
streamline the collection, processing, analysis, and visualisation of data pertinent to
decision-making in Hyperloop technology according to FBD (Figure 2).
Figure 3: Hyperloop decision-making ecosystem technical solution blueprint. Data load
stages are presented in green colour and component codes are highlighted in blue colour.
Detailed ecosystem’s components are presented in Table 1. The framework starts with
the ingestion of diverse datasets through external databases, web crawlers, and API
connectors. Once data is collected, a dedicated component utilising AI technology
interprets and extracts relevant information from various. This data is transferred to a
centralised data warehouse managed in Snowflake. In the next phase, data is cleaned and
enriched to improve its quality with the help of AI. To supplement decision-making in
cases when data is missing, synthetic data is generated based on existing statistical
patterns using Generative AI. The architecture emphasises data resilience through
multiple backups within Snowflake.
System dynamics models used to simulate various scenarios, thereby aiding in the
prediction of outcomes based on different Hyperloop design and operational strategies.
The final stage is data visualization using PowerBI interactive dashboards, built on top of
Streamlit applications.
5. Implementation Framework
In this Section Implementation framework is proposed, implementation challenges are
discussed, and framework approbation is conducted. It contributes to the completion of
Research Objectives 3, 4 and 5 (O3, O4, O5).
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Table 1
Hyperloop decision-making ecosystem components
Component Name Technology Description
COM1 Data extraction ChatGPT 4 Extracts from GenAI knowledge base.
COM2 API connector Python Connects to external APIs to fetch data
directly into the system from various
Internet of Things devices.
COM3 Web crawler Python Gathers data from various online sources
to be processed and analysed.
COM4 External data PostgreSQL External data collected from various
source sources for processing.
COM5 Data transfer Snowflake Transfers extracted data into Snowflake
for centralized data handling.
COM6 Database backup Snowflake Provides backup services for data.
COM7 Data enrichment ChatGPT 4 Enhances data quality by adding missing
information or correcting errors.
COM8 Synthetic data Python Generates artificial data based on
generation patterns learned from real data.
COM9 Data cleanup ChatGPT 4 Cleans and prepares data for analysis,
removing errors and inconsistencies.
COM10 Python Python Application built in Python to process
application and handle data operations.
COM11 Data transfer Python Facilitates the movement of processed
data to different parts of the system.
COM12 Data analytics Snowflake Component that analyses big data,
applies machine learning and advanced
analytics
COM13 Data processing, Snowflake Processes and further transforms data
transformations applying business logic.
COM14 Database backup Snowflake Provides backup services for data.
COM15 Simulation data ChatGPT 4 Uses advanced models to simulate data
scenarios for further analysis.
COM16 Database backup Snowflake Snowflake database containing
production data gathered in all previous
steps.
COM17 API enrichment ChatGPT 4 Uses APIs to enrich data with additional
external insights.
COM18 Data enrichment ChatGPT 4 Further enriches data post-transfer with
additional context and information.
COM19 Database backup Snowflake Provides backup services for data.
COM20 Application Streamlit A bridge between data and
visualizations.
COM21 System Vensim System modelling core that will be used
modelling for visualization in COM22.
COM22 Dashboard Power BI Creates interactive dashboards for data
visualization analysis and reporting.
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5.1. Framework Overview
Existing software development frameworks, methods, and paradigms are analysed. Agile
and Waterfall do not exactly fit given ecosystem, because they are mainly aimed at the
team’s approach to managing project tasks, rather specific digital system implementation
framework. They can be adopted to develop specific components of ecosystem as different
teams can be involved for their development. For example, COM6 and COM16 can be done
by one team using Waterfall method and COM1-COM4 can be implemented by another
team which adopted Agile. Proposed framework utilizes hybrid methodology adopting
selected methods features (Table 2).
Table 2
Software development methods adopted in the framework
Method Features selected for framework Source
DevOps Continuous integration/deployment, automated testing, [10]
collaboration.
Lean Value stream mapping, continuous improvement. [11]
DataOps Collaboration, rapid data delivery, integration, automation. [12]
RAD Prototyping, user feedback, iterative. [13]
PRINCE2 Roles-responsibility model, control, stage-based process. [14]
Data Data as a product, decentralized data ownership, federal [15]
mesh computational governance.
Six Sigma Data-driven approach, control, quality improvement. [16]
The framework is presented in Figure 4. It is purposed for evaluating and guiding the
deployment of Hyperloop decision-making ecosystem. At the end of each cycle project
maturity model is reevaluated according to Capability Maturity Model Integration (CMMI)
[17].
Figure 4: Hyperloop decision-making ecosystem implementation framework.
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Implementing a digital ecosystem for Hyperloop projects presents several challenges.
Integration of diverse and vast data sources can pose a challenge as Hyperloop systems
generate large amounts of data from various sensors, operational logs, and maintenance
records. Ensuring data quality and consistency across these different sources is crucial for
accurate analysis and decision-making, involving data cleaning, transformation, and
enrichment processes that can be resource-intensive and complex to manage, however
integrated in the ecosystem by design (Figure 3, COM7-9).
Ecosystem and implementation framework can be considered as part of digital
transformation processes of the society and introduces shift to digital business system
from monolithic by its design. It incorporates such technologies as AI, Big Data, Data
Analytics, Internet of Things, Cloud Computing, Data Warehousing, and others (Table 1)
which are essential drivers of digital transformation. Digital transformation has an
influence on human lives, industrial processes, circular economy, various systems, and
their Key Performance Indicators. Data privacy and cybersecurity of ecosystem affect all
mentioned above impacts [18]. Therefore, each component of ecosystem must answer
regional data protection policies, such as General Data Protection Regulation (GDPR) [19]
in EU and incorporate best software development practices in mitigating cybersecurity
risks. Further, pursuit to comply with European strategy for data [20] principles must be
considered as summarized in Figure 5.
Figure 5: European strategy of data for Hyperloop decision-making ecosystem [20].
Another challenge is the need for seamless collaboration among a wide range of
stakeholders, including engineers, regulatory bodies, investors, and business users. The
project leadership must provide transparent and accessible means for stakeholders to
monitor system performance, simulate scenarios, and make informed decisions. The
implementation process must address the scalability and flexibility of the ecosystem to
accommodate the evolving needs of the Hyperloop industry. To conclude, the success of
the Hyperloop decision-making ecosystem depends on effectively managing these
challenges to create a scalable, efficient, and reliable framework for data-driven decision-
making.
5.2. Framework Approbation
Framework approbation is conducted on hypothetical case of Hyperloop Baltics
implementation, for which synthetic data was generated and included in Python script
(Appendices A, files tests/unit/synthetic_data.py and
tests/unit/synthetic_maturity_data.py).
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Figure 6: Implementation framework validation using unit testing strategy and results.
Implementation framework approbation is conducted using unittest method in Python
programming language pytest library on synthetic data generated for Hyperloop Baltics
project. Unit testing strategy and results are presented in Figure 6, source code is publicly
available on GitHub, see Appendices A. In scope of tests, 43 unit tests were executed with
SUCCESS status. Project maturity level check indicates an increase of maturity model
CMMI metrics which include Hyperloop project success factors (Section 3.1). As a result,
project maturity model increased from defined to capable and each of underlying
contributing factors increased as well.
6. Conclusions
The research goal is completed by proposing a framework (Figure 4) for implementing a
digital decision-making ecosystem designed specifically for Hyperloop projects (Figure 3).
The framework integrates ETL process enhanced with Generative AI envisioned in
solution components (COM1-COM22, Table 1). Structured technical solution blueprint is
based on FBD (Figure 2) correlating to system inputs, outputs, and desired result which
contributes to completion of research aim. By unifying diverse data sources into a
centralized platform, the ecosystem facilitates informed decision-making, predictive
maintenance, and process optimization, providing knowledge and insights to end users.
Implementation framework guides ecosystem development and integration. It is scalable
and flexible as it incorporates selected features from seven software development
methods (Table 2) and can be adopted to other digital systems with similar parameters.
The approbation is conducted using unit tests on synthetic data (Figure 6), contributing to
implementation strategy adoption to given use case. The output of each implementation
cycle is revised project maturity model according to CMMI.
Contribution. Research contributes to the field of telematics and logistics, data
engineering and project management, within the context of Hyperloop technology. It
supports EU digital targets 2030 [21] by enabling digital transformation of businesses and
European strategy for data [20]. Framework is designed for use by Hyperloop project
stakeholders as guiding steps for digital system deployment and integration. It is
committed to increasing the Hyperloop technology project’s chances of success given the
dynamic and innovative nature of HTS and ability to adapt to scope changes.
Limitations. Synthetic data is used for implementation framework approbation.
Future steps. The next step involves further tests and framework approbation using
real world data for various real Hyperloop projects from different regions and using
different methods. It is planned to apply the given framework to the Hyperloop Baltics
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startup project, aiming to implement Hyperloop technology for passenger and freight
transport in the Baltic and Nordic regions. This development will contribute to the green
and digital transformation aligned with the EU Mobility Strategy [22].
Acknowledgements
Research supervisor – Professor Dr.Sc.Ing Mihails Savrasovs, Transport and
Telecommunication Institute.
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A. Online Resources
The code for framework approbation can be accessed via public GitHub repository at
https://github.com/pirrencode/tsi_hl_framework_implementation/tree/main.
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