Emerging Technologies in the Era of Digital Transformation: State of the

Art in the Railway Sector

Dietmar M

oller

, Lukas Ifﬂ

ander

, Michael Nord

, Patrik Krause

, Bernd Leppla

, Kristin M

uhl

Nikolai Lensik

and Peter Czerkewski

Clausthal University of Technology, Institute for Mathematics, Erzstr. 1, 38678 Clausthal-Zellerfeld, Germany

German Centre for Rail Trafﬁc Research (DZFS), August-Bebel-Str. 10, 01219 Dresden, Germany

IABG mbH, Einsteinstr. 20, 85521 Ottobrunn, Germany

3DSE Management Consultants GmbH, Seidelstr. 18a, 80335 M

unchen, Germany

Fraunhofer AISEC, Breite Str. 12, 14199 Berlin, Germany

Institute of Railway Technology GmbH, Carnotstr. 6, 10587 Berlin-Charlottenburg, Germany

nikolai.lenski@aisec-fraunhofer.de, pc@bahntechnik.de

Keywords:

Emerging Technologies, New Technologies, Railway Sector, Maturity Level Analysis, SWOT Analysis,

Digital Transformation.

Abstract:

Emerging technologies and digital transformation are essential indicators in today’s industrial sectors. The

railway and public transportation sectors are undergoing a substantial transformation through digitalization and

emerging technologies. However, little is known about the manifold of applications in the industrial sectors and

progress so far. In this study, we consider various emerging technologies and proposed use-cases. Next, using a

two-step survey and a SWOTA analysis, we analyze both sector’s maturity levels regarding these technologies.

The analysis indicates technologies currently permeating the analyzed sectors, shows discrepancies between

technology application and knowledge, and multiple issues hamper their implementation.

1 INTRODUCTION

The digital transformation transcends currently prac-

ticed roles in development and design, marketing and

sales, customer services, and many other areas. In this

regard, digital transformation begins and ends with

enabling intelligent digital technologies in design and

development. Moreover, it takes people’s interaction

from paper over spreadsheets and intelligent applica-

tions to digitally manage all business processes and

tasks. However, digital transformation also causes a

disruption (M

oller, 2020).

Emerging technologies is a term used to describe

technological advancements, but may also refer to

the continuing development of existing technology.

Against this background, the term emerging technol-

ogy commonly refers to a currently developing tech-

nology available within a short period.

The railway and public transportation sectors un-

dergo a signiﬁcant transformation through digitaliza-

tion and emerging technologies. However, little re-

search on the scale of the application of emerging

technologies exists. Therefore, we initiated a project

to, on the one hand, evaluate the current conditions of

emerging technologies and possible opportunities for

their usage, and on the other hand, to fathom the rea-

sons why the sectors do not apply seemingly promis-

ing technologies.

Against this background, we evaluated the current

state of emerging technologies in the railway and pub-

lic transportation sectors by applying a multi-method

approach to assess, on the one hand, the everyday use

of emerging technologies and, on the other hand, op-

portunities for their potential usage. To this end, we

describe a two-stage survey comprising an online sur-

vey and an interview survey. We evaluate the replies

to the surveys and analyze, e.g., contradictory an-

swers and inconsistencies.

In the remainder of this work Section 2 describes

the considered emerging technologies, accompanied

by their placement inside the analyzed sectors. Next,

we describe the survey method in Section 3 and eval-

uate the results in Section 4. Section 5 concludes the

paper and gives an outlook on future work.

Möller, D., Ifﬂänder, L., Nord, M., Krause, P., Leppla, B., Mühl, K., Lensik, N. and Czerkewski, P.

Emerging Technologies in the Era of Digital Transformation: State of the Art in the Railway Sector.

DOI: 10.5220/0011141900003271

In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2022), pages 721-728

ISBN: 978-989-758-585-2; ISSN: 2184-2809

721

2 EMERGING TECHNOLOGIES

Emerging technology is a term used to describe the

continuing development of technology to expand or

enable technical features or applications. We excpect

such technologies’ availability in the next ﬁve to ten

years, such as advances in semiconductors through

nanotechnology to overcome the Heisenberg uncer-

tainty relation or advances in product design through

new materials (Soares et al., 1997).

Emerging technologies are manifold. We brieﬂy

introduce these technologies that we see relevant for

the studied sectors. We present the technologies in

general and later place them inside the railway sector.

Artiﬁcial Intelligence (AI): Describes the ability of

machines to “think” like humans. “Thinking”

upon others comprises awareness of the surround-

ing environment and acting based on this environ-

ment.

Additive Manufacturing: Manufacturing process

creating 3D objects through layered production.

Big Data and Analytics: Enables the processing of

large and complex data sets that are difﬁcult to

analyze with traditional tools.

Blockchain: A directory of information crypto-

graphically hashed and authenticated within a net-

work of participants using cryptographic proof for

secure interacion.

Cloud Computing: Provides infrastructure, applica-

tion, and service resources via the Internet. In this

context, cloud computing enables complex and in-

novative IT infrastructures to integrate into com-

panies quickly.

Cloud Services: Represent infrastructure, platforms,

software, or technology hosted by a third party

and made available to potential users over the

Internet, e.g., Infrastructure- (IaaS), Platform-

(PaaS), Software- (SaaS), and Function-as-a-

Service (FaaS).

Container: A form of secure logical process isola-

tion on operating system level.

Fiber Optics: Technology that uses glass and plastic

ﬁbers to transmit data loss-free as light pulses over

large distances or allows measurements of, e.g.,

vibrations in proximity to the ﬁre.

Internet of Things (IoT): Represents a self-conﬁg-

uring network where sensors and actuators can be

connected to the Internet. IoT can manage and

monitor any number of objects.

Machine Learning (ML): A computer system with

access to data to learn using different learning

methods. Machine learning methods are:] Unsu-

pervised Learning, Supervised Learning, and Re-

inforcement Learning.

Networks Function Virtualization (NFV): Re-

places pysical network functions like routers

or ﬁrewalls with virtualized network functions

(VNFs).

Software Deﬁned Network (SDN): Network archi-

tecture separating control and data planes.

Virtualization: Software-based or virtual represen-

tation of appliances.

Wireless Sensor Networks (WSN): WSN connect

many sensor nodes in a communication infras-

tructure to monitor conditions at different loca-

tions. Connection establishment uses ad-hoc rout-

ing.

5G: Fifth generation of mobile wireless broadband

technology.

The preceding introduced emerging technologies

that can be used and previously used technologies and

future use in various industrial sectors. Indeed, these

emerging technologies are ideal for use in the railway

and public transportation sectors and infrastructure.

Railway Sector Applications

The technologies we just introduced are not merely

“hype technologies” in the general IT sector. Either

an existing implementation or statements of intention

for introduction exist for every technology. In the fol-

lowing, we will show at least a scientiﬁc publication

or a press statement by a major stakeholder for each

technology. Some applications cover multiple tech-

nologies and demonstrate their interactions.

Deutsche Bahn AG uses Artiﬁcial Intelligence and

Big Data Analytics to predict train delays. To this

end, (Hauck and Kliewer, 2020) employ a supervised

Machine Learning model based on a neural network

and achieve promising results.

Additive Manufacturing became relevant for re-

placement parts, especially non-safety relevant com-

ponents like driver armrests. However, (Fu and

Kaewunruen, 2021) note in their survey of the appli-

cation in the sector that it is still in the exploratory

stage. This is especially true for safety-critical com-

ponents like rail track infrastructure components.

(Kuperberg et al., 2020) introduce an application

for a distributed ledger (Blockchain) in interlocking.

Instead of a central authority (e.g., the classical sig-

nal box), trains and infrastructure components (e.g.,

switches) interact using the decentralized information

and agree on driveways and access to track segments.

ICINCO 2022 - 19th International Conference on Informatics in Control, Automation and Robotics

722

The authors consider their work suitable for branch

lines and a backup for regular lines.

Deutsche Bahn moved their regular IT to the

Cloud and completed the process by 2020 (Koenen,

2020). They use a comprehensive set of available

cloud technologies, including various Cloud Comput-

ing, Virtualization, Cloud Services, and Container

solutions, and their multi-cloud strategy comprises

the Amazon and Microsoft clouds. While the left-

wing NGOs “Prellbock Altona” and “Bahn f

ur Alle”

tried to cause an outrage that “no switch could be

switched” (Jung and Wolf, 2020) without this system,

this is not the case. Signaling and Interlocking remain

on their localized infrastructure. Especially, older me-

chanical and electro-mechanical interlocking systems

run entirely independently of any IT infrastructure.

Fiber Optics act as monitors for railway infras-

tructure components. E.g., (Wheeler et al., 2019) use

ﬁber optic sensing to measure the stress in a rail. Fur-

ther experimental applications detect persons on the

tracks or continuously locate trains without using axle

counters. However, since these approaches are usu-

ally based on Machine Learning models, they lack

proper authorization for production use.

IoT in railways comprises a more extensive set

of approaches. (Brandm

uller, 2022) from Deutsche

Bahn introduced his passenger counter open-source

tool running on an ESP32 microcontroller to count

passengers based on the number of nearby Blue-

tooth and Wi-Fi devices and report the results via Lo-

RaWAN. Thereby, this approach also becomes an ex-

ample for WSN. Meanwhile, (Gbadamosi et al., 2021)

identiﬁed and discussed rail asset management prob-

lems and proposed an implementation strategy for

IoT-based predictive asset maintenance in the UK.

(Ruscelli et al., 2019) discuss the adaptation and

introduction of SDN and NFV in railway control sys-

tems and suggest two applications: an SDN failure

recovery and a secured NFV-based maintenance ser-

vice. Such systems can also become a reason to intro-

duce NaaS, e.g., to provide failure recovery networks.

5G will be the next standard for railway wire-

less communication. The 5G-based future railway

mobile communication system (FRMCS) will replace

the current 2G-based GSM-R. The sector decided

to forego the initially proposed 4G-based LTE-R for

future-proof solutions (Pierre and Christophe, 2019).

While the different technologies ﬁnd application

in other areas of the railway and transport sectors and

are at seemingly different stages of maturity, the ex-

istence of either scientiﬁc publications or statements

of intent regarding their application shows that these

technologies spill over to the relevant sectors and are

worthy of considering in our survey.

3 METHODS

We used two survey methods were to identify the em-

bedment of emerging technologies in the German rail-

way and public transportation sectors. To this end,

we used a two-stage survey to determine their cur-

rent market share obstacles for their introduction. We

applied a quantitative online survey and a qualitative

semi-structured interview method.

3.1 Online Survey

We conducted an online survey as the ﬁrst part of our

two-stage process. Answers refer to maturity levels in

a ﬁxed range between 0 and 5 regarding the emerging

technology questioned emerging technologies. Sub-

sequent expert interviews followed in a second step

(see Section 3.2) to increase individualization.

We chose Limesurvey to realize the survey based

on availability, acceptance, ease of use, and compli-

ance with data protection requirements. The online

survey consisted of two parts with speciﬁc questions

on cybersecurity (not addressed in this paper) and

emerging technologies. For each emerging technol-

ogy mentioned in Section 2, we inquired about the

know-how, the applicability, the temporal and perma-

nent inﬂuence of adaption, and the risk. We used

a linear answer scale referring to the maturity lev-

els. In total, the survey comprised 60 questions.

The question about know-how served as a ﬁlter, i.e.,

only technologies the participant had any knowledge

of, were displayed in the following questions. Fi-

nally, we recorded demographic data on the company,

such as employee numbers and turnover. In addi-

tion, the company had to map itself to the respective

sub-sectors of the railway industry: (1) railway op-

erators, (2) rail-way infrastructure manager, (3) rail-

way energy supplier, (4) railway vehicle manufac-

turers and maintenance workshops, (5) railway in-

frastructure supplier, (6) transit authorities and public

transport operators, and (7) distributors.

3.2 Interview Survey

The questionnaire of the interview survey served as a

guide for the follow-up survey. The opportunity is

used here to delve deeper into selected topics with

open-ended questions to compensate for the disadvan-

tage of the closed questions from the online question-

naire. For this reason, the form of the semi-structured

interview (see for example (Adams and others, 2015))

was chosen. As a compromise between the large

number of relevant topics that could have been deep-

ened in such an interview and the consideration of the

Emerging Technologies in the Era of Digital Transformation: State of the Art in the Railway Sector

723

20% 40%

60%

80% 100%

WSN

Virtualization

Open Data

AI & ML

IoT

Fiber Optics

Cl. Services

Cl. Comput.

Blockchain

Big Data

Addit. Manuf.

unknown low medium high very high

Figure 1: Knowledge about emerging technologies.

time of our interview participants, a total duration in-

cluding welcome and farewell of about one hour was

planned. Due to the pandemic situation and the phys-

ical distance to the interviewees, the interviews were

conducted virtually.

Essentially, the interviews pursued three core

goals: Clarifying contradictions and abnormalities,

deepening speciﬁc questions regarding the degree of

maturity, and identifying strengths, weaknesses, op-

portunities, and threats (SWOT analyses) of the cy-

bersecurity and selected emerging technologies.

The main question for collecting data for the

SWOT analysis was ”why,” i.e., investigating the ac-

tual cause behind the status quo, which was explored

with every interview question. Questions were asked

about achieving a good status and signiﬁcant support

factors for the strengths and opportunities. In turn, a

self-assessment of existing barriers to the introduction

and use of emerging technologies served to explore

weaknesses (barriers as internal factors) as well as

risks (in the case of external factors). The necessary

measures and assistance to overcome these obstacles

were also explored. Finally, data were collected for

the SWOT analysis of selected emerging technologies

in which the surveyed companies already showed ex-

tensive experience. To do so, the strengths, opportuni-

ties, weaknesses and threats of the affected technolo-

gies were promptly addressed.

The interview questions for each company were

adapted to the results obtained from the online survey

to achieve the goals described. Criteria were created

to deepen the topics based on the answers to one or

more questions, which were the most promising re-

garding the above goals. This is exempliﬁed by the

described selection of technologies a company should

be asked about, based on their experience with the rel-

0% 20% 40%

60%

80% 100%

WSN

Virtualization

Open Data

AI & ML

IoT

Fiber Optics

Cl. Services

Cl. Computing

Blockchain

Big Data

Addit. Manuf.

no plans no plans yet plans pilots in use

Figure 2: Usage of emerging technologies.

evant technologies.

The interview partners were chosen based on var-

ious criteria from the participants in the online sur-

vey to ﬁnd a representative subset. To this end, we

covered all sectors (see Section 3.1) and adjusted the

number of interviewees per sector to the relative size

of each sector.

4 RESULTS

4.1 Online Survey

First, the level of knowledge about emerging tech-

nologies and their possible use in the railway sector

was of signiﬁcant interest. Figure 1 shows the re-

sult of the knowledge assessment regarding emerging

technologies.

As shown in Figure 1, for high and very high

percentages, the level of knowledge for Cloud Com-

puting, Virtualization, and Fiber Optics is most pro-

nounced. Emerging technologies with the lowest

level of expertise represent Additive Manufacturing,

Blockchain, and Wireless Sensor Networks. For as-

sessing possible usage of emerging technologies, their

gradation ranges from not planned to already in use,

as shown in Figure 2.

Comparing piloting and in-use results in Figures 1

and 2, it is evident that Cloud Services, Fiber Op-

tics, and Virtualization already are in widespread use,

which shows:

Virtualization: is in advanced usage in the partici-

pating railway subsectors. It makes it easier for

administrators to manage services and networks

in many respects by abstracting the lower func-

tions in the network. However, the knowledge of

ICINCO 2022 - 19th International Conference on Informatics in Control, Automation and Robotics

724

Figure 3: Maturity level in the railway sub-sectors based on

categorization.

this technology is not as pronounced as the use

suggests. Furthermore, virtualization in connec-

tion with SDN / NFV abstracts the lower levels of

the network, offering central control without man-

ual access to the physical network components.

Consequently, one decouples oneself from the un-

derlying technology and replaces it with easy-to-

administer services. These services increasingly

replace the previously required knowledge and

explain the discernible difference between use and

knowledge.

Cloud Services: are another technology that needs

to consider in more detail. A high proportion of

realization opposes a less pronounced knowledge.

In the context of cloud services, the question must

be asked to what extent in-depth understanding of

the technology is still required. Cloud services are

outsourced by the railway sub-sectors, provided

via a Service Level Agreement. The railway sub-

sector over-buys the loss of knowledge a service

that operates these services in a kind of black box

that is not transparent for the railway sub-sector.

This explains why the knowledge lags behind the

degree of use.

Another question arises in the survey: What role

does railway sub-sector size [number of employees]

play on knowledge and use of emerging technolo-

gies? Figure 3 shows the distribution of railway

sub-sectors categorized as small, medium, and large

[< 50· · · > 10.000] in the railway infrastructure man-

ager (RIM) and public transport operators (PTO). We

selected these three sub-sectors out of 8 because at

least ten companies from each sector took part in the

online survey.

Figure 3 shows that primarily small companies

took part in the RIM sub-sector, while the PTO sub-

sector includes medium-sized and large companies.

Therefore, both subsectors investigated the level of

knowledge and usage of emerging technologies more

in detail. Figure 4 shows the level of expertise in

boxplots about the 12 selected emerging technologies

within the RIM sub-sector.

Figure 4 shows that the levels of knowledge range

between the lower threshold value / lower quartile at

0.0 “is not known” and reach a maximum of approx.

2.5 “medium,” without going into outliers. In com-

parison, Figure 5 shows the level of knowledge in the

public transport operator’s sub-sector.

Against this background, knowledge does not al-

ways correspond to the level of commitment, e.g., us-

ing emerging technologies and the possibility of out-

sourcing. It should be noted; that small-sized com-

panies fall behind both knowledge and assessment of

possible usage, which can be explained by consider-

ing capacities and resources. However, it can be seen

in small companies that there is a need to catch up

with emerging technologies, which offers them bet-

ter knowledge about them and examples of best prac-

tices.

The distribution is recognizably different here.

Figure 4: Knowledge in emerging technologies in the RIM

sub-sector.

Figure 5: Knowledge in emerging technologies in the PTO

sub-sector.

Emerging Technologies in the Era of Digital Transformation: State of the Art in the Railway Sector

725

The values of knowledge are about one level higher

than in the RIM sub-sector. Medium-sized and larger

companies acquire higher expertise than smaller com-

panies in the RIM sector. The reason is that the small

companies in the RIM sub-sector cannot deal with

emerging technologies. In contrast, medium-sized

and large companies have more time and staff to ac-

quire knowledge. This outcome is also a result of the

online survey on emerging technologies in the overall

railway sub-sectors.

A direct comparison shows that medium-sized and

large-sized companies assume more application pos-

sibilities for emerging technologies or are already us-

ing them. While only two technologies are being pi-

loted or used in the RIM sector (cloud services and

virtualization), there are already 5 in the public trans-

port sub-sector. Furthermore, It is interesting that

blockchain will not play a role in the PTO sub-sector.

The reason is that the small companies in the RIM

sub-sector have no capacity/resources to deal with it

or do not assume any value “Not intended for use” and

“imaginable, but not in the planning stage”). In con-

trast, medium-sized and large-sized companies un-

dergo more efforts to keep up with emerging tech-

nologies and acquire knowledge.

4.2 Interview Survey

The qualitative nature of the interview results and

their semi-structured implementation makes an eval-

uation based on individual questions impractical. In-

stead. Thus, we carried out an assessment to clarify

contradictions and abnormalities by deepening spe-

ciﬁc questions about selected emerging technologies

and enabling data collection for a SWOT analysis.

Twenty-four companies of the 60 online survey

participants agreed to participate in the interview sur-

vey. Of these twenty-four companies, 12 were se-

lected for an in-depth discussion (criteria used see

Section IV), paying attention to a representative selec-

tion of online participants. Companies that signed up

for the follow-up interview but did not respond were

replaced with suitable candidates. For each of the in-

terviewed companies, an individual questionnaire was

created consisting of an initial question about the mo-

tivation to participate, six to ten key questions. We de-

pend on the company’s online survey results to spec-

ify its previous answers and questions on emerging

technologies in the respective railway sub-sector.

The questions concerning emerging technologies

can be divided into two main directions: 1) identify

reasons why emerging technologies are not used and

2) understand the level of the potential use of certain

emerging technologies. While 1) can be answered

with one question, 2) must be corroborated through

several questions.

Due to anonymization, the data of sector “Others”

and a split by interviewed participants by technolo-

gies are not displayed by sub-sectors. Some tech-

nologies are only relevant for one participant. The

reason for this is that the sample we chose the par-

ticipants from was limited regarding outlining re-

sults from the online survey. Therefore, only a brief

overview of the number of participants by technology

can be given: Regarding Additive Manufacturing, Big

Data, Blockchain, Cloud Computing, Cloud Services,

Fiber Optics, Internet of Things, AI/Machine Learn-

ing, Open Data and Virtualization/SDN/NFV 1 to 3

companies were interviewed. Every participant was

interviewed about at least two different technologies.

For 5G and Wireless Sensor Networks, no interview

participant was identiﬁed. One participant was inter-

viewed, answering the online survey that no emerging

technologies were used.

The interviews lasted between one and two hours,

depending on the company. The interviewers took

minutes in the form of bullet points. It was strik-

ing that regardless of the technology, similar answers

were given. Table 1 shows an example resulting from

the interview part per question, and Table 2 shows

SWOT data for each selected new technology. In the

following, we will correlate the results from the inter-

view survey with the results from the online survey.

4.3 General Findings and Limitations

As an essential issue of the research, we examined the

state of the art of emerging technologies in the rail-

way sub-sectors in the era of digital transformation.

We identiﬁed outliers from the online questionnaire.

This qualitative picture across all interviews provides

background information on the online survey results.

In this regard, the following four statements can be

made about emerging technologies in the railway sec-

tor:

1. The extent of certain technologies’ application

varies in a broad range from not used to fully im-

plemented – even within the same sector.

2. The general statement that emerging technologies

are favorable for the company, its customers, or

employees can be derived from the strengths and

opportunities of emerging technologies. For ex-

ample, participants see AI and Big Data as beneﬁ-

cial as it helps to speed up processes and identify

new business models or outline business issues.

In addition, it can be mentioned that certain tech-

nologies are even more beneﬁcial when used in

ICINCO 2022 - 19th International Conference on Informatics in Control, Automation and Robotics

726

Table 1: Example results from the interview survey.

Focus of Question Answers

Reasons why emerging

technologies are not

used

• Railway sector is a highly conservative sector using reliable and secure (closed

networks) technology

• Fiber optics is not considered a new technology

• Early-stage IoT research projects started

Strength and

opportunities of

emerging technologies

• Realize revenue potential through new business models

• Create business solutions that are now technically feasible

• Improve process efﬁciency to increase process speed or quality or to achieve cost

savings

• Offer new services to the customers

• Make employees’ daily business easier and faster

Biggest obstacles to

overcome

• Convincing staff/management of advantages of emerging technologies

• Convincing management that an investment beneﬁcial

• Gain basic and ﬁnally deep knowledge on emerging technologies to create use cases

• Find the right staff to realize proof of concepts and to implement new solutions

Effect on employees’

work and how changes

can be supported

• Defensiveness towards emerging technologies as purpose technology is used for is

often unclear

• Lighthouse projects help to create acceptance

• Involvement of key players within a company and early-stage training of employees

Table 2: SWOT Analysis of emerged Technologies.

Emerging

Technologies

Strengths & Opportunities Weakness & Threats

Additive Manu-

facturing

Production of parts in small quantities, e.g., for

older trains, hardly any cybersecurity risks

Cost / beneﬁt assessment, quality

Big Data New business models, service improvement Data acquisition, data ownership

Blockchain Trust (traceability), efﬁciency Not really arrived yet (lack of acceptance and un-

derstanding), undesirable CO2 value, inefﬁcient

Cloud Services

& Cloud Com-

puting

Fast exchange, access, versioning (mail reduc-

tion), customer proximity, ﬂexibility reduced ef-

forts (outsourcing)

Lack of trust, dependency, implementation, cost

Fiber optics High data throughput None

Internet of

things

Acquisition of data, the enabler for predictive

maintenance

Gateways for cyberattacks

AI & Machine

Learning

Automated image and data evaluation of / dam-

age to components → saves money, time and it is

safer

Lack of trust (in AI, possibly also in part, lack of

explain ability)

Open Data New business models, service improvement Data acquisition, data ownership

Virtualization,

SDN, NFV

Lower cost of infrastructure, ﬂexibility, the pos-

sibility for ‘digital twin’

Cost / beneﬁt assessment, lack of use cases, lack

of knowledge

a combination, e.g. Big Data, Open Data, Cloud

and AI.

3. The main obstacles are the missing realization of

beneﬁts, management buy-in, and lack of money.

4. Management, employees, and customers need to

be involved from the beginning while lighthouse

projects prove the beneﬁts of technology.

The main limitation of this study is the sub-

participation in some subsectors, which challenges its

representativeness. Cyber-security and related emerg-

ing technologies are sensitive topics that many com-

panies are reserved for discussing, fearing revealing

signiﬁcant weaknesses. For example, there were no

answers of interview participants representing 5G or

wireless sensor networks, while other technologies

were only represented by only up to three to one

participant. Increasing this study’s representativeness

would require more interview participants or an ex-

tended questionnaire by participants.

Emerging Technologies in the Era of Digital Transformation: State of the Art in the Railway Sector

727

5 CONCLUSION

Emerging technologies permeate the railway and pub-

lic transport sub-sectors as part of the digital trans-

formation. While these sub-sectors are traditionally

change-resistant, they can no longer be isolated from

these trends. While press releases continuously an-

nounce a new project to modernize processes, rolling

stock, and infrastructures and scientiﬁc articles talk

about concepts, prototypes, and demonstrators, re-

lated work on the latest technologies’ embedment

level falls short.

Thus, in this work, we addressed this issue. Af-

ter discussing the advantages and challenges of dig-

ital transformation, we introduced several emerging

technologies. We placed them inside the railway sub-

sectors and referenced their proposed or actual appli-

cation examples.

Next, we introduced our quantitative online sur-

vey and the follow-up qualitative interview survey. In

the ﬁrst survey, we analyzed the application of and the

knowledge about technologies. Here we made sev-

eral interesting ﬁndings. We found that signiﬁcantly

more companies use cloud services and virtualization

than rate high or very high on the knowledge metric.

Moreover, the commonly excited blockchain hardly

receives attention and application.

The following interviews showed selected emerg-

ing technologies’ strengths, weaknesses, opportuni-

ties, and threats. Common reasons for not introduc-

ing certain technologies included a lack of trust in

the technology or a negative beneﬁt-cost assessment.

Major obstacles lie in convincing conservative staff or

management of the opportunities and ﬁnding the right

personnel to implement new approaches.

This work shows which technologies permeate

the railway and transport sectors and queried the

perceived strengths, weaknesses, opportunities, and

threats. In future work, we intend to assess the per-

ceived impact on cybersecurity by introducing these

technologies. Furthermore, we plan to associate the

afﬁnity for emerging technologies with the cyberse-

curity awareness of the studied sub-sectors. Lastly,

we plan to derive proposed actions for the legislative

and executive bodies to improve upon the application

of selected technologies.

ACKNOWLEDGEMENT

This research paper originates from the German Cen-

tre for Rail Trafﬁc Research (DZSF) project “Study

Security & New Technologies”.

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