Policy Briefs

 
NOESIS Policy Briefs - Page 2 
- Table of Contents - 
 
NOESIS project ........................................................................................................................... 3 
Big Data in Transport / Overview............................................................................................... 4 
Main obstacles identified........................................................................................................... 6 
Policy recommendations ......................................................................................................... 12 
Skilled personnel within transport authorities and companies ........................................... 12 
Promoting cooperation among stakeholders ...................................................................... 13 
Transferability of Big Data “know-how” ............................................................................... 14 
Improve the quality of data.................................................................................................. 15 
Opening data to boost the economy and research activities .............................................. 16 
Ensuring data protection compliance and supporting the implementation of the GDPR... 17 
Understand the benefits of big data solutions to better incorporate them in transport ... 18 
Encouragement of feasible business models and access to financing ................................ 19 
References ............................................................................................................................... 20 
 
   

NOESIS Policy Briefs - Page 3

NOESIS project

The NOESIS project has been a two-year Horizon 2020 coordination and support action, from

November 2017 until October 2019 and is part of the ‘Smart Green and Integrated Transport’

Programme. The consortium consisted of eight (8) partners from six (6) EU Member States

and comprised of experts from ITS, transport management and operation, decision making

and data management fields.

The NOESIS project identified critical factors/features which lead to successful

implementation of Big Data technologies and services in the field of transport and logistics

with significant value generation from a socioeconomic viewpoint. The impact of Big Data has

been evaluated by analysing a series of transportation use cases. The project has been

developing the Big Data in Transport Library (BDTL), the first collection of Big Data use cases

in transport. Based on this library, the project developed a Decision Support tool that is able

to predict the socioeconomic value generated from Big Data investments, taking as input the

specific characteristics and contextual information of the transport system under evaluation

and associating it with a predefined set of use cases with similar characteristics by employing

Big Data (machine learning) techniques.

The goal of the NOESIS policy briefs document is to share insights derived from our experience

working with more that 100 Big Data transportation use cases. The policy briefs focus on

suggesting pathways and good practices to policy makers, transport stakeholders and

businesses about the successful implementation of Big Data in transport projects on the EU,

national and regional level.

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Big Data in Transport / Overview

Data size grows tremendously every day, but not all transport organisations use it to derive

insights and make decisions. Big Data has been the next big technology phenomenon for a

long time, but organisations are still evaluating ways to successfully leverage Big Data and

analytics to:

✓ create a better customer experience providing more efficient, effective and quality

services and modify user behaviour,

✓ achieve organisation objectives (e.g. optimize operational efficiency, avoid failures,

financial performance) and

✓ gain a competitive advantage (innovate).

Things are changing quite fast in the transportation sector. Powered by Big Data, Artificial

Intelligence (AI) and the Internet of Things (IoT), the transportation system can become safer,

more efficient and smarter.

Though there is a growing number of Big Data applications and use cases in the transportation

sector, we can argue that there are still many things that need to be done in order to realise

the full potential of Big Data in this sector.

On the one hand side, both government and public transport bodies begin to offer support

by creating and providing access to public datasets. On the other side, data collection is being

further supported by an entire ecosystem of private data providers and software developers

supporting the public sector capacity. Data collection is further boosted by the rapid

penetration of connected devices into everyday life providing e.g. location data at a scale

never seen before, IoT devices. Smart phones, telematics, sensors, credit card transactions

etc. offer real-time and highly detailed data about people’s transportation demands. Big Data

technologies offer the ability for the production of new analytics introducing new

opportunities to plan and build a sustainable transportation ecosystem that will fulfil user

needs.

Data is vital to how we plan, invest, and evaluate transportation networks. However, there

are important obstacles at all levels that need to be taken into account regarding data

collection and use in the EU. Before presenting these obstacles, it is important to mention

some important observations we made when working with Big Data in transport use cases:

Data collector / provider is changing: Though historically the public sector had a leading role

and was investing resources to collect and analyse transportation related data, we now see

that the private sector is becoming the leading source of Big Data that can be later utilised by

public authorities and transport organisations. As an example, Google now knows more about

NOESIS Policy Briefs - Page 5

where people move on a daily basis than every government in the EU or in the world. In most

cases we observe that authorities responsible for building and maintaining transportation

networks have less data then Google has.

Types of data are evolving and changing over time: Data collected until now were e.g. mainly

data concerning travel patterns of a sample set of commuters, data from traffic counters that

were collected and analysed regularly etc. Now, mobile phones, sensors, GPS trackers, and

other navigation devices offer real-time demand-side data. This is real-time Big Data at a level

of detail that simply did not exist before. The Internet of Things is predicted to contain 50

billion devices in 2020, rapidly expanding the volume, velocity and variety of data related to

transport and mobility. This data is expected to become a “new form of oil” for transport

systems. While earlier datasets focused more on vehicular transportation, emerging data

sources can offer comparable information regarding bicycle, pedestrian, and mass transit

travel - plus multimodal trips.

Transport authorities in most cases do not use private data sources: this is a missed

opportunity and it also most of the time characterizes the public sector’s refusal to develop

innovative geospatial data or to establish synergies with the private sector to acquire such

datasets.

Returns on data investments might take time: Innovation in transport requires time, and

commitment. Using Big Data to create value does not mean that automatically Big Data

investments can lead to quick and easy results. Instead, introducing new technologies into

the transport planning process takes time, and strong leadership and adequate resources are

needed.

In order to better understand the policy actions needed in the Big Data in transport sector,

NOESIS identified some gaps/obstacles that transport organisations face when developing Big

Data solutions. Based on these obstacles, NOESIS consortium suggest related Policies that

should be taken into account by Policy Makers and Transport experts.

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Main obstacles identified

Lack of skilled technical personnel to work with data for Transport Authorities

There are important technical challenges that transport authorities should take into

account when deciding to work with Big Data and make datasets available and/or develop

insights from these datasets. Often in-house personnel lack the data skills to create

appropriate and reliable data feeds (Wani and Jabin, 2018) and to conduct analytics that

will help transport authorities reach their goals. In most cases transport authorities do not

have the financial capacity to hire data scientists; salaries offered in the private sector for

them are much higher and they have plenty of opportunities to work in a more start-up

business environment. Leveraging data analytics for transport planning requires specific

skills and knowledge that might not be available in the local job market. Furthermore, at

the managerial level, there is a lack of training to know the potential Big Data analytics for

the transportation sector. This lack of training limits the agency’s ability to obtain data in

a usable format, analyse it, and effectively deploy it at the decision-making stage.

Lack of data collaboration and sharing among institutions

Partnerships and synergies are needed among public and private data providers and other

transport organisations. The large number of emerging datasets reveal the need to

establish synergies in order to share costs and to be able to establish a wider and

synergetic Big Data strategy at regional or national level. For instance, the lack of sharing

transport data is a big headache for potential MaaS providers who want to provide a

better travel offer.

At the same time, many transport organisations lack the technical capacity to experiment

with cutting-edge technologies. To overcome these hurdles, partnerships with

development agencies and specialized tech firms are essential.

Lack of understanding between transportation experts and data scientists

Transportation experts should play a key role to help interpret the outcomes of Big Data

analytics, but they often lack machine learning and statistical training to benefit from data.

The Big Data tools that exist are generic and designed for data scientists to model big data

with different characteristics and to apply to different domains to serve their own

purposes. These tools may not maximise their utilities without domain expert’s

knowledge.

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Lack of access to different data sources and data variability

A large number of NOESIS Big Data in Transport Library (BDTL) use cases rely solely on

data that transport organisations collected themselves. This contradicts to the purpose of

using large quantities of data from several sources in order to gain a better understanding

of what is happening in the area of interest. The reason for this is partially because of

technical limitations, such as not having access to the sources that could supply the data

needed, but also because of policy decision where the collectors of data do not want to

share their data or are not aware of how they could share their data. Furthermore,

disperse data generated from different sources with disparity speeds and resolutions tend

to be a challenge for data integration to serve business objectives. They may be not useful

at all, if they are too diverse.

Lack of standards in databases

There is a lack of standards regarding data formats and structures for many types of data

relevant to transport (Kumar and Prakash, 2014). For example, in many open data portals,

raw data feeds are presented as CSV, XLS and XML files, as well as PDF documents and a

range of other forms of data. That makes the process of finding data time-consuming, and

in most cases difficult to identify the appropriate datasets. One of the barriers to

multimodal journey planning is the lack of standardized data formats for all transportation

modes. Lack of standardized data formats also means that transportation planners have

a less complete picture of the multimodal transportation network and traveller behaviour.

Lack of data quality

Private users and organisations make their datasets publicly available, but the quality of

these data can be put into question, as they may not be consistent, complete,

understandable, or trustworthy. For instance, data that is old or redundant might lead to

conclusions and realisations that might be no longer relevant. Therefore, it is important

to keep quality of datasets. Furthermore, Big Data consists of oversized data, frequently

unusable without pre-processing. Only a small portion of this data is relevant for

stakeholders to use. Therefore, cleaning the data and removing noise is important.

Lack of infrastructure for storage

Data needs to be represented in a valid format and stored in a repository in a consistent

way, so that it can be searched, retrieved and manipulated consistently and efficiently.

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The secure storage, management and analysis of Big Data requires investment both in

hardware and software. A data infrastructure such as servers or cloud storage and

software services are needed. However, many transport organisations still use legacy

systems and are risk averse to testing new products.

Cost of purchasing the data

Nowadays, when transport organizations intend to implement Big Data techniques within

their activities, they find that data has to be purchased. Some big data sources are

currently expensive (e.g. CDR data), others are provided free through APIs (e.g. social

media) while not exceeding a certain limit of data downloaded. At this point, the

development of big data solutions run the risk that in the future data providers would

make the access to free data more restricted or charge large amounts of money to acquire

information, thereby making big data analysis costly and, for some purposes, even not

profitable.

Potential of open data not fully realized

There exist a wide range of transportation related datasets available, ranging from closed

to open. Until now, public or private transport organisations have not yet recognized the

full potential of opening up their data (Soriano et al. 2016). Though transport

organisations already collect a significant amount of data (timetables, status of the

service, etc.), in most cases they do not publish this data as open mainly because: a) data

is not open (there are restrictions for releasing data as open), b) they do not wish to share

data at all, c) they do not have the resources to release data. Despite the fact that the

potential benefit for all the society (including the government and the transport company)

is high, many of these companies are reluctant to open up the data they collect (EU, 2016).

Uncertainty about data ownership regulation

Currently there are no legal regulations for "data ownership", but EU could be considering

the introduction of a property right over data in its efforts to boost the European data

economy (Stepanov, 2019). When we speak of data ownership, we do not mean the right

to use the data (e.g. through a license), but an absolute right similar to tangible property.

Unlike physical objects, data is characterized by non-rivalry, non-exclusivity, and non-

“wear and tear”. This means that a large number of users can use data without affecting

the use of the other. In addition, data can be copied without particular financial expense

and is not subjected to wear and tear or aging. All this is different for physical objects and

explains the existence of an absolute property right for physical objects (Hoeren, 2019).

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In addition, it could further exacerbate existing power disparities and dependencies. As

an example, data from connected cars could be mentioned here, where the dependency

on the car manufacturer could become even greater through the introduction of data

ownership.

This obstacle is also related to open data, knowing that many organisations do not share

their data. We have seen that many transport authorities contract external organisations

to manage their data infrastructures and these contracts do not have data ownership

provisions. Contractors can be reluctant to share or exchange data with their partners

(Kennedy and Moss, 2015) mainly due to competition (Jagadish et al., 2014). There are

some cases were organisations who own the data do share it with researchers or agencies

under a non-disclosure agreement. Many organisations are reluctant to make data

available or to identify new business models for providing their data.

Uncertainty on data privacy and protection law

Data protection law was comprehensively reformed with the General Data Protection

Regulation (GDPR)

. GDPR is directly applicable, as it is an EU-Regulation and not an EU-

Directive. Although the Member States also have their own data protection laws, these

aim to harmonise the laws of the Member States with the GDPR or to use opening clauses

provided within the GDPR for individual legal areas (e.g. data processing in the context of

employment). There are no such opening clauses for Big Data applications. For such

applications, the GDPR is therefore primarily important.

The GDPR was intended to harmonise data protection law and follows a “one size fits it

all”- approach in two respects: Firstly, it does not differentiate according to the type of

organisation processing the data. Second, there is no differentiation according to the

amount of data being processed. The GDPR thus applies to the small handicraft enterprise

that processes customer data, as well as to large social networks. The same rules apply

for a small spreadsheet with personal data as for large Big Data applications. This,

undoubtedly in good intention pursued, “one size fits it all”-principle, represents also the

biggest problem of the GDPR. Since the GDPR must capture so many different situations,

it is very broadly formulated. That leads to many unclear regulations that, depending upon

point of view, can be interpreted differently. Thereby a big legal insecurity exists, that

might be settled only in some years, when courts and supervisory authorities have fine-

tuned the GDPR. This legal uncertainty is causing many companies to adopt a rigorous

approach. Before they expose themselves to the risk of a fine, they take the safe option

REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 27 April 2016 on the

protection of natural persons with regard to the processing of personal data and on the free movement of such

data, and repealing Directive 95/46/EC (General Data Protection Regulation).

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and prefer to forego the development and introduction of innovative Big Data

applications.

There is a concern by transport authorities that creating data and making data available

(even in anonymised form) may violate the GDPR. Big Data projects capture large amounts

of personal or commercially sensitive data. Organisations responsible for collecting,

storing and analysing Big Transport Data should make sure that data is available only in

anonymised and aggregated form and prevent its misuse.

Uncertainty in the benefits provided by the investment in Big Data

Big Data in transport is a new area and there are not enough insights on how an

organization can move from traditional approaches to new Big Data solutions. It is also a

complicated area that it makes it difficult to predict from the beginning the economic

benefits and the social impact of Big Data investments. This uncertainty can become an

obstacle taking into account that in most cases the return on Big Data investments might

take time.

Lack of financing or business models

In order to face the new challenges and meet the requirements in the new information

era, digitization and adoption of state-of-the-art technologies is necessary for all transport

and logistic companies to improve their activities and services. This digitization is giving

birth to huge and increasingly growing datasets that can be efficiently analysed to create

new business values in the transport and logistics domains (Borgi et al, 2017).

Transport companies are now taking advantage of the huge amount of data created in the

transport chain and start developing new or extend the already established services and

generate new assets. However, there is still a lack of financing and/or sustainable business

models to support their initiatives.

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Table 1: Main obstacles identified

→ Lack of skilled technical personnel to work with data for Transport Authorities

→ Lack of data collaboration and sharing among institutions

→ Lack of understanding between transportation experts and data scientists

→ Lack of access to different data sources and data variability

→ Lack of standards in databases

→ Lack of data quality

→ Lack of infrastructure for storage

→ Cost of purchasing the data

→ Potential of open data not fully realized

→ Uncertainty about data ownership regulation

→ Uncertainty on data privacy and protection law

→ Uncertainty in the benefits provided by the investment in Big Data

→ Lack of financing or business models

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Policy recommendations

Skilled personnel within transport authorities and companies

The following actions are proposed to increase the rate of skilled personnel within transport

authorities and companies:

✓ To promote university programs, such as a Master of Science on Big Data for transport

management and systems, to improve the amount of people with common skills on

both Big Data and transport.

✓ To encourage transport companies, the establishment of a Chief Data Officer (CDO)

position, at the highest level of the Data Governance Strategy of the company (NOESIS

D4.2, 2019). CDO will be a senior executive who is responsible for organization-wide

governance, management and utilization of data as an asset, data availability,

business intelligence, data mining, reporting, data quality initiatives, etc.

Main actors involved are:

✓ Research and academia communities in Europe should promote a Master of Science

on data analytics for transport. To do so, academia and research centres from

different background such as data science, transportation science, law and business

management areas should cooperate. The Big Data in Transport Working Group

proposed by NOESIS could be the first step in the definition of this program.

✓ Transport operators and authorities are encouraged to implement a Data Governance

Strategy including a Chief Data Officer with required skills in data science.

Policy recommendation 1

Educating personnel is as important as developing new tools. This includes training

staff on the use of data and creating business strategies that emphasize objective

data-driven analysis. The use of Internet of Things (IoT) devices is creating a

significant amount of transport related data as more and more people are connected

and are using such devices. This provides opportunities for Big Data application

developers and Big Data experts. The profile of skilled personnel should combine

analytical and statistical skills with appropriate level of business understanding.

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Promoting cooperation among stakeholders

The following points are important for this:

✓ The establishment of data collaboration communication and clear agreements

between partners. For instance, a Memorandum of Understanding (MoU) between

data providers and transport authorities can be established to develop new products.

✓ The development of a “data ecosystem platform” to bring together large software

firms, SMEs and start-ups, the research and academia communities, and other

organisations interested in Big Data for transport. An important step for creating a

“data ecosystem” is to make IT interfaces explicit, well documented, less expensive

and easy to interrogate in order to acquire the data.

Main actors involved:

✓ EU Institutions and Member States Governments at different levels could be a source

of public funds for promoting collaboration.

✓ Investors, Venture Capitalist, and Incubators could also provide the funding by

collaborations with the government and policy makers as long as they foresee

significant returns in their investment.

✓ Transport authorities could influence the development of certain big data solutions

and impose collaboration among stakeholders for the sake of coordination and the

wellbeing of society. It could for example require transport operators to share certain

data when they are operating transport services under their jurisdiction.

✓ Transport operators, research and academia communities, start-ups and IT

companies. Transport operators could benefit from the financial instrument of

governments and investors for the implementation of big data solutions and establish

agreements with data providers, research community and IT companies. These

agreements may be the first step of data ecosystem platforms.

Policy recommendation 2

In the Big Data for transport sector there is a certain need for cooperation between

transport authorities, transport operators, research centres, IT companies, users, etc.

Transport operators have the opportunity to work together with IT companies and

the research community to develop intellectual capital. This way, academics and

companies will focus their research on transportation related problems. Transport

authorities, IT companies, researchers and users should work together to create

sustainable Big Data products and services.

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Transferability of Big Data “know-how”

For this action, it could be proposed the following:

✓ Promotion of data re-use across interoperable applications.

✓ Promote data access through open data portals (especially for public authorities) and

understand the role open data will play in shaping future transport planning and

modelling.

✓ Encourage the development of sustainable business models for commercializing data.

Main actors:

✓ Transport operators and authorities are encouraged to share their data and develop

sustainable business models.

✓ IT companies (Start-ups and SMEs) are encouraged to develop new business models

based on already existing datasets.

Policy recommendation 3

Transferability refers to applying Big Data solutions designed in one context to a

different context (e.g., Lycett, 2013; Tallon et al. 2014). For example, a transport

authority that has been collecting GIS data from buses can sell these data to agencies

dealing with road maintenance to find locations that require repairs. Data that has

been collected in a very specific context may have a different value when it is used

by organisations in other contexts. Transferability is crucial for Big Data that may

leverage large volumes of varied data from many sources, considerably beyond

organisational boundaries.

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Improve the quality of data

This action requires:

✓ To promote certification mechanisms to ensure the standardization and quality of

data.

Main actors:

✓ Standardisation bodies could propose, develop, establish, monitor, and/or coordinate

voluntary standards or quality requirements for transport sector data. Those bodies

could be public or private sector, domestic or international organizations.

✓ Transport authorities and operators are the organisations, that may require

certification or standardization for certain activities.

✓ IT companies can start building databases and platforms according to the quality

requirements and standards defined above

Policy recommendation 4

This action includes the standardization of data and processes in order to facilitate

the use of data. Fostering the development of data standards will help to achieve

data quality, homogenization, accessibility and availability. Reach a wider audience

for any possible use of different transportation related applications could reduce the

cost of data collection and make intelligent transportation systems more feasible and

economical. Standardising interfaces and making them friendly and explicitly to

install and access can facilitate data management. This allows communication

network providers to work together to enable data scientists to cut their efforts in

gaining their raw data for transportation data analytics.

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Opening data to boost the economy and research activities

Main actors:

✓ European and Member States Governments are encouraged to help by stating clearly

how and what data is possible to open up among companies and governmental

bodies. As described previously, many actors are afraid of opening their data as they

do not know the repercussions.

✓ Transport Authorities by gaining the right to distribute data among research groups

and students, great projects have spawned from opening up data. In the same sense,

transportation agencies could gain a much better insight by opening up their data to

let others do research on it, which they later can benefit from.

✓ Research and Academia communities could help by utilising previously accessible data

to show how the results of utilising said data can benefit the organisations that have

opened their data. For instance, KTH in Sweden helped the public agency of Stockholm

to better understand how the expansion and alteration of the metro system is

affecting the how and where people decide to live (Tidebanan, 2019).

✓ IT companies and start-ups similar as Research and Academia communities could use

open data to create their products.

Policy recommendation 5

Most transportation related data could and should be made publicly available to

organisations and consumers when there is no risk of data privacy being

compromised. This will encourage development of new tools and services and will

generate public excitement and participation. Transport organisations and other

public sector bodies can choose to open up data to stimulate innovation and provide

transparency. Mobility as a Service (MaaS) experts have suggested that public

transport operators, taxi companies, car clubs, bike share services and even

carmakers, should be obliged to make their data accessible to mobility companies.

Many large government agencies have the capacity and funding to gather huge

amounts of data. In addition, opening up those datasets can have major economic

benefits. For transport government/public organisations, the aim of the member

states should be to implement the revised PSI Directive quickly and fully. The recast

of the “Directive of the European Parliament and of the Council on open data and the

re-use of public sector information” is an important step, so that public sector

information can be reused. Especially in the field of transport and logistics, a great

potential for the use of public sector information can be identified.

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Ensuring data protection compliance and supporting the

implementation of the GDPR

The following recommendations can increase the acceptance of data protection by

companies and organizations and, on the other hand, help them to work in compliance with

data protection law:

✓ Resources for consultation to support companies and organisation in implementing

GDPR.

✓ Further efforts should ensure to harmonised data protection framework in order to

lead to a uniform application of the GDPR. The European Data Protection Board

(EDPB) has a decisive coordinating role to play here.

✓ To appoint a Data Protection Officer in the organization/company.

✓ If GDPR is subject to an evaluation, to take into account expert opinions from research,

consulting and practice.

Main actors:

✓ European and Member States Governments. On the one hand, supervisory authorities

of Member States should support organisation in the right implementation of GDPR.

On the other hand, EDPB should coordinate the harmonised data protection

application as unified as possible across member states and regions.

✓ Public and private transport enterprises or organizations could appoint a DPO within

their organisation in charge of all data privacy, ownership, protection, and regulation

policies.

Policy recommendation 6

Data protection is often used as an argument that innovative projects cannot be

implemented. Although there are no exact numbers or studies, this argument only

seems to be a pretext. Even though the new data protection law is complex, as

outlined above, it should not be ignored.

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Understand the benefits of big data solutions to better incorporate

them in transport

To implement this action, the following measures are recommended:

✓ To promote the application of impact assessment methodologies to evaluate the

effects of big data solutions according to the goals of the organization and the society.

✓ To develop detailed studies on the benefits, costs and risks of implementing big data

solutions to assist decision-makers.

Main actors:

✓ Transport operators and authorities are encouraged to implement methods to assess

the value for money of big data solutions. This will help them to set a long-term vision

regarding the use of big data solutions.

✓ Research and academia communities are recommended to study the impacts and risks

of big data solutions in transport in Europe by, for instance, taking as reference the

NOESIS IAM (NOESIS D5.1, 2019).

Policy recommendation 7

Understanding the benefits and costs of big data compared to traditional models is

key to justify the need to invest and finance new applications. The analysis of benefits

of using big data solutions in transport versus the costs of obtaining the data,

maintenance and operation, and their risks would permit to identify in which areas

of transportation the use of big data may be value for money. This analysis will help

transport organizations and investors to promote big data solutions in transport.

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Encouragement of feasible business models and access to financing

To implement this action, the following measures are recommended:

✓ Access to financing for start-ups should be facilitated such as venture capital and

other forms of new enterprise financing.

✓ Promotion of alternative funding sources such as crowdfunding or PPPs

✓ Using European financial instruments such as SME Instrument project (H2020) to

encourage cooperation between data driven start-ups and established large

corporate players.

Main actors:

✓ Transport operators and authorities are encouraged to identify other financing

schemes to support their initiatives.

✓ Businesses and start-ups are encouraged to develop new business models.

✓ Governments/Public administration are encouraged to promote access to financing

for SME, PPPs.

Policy recommendation 8

Access to financing for start-ups should be facilitated such as venture capital and

other forms of new enterprise financing. Furthermore, alternative funding sources

should also be promoted such as crowdfunding. Various schemes representing

public-private partnerships (PPP) represent another funding source supporting new

business models for big data in transport.

NOESIS Policy Briefs - Page 20

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