Leveraging Big Data to Manage Transport Operations (LeMO) project will address these issues by investigating the implications of the utilisation of such big data to enhance the economic sustainability and competitiveness of European transport sector.

1. Leveraging Big Data to Manage Transport Operations
Big Data in Transport
Next Steps with LeMO project
Rajendra Akerkar
Western Norway Research Institute

2. LeMO project
Leveraging Big Data to Manage Transport Operations (LeMO) project
Grant agreement no: 770038
• H2020‐EU.3.4. ‐ SOCIETAL CHALLENGES ‐ Smart, Green and Integrated Transport
• In order to meet this challenge, LeMO:
• identifies methodological and technological issues to allow for effective data
mining and data exploitation
• analyses the barriers and limitations of the transportation system to exploit
big data opportunities
• designs a research and policy recommendations for big data in transport

3. Project details
• Nov 2017 – Oct 2020; 36 months
• 5 Partners
• 5 Countries

4. LeMO objectives
• To produce research and policy roadmap towards data openness,
collection, exploitation and data sharing to support European transport
stakeholders in capturing and addressing issues, that range from technical
to institutional, including legitimacy, data privacy and security.
• To involve European transport sector actors in order to identify and
analyse concrete opportunities, barriers and limitations of the
transportation systems to exploit big data opportunities.
• To disseminate the LeMO findings, recommendations and the
contribution of the LeMO to evidence–based decision making by
improving knowledge on methodological and exploitation issues taking
also into account economic, legal, social, institutional and technical
aspects.

5. Transport themes
The LeMO project will
study and analyse big data
in the transport domain
with respect to these five
transport dimensions.
Source: Transport Research & Innovation Portal

6. Big data
LeMO project is uniquely positioned
to help stakeholders capitalize on
the power of big data to:
• Significantly improve the customer
experience
• Enhance services to increase revenue
and manage capacity
• Maximize the availability of assets
and infrastructure
• Improve operational efficiency
5 Vs

7. Case studies – Overview (1)
Issues to be addressed: innovation in infrastructure,
efficiency in transport routes, resource management and
supply chain, environmental impacts, vulnerability in
potential crisis and data protection.
Issues to be addressed: enabling ‘mass
mobilisers’ (training journalists and civic
groups) to disseminate and make data
understandable by the general public, not just
data scientists.
Rail transport data
Open data
and transport sector

8. Case studies – Overview (2)
Issues to be addressed: discuss a methodology to
estimate passenger demand for public transport
services using social media and mobile phone data.
Issues to be addressed: innovative big data
acquisition procedures; test the capability to
reach an alternative, less expensive and faster
administrative procedure of acquiring
stakeholder‐specific big data.
Real‐time traffic
management
Logistics and
consumer
preferences

9. Case studies – Overview (3)
Issues to be addressed: Use
Inland AIS to track vessel
movements in and around locks
Issues to be addressed: explore various
network models for efficient
transportation planning that eliminates
service delays.
Issues to be addressed:
Multimodal, multiagency approach
to provide useful suggestions on
the prerequisites of successful big
data implementation in ITS
Smart inland
shipping
Optimized transport
and improved
customer service
Big data and
intelligent
transport
systems

10. Case studies
LeMO will use an overview of
opportunities‐limitations‐barriers,
which will be filtered through the
case studies, resulting in key
recommendations and roadmap.

11. Expected impacts
• LeMO recommendations and roadmap can help policy makers at the
EU, national and regional level, as well as relevant decision makers to
take informed decisions
• Contribution to evidence–based decision making by improving knowledge on
methodological and exploitation issues taking also into account economic and
technical considerations
• Support to industry in capturing benefits (efficiencies, new business models,
etc.) and addressing limitations before beginning a project, initiative or
programme
• Contribution to an early identification of critical issues linked to privacy, data
security, legal and institutional aspects

12. Thank you
• Any questions?
• Key contact:
• Rajendra Akerkar rak@vestforsk.no
• LeMO website:
• lemo‐h2020.eu/