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Big Data and ITS for improving
the governance of road freight
Dr. Tom Voege, OECD - International Transport Forum
Big Data and ITS for improving
the governance of road freight
1. Introduction and background
2. New regulatory approaches
3. Technical issues and needs
4. Other related developments
5. Some international examples
6. Conclusion and way forward
1. Introduction and
background
• Legislative environments for road freight
• Current issues for enforcement practices
• Intelligent Transport System and Big Data
Legislative environments for road freight
• Market-based rules
apply to the road haulage operators and regulate their market access
or access to its specific segments
• Driver-based rules
apply to the actions of drivers when at work, resting, or in the state of
availability to perform their duties
• Vehicle condition rules
relate to the technical condition that the vehicle must meet to perform
freight transport operations in the market
Current issues for enforcement practices
Current issues for enforcement practices
• On-the-spot roadside checks
Carried out either at the roadside or near it at dedicated sites where
authorities check vehicle condition rules (weight, roadworthiness),
market-based rules (licenses) and driver-based rules (tachograph)
• Checks on company premises
Checks on company premises allows enforcing specific legislation,
e.g. the company registration or some of the driver-based social
rules, through physical check in paper or digital form.
Intelligent Transport System and Big Data
Intelligent Transport System and Big Data
Intelligent Transport System and Big Data
• Various relevant systems
- In-vehicle: Navigation and tracking, driver assistance systems
- Road-side: Weigh-in-motion, variable/ dynamic message signs
- And other emerging transport-related big data opportunities
• Improved regulatory framework
- Better enforcement
- New approaches
Intelligent Transport System and Big Data
• Other related ITS applications
- Road user charging
- Infrastructure monitoring/ maintenance
- Larger and heavier vehicles
- Platooning/ vehicle automation
- Freight matching platforms
• Current limitations
- Fragmentation of technologies
- Geographical differences
- Lack of interoperability
2. New regulatory
approaches
• Examples for regulatory frameworks
• Data types and other characteristics
• Additional data-specific consideration
Examples for regulatory frameworks
• Enforcement of weight and dimensions
- Total laden weight protection/ axle load protection
- Tailored restriction for specific infrastructure
• User-based traffic signs
- VMS content on on-board display
- Traffic, routing, logistics, etc.
• New regulatory approaches
- Use of electronic licenses
- Complete overhaul based on big data
Data types and other characteristics
• Vehicle condition and technical characteristics
• Vehicle routing information
• Weather conditions on this route
• Traffic conditions currently and short-term forecasts
• Cargo (type, weight, origin, destination, owner, CMR info)
• Performance (speed, acceleration, deceleration, jerk)
• Various driver drowsiness indicators
• Traffic data (loops or video)
Additional data-specific consideration
• Data exchange
- Vehicle to cloud (servers)
- Cloud to vehicle
• Data storage
- Onboard (data follows the vehicle)
- On drivers licence, electronic file (data follows driver)
- Company data (all its drivers and vehicles)
- National cloud storage (data on the system)
• For international freight transport
- Data exchange internationally (driving/ rest periods, licencing, etc.)
- Short term data storage during data exchange
- Long term data storage for immediate retrieval/ service level
3. Technical issues
and needs
• Overview of new approaches
• Key issues for implementation
• Specific governance challenge
Overview of new approaches
• First step: enforcement of existing rules and regulations
• Followed by: implications of many related developments
• Consider complete overhaul of the regulatory approach
• Move away from current descriptive regulatory approach
• Lighter system of less ex-ante data needs for regulation
• Pre-defined policy objectives and continuous monitoring
• Transition: schemes streamlined, replaced, or removed
• Wider move towards data-led regulation in other sectors
Key issues for implementation
• Geographic context should not hinder implementation
• Ensure transition to a higher coverage of vehicle fleet
• Enforcement practices should be politically acceptable
• Sufficient service/ enforcement level need to be ensured
• Views on trustworthiness/ reliability of big transport data
• Address access modalities for on-board vehicle data
• Data Ownership, architecture, privacy, cyber security
• Need for legislation on data-led enforcement approaches
Specific governance challenge
• Using two-tier system for different technologies
• Shortening regulatory implementation periods
• Dealing with the uneven regulatory landscape
• Outsourcing functions of enforcement authorities
• Convincing actors to participate in data sharing
• Adjusting specific procedures for procurement
• Addressing cost/ revenue sharing amongst actors
• Addressing the question of universal applicability
4. Other related
developments
• Using larger and heavier vehicles
• Vehicle automation and platooning
• Platform-based freight matching
Using larger and heavier vehicles
+ Intelligent Access Programme (IAP)
+ Performance-Based Standards (PBS)
Using larger and heavier vehicles
• Societal and political concerns
 In different geographical areas implementation is effected by this
• Use of infrastructure categories
 Identify parts of the network and infrastructure suitable to vehicles
• Tracking of vehicles and drivers
 Network and infrastructure access based on real-time locations
• Potential for widening of scheme
 Broader regulatory framework covering other vehicle categories
• Privacy& cyber-terrorism concerns
 Both threats and potentials to be considered during the planning
Vehicle automation and platooning
Vehicle automation and platooning
• Vehicle automation high on political and R&D agendas
Worldwide race for market dominance in automation and digitalisation
• Driver regulation if driver in cab or use of control centre
Relaxation of rules in response to the changes of roles and activities
• Potential move from platooning to driverless operation
Evolution of Systems with increased sophistication and consequence
• Tracking vehicles, data collection, vehicle connectivity
Data-rich environment both requiring and enabling data-led regulation
Platform-based freight matching
Platform-based freight matching
• Changes to current business model
New data-driven app-based processes to match supply and demand
• Enhancing supply chain efficiency
Improved vehicle load factors and minimising empty running vehicles
• Sharing economy and/ or disruption
Many positive aspect but emerging new players and business models
• Part-time work & flexibility for drivers
Positive changes to the work practice but potentially less job security
• Question of more or less regulation?
Government oversight might be required for labour market protection
5. Some international
examples
• Intelligent Access Programme
• Operator Compliance Risk Score
• e-CMR and UNECE e-TIR System
Intelligent Access Programme
Intelligent Access Programme
Operator Compliance Risk Score
e-CMR and UNECE e-TIR System
6. Conclusion and
way forward
• Summary of our study - what we did
• Recap study results - what we found
• Looking ahead - what we recommend
Summary of our study - what we did
 Gap between regulatory framework and policy objectives
 Unforeseen outcomes/ specific enforcement challenges
 Big data in transport for more data-driven policy making
 Bridging gap with targeted flexible regulatory framework
 Review of current legislation and enforcement practices
 Discussions with international freight transport experts
 Research, workshop, consultation with key stakeholders
Recap study results - what we found
 Clear potential for data-driven approaches to regulation
 But many specific challenges still need to be overcome
 No uniform level of readiness for wider implementation
 Two-tier system which provides minimum functionality
 Disparity between innovation and regulatory responses
 Issues from changed roles of the stakeholders involved
 Will requires outsourcing of tasks to the private sector
Looking ahead - what we recommend
 Use currently available data within existing framework
 Consider new fully data-driven regulatory approaches
 Cross-sector approaches to data handling/ processing
 Best uses of new technologies, systems, data science
 Access control, tracking, ”geo-timing”, and dashboard
 Applicability of wider and less structured big data sets
 Consider impacts of automation of road freight vehicles
www.itf-oecd.org/sites/default/files/docs/data-led-governance-road-freight-transport.pdf
Thank you for
you attention!
TOM.VOEGE@ITF-OECD.ORG

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Big Data and ITS for improving the governance of road freight

  • 1.
  • 2. Big Data and ITS for improving the governance of road freight Dr. Tom Voege, OECD - International Transport Forum
  • 3. Big Data and ITS for improving the governance of road freight 1. Introduction and background 2. New regulatory approaches 3. Technical issues and needs 4. Other related developments 5. Some international examples 6. Conclusion and way forward
  • 4. 1. Introduction and background • Legislative environments for road freight • Current issues for enforcement practices • Intelligent Transport System and Big Data
  • 5. Legislative environments for road freight • Market-based rules apply to the road haulage operators and regulate their market access or access to its specific segments • Driver-based rules apply to the actions of drivers when at work, resting, or in the state of availability to perform their duties • Vehicle condition rules relate to the technical condition that the vehicle must meet to perform freight transport operations in the market
  • 6. Current issues for enforcement practices
  • 7. Current issues for enforcement practices • On-the-spot roadside checks Carried out either at the roadside or near it at dedicated sites where authorities check vehicle condition rules (weight, roadworthiness), market-based rules (licenses) and driver-based rules (tachograph) • Checks on company premises Checks on company premises allows enforcing specific legislation, e.g. the company registration or some of the driver-based social rules, through physical check in paper or digital form.
  • 10. Intelligent Transport System and Big Data • Various relevant systems - In-vehicle: Navigation and tracking, driver assistance systems - Road-side: Weigh-in-motion, variable/ dynamic message signs - And other emerging transport-related big data opportunities • Improved regulatory framework - Better enforcement - New approaches
  • 11. Intelligent Transport System and Big Data • Other related ITS applications - Road user charging - Infrastructure monitoring/ maintenance - Larger and heavier vehicles - Platooning/ vehicle automation - Freight matching platforms • Current limitations - Fragmentation of technologies - Geographical differences - Lack of interoperability
  • 12. 2. New regulatory approaches • Examples for regulatory frameworks • Data types and other characteristics • Additional data-specific consideration
  • 13. Examples for regulatory frameworks • Enforcement of weight and dimensions - Total laden weight protection/ axle load protection - Tailored restriction for specific infrastructure • User-based traffic signs - VMS content on on-board display - Traffic, routing, logistics, etc. • New regulatory approaches - Use of electronic licenses - Complete overhaul based on big data
  • 14. Data types and other characteristics • Vehicle condition and technical characteristics • Vehicle routing information • Weather conditions on this route • Traffic conditions currently and short-term forecasts • Cargo (type, weight, origin, destination, owner, CMR info) • Performance (speed, acceleration, deceleration, jerk) • Various driver drowsiness indicators • Traffic data (loops or video)
  • 15. Additional data-specific consideration • Data exchange - Vehicle to cloud (servers) - Cloud to vehicle • Data storage - Onboard (data follows the vehicle) - On drivers licence, electronic file (data follows driver) - Company data (all its drivers and vehicles) - National cloud storage (data on the system) • For international freight transport - Data exchange internationally (driving/ rest periods, licencing, etc.) - Short term data storage during data exchange - Long term data storage for immediate retrieval/ service level
  • 16. 3. Technical issues and needs • Overview of new approaches • Key issues for implementation • Specific governance challenge
  • 17. Overview of new approaches • First step: enforcement of existing rules and regulations • Followed by: implications of many related developments • Consider complete overhaul of the regulatory approach • Move away from current descriptive regulatory approach • Lighter system of less ex-ante data needs for regulation • Pre-defined policy objectives and continuous monitoring • Transition: schemes streamlined, replaced, or removed • Wider move towards data-led regulation in other sectors
  • 18. Key issues for implementation • Geographic context should not hinder implementation • Ensure transition to a higher coverage of vehicle fleet • Enforcement practices should be politically acceptable • Sufficient service/ enforcement level need to be ensured • Views on trustworthiness/ reliability of big transport data • Address access modalities for on-board vehicle data • Data Ownership, architecture, privacy, cyber security • Need for legislation on data-led enforcement approaches
  • 19. Specific governance challenge • Using two-tier system for different technologies • Shortening regulatory implementation periods • Dealing with the uneven regulatory landscape • Outsourcing functions of enforcement authorities • Convincing actors to participate in data sharing • Adjusting specific procedures for procurement • Addressing cost/ revenue sharing amongst actors • Addressing the question of universal applicability
  • 20. 4. Other related developments • Using larger and heavier vehicles • Vehicle automation and platooning • Platform-based freight matching
  • 21. Using larger and heavier vehicles + Intelligent Access Programme (IAP) + Performance-Based Standards (PBS)
  • 22. Using larger and heavier vehicles • Societal and political concerns  In different geographical areas implementation is effected by this • Use of infrastructure categories  Identify parts of the network and infrastructure suitable to vehicles • Tracking of vehicles and drivers  Network and infrastructure access based on real-time locations • Potential for widening of scheme  Broader regulatory framework covering other vehicle categories • Privacy& cyber-terrorism concerns  Both threats and potentials to be considered during the planning
  • 24. Vehicle automation and platooning • Vehicle automation high on political and R&D agendas Worldwide race for market dominance in automation and digitalisation • Driver regulation if driver in cab or use of control centre Relaxation of rules in response to the changes of roles and activities • Potential move from platooning to driverless operation Evolution of Systems with increased sophistication and consequence • Tracking vehicles, data collection, vehicle connectivity Data-rich environment both requiring and enabling data-led regulation
  • 26. Platform-based freight matching • Changes to current business model New data-driven app-based processes to match supply and demand • Enhancing supply chain efficiency Improved vehicle load factors and minimising empty running vehicles • Sharing economy and/ or disruption Many positive aspect but emerging new players and business models • Part-time work & flexibility for drivers Positive changes to the work practice but potentially less job security • Question of more or less regulation? Government oversight might be required for labour market protection
  • 27. 5. Some international examples • Intelligent Access Programme • Operator Compliance Risk Score • e-CMR and UNECE e-TIR System
  • 31. e-CMR and UNECE e-TIR System
  • 32. 6. Conclusion and way forward • Summary of our study - what we did • Recap study results - what we found • Looking ahead - what we recommend
  • 33. Summary of our study - what we did  Gap between regulatory framework and policy objectives  Unforeseen outcomes/ specific enforcement challenges  Big data in transport for more data-driven policy making  Bridging gap with targeted flexible regulatory framework  Review of current legislation and enforcement practices  Discussions with international freight transport experts  Research, workshop, consultation with key stakeholders
  • 34. Recap study results - what we found  Clear potential for data-driven approaches to regulation  But many specific challenges still need to be overcome  No uniform level of readiness for wider implementation  Two-tier system which provides minimum functionality  Disparity between innovation and regulatory responses  Issues from changed roles of the stakeholders involved  Will requires outsourcing of tasks to the private sector
  • 35. Looking ahead - what we recommend  Use currently available data within existing framework  Consider new fully data-driven regulatory approaches  Cross-sector approaches to data handling/ processing  Best uses of new technologies, systems, data science  Access control, tracking, ”geo-timing”, and dashboard  Applicability of wider and less structured big data sets  Consider impacts of automation of road freight vehicles
  • 37. Thank you for you attention! TOM.VOEGE@ITF-OECD.ORG