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BDVe Webinar Series - TransformingTransport – Big Data in the Transport Domain

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Learn about how the Transforming Transport Lighthouse Project is helping to transform the Transport and Logistics domains using big data technologies. Lessons learned, pitfalls, innovation potential and business insights.

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BDVe Webinar Series - TransformingTransport – Big Data in the Transport Domain

  1. 1. Big Data in the Transport Domain: Transforming Transport Andreas Metzger (paluno, TT Technical Coordinator) Tonny Velin (CEO Answare)
  2. 2. Agenda 1. TT in a Nutshell (15’) 2. Q&A (10’) 3. Innovation and Business (20’) 4. Q&A (15’) 2
  3. 3. TT in a Nutshell
  4. 4. About TT EU Horizon 2020 Big Data Value PPP Large Scale Pilot Action • Demonstrates transformations big data has on mobility and logistics • Part of • 48 members - 18.7 MEUR budget - 30 months duration 4
  5. 5. About TT 13 pilots in 7 domains 5 Available data
  6. 6. TT Methodology Rationale • Each data set, domain, use case is different • Diversity of data sources and infrastructures • “No free lunch”  Each pilot • Analytics solutions best suited for requirements and data • Infrastructure best linked to data sources • Big data pipelines and tools fit for purpose  Cross-cutting sharing of • best practices, architecture patterns, KPIs, lessons learned, … 6
  7. 7. TT Methodology 3-Stage validation and scale-up Stage Embedding Scale of Data Technology Validation Problem understanding and validation of key solution ideas (Historic) data pinpointing problems and opportunities Large-scale Experiments Controlled environment (not productive environment) Large historic and real-time data, possibly anonymized / simulated In-situ (on site) trials Trials in the field, involving actual end-users Real-time, live production data complementing historic data 7
  8. 8. Transport Innovation via Big Data 8 (IconSource:DHL/DETECON) Efficiency Customer Experience Business Models Smart Highways ++ ++ o Sustainable Connected Vehicles ++ ++ o Proactive Rail Infrastructures ++ + o Ports as Intelligent Logistics Hubs ++ + o Smart Airport Turnaround ++ + + Integrated Urban Mobility ++ ++ o Dynamic Supply Networks + + + New Business Models Improved Operational Efficiency Better Customer Experience Transport Domains
  9. 9. Transport Innovation via Big Data 9 Run-time visualization of operations to increase terminal productivity Deep Learning for proactive transport management Enhanced decision support for terminal operators (risk and reliability of warnings) Predictive analytics for proactive terminal process management @ duisport inland port terminal
  10. 10. Transport Innovation via Big Data 10 Deep learning for proactive terminal management Integrated data of container moves (10,000 moves / month) Data Integration and Aggregation (GPS / XYZ mapping; from states to moves) Data streams from terminal equipment (1.3 mio states / month)
  11. 11. Transport Innovation via Big Data 11 Deep learning for proactive terminal management 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0 10 20 30 40 50 60 70 80 90 Diagrammtitel nums2enoplannednopath mlp Checkpoint [sequence prefix] Accuracy[MCC] RNN MLP Predicting Delays in Container Transport (Recurrent Neural Networks) +42% Prediction Reliablity for Decision Support (Ensemble Neural Networks / Bagging) Cost Savings Frequency Cost savings of 14% on average [Metzger & Föcker, “Predictive business process monitoring considering reliability estimates”, CAiSE 2017] [Metzger & Neubauer, “Considering non-sequential control flows for process prediction with recurrent neural networks”, SEAA 2018]
  12. 12. Transport Innovation via Big Data 12 Advanced analytics solutions (Indra HORUS) for improved traffic distribution along road corridor Better information and decision tools for road users Real-time incident warnings based on novel sensor technology Improved driving and travel experience @ CINTRA/Ferrovial-managed highways
  13. 13. Transport Innovation via Big Data 13 Real-time road incident warnings using novel sensor technology Optical fiber-based sensor (0.88 GB/sec) Time Distance Filtered data (1-5 GB/day) Isolating Signals from Noise (classification, adaptive thresholds, clustering etc.) = 3,500 virtual sensors
  14. 14. Transport Innovation via Big Data 14 Real-time road incident warnings using novel sensor technology Individual Mobility Pattern Detection (trucks) Aggregate Mobility Pattern Detection (traffic jams)
  15. 15. Transport Innovation via Big Data Data-driven decision making in retailing @ Athens International Airport 15 Advanced big data analytics solutions (Indra INPLAN) to anticipate passenger flow and preferences Adapt marketing to expected passenger typology per time slot Use data insights to exploit market niches
  16. 16. Conclusions Opportunities Deep learning e.g., RNNs Cross-sector data sharing e.g., TT Data Portal Challenges Data protection e.g., GDPR vs. IPR Lack of skills e.g., lack of up ½ million data professionals in 2020 [IDC] 16 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0 10 20 30 40 50 60 70 80 90 Diagrammtitel nums2enoplannednopath mlp  Checkpoint Accuracy „deep“ „classical“ Commercial data: 68% Personal data: 1%
  17. 17. Thank You! 17 This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 731932 Contact: Andreas Metzger paluno andreas.metzger@paluno.uni-due.de Skype: ammetzger http://www.transformingtransport.eu

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