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Big Data Value in Mobility and Logistics

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Big data is expected to have a profound economic and societal impact in mobility and logistics. Examples include 500 billion USD in value worldwide in the form of time and fuel savings, and savings of 380 megatons CO2. With freight transport activities projected to increase by 40% in 2030, transforming the current mobility and logistics processes to become significantly more efficient, will have a profound impact. A 10% efficiency improvement may lead to EU cost savings of 100 billion EUR. This keynote will highlight the key value dimensions for big data in mobility and logistics. The talk will present examples from the EU Horizon 2020 lighthouse project TransformingTransport, demonstrating the transformations that big data can bring to the mobility and logistics sector. TransformingTransport addresses 13 pilots in seven highly relevant pilot domains within mobility and transport that will benefit from big data solutions and the increased availability of data. The talk will close with an outlook on barriers and future opportunities.

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Big Data Value in Mobility and Logistics

  1. 1. Big Data Value in Mobility and Logistics Andreas Metzger (TT Technical Coordinator)
  2. 2. Agenda 1. TT and the Big Data Value Ecosystem 2. TT Methodology 3. Transport Innovation via Big Data 4. Future Opportunities and Barriers BMDA 2018, Vienna 2
  3. 3. About TT • EU Horizon 2020 Big Data Value PPP Large Scale Pilot Action • Goal: demonstrate transformations big data has on mobility and logistics • 46 members - 18.7 MEUR budget - 30 months duration BMDA 2018, Vienna 3
  4. 4. About TT 13 pilots in 7 domains BMDA 2018, Vienna 4 Smart Highways Smart Airport Turnaround Ports as Intelligent Logistics Hubs Proactive Rail Infrastructures Sustainable Connected Vehicles Integrated Urban Mobility Dynamic Supply Networks
  5. 5. Big Data Value Ecosystem Big Data Value means… • Achieving socio-economic impact with Big Data • Increased efficiency, higher customer satisfaction, new business models, … ETP4HPCEOSC ECSO AIOTI 5G PPP EFFRA BMDA 2018, Vienna 5
  6. 6. Big Data Value Ecosystem Big Data Value Association (BDVA) • Industry-led; 55% industry • > 180 members from 28 different EU countries • Annual Research & Innovation Agendas Technical Priorities • Data Management • Data Processing Architectures • Data Analytics • Data Visualisation and User Interaction • Data Protection • Engineering & DevOps for Big Data • Big Data Standardisation Non-technical Priorities • Skills development • Ecosystems and Business Models • Policy and Regulation • Social perceptions and societal implication BMDA 2018, Vienna 6
  7. 7. Big Data Value Ecosystem Big Data Value Public-Private Partnership (BDV PPP) • European Commission (public side) + BDVA (private side) • Implemented through calls for actions / projects under Horizon 2020 • Current work programme (WP2018-2020) – https://ec.europa.eu/programmes/horizon2020/en/h2020-section/information-and- communication-technologies • Types of projects: BMDA 2018, Vienna 7 Data Platforms (Personal/Industrial) WP Topic: ICT-13a/b-WP2018-2020 Lighthouse Projects (Large-scale Pilots / Test-beds) WP Topic: ICT-11/14-WP2018-2020 Technical Projects WP Topic: ICT-12@WP2018-2020 Collaboration & Support Actions WP Topic: ICT-13c@WP2018-2020
  8. 8. Agenda 1. TT and the Big Data Value Ecosystem 2. TT Methodology 3. Transport Innovation via Big Data 4. Future Opportunities and Barriers BMDA 2018, Vienna 8
  9. 9. TT Methodology Rationale • “No free lunch”[1] – Each data set, domain, use case is different – Using a single data analytics solution will most probably not work • Thus: For each of the 13 Pilots – Dedicated data analytics solutions best suited for requirement and datasets – Dedicated infrastructures best linked to data sources • Still: Reuse of do‘s/don‘ts, best practices, common requirements, lessons learned, … – Within pilots, across pilots, beyond project [1] David Wolpert, William G. Macready: No free lunch theorems for optimization. IEEE Trans. Evolutionary Computation 1(1): 67-82 (1997) BMDA 2018, Vienna 9
  10. 10. TT Methodology BMDA 2018, Vienna 10 Replication Data Integration
  11. 11. 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 BMDA 2018, Vienna 11
  12. 12. Agenda 1. TT and the Big Data Value Ecosystem 2. TT Methodology 3. Transport Innovation via Big Data 4. Future Opportunities and Barriers BMDA 2018, Vienna 12
  13. 13. Transport Innovation via Big Data 13 (Icon Source: 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 BMDA 2018, Vienna Key Value Dimensions:
  14. 14. Transport Innovation via Big Data Data-driven decision making in retailing @ Athens International Airport 14 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 BMDA 2018, Vienna
  15. 15. Transport Innovation via Big Data 15BMDA 2018, Vienna 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
  16. 16. Transport Innovation via Big Data 16BMDA 2018, Vienna Run-time visualization of operations to increase terminal productivity Predictive analytics to generate warnings 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
  17. 17. Predictive analytics for proactive process management BMDA 2018, Vienna 17 monitor predict real-time decision proactive management time t t +  planned / acceptable situations = Violation = Non- Violation  e.g., delay in freight delivery time e.g., schedule faster means of transport
  18. 18. Predictive analytics for proactive process management Prediction accuracy key for proactive process management Prediction accuracy = ability of prediction technique – to forecast as many true violations as possible, – while generating as few false alarms as possible • True violation  triggering of required adaptations – Missed required adaptation = less opportunity for proactively preventing or mitigating a problem • False alarm  triggering of unnecessary adaptation – Unnecessary adaptation = additional costs for executing the adaptations, while not addressing actual problems BMDA 2018, Vienna 18
  19. 19. Predictive analytics for proactive process management “Utility” of adaptation decisions depends on… (1) Accuracy of individual prediction • Research focused on aggregate accuracy – E.g., precision, recall, mean average prediction error, … – But: aggregate accuracy gives no direct information about error of an individual prediction  Use reliability estimate to quantify probability of violation BMDA 2018, Vienna 19 Aggregate Accuracy 75% 75% 75% 75% Prediction # 1 2 3 … Reliability Estimate 60% 90% 70% …
  20. 20. Predictive analytics for proactive process management “Utility” of adaptation decisions depends on… (2) Severity of violation – E.g., contractual penalties (such as stipulated in SLAs)  Use estimated penalty to quantify severity (in terms of costs) based on size of deviation: c() BMDA 2018, Vienna 20 δ Linear with cap clin c 0 1 δ Constantc 0 cconst 1 c Step-wise (s steps) δ 1/s 2/s cstep 1 2/s·cstep 1/s·cstep (s-1)/s … 0
  21. 21. Risk estimate for proactive process management  Risk = Probability of occurrence × Severity [ISO 31000:2009] Reliability estimate × Estimated penalty BMDA 2018, Vienna 21
  22. 22. Risk estimate for proactive process management 22BMDA 2018, Vienna Prediction T Process Moni- toring Data { Regression Model 1 Regression Model n  a1  an { Deviation   Penalty c() Reliability estimate  Classification Model 1 Classification Model m{{{ Each model of ensemble trained differently (bagging)  T1  Tm Ensemble Prediction:
  23. 23. Risk estimate for proactive process management BMDA 2018, Vienna 23 monitor predict real-time decision proactive management time t t +  planned / acceptable situations = Violation = Non- Violation  R ≤ threshold  no adaptation R > threshold  adaptation + Risk R
  24. 24. Risk estimate for proactive process management BMDA 2018, Vienna 24 Costs Adaptation Cost Adaptation Cost + Penalty R >  R ≤  No Adaptation Adaptation Risk R Violation Non-Violationeffective not effective 0 PenaltyViolation Non-Violation • Risk threshold  • Adaptation effectiveness  „Utility“ measured in terms of saved costs:
  25. 25. Risk estimate for proactive process management Initial experimental evaluation based on air cargo process 5 months of operational data 3 942 process instances 56 082 service invocations 25BMDA 2018, Vienna Point of Prediction
  26. 26. Constant penalty Nonconstant Penalties BMDA 2018, Vienna 26 Metzger & Föcker, CAiSE 2017 https://doi.org/10.1007/978-3-319-59536-8_28 Metzger & Bohn, ICSOC 2017 https://doi.org/10.1007/978-3-319-69035-3_25 Cost Savings Frequency Cost savings of 14% on average (in 82.9% of cases) Additional cost savings of 23% on average Additional Cost Savings Risk estimate for proactive process management
  27. 27. Agenda 1. TT and the Big Data Value Ecosystem 2. TT Methodology 3. Transport Innovation via Big Data 4. Future Opportunities and Barriers BMDA 2018, Vienna 27
  28. 28. Opportunities Cross-sector data sharing (e.g., traffic flow  passenger flow  flights) Open data: http://europeandataportal.eu/data/en/group/transport Meta data repositories: e.g., TT Data Portal: BMDA 2018, Vienna 28
  29. 29. Opportunity Deep Learning Deep Learning (“AI”) • Recurrent Neural Networks (RNNs) with LSTM • Can handle arbitrary length sequences of events • Initial results for predictive transport process management – 27% higher accuracy than classical Multi-Layer Perceptron (MLP) – Robust against how data is encoded (not need to „tweak“) BMDA 2018, Vienna 29 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 num s2e noplannednopath mlpCheckpoint [relative prefix] Accuracy[MCC] RNN MLP
  30. 30. Barriers Protection of Personal Data (EU GDPR) – e.g., TT: 1% of TT data Protection of Commercial Data / IPR – e.g., 68% of TT data sources BMDA 2018, Vienna 30 https://www.eugdpr.org/ http://www.industrialdataspace.org/en
  31. 31. Barriers Lack of skills • Demand for “data professionals” exceeds supply on the labour market • (Some) ongoing activities – EU “Digital Skills and Jobs” Coalition – BDV PPP Education Hub: (Sources: [OECD, 2015; IDC 2015]) Year Gap (total EU) Gap (% EU) 2014 500,000 8% 2020 (baseline) 530,000 6% 2020 (challenged) 150,000 2% 2020 (high-growth) 3,500,000 30% BMDA 2018, Vienna 31
  32. 32. Thank You! BMDA 2018, Vienna 32 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 731932
  33. 33. Mobility and Logistics One of most-used industries in the world and in EU… • 15% of GDP (source: KLU) • Employment of 11.2 million persons in EU-28 (source: DG MOVE) • 3,768 billion tonne-kilometres and 6,391 billion person-kilometres in EU-28 • Key contributor to emissions: 4,824 megatonnes CO2 (source: DG MOVE) …and growing • Business and tourism travel expected to grow significantly over next decades • Freight transport slated to increase by 40 % in 2030 and by 80% in 2050 (source: ALICE ETP) Need for paradigm shift! • 10% efficiency improvement = EU cost savings of 100 B€ (source: ALICE ETP) • Big Data expected to lead to 500 billion USD in value worldwide in the form of time and fuel savings, and savings of 380 megatons CO2 in transport and logistics (source: OECD) BMDA 2018, Vienna 33

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