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Big Data in Shift2Rail: Connective and in2Stempo projects

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Big Data in Shift2Rail: Connective and in2Stempo projects

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Big Data in Shift2Rail: Connective and in2Stempo projects

  1. 1. ResearchandDevelopment Innovationisinournature Big Data in Shift2Rail: CONNECTIVE and In2Stempo projects European Big Data Value Forum, 14th October 2019 Helsinki, Finland Aneta Tumilowicz, Network Rail (UK)
  2. 2. ResearchandDevelopment Innovationisinournature What is Shift2Rail (S2R) Shift2Rail is the first pan-European rail industry initiative that aims to foster innovation in the railway sector by accelerating the integration of new and advanced technologies into innovative rail product solutions. 07/11/2019 3 S2R research priorities are split into 5 innovation programmes (IP): Each IP aims to deliver specific area technology demonstrators and is split into smaller projects
  3. 3. ResearchandDevelopment Innovationisinournature Shift2Rail catalogue of 12 Railway Innovation Capabilities 07/11/2019 4
  4. 4. ResearchandDevelopment Innovationisinournature IP4 context (overview and objectives) 07/11/2019 5 • Put the traveller back at the centre, ease access to rail, increasing its attractiveness • Complete multimodal travel offer connecting the first and last mile to long distance journeys • Give access to all multimodal travel services (shopping, ticketing, and tracking) through its travel-companion • Build an open framework providing full interoperability whilst limiting impacts on existing systems Across Europe Across Modes Door - to - Door Across Services Planning Shopping Ticketing Navigating Tracking Aftersales
  5. 5. ResearchandDevelopment Innovationisinournature CONNECTIVE Project 07/11/2019 6 CONNECTIVE: Connecting and Analyzing the Digital Transport Ecosystem The CONNECTIVE project works towards the digital transformation of rail and all transport services, providing the framework, tools and technologies to allow data exchange among different actors of the transport ecosystem and facilitating interoperability among systems, but also the creation of added value services using all available information. TD4.1 Interoperability Framework TD4.4 – Trip Tracker TD4.5 - Travel Companion TD4.2 - Travel Shopping TD4.6 - Business Analytics TRANSPORTATION DATA TD4.3 - Booking, Ticketing This project has received funding from the Shift2Rail Joint Undertaking under the European Union's Horizon 2020 grant agreement no 777522
  6. 6. ResearchandDevelopment Innovationisinournature IP3 context (overview and objectives) 07/11/2019 7 • The design, construction, operation and maintenance of rail network infrastructure has to be safe, reliable, supportive of customer needs, cost-effective and sustainable • There is a need for a step change in the productivity of infrastructure assets. These will have to be managed in a more holistic and intelligent way, using lean operational practices and smart technologies that can ultimately help improve the reliability and responsiveness of customer service, as well as the capacity and overall economics of rail transportation. • Rail infrastructure must ensure compatibility between infrastructures (interoperable and standardised infrastructure), as well as with other modes (intermodal infrastructure, including stations and passenger and freight hubs). Improving crowd management in high and low capacity stations, improving station design and components, improving accessibility to trains and improving the safety and security of passengers and employees at stations.
  7. 7. ResearchandDevelopment Innovationisinournature In2Stempo Project 07/11/2019 8 In2Stempo: Innovative Solutions in Future Stations, Energy Metering and Power Supply This project has received funding from the Shift2Rail Joint Undertaking under the European Union's Horizon 2020 grant agreement no 777515 This project aims to improve cost efficiency and ensure reliable, high capacity, infrastructure. In2Stempo has 3 primary objectives: developing a smart railway power grid in a interconnected and communicated system, mapping of energy flows and usage within the entire railway system allowing for more effective energy management strategies in the future and improving the customer experience at railway stations.
  8. 8. ResearchandDevelopment Innovationisinournature Business Analytics (BA) Overview 07/11/2019 9 Descriptive Analysis Use data aggregation and data mining to provide insight into the past and answer: “What has happened?” I Predictive Analysis Use statistical models and forecasts techniques to understand the future and answer: “What could happen?” Prescriptive Analysis Use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?” II III UC1 UC2
  9. 9. ResearchandDevelopment Innovationisinournature UC1: BA in the context of Future stations (IP3) 07/11/2019 11 Assets management Demand management Passenger & Services Management Multimodal Business Data Analytics • Monitoring stations; optimize maintenance; optimize passenger flow • Optimize revenue collection • Fraud control • Characterize problems/incidences and impact depending on type of day • Mobility patterns, demand prediction, OD Matrix • Correlate demand and day type, events, weather, etc. • What-If analysis, support to the deployment of strategies and offer-demand adjustment • Control passenger entrance at stations • Patterns of transport usage depending on sociodemographic profiles • Business KPI: cost and revenues • Loyalty programs, ancillary services, preferred bookings, etc. • Combining data from different TSP, users and IP4 services: − Travel patterns: preferred multimodal itineraries, etc. − Multimodal KPIs − Multimodal hub analyses − Resource management depending on other modes
  10. 10. ResearchandDevelopment Innovationisinournature UC1: BA in the context of Future stations (IP3) 07/11/2019 13 BA: UC1: Crowd management in stations  Infrastructure design  Offline analysis: Evaluate and enrich evacuating existing scenarios and train operators  Online analysis: Forecasting analysis  Online analysis: What-if analysis  Analytics: • Machine Learning (virtual sensors and labelled data)+Transfer learning (real data and unlabelled or partially labelled data) • Data imputation algorithms • Predictive Analytics: short and medium terms predictions • Prescriptive Analytics: What-if analysis  Visualization:  Visualizing simulation, KPIs Warsaw West station  Real situation analyses  Sensors installation inside the station allowing simulation model recalibration  Video Analytics  Creation of virtual sensor data based on the simulated data Objectives Data available Technical details This project has received funding from the Shift2Rail Joint Undertaking under the European Union's Horizon 2020 grant agreement no 777515
  11. 11. ResearchandDevelopment Innovationisinournature UC1: BA in the context of Future stations (IP3) 07/11/2019 14 This project has received funding from the Shift2Rail Joint Undertaking under the European Union's Horizon 2020 grant agreement no 777515 From Real World … … To Simulated World/Digital Twin… … And Back to Real World Solve the reality gap problem • Get simulations close to reality • Get finer results (predictions, what-if analyses) Populate Digital Twin with people Calibrate simulation with real data (VCA, Traveller companion data, TSP data…)
  12. 12. ResearchandDevelopment Innovationisinournature UC2: BA in the context of Bus company management 07/11/2019 15 This project has received funding from the Shift2Rail Joint Undertaking under the European Union's Horizon 2020 grant agreement no 777522 Assets management Demand management Passenger & Services Management Multimodal Business Data Analytics • Monitoring stations; optimize maintenance; optimize passenger flow • Optimize revenue collection • Fraud control • Characterize problems/incidences and impact depending on type of day • Mobility patterns, demand prediction, OD Matrix • Correlate demand and day type, events, weather, etc. • What-If analysis, support to the deployment of strategies and offer-demand adjustment • Control passenger entrance at stations • Patterns of transport usage depending on sociodemographic profiles • Business KPI: cost and revenues • Loyalty programs, ancillary services, preferred bookings, etc. • Combining data from different TSP, users and IP4 services: − Travel patterns: preferred multimodal itineraries, etc. − Multimodal KPIs − Multimodal hub analyses − Resource management depending on other modes
  13. 13. ResearchandDevelopment Innovationisinournature UC2: BA in the context of Bus company management 07/11/2019 16 This project has received funding from the Shift2Rail Joint Undertaking under the European Union's Horizon 2020 grant agreement no 777522 Example UC: Analytics on Ticketing and service performance Use available information from ticketing equipment in metro stations (vending machines, access gates), which could be useful to:  Enhance operators performance and maintenance  Allow users to know in advance the situation of the equipment in the stations as well as peak times  Total sales, sales per hour, sales per location, average of sales.  Validation per line, validation per profile, validation per equipment.  Demand prediction by line, stop, at peak hour  Total benefits, Payed transactions, number of cards on the black list, Transaction per minutes.  Delay time for each stop, occupation per service, occupation per service and per stop place.  Alarms and failures patterns  Prediction of delays Databases of urban operator (Interbus, Madrid): • Operation Assistance Services (OAS) and Automatic Vehicle Location (AVL) • On board/ on station equipment: • Sales • Validations • Alarms Objectives Data available Analytics
  14. 14. ResearchandDevelopment Innovationisinournature Thank you for your attention 07/11/2019 17 https://projects.shift2rail.org/

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