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Predictive analytics in mobility

Presentation on EDIT summer school

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Predictive analytics in mobility

  1. 1. Predictive analytics The next generation of MaaS Rok Okorn, Ektimo d.o.o.
  2. 2. Agenda About predictive analytics Supervised, unsupervised or reinforcement Methods Examples of usage
  3. 3. What is data science? Data science reveals previously unknown cause and effect relationships and possibly forecasts future events by a systematic analysis (of large amounts) of data. Objective: usage of data for improved business cases. Better decisions Higher effiecency Cost optimization Improved experience New products
  4. 4. dfsdf New levels of analytics with a data science Complexity Addedvalue Descriptive analytics Diagnostic analytics Predictive analytics Prescriptive analytics What happened? Why it happened? What will happen? Which decision leads to the best outcome? Survey results: Big data use cases 2015, BARC: 39% 31% 8% 10% EU companies perform big data projects. companies implemented predictive analytics. increase in income due to big data projects.. mean cost reduction due to big data projects.
  5. 5. In the center of data science is artificial intelligence These algorithms enable computers to: • learn from past data without explicit programming • improve with new data. • effectively recognize patterns in complex data from a variety of sources.
  6. 6. Supervised, unsupervised or reinforcement? Example: object recognition •Supervised learning: Learn by examples as to what object it is in terms of structure, color, shape, etc. So that after several iterations it learns to define an object. •Unsupervised learning: There is no desired output that is provided, therefore categorization is done so that the algorithm differentiates correctly between bikes, cars, houses or people (clustering of data). •Reinforcement learning: The predictions are continuously updated, unlike in the previous types. For example, when a robot sees an object: first classify it and then go around it and classify it again on new observed parameters. Alternatively, when the robot learns that some object is dangerous, it will avoid it, next time
  7. 7. Usable tools
  8. 8. Typical process Obtain the data Data preparation Feature creation ValidationModelling Application Identification Enrichment Import Integration Cleaning Exploration Transformation Normalization Categorization Statistical Business Splitting Subsetting Learning Optimization Implementation Monitoring Visualization UI Feedback
  9. 9. Data preparation DA Transactions Products Demographics Campains CallsE-mail Mobile media External data What we knew about person A up to some date T? What happened 1 month after T? Buys new product Integration and transformation of data Features (predictors) Response 301.2 1 4.5 1 10.9 Person A’s digital print
  10. 10. D A D D D D D D D Modelling HistoricaldataCurrentdata Learning set Test set ?? Predictive model Learning/training Model validation Input (features) Output (prediction) 67% The model predicts a purchase for person X with 67% probability
  11. 11. Ensembles – diversification at the level of models Predictive models Input Prediction Final prediction based on some function of the individual models, e.g. mean Instead of one single model we train multiple different models. 65%?? 90% 10% 55%
  12. 12. Some useful algorithms Regressions: linear, logistic, poisson, lasso SVM: linear, kernel, hard/soft margin Clustering: k-means, kNN, hierarchical Decision trees: decision tree, randomForest Deep learning: Boltzman machines, autoencoders, recurrent networks Ensemble methods: AdaBoost, VotingClassifier
  13. 13. Variouos use cases • Demand forecasting • Loyalty programs • Dynamic pricing • Recommendation systems • Optization of asortment • Credit scoring • Claims prediction • Fraud detection • Predictive lead scoring • Targeting • Optimization • Susceptibility to the purchase • Personalization • Churn prediction • Customer lifetime value prediction • Routing optimization
  14. 14. Self- driving cars Predictive maintenance Optimization of supply Usage of PA in mobility What elementary problems need to be solved? • Basic infrastructure • Data gathering • What are the KPIs? Predictive analytics tasks: • Predict (stochastic) demand and supply • Predict defects, malfunctions or failures • Recognize objects on paths and deal with them • etc.
  15. 15. Examples of predictive analytics capabilities
  16. 16. Image recognition – problem formulation •What is it? Handwriting, CAPTCHAs; discriminating humans from computers •Where is it? Detecting objects regions in images •How is it constructed? Determining how a group of something is related (e.g. math symbols) or determining some structure of objects Given a database of objects and an image determine what, if any of the objects are present in the image.
  17. 17. Image recognition – solution I source: Bernd Heisele,Visual Object Recognition with Supervised Learning
  18. 18. Image recognition – solution II source: https://s3.amazonaws.com/datarobotblog/images/deepLearningIntro/013.png
  19. 19. Image recognition – mobility usage • Obstacle detection • Terrain reconstruction • Convoying • Collision detection • Road recognition
  20. 20. Demand prediction - problem formulation Different forecasts for different types of products: • Nondurable consumer goods • vanish after a single act of consumption • depends upon price of the commodity and the related goods and population and characteristics • Durable consumer goods • can be consumed a number of times or repeatedly used • depends upon social status, level of money income, taste and fashion, the provision of allied services and their cost, sensitive to price changes • Capital goods • used for further production • depends on the specific markets they serve and the end uses for which they are bought, consumption per unit of each end-use product • New-products • new to the consumers • depends on type (evolution, substitute), same group products demand Given current and past data, predict the demand of a given product.
  21. 21. Demand prediction – solution I Classical time series approach • Seasonality • Trend • ARIMA, GARCH
  22. 22. Demand prediction – solution II Machine learning methods source: Application of machine learning techniques for supply chain demand forecasting Original Research Article, European Journal of Operational Research, Volume 184, Issue 3, 1 February 2008
  23. 23. Demand prediction– mobility usage • Predicting demand in a specific location • Adding new infrastructure elements (stations, cars) • Dynamic pricing • Power demand
  24. 24. Predictive maintenance - problem formulation Can you tell me, when to perform maintenance? Three types of maintenance: • emergency; when failure occurs • preventive; regularly on time, cleaning cycle of x weeks • predictive; when it is needed Predictive maintenance is condition based using advanced technology and instrumentation Assumes installed indicators; read and reported by operators or sensors •What symptoms indicate the pending failure under review? •How can the symptom be detected? •Which methods of detection might be useful? •How long is the anticipated failure development period? •What does this suggest about inspection intervals?
  25. 25. Predictive maintenance – solution I source: Architecture diagram: Solution Template for predictive maintenance
  26. 26. Predictive maintenance – solution II source: http://revolution-computing.typepad.com/.a/6a010534b1db25970b01bb08cba61c970d-800wi
  27. 27. Predictive maintenance – mobility usage • Safety, motor breakdowns • Infrastructure faults • Electric component failures • Battery performance / failure
  28. 28. Beware: Issues • Methods gathering and labeling data, problem formulation • Image recognition range of viewing conditions, 2D vs. 3D, point of view, size of known image pool • Demand prediction seasonality, special events, weather, location, only sales data (instead of demand) • Preventive maintenance immediate critical faults, sensor placements
  29. 29. Thank you for your attention! & Q&A