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[EVENT IOT] MOTION-S

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Mobility Data Augmentation

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[EVENT IOT] MOTION-S

  1. 1. MOBILITY DATA AUGMENTATION From Telematics Data to Portable Scores Dr.-Ing. German Castignani CTO / Co-founder MOTION-S S.A. Internet of Things September 25th 2018 Chambre de Commerce
  2. 2. ABOUT MOTION-S Game of Roads Best Mobile App of the Year Marketers Award 2015 Game of Roads 2015 Motion-S 2016 Motion-S 2017 GoodDrive 2017 Motion-S 2018 Mind & Market Award Creative Young Entrepreneur Luxembourg 2nd Place Startup of the Year IT-Nation GOLDEN-i Award Best Innovation in Insurance Infinance Award Representative of G.D. Luxembourg at the CES Las Vegas 5 MOBILE APPS LAUNCHED 13.500+ profiles • 1.000.000+ Trips 15.000.000+ km • EU, US, MEX, UAE Motion-S 2018 Selected as one out of five participants in the first batch Motion-S 2018 Strategic partnership and investment of 1m€.
  3. 3. OUR BUSINESS MODEL SOURCE STORED or LIVE GPS, sensor and car data provided by any device (smartphones, dongles, CAN bus) We contextualize and profile the data on our Profiling Platform. PROFILING SCORING The augmented data gets correlated with individual risk factors and predictive models on our Scoring Platform. We create dynamic dashboards for advanced data visualisation to assist decision making and smartphone apps to provide feedback to drivers VISUALISATION OUR USP
  4. 4. OUR WHITELABEL SOLUTION
  5. 5. PLATFORM AND METHODOLOGY
  6. 6. Claim-based Correlation Context-based Drivers having frequent harsh events are more risky Drivers having frequent harsh events in a given context are more risky Drivers having frequent harsh events in a given context with proven correlation to claim statistics are more risky Event-based Approach: From subjective to objective scoring MEASURING RISK WITH TELEMATICS
  7. 7. 60% of reported accidents with 22 CFs MEASURABLE CONTRIBUTORY FACTORS - STAT19 GPS + Augmentation GPS + Car + Augmentation
  8. 8. Metric n CF n 1. Trip Profiling 2. CF Calculation 3. Scoring ● The concept: Reconstructing the CF form with Telematics ○ Measure exposure to CF through Telematics Metrics ○ For each trip, calculate Telematics Metrics and decide which CF would have been triggered ○ Use the distribution of accidents per contributory factors to come out with a dist score Road Safety Statistics Behavioural Exposure RISK ASSESSMENT
  9. 9. TEMPORAL AND SPATIAL FACTORS (UK EXAMPLE) Slight Serious Fatal Slight Serious Fatal
  10. 10. Best participant -56% from avg fleet risk (1.600 km driven) Worst participant +136% from avg fleet risk (2.237 km driven) BEST AND WORST DRIVERS Difference in score is mainly generated by CF and road distribution of Worst Participant being present in 26% more of fatal accidents
  11. 11. ● Use telematics metrics to find groups of drivers according to their driving patterns ● 1.580 drivers from our DBs- 162.145 trips - 85 trips per driver (median) CLUSTERING OF DRIVING PATTERNS Visualization with t-SNE Cluster ID Number of drivers Interpretation 1 462 Respect speed limits, soft driving maneuvers. 2 154 Harsh braking close to traffic signs, aggressive driving. 3 186 Disrespect speed limits, sensation seeking (traffic and curves speeding), aggressive driving. 4 284 Soft driving maneuvers, moderate to low values on other TM. 5 446 Do not present extreme values on TM. 3,2 1,4,5
  12. 12. We are hiring ! jobs@motion-s.com Motion•S S.A. +352 - 26 20 21 56 2a, rue du Belvédère L-5512 Remich www.motion-s.com info@motion-s.com

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