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Big data Europe the transport pilot in Thessaloniki - Josep Maria Salanova

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Webinar presentation "Big data Europe the transport pilot in Thessaloniki " by Josep Maria Salanova

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Big data Europe the transport pilot in Thessaloniki - Josep Maria Salanova

  1. 1. Dr. Josep Maria Salanova Grau Center for Research and Technology Hellas - Hellenic Institute of Transport Head of “Data collection and processing, algorithm design, and use of specialized transport software packages” laboratory Email: jose@certh.gr Web: www.hit.certh.gr Big Data Europe. The transport pilot in Thessaloniki.
  2. 2. Mobile sensors in Thessaloniki • Stationary sensors network: Point to point tracking of MAC ids along the network through 43 Bluetooth device detectors. ▫ Travel time estimation ▫ Route choice model calibration ▫ Origin – Destination matrix estimation / Mobility patterns estimation ▫ Traffic flow extrapolation • Dynamic sensors fleet: Floating Car Data provided in real time by a professional fleets composed of 1.200 taxis and 600 buses ▫ Traffic status estimation (average speed) ▫ Origin – Destination matrix estimation / Mobility patterns estimation ▫ Taxi/bus performance indicators • Social media (geolocated tweets & Facebook check-in events) ▫ Activity patterns estimation ▫ Events / incidents detection ▫ Attraction models estimation
  3. 3. Mobile sensors in Thessaloniki • http://160.40.63.110/desktop/
  4. 4. Mobile sensors in Thessaloniki • www.trafficthess.imet.gr/
  5. 5. Mobile sensors in Thessaloniki • www.trafficpaths.imet.gr
  6. 6. Mobile sensors in Thessaloniki • http://opendata.imet.gr/ Datatank (Back office + APIs) CKAN (front end)
  7. 7. BDE pilot in Thessaloniki
  8. 8. • Probe data that is used ▫ Floating Car Data (500-2.500 locations per minute) ▫ Bluetooth detections (millions of daily detections in 43 locations) • Services that are being implemented ▫ Map matching ▫ Mobility patterns recognition and forecasting (ARIMA) BDE pilot in Thessaloniki – pilot 1 GPS Data Map Matching Traffic Classification and Prediction Classification and Prediction Data Bluetooth Data Results
  9. 9. • Probe data that is used ▫ Floating Car Data (500-2.500 locations per minute) ▫ Bluetooth detections (millions of daily detections in 43 locations) • Services that are being implemented ▫ Improved topology-based map matching ▫ Mobility patterns recognition and forecasting (ARIMAX + NN) GPS Data Map Matching Traffic Classification and Prediction Classification and Prediction Data Bluetooth Data Results BDE pilot in Thessaloniki – pilot 2&3
  10. 10. Map Matching Algorithm BDE pilot in Thessaloniki – pilot 2&3
  11. 11. rownum recorded_times tamp transfer osmids 263005 08:43:18 1 213910068 263171 08:43:27 1 16457977 263350 08:43:37 1 213910068 263657 08:43:55 1 213910068 263919 08:44:10 1 22564731 264174 08:44:25 1 222137984 264701 08:44:56 1 222137984 264813 08:45:04 1 222137984 265071 08:45:19 0 22564731 265363 08:45:35 0 213910068 265565 08:45:48 0 213910068 265737 08:45:57 0 16457977 265906 08:46:07 0 213910068 Map Matching Algorithm BDE pilot in Thessaloniki – pilot 2&3
  12. 12. Start Historical Link Traffic State (FCD) Historical Link Traffic State Classification Historical Link Traffic State (Loop Detectors) Historical Link Traffic State BT Compare Traffic States (ML, NN) Define Current Link Traffic State Predict (ARIMAX | NN) Store in Historical States Validate Prediction Traffic Classification and Prediction BDE pilot in Thessaloniki – pilot 2&3
  13. 13. Dr. Josep Maria Salanova Grau jose@certh.gr +30 2310 498 433 Thank you!

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