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Movement behavior study using GPS/GIS integration

Movement behavior study using GPS/GIS integration

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Movement behavior study using GPS/GIS integration

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  1. Movement behavior study using GPS/GIS integration Algorithm design for extraction of movement behavior from GPS data logs, user profiles and GIS spatial datasets. GISt Lunchmeeting Arnoud de Boer

  2. Presentation • Introduction • Preliminary results • Current and future work • Discussion Movement behavior study using GPS/GIS integration

  3. user profiles GIS spatial data sets Objective design algorithms position + time modality + category Movement behavior study using GPS/GIS integration

  4. PostGIS and QuantumGIS • PostGIS: spatial extension to PostgreSQL DBMS • QuantumGIS: visualization of PostGIS data Movement behavior study using GPS/GIS integration

  5. GPS data only + user profiles • Design algorithms to identify • modality using moving average e.g. • if average speed < 10 km/h  foot • if average speed between 10 and 20 km/h  bike • if average speed between 20 and 200 km/h and • user has car  car • user has no car  train • category using e.g. location of home and work Movement behavior study using GPS/GIS integration

  6. GPS/GIS integration • Use spatial datasets for • Modalities, e.g. movements along a railway train • Categories, e.g. POIs, train stations and shopping centres Movement behavior study using GPS/GIS integration

  7. Problems (1/2) Movement behavior study using GPS/GIS integration

  8. 64% of GPS trackpoints intersects railway 39% of GPS trackpoints intersects railway Problems (2/2) >40% for modality ‘train’ Movement behavior study using GPS/GIS integration

  9. Preliminary results • Results • for modalities: 60% identified correctly • for categories: < 25% identified correctly • How to improve the results? • More (detailed) spatial datasets, e.g. busstops, platforms? • More constraints/conditions e.g. distances, acceleration? Movement behavior study using GPS/GIS integration

  10. id id 1 1 2 2 3 3 4 4 point1 point2 point3 avgspeed1 + avgtime1 avgspeed2 + avgtime2 acceleration = (avgspeed2-avgspeed1) / (avgtime2-avgtime1) Acceleration (1/3) • Assumption: • Modality train shows a more constant speed and acceleration than modality car • Compute acceleration from speed and time differences Movement behavior study using GPS/GIS integration

  11. Acceleration (2/3) Movement behavior study using GPS/GIS integration

  12. Acceleration (2/3) • Assumption not true… • Reasons: • GPS inaccuracies? • Low-resolution log time interval? Movement behavior study using GPS/GIS integration

  13. More detailed spatial datasets (1/2) Movement behavior study using GPS/GIS integration

  14. Platform length More detailed spatial datasets (2/2) • TOP10NL: tramroutes, tramstations and metrostations • ProRail: platform lengths and passenger buildings footprints • NWB: busstations? • Locatus: shops? Platform width Movement behavior study using GPS/GIS integration

  15. Other ideas • “Reverse-order”: • use validated trips to determine values for e.g. average speed, maximum speed, distance, time • “Likelyhood”: • add value if a certain condition is true and select modality or category with highest score Movement behavior study using GPS/GIS integration

  16. “Reverse order” • Large amount of validated • 3,395,958 GPS trackpoints • 36,811 distinct trips • 1290 distinct users (approx. 1100 validated) • Use validated trips for assumed values • 90% of validated trips in should match condition of a.o. • average and maximum speed • trip distance • intersection of with railway Movement behavior study using GPS/GIS integration

  17. Incorrect validated trips Movement behavior study using GPS/GIS integration

  18. Correct validated Movement behavior study using GPS/GIS integration

  19. Results (1/2) Movement behavior study using GPS/GIS integration

  20. Modality Average speed (km/h) Maximum speed (km/h) Time (min) Distance (m) air too scattered too scattered too scattered 100 to 100,000 bike 5 to 25 0 tm 30 km/h 0 to 20 100 to 5,000 Bus-tram-metro 0 to 60 0 tm 85 km/h 0 to 35 250 to 50,000 car 5 to 85 25 to 120 km/h 0 to 50 500 to 100,000 ferry 0 to 15 0 to 20 km/h 3 to 50 AND >120 100 to 2,500 foot 0 to 10 10 to 15 km/h 0 to 25 250 to 5,000 AND 10,000 to 50,000 scooter 5 to 40 AND 50 to 60 15 to 80 km/h 1 to 55 IS NULL AND 250 to 50,000 train IS NULL IS NULL IS NULL 100 to 500 Results (2/2) Movement behavior study using GPS/GIS integration

  21. “Likelyhood” • GIS data only • Speed, trip distance, time-of-day? • GPS/GIS integration: intersection with railways, rivers, motorways • User profiles: • User prefers modality for certain category, e.g. to ‘shopping centre by car’ or ‘work by train’ • User ownership of scooter, car, reduced-fare card Movement behavior study using GPS/GIS integration

  22. Future work • Intersection of full dataset with railways and water • Very computational: >3,000,000 intersections • Integration of user profiles with GPS/GIS approach • User questionnaires for likeliness • Acceleration with 1-second interval datalogs • Collect some test data with Amaryllo device Movement behavior study using GPS/GIS integration

  23. Movement behavior study using GPS/GIS integration Falk data GPS tracklines GISt Lunchmeeting Arnoud de Boer