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Movement behavior study using GPS/GIS integration

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. Presentation. Introduction Preliminary results Current and future work Discussion.

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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

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