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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

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

Movement behavior study using GPS/GIS integration

objective

user profiles

GIS spatial data sets

Objective

design algorithms

position + time

modality + category

Movement behavior study using GPS/GIS integration

postgis and quantumgis
PostGIS and QuantumGIS
  • PostGIS: spatial extension to PostgreSQL DBMS
  • QuantumGIS: visualization of PostGIS data

Movement behavior study using GPS/GIS integration

gps data only user profiles
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

gps gis integration
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

problems 1 2
Problems (1/2)

Movement behavior study using GPS/GIS integration

problems 2 2

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

preliminary results
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

acceleration 1 3

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

acceleration 2 3
Acceleration (2/3)

Movement behavior study using GPS/GIS integration

acceleration 2 312
Acceleration (2/3)
  • Assumption not true…
  • Reasons:
    • GPS inaccuracies?
    • Low-resolution log time interval?

Movement behavior study using GPS/GIS integration

more detailed spatial datasets 1 2
More detailed spatial datasets (1/2)

Movement behavior study using GPS/GIS integration

more detailed spatial datasets 2 2

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

other ideas
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

reverse order
“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

incorrect validated trips
Incorrect validated trips

Movement behavior study using GPS/GIS integration

correct validated
Correct validated

Movement behavior study using GPS/GIS integration

results 1 2
Results (1/2)

Movement behavior study using GPS/GIS integration

results 2 2

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

likelyhood
“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

future work
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

movement behavior study using gps gis integration23

Movement behavior study using GPS/GIS integration

Falk data

GPS tracklines

GISt Lunchmeeting

Arnoud de Boer

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