extraction of bicycle commuter trips from day long gps trajectories

Extraction of bicycle commuter tripsfrom day-long GPS trajectories

Cycling Data Challenge 2013Leuven, Belgium

workshop presentation

Gerald Richter 1 Christian Rudloff 1 Anita Graser 1

1Austrian Inst. of Technology – Mobility Dept. – Dynamic Transportation Systems

G. Richter | AIT | mobility | DTS May 14, 2013 1 / 19

The Austrian Institute of TechnologyAIT – who we are and what we do

Austria’s largest non-university research instituteAIT: 5 departments focussing on applied research topics

• Energy• Mobility

business units:• Transportation Infrastructure Technologies• Dynamic Transportation Systems• Electric Drive Technologies• Light Metals Technologies Ranshofen

• Safety & Security• Health & Environment• Foresight & Policy Development


Dynamic Transportation Systems“develop efficient, safe and cost-effective multimodaltransportation solutions for transportation networks, hubs andservices”

Airports / Train Stations

Shopping Centres / Events

Multi-Modal Transportation

Networks Transport Logistics

Crowd Dynamics Traffic Flow Modelling Dynamic Vehicle Routing

Optimisation Simulation /

Prediction Data Analysis Data Collection


GPS measurementsand some peculiarities

Proper GPS measurement requires 4 satelitesto be visible by device.Measurement is stochastic process by nature.Positional precision is gaussian distributedunder clear-view conditions.Additional effects arise from obstructed view(signal shadowing, reflection by obstacles).

• outliers: sudden change in signal receptionconditions

• drift: longer phases of signal impairment,receiver-internal error correction walking amisguided path.

snap-back

true path


The input data. . . hence this initial situation

some points not out of thisworldsome tracks far outside theregion of interestmost likely due to GPSinitialisation phase– fixable by bounding boxclipping

Figure: detail UK


A simple yet efficient approachstages of processing

Cleaning• Outliers and unlikely points in the data are removed

i.e.: some trajectory smoothness is ascertained• Data is split into trip trajectories inbetween stops or

activitiesi.e.: a journey’s segments are identified

Mode Detection• A training set of data is used to identify decision criteria

within a manually chosen set of variables (trip parameters).• With those criteria modes of trips are detected to separate

bike trips from other trips

Details found in [1, 3, 2]


Cleaning the dataSteps of the data cleaning algorithm

Outliers are removed according to• geographic location: within bounding box around area of

interest• accesiblity: reachable by realistic speeds (here ≤ 50 m

s )• GPS drifts: points before trajectory snap-backs are deleted

until the remaining trajectory only contains realistic speeds

Stop detection and trip separation• Stop is detected when trajectory does not

leave circle of radius 30m for at least 5minutes.

• GPS trajectories are cut into trips at stoppoints (removal of tumbleweed)

• Next trip starts when trajectory leavescircle


Unlikely points

Tumbleweed also found atshorter stops (e.g. traffic lights)

Removed by loop detection(look ahead 3 minutes andfind very low effectivevelocities to reach asuccessive trajectory pointin given time interval)All points in loop arereplaced by one middlepoint between start andend of loop.


Modal Decisionprinciple

Classification of cycling tracksusing a decision treeOther methodologies (logisticregression, support vectormachines, neural network)show similar out of sampleperformanceDecision tree are easy to useand interpret

exemplary diagram:(2-dimensional feature space)

Training data from the Vienna region with 8 different modes


Mode Detectionalgorithmic choices

For CDC data set distinction was made between 3 Modes

Walking

Cycling

Other

Algorithmic separability optimisation left 3 separation variables:

maximum velocity

percentage of time over 16 km/h

maximum acceleration


Processing outcomevisually

black: refined tracks; green: processed and detected cycling tracksG. Richter | AIT | mobility | DTS May 14, 2013 11 / 19

Bird’s eye comparisonin numbers

Comparison of no. cycle trips and trip lengthrefined all modes cycling

No. cycle trips 941 1,734 749Total trip [km] 4,483 6,800 3,014

Oct 12 2011

Oct 19 2011

Oct 26 2011

Nov 02 2011

Nov 09 2011

Nov 16 2011

Nov 23 20110

20000

40000

60000

80000

100000

tota

l trip

tim

e [s

]

trips per day comparisonwrt. total time

diaryprocessed

Oct 12 2011

Oct 19 2011

Oct 26 2011

Nov 02 2011

Nov 09 2011

Nov 16 2011

Nov 23 20110

10

20

30

40

50

60

70

tota

l num

ber o

f trip

s

trips per day comparisonwrt. number of trips

diaryprocessed


Comparing track densitiesprinciple

fewer trips weredetected than in refineddataalgorithm unlikely tofalsely qualify tracks ascyclingcoordinate shift in initialdata along thebackslash diagonal

(processed cycling trips) – (refined trips)


Different cyclists

0 100 200 300 400 500 600 700avg. number of pts per trip

5

0

5

10

15

20

25

num

ber

of

trip

s

processed trip scatterfor all cyclists

quite different profilesby cycling habit ortrajectory cleaning?⇒ look associatedvelocity profiles

0 10 20 30 40 50 60speed [km/h]

0

100

200

300

400

500

# G

PS p

oint

s

speed distribution: cyclist 101(high number of trips)

0 10 20 30 40 50 60speed [km/h]

0

50

100

150

200

250

300

350

400

450

# G

PS p

oint

s

speed distribution: cyclist 113(high avg. number of points per trip)


Cyclist differences on map

high number of points per trackcyclist 113

high number of trackscyclist 101


Big visual


Summary & conclusionsApplied methods successfully discern useful GPS tracking datafrom technological artifacts.

Not too complex methods, good classification of the cyclingtransport mode

Results display periodic features of protocolled travel activity wrt.number of trips and travel times.

Algorithm cannot identify all cycling tracks of reference data.

Differences most likely due to dissimilar training set.

Low rate of false modal identification for cycling, while retainingthe substantial part of useable tracking data.

Compared to reference data, removal of erratic GPSmeasurement errors with appreciable reliability.

TODO: Use of homologous training data (road network topologyand traffic densities) expected to yield consistently better results.


Remarks

Thanks to:CDC2013 organisersThe other contributers and colleagues who I work with. . . a patient audience

Questions & comments to:[email protected]@[email protected]


References

[1] D. Bauer et al. “On Extracting Commuter Information fromGPS Motion Data”. In: Proceedings InternationalWorkshop on Computational Transportation Science(IWCTS08). 2008.

[2] R. Hariharan and K. Toyama. “Project Lachesis: Parsingand Modeling Location Histories.” In: Proceedings of theThird International Conference on GIScience. Adelphi,MD, USA, 2004.

[3] C. Rudloff and M. Ray. “Detecting Travel Modes andProfiling Commuter Routes Solely Based on GPS Data”.In: TRB 89th Annual Meeting. 2010.
