Automatic Recognition of PublicTransport Trips from Mobile Device

Sensor Data and TransportInfrastructure Information

Mikko Rinne Mehrdad Bagheri Tuukka TolvanenJaakko Hollmen

Aalto University, Department of Computer ScienceEspoo, Finland

Presentation at the KNOWMe: 1st International Workshop on KnowledgeDiscovery from Mobility and Transportation Systems

September 22, 2017

TrafficSense Research Project (2013–2017)

I TrafficSense — Energy Efficient Traffic withCrowdsensing (2013-2017)

I Research project funded by Aalto Energy EfficiencyResearch Programme (AEF)

I Project desciption:http://aef.aalto.fi/en/research/trafficsense/

I Motivational video:https://player.vimeo.com/video/109910585

Motivation for automatic detection of public

transport usage

I Understand the commuting habits of passengers at largeand over longer periods of time

I Enable compilation of door-to-door trip chains to assistpublic transport providers in improved optimisation oftheir transport networks

I Predictions of future trips based on past activities can beused to assist passengers with targeted information aboutopportunities and problems on the predicted trip chain

I Connecting passenger assistance

Targets of the experiment

I Create a dataset of mobile device sensor measurementsand public transport infrastructure data to test algorithmsfor automatic recognition of public transport trips

I Test initial algorithms

I Recognize both the public transport vehicle type (bus,tram, train, subway) and current line

I Fully automated sensor data collection with no effortfrom the user

I Power saving features to facilitate ’always-on’measurements with minimum impact on mobile devicebattery consumption

TrafficSense system and mobile application

Screenshots from the TrafficSense mobile application.

What was collected?

Mobile device:

I Position (combination ofsatellite, WiFi and Cell-IDmethods)

I Activity (’still’, ’walking’,’running’, ’on bicycle’, ’invehicle’ with confidencepercentages as estimatedby Google Play Services)

I Model of each device

Manual bookkeeping:

I trip start and end times,lines, boarding and exitstops etc.

Infrastructure:

I Static public transporttimetables and routeinformation

I Live samples of publictransport vehiclepositions.

I Measured train stop timesat stations for the date ofthe experiment.

Mobile application power saving state diagram

Sleep

Active

Timer running

No timer

Start timer

Timer expired

Activity not STILL

Activity not

STILL

Activity STILL

Location change

Sensor data filter algorithm

New Location

X min after last queued

Accept

Yes

Accuracy <= Y m

‘Good’ Activity !=

last queued

Yes Yes No

Distance to last queued > Accuracy

No Yes

Imperfections of sampled data

I Activity estimate sometimes incorrect, e.g. passengerriding a bicycle during a tram trip

I Intermittent or missing measurements (power saving,problems in positioning for rail traffic and in undergroundconditions)

I Positioning location hopping between the correct positionand a single distant point

I Live locations not available from all public transportvehicles at the time of the trial

Matching with live vehicle location

distance threshold R

distance r

score max(0, R-r)

user path samples

vehicle path

vehi

cle

path

dt

Matching with static timetables

Boarding stop End stop

Actual trip:

Sampled trip:

Planned trip:

IN_VEHICLE leg’s origin

IN_VEHICLE leg’s destination

IN_VEHICLE

BUS

2.BUS

1.WALK 3.WALK

tPT

tWb tWe

tV

WALK WALK

WALK WALK

tPTb tPTe

Recognition results

Table: Trip matching statistics for all recognition methodscompared to the manually logged trips (“line name” = matchingline name, “line type” = matching line type).

Static Old live New live Combined LoggedBus 8 3 4 9 15Bus (line name) 6 3 4 7 15Tram 8 8 8 9 15Tram (line name) 8 7 7 9 15Train 4 0 0 4 9Train (line name) 4 0 0 4 9Subway 19 9 17 25 58Public transport 40 20 29 48 97Public transport (line type) 39 20 29 47 97

Conclusions

I Combining the live and static methods 60% of bus andtram trips, and 43–44% of train and subway tripsrecognised with correct vehicle type

I Requiring bus line name dropped recognition to 47%

I Joint combined public transport recognition at 48% forthe correct line type

I Level adequate for e.g. public transport disruptioninformation filtering for frequently used lines

I Dataset available for improvements:https://github.com/aalto-trafficsense/

public-transport-dataset

Contact information

TrafficSense research project home page

I http://aef.aalto.fi/en/research/trafficsense/

Author contact information

I Mikko Rinne Mikko.Rinne@iki.fi

I Mehrdad Bagheri, Mehrdad.Bagheri@aalto.fi

I Tuukka Tolvanen, Tuukka.Tolvanen@aalto.fi

I Jaakko Hollmen, Jaakko.Hollmen@aalto.fi