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Automatic detection of public transport (PT) usage has important applicationsfor intelligent transport systems. It is crucial for understanding thecommuting habits of passengers at large and over longer periods of time. Italso enables compilation of door-to-door trip chains, which in turn can assistpublic transport providers in improved optimisation of their transportnetworks. In addition, predictions of future trips based on past activities canbe used to assist passengers with targeted information. This article documentsa dataset compiled from a day of active commuting by a small group of peopleusing different means of PT in the Helsinki region. Mobility data was collectedby two means: (a) manually written details of each PT trip during the day, and(b) measurements using sensors of travellers' mobile devices. The manual log isused to cross-check and verify the results derived from automatic measurements.The mobile client application used for our data collection provides a fullyautomated measurement service and implements a set of algorithms for decreasingbattery consumption. The live locations of some of the public transportvehicles in the region were made available by the local transport provider andsampled with a 30-second interval. The stopping times of local trains atstations during the day were retrieved from the railway operator. The statictimetable information of all the PT vehicles operating in the area is madeavailable by the transport provider, and linked to our dataset. The challengeis to correctly detect as many manually logged trips as possible by using theautomatically collected data. This paper includes an analysis of challenges dueto missing or partially sampled information in the data, and initial resultsfrom automatic recognition using a set of algorithms. Improvement of correctrecognitions is left as an ongoing challenge.

Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information