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

Mikko Rinne, Mehrdad Bagheri Majdabadi, Tuukka Tolvanen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)
137 Downloads (Pure)


Automatic detection of public transport (PT) usage has important applications for intelligent transport systems. It is crucial for understanding the commuting habits of passengers at large and over longer periods of time. It also enables compilation of door-to-door trip chains, which in turn can assist public transport providers in improved optimisation of their transport networks. In addition, predictions of future trips based on past activities can be used to assist passengers with targeted information. This article documents a dataset compiled from a day of active commuting by a small group of people using different means of PT in the Helsinki region. Mobility data was collected by 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 is used to cross-check and verify the results derived from automatic measurements. The mobile client application used for our data collection provides a fully automated measurement service and implements a set of algorithms for decreasing battery consumption. The live locations of some of the public transport vehicles in the region were made available by the local transport provider and sampled with a 30-s interval. The stopping times of local trains at stations during the day were retrieved from the railway operator. The static timetable information of all the PT vehicles operating in the area is made available by the transport provider, and linked to our dataset. The challenge is to correctly detect as many manually logged trips as possible by using the automatically collected data. This paper includes an analysis of challenges due to missing or partially sampled information, and initial results from automatic recognition using a set of algorithms comparing measured trips with both live vehicle locations and static timetables. Improvement of correct recognitions is left as an ongoing challenge.
Original languageEnglish
Title of host publicationPersonal Analytics and Privacy
Subtitle of host publicationAn Individual and Collective Perspective - 1st International Workshop, PAP 2017, Held in Conjunction with ECML PKDD 2017, Revised Selected Papers
Number of pages22
Publication statusPublished - 14 Jun 2017
MoE publication typeA4 Article in a conference publication
EventEuropean Conference on Principles and Practice of Knowledge Discovery in Databases - Croke Park Conference Centre, Skopje, Macedonia, The Former Yugoslav Republic of
Duration: 18 Sep 201722 Sep 2017
Conference number: 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10708 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Principles and Practice of Knowledge Discovery in Databases
Abbreviated titleECML PKDD
CountryMacedonia, The Former Yugoslav Republic of


  • intelligent transport systems
  • public transport
  • mobile applications

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