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

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Rinne, Mikko
dc.contributor.author Bagheri Majdabadi, Mehrdad
dc.contributor.author Tolvanen, Tuukka
dc.date.accessioned 2017-06-20T11:17:45Z
dc.date.available 2017-06-20T11:17:45Z
dc.date.issued 2017-06-14
dc.identifier.citation Rinne , M , Bagheri Majdabadi , M & Tolvanen , T Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information . en
dc.identifier.other PURE UUID: 652620e5-30aa-4f94-adeb-0b316b9da3eb
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/automatic-recognition-of-public-transport-trips-from-mobile-device-sensor-data-and-transport-infrastructure-information(652620e5-30aa-4f94-adeb-0b316b9da3eb).html
dc.identifier.other PURE LINK: http://arxiv.org/abs/1706.04047
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/28359279/SCI_Rinne_Mehrdad_Automatic_Recognition_of_Public_Transport_Trips_from_Mobile_Device_Sensor_Data_and_Transport_Infrastructure_Information.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/26963
dc.description.abstract 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. en
dc.format.extent 22
dc.format.mimetype application/pdf
dc.language.iso en en
dc.rights openAccess en
dc.subject.other Theoretical Computer Science en
dc.subject.other Computer Science(all) en
dc.subject.other 113 Computer and information sciences en
dc.subject.other 218 Environmental engineering en
dc.subject.other Software engineering, operating systems, man-computer interaction en
dc.title Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Non peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Professorship Hyvönen E.
dc.subject.keyword intelligent transport systems
dc.subject.keyword public transport
dc.subject.keyword mobile applications
dc.subject.keyword Theoretical Computer Science
dc.subject.keyword Computer Science(all)
dc.subject.keyword 113 Computer and information sciences
dc.subject.keyword 218 Environmental engineering
dc.subject.keyword Software engineering, operating systems, man-computer interaction
dc.identifier.urn URN:NBN:fi:aalto-201706205687
dc.identifier.doi 10.1007/978-3-319-71970-2_8
dc.type.version publishedVersion


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive


Advanced Search

article-iconSubmit a publication

Browse

My Account