Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Recognition of Public Transport Trips from Mobile Device
  Sensor Data and Transport Infrastructure Information   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-second 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 in the data, and initial results
from automatic recognition using a set of algorithms. Improvement of correct
recognitions is left as an ongoing challenge.

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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph