Details
| Original language | English |
|---|---|
| Pages (from-to) | 215-221 |
| Number of pages | 7 |
| Journal | Transportation Research Procedia |
| Volume | 78 |
| Early online date | 23 Feb 2024 |
| Publication status | Published - 2024 |
| Event | 25th Euro Working Group on Transportation Meeting, EWGT 2023 - Santander, Spain Duration: 6 Sept 2023 → 8 Sept 2023 |
Abstract
Knowledge about people's daily travel behavior is very relevant for transportation planning, but also for urban and regional planning in general. This information is typically collected through questionnaires or surveys. With the increasing availability of mobile devices capable of using Global Navigation Satellite Systems, it is possible to derive individual mobility behavior on a large scale and for a variety of different users. However, the challenge is to derive the relevant information from the mere GNSS trajectories; in this paper, the relevant information is semantic locations such as home, work place or leisure places. This paper presents an approach to first detect and cluster stop points as potential semantic locations of a user, which are then enriched with Points of Interest from OpenStreetMap and additional features, and finally a Viterbi optimization assigns the most probable semantics to these locations. Overall, this approach produces promising results for predicting user location semantics on a generalized level.
Keywords
- clustering, GPS data, location of interest, semantic place annotation, Viterbi Optimization
ASJC Scopus subject areas
- Social Sciences(all)
- Transportation
Sustainable Development Goals
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In: Transportation Research Procedia, Vol. 78, 2024, p. 215-221.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Determining user specific semantics of locations extracted from trajectory data
AU - Golze, Jens
AU - Sester, Monika
PY - 2024
Y1 - 2024
N2 - Knowledge about people's daily travel behavior is very relevant for transportation planning, but also for urban and regional planning in general. This information is typically collected through questionnaires or surveys. With the increasing availability of mobile devices capable of using Global Navigation Satellite Systems, it is possible to derive individual mobility behavior on a large scale and for a variety of different users. However, the challenge is to derive the relevant information from the mere GNSS trajectories; in this paper, the relevant information is semantic locations such as home, work place or leisure places. This paper presents an approach to first detect and cluster stop points as potential semantic locations of a user, which are then enriched with Points of Interest from OpenStreetMap and additional features, and finally a Viterbi optimization assigns the most probable semantics to these locations. Overall, this approach produces promising results for predicting user location semantics on a generalized level.
AB - Knowledge about people's daily travel behavior is very relevant for transportation planning, but also for urban and regional planning in general. This information is typically collected through questionnaires or surveys. With the increasing availability of mobile devices capable of using Global Navigation Satellite Systems, it is possible to derive individual mobility behavior on a large scale and for a variety of different users. However, the challenge is to derive the relevant information from the mere GNSS trajectories; in this paper, the relevant information is semantic locations such as home, work place or leisure places. This paper presents an approach to first detect and cluster stop points as potential semantic locations of a user, which are then enriched with Points of Interest from OpenStreetMap and additional features, and finally a Viterbi optimization assigns the most probable semantics to these locations. Overall, this approach produces promising results for predicting user location semantics on a generalized level.
KW - clustering
KW - GPS data
KW - location of interest
KW - semantic place annotation
KW - Viterbi Optimization
UR - http://www.scopus.com/inward/record.url?scp=85187571653&partnerID=8YFLogxK
U2 - 10.1016/j.trpro.2024.02.028
DO - 10.1016/j.trpro.2024.02.028
M3 - Conference article
AN - SCOPUS:85187571653
VL - 78
SP - 215
EP - 221
JO - Transportation Research Procedia
JF - Transportation Research Procedia
SN - 2352-1457
T2 - 25th Euro Working Group on Transportation Meeting, EWGT 2023
Y2 - 6 September 2023 through 8 September 2023
ER -