Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Geospatial Data in a Changing World |
| Untertitel | Selected papers of the 19th AGILE Conference on Geographic Information Science |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 297-314 |
| Seitenumfang | 18 |
| ISBN (elektronisch) | 978-3-319-33783-8 |
| ISBN (Print) | 978-3-319-81601-2, 978-3-319-33782-1 |
| Publikationsstatus | Veröffentlicht - 15 Mai 2016 |
| Veranstaltung | 19th AGILE Conference on Geographic Information Science, 2016 - Helsinki, Finnland Dauer: 14 Juni 2016 → 17 Juni 2016 |
Publikationsreihe
| Name | Lecture Notes in Geoinformation and Cartography |
|---|---|
| Herausgeber (Verlag) | Springer International Publishing AG |
| ISSN (Print) | 1863-2351 |
Abstract
Many drives in crowded cities end with a challenging parking search, and visitors often do not know which streets allow on-street parking. Therefore, we present a learning-based approach to automatically generate on-street parking maps from parked vehicle positions detected by sensing vehicles. Multiple sets of features are proposed to describe the occupancy of every small road segment and its surroundings at different time instances. The usage of k-means algorithm as unsupervised learning and random forests as supervised learning are compared by applying these feature sets. The proposed approach is evaluated with repeated LiDAR measurements on more than five kilometers of potential parking space length. Our approaches, while keeping the model more generic, reveal slightly better results than an approach from literature. In particular, the unsupervised approach does not need a training data set and is free of any area specific parameter choice.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
- Erdkunde und Planetologie (insg.)
- Erdoberflächenprozesse
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
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Geospatial Data in a Changing World: Selected papers of the 19th AGILE Conference on Geographic Information Science. Cham: Springer Verlag, 2016. S. 297-314 (Lecture Notes in Geoinformation and Cartography).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Learning on-street parking maps from position information of parked vehicles
AU - Bock, Fabian
AU - Liu, Jiaqi
AU - Sester, Monika
N1 - Funding information: Acknowledgments This research has been supported by the German Research Foundation (DFG) through the Research Training Group SocialCars (GRK 1931). The focus of the SocialCars Research Training Group is on significantly improving the city’s future road traffic, through cooperative approaches. This support is gratefully acknowledged.
PY - 2016/5/15
Y1 - 2016/5/15
N2 - Many drives in crowded cities end with a challenging parking search, and visitors often do not know which streets allow on-street parking. Therefore, we present a learning-based approach to automatically generate on-street parking maps from parked vehicle positions detected by sensing vehicles. Multiple sets of features are proposed to describe the occupancy of every small road segment and its surroundings at different time instances. The usage of k-means algorithm as unsupervised learning and random forests as supervised learning are compared by applying these feature sets. The proposed approach is evaluated with repeated LiDAR measurements on more than five kilometers of potential parking space length. Our approaches, while keeping the model more generic, reveal slightly better results than an approach from literature. In particular, the unsupervised approach does not need a training data set and is free of any area specific parameter choice.
AB - Many drives in crowded cities end with a challenging parking search, and visitors often do not know which streets allow on-street parking. Therefore, we present a learning-based approach to automatically generate on-street parking maps from parked vehicle positions detected by sensing vehicles. Multiple sets of features are proposed to describe the occupancy of every small road segment and its surroundings at different time instances. The usage of k-means algorithm as unsupervised learning and random forests as supervised learning are compared by applying these feature sets. The proposed approach is evaluated with repeated LiDAR measurements on more than five kilometers of potential parking space length. Our approaches, while keeping the model more generic, reveal slightly better results than an approach from literature. In particular, the unsupervised approach does not need a training data set and is free of any area specific parameter choice.
KW - Crowd-sensing
KW - Machine learning
KW - Map generation
KW - Parking management
UR - http://www.scopus.com/inward/record.url?scp=85002039152&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-33783-8_17
DO - 10.1007/978-3-319-33783-8_17
M3 - Conference contribution
AN - SCOPUS:85002039152
SN - 978-3-319-81601-2
SN - 978-3-319-33782-1
T3 - Lecture Notes in Geoinformation and Cartography
SP - 297
EP - 314
BT - Geospatial Data in a Changing World
PB - Springer Verlag
CY - Cham
T2 - 19th AGILE Conference on Geographic Information Science, 2016
Y2 - 14 June 2016 through 17 June 2016
ER -