Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Machine Learning and Knowledge Discovery in Databases |
| Untertitel | European Conference, ECML PKDD 2017, Proceedings |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 358-362 |
| Seitenumfang | 5 |
| ISBN (Print) | 9783319712727 |
| Publikationsstatus | Veröffentlicht - 30 Dez. 2017 |
| Veranstaltung | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 - Skopje, Nordmazedonien (Mazedonien) Dauer: 18 Sept. 2017 → 22 Sept. 2017 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 10536 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Finding a parking space is a key problem in urban scenarios, often due to the lack of actual parking availability information for drivers. Modern vehicles, able to identify free parking spaces using standard on-board sensors, have been proven to be effective probes to measure parking availability. Nevertheless, spatio-temporal datasets resulting from probe vehicles pose significant challenges to the machine learning and data mining communities, due to volume, noise, and heterogeneous spatio-temporal coverage. In this paper we summarize some of the approaches we proposed to extract new knowledge from this data, with the final goal to reduce the parking search time. First, we present a spatio-temporal analysis of the suitability of taxi movements for parking crowd-sensing. Second, we describe machine learning approaches to automatically generate maps of parking spots and to predict parking availability. Finally, we discuss some open issues for the ML/KDD community.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Proceedings. Springer Verlag, 2017. S. 358-362 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10536 LNAI).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Data-Driven Approaches for Smart Parking
AU - Bock, Fabian
AU - Di Martino, Sergio
AU - Sester, Monika
PY - 2017/12/30
Y1 - 2017/12/30
N2 - Finding a parking space is a key problem in urban scenarios, often due to the lack of actual parking availability information for drivers. Modern vehicles, able to identify free parking spaces using standard on-board sensors, have been proven to be effective probes to measure parking availability. Nevertheless, spatio-temporal datasets resulting from probe vehicles pose significant challenges to the machine learning and data mining communities, due to volume, noise, and heterogeneous spatio-temporal coverage. In this paper we summarize some of the approaches we proposed to extract new knowledge from this data, with the final goal to reduce the parking search time. First, we present a spatio-temporal analysis of the suitability of taxi movements for parking crowd-sensing. Second, we describe machine learning approaches to automatically generate maps of parking spots and to predict parking availability. Finally, we discuss some open issues for the ML/KDD community.
AB - Finding a parking space is a key problem in urban scenarios, often due to the lack of actual parking availability information for drivers. Modern vehicles, able to identify free parking spaces using standard on-board sensors, have been proven to be effective probes to measure parking availability. Nevertheless, spatio-temporal datasets resulting from probe vehicles pose significant challenges to the machine learning and data mining communities, due to volume, noise, and heterogeneous spatio-temporal coverage. In this paper we summarize some of the approaches we proposed to extract new knowledge from this data, with the final goal to reduce the parking search time. First, we present a spatio-temporal analysis of the suitability of taxi movements for parking crowd-sensing. Second, we describe machine learning approaches to automatically generate maps of parking spots and to predict parking availability. Finally, we discuss some open issues for the ML/KDD community.
UR - http://www.scopus.com/inward/record.url?scp=85040242372&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-71273-4_31
DO - 10.1007/978-3-319-71273-4_31
M3 - Conference contribution
AN - SCOPUS:85040242372
SN - 9783319712727
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 358
EP - 362
BT - Machine Learning and Knowledge Discovery in Databases
PB - Springer Verlag
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
Y2 - 18 September 2017 through 22 September 2017
ER -