Abstract
High-quality location based services rely on complete and accurate information of road segments. However, the attributes of road segments in online maps are often incomplete. For example, to compute fastest routes, a navigation system requires information, such as speed limits and road categories, of all road segments. While in OpenStreeMap, such attributes are often missing for many road segments. To contend with incomplete attributes, we propose a system that is able to utilize different machine learning techniques, including both non-deep learning and deep learning algorithms, to fill in the missing attributes. The system is developed and integrated into aSTEP, a spatio-Temporal data analytic platform developed by Aalborg University, and is tested using data collected from four major Danish cities.
Original language | English |
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Title of host publication | Proceedings - 2020 21st IEEE International Conference on Mobile Data Management, MDM 2020 |
Number of pages | 2 |
Publisher | IEEE |
Publication date | Jun 2020 |
Pages | 236-237 |
Article number | 9162212 |
ISBN (Electronic) | 9781728146638 |
DOIs | |
Publication status | Published - Jun 2020 |
Event | 21st IEEE International Conference on Mobile Data Management, MDM 2020 - Versailles, France Duration: 30 Jun 2020 → 3 Jul 2020 |
Conference
Conference | 21st IEEE International Conference on Mobile Data Management, MDM 2020 |
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Country/Territory | France |
City | Versailles |
Period | 30/06/2020 → 03/07/2020 |
Sponsor | IEEE, IEEE Computer Society TCDE |
Series | Proceedings - IEEE International Conference on Mobile Data Management |
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Volume | 2020-June |
ISSN | 1551-6245 |
Bibliographical note
Funding Information:Acknowledgements: This work was supported by Independent Research Fund Denmark under agreements 8022-00246B and 8048-00038B.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.