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Machine Learning Framework for the Estimation of Average Speed in Rural Road Networks with OpenStreetMap Data

Keller, Sina; Gabriel, Raoul; Guth, Johanna

Abstract:
Average speed information, which is essential for routing applications, is often missing in the freely available OpenStreetMap (OSM) road network. In this contribution, we propose an estimation framework, including different machine learning (ML) models that estimate rural roads’ average speed based on current road information in OSM. We rely on three datasets covering two regions in Chile and Australia. Google Directions API data serves as reference data. An appropriate estimation framework is presented, which involves supervised ML models, unsupervised clustering, and dimensionality reduction to generate new input features. The regression performance of each model with different input feature modes is evaluated on each dataset. The best performing model results in a coefficient of determination R$^{2}$=80.43%, which is significantly better than previous approaches relying on domain-knowledge. Overall, the potential of the ML-based estimation framework to estimate the average speed with OSM road network data is demonstrated. This ML-based approach is data-driven and does not require any domain knowledge. In the future, we intend to focus on the generalization ability of the estimation framework concerning its application in different regions worldwide. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000125376
Veröffentlicht am 28.10.2020
Originalveröffentlichung
DOI: 10.3390/ijgi9110638
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2220-9964
KITopen-ID: 1000125376
Erschienen in ISPRS International Journal of Geo-Information
Band 9
Heft 11
Seiten Art.-Nr.: 638
Vorab online veröffentlicht am 27.10.2020
Schlagwörter machine learning; regression; OpenStreetMap; volunteered geographical information; supervised learning; unsupervised learning; Self-Organizing Maps; estimation framework; average speed
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