Multi-Parameter Estimation of Average Speed in Road Networks Using Fuzzy Control

• Published in: ISPRS international journal of geo-information 2020-01, Vol.9 (1), p.55
• Main Authors: Guth, Johanna, Wursthorn, Sven, Keller, Sina
• Format: Article
• Language: English
• Subjects: Application programming interface; Case studies; Control; Control systems; Data; Datasets; digital elevation model; Digital Elevation Models; Digital mapping; Fuzzy control; fuzzy control system; Fuzzy systems; Knowledge bases (artificial intelligence); Learning algorithms; link travel time; Machine learning; Methods; openstreetmap; Parameter estimation; Roads; Roads & highways; routing; Rural areas; Rural roads; Source code; System theory; Topography; Traffic speed; Travel; Travel time
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi9010055

Average speed is crucial for calculating link travel time to find the fastest path in a road network. However, readily available data sources like OpenStreetMap (OSM) often lack information about the average speed of a road. However, OSM contains other road information which enables an estimation of average speed in rural regions. In this paper, we develop a Fuzzy Framework for Speed Estimation (Fuzzy-FSE) that employs fuzzy control to estimate average speed based on the parameters road class, road slope, road surface and link length. The OSM road network and, optionally, a digital elevation model (DEM) serve as free-to-use and worldwide available input data. The Fuzzy-FSE consists of two parts: (a) a rule and knowledge base which decides on the output membership functions and (b) multiple Fuzzy Control Systems which calculate the output average speeds. The Fuzzy-FSE is applied exemplary and evaluated for the BioBío and Maule region in central Chile and for the north of New South Wales in Australia. Results demonstrate that, even using only OSM data, the Fuzzy-FSE performs better than existing methods such as fixed speed profiles. Compared to these methods, the Fuzzy-FSE improves the speed estimation between 2% to 12%. In future work, we will investigate the potential of data-driven machine learning methods to estimate average speed. The applied datasets and the source code of the Fuzzy-FSE are available via GitHub.