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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi9010055