Abstract
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.
Highlights
Applications like route planning, disaster risk management or transportation depend on finding the fastest path in a road network
A fixed speed profile that consists of the average speed for each class of the Google Directions application programming interface (GD-API) speed data is calculated as a baseline
The baseline we calculate is most likely more adapted to the regions characteristics than other speed profiles as it uses the average speed of the GD-API, which is an information most routing engines lack
Summary
Applications like route planning, disaster risk management or transportation depend on finding the fastest path in a road network. For the computation of the fastest path, average speed values are assigned to every edge in the road network to calculate link travel times. In studies on critical road infrastructure and accessibility, the link travel time often serves as a cost factor for the road network [2,3,4,5]. Many of these approaches use OpenStreetMap (OSM) data. Barrington-Leigh and Millard-Ball [6] have concluded in 2017 that the OSM road network is more than 80 % complete. 40 % of all countries in the worldwide OSM dataset have a fully mapped road network. The completeness of the road network is high at a national level [15,16]
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