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 R2=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. The implementation of our estimation framework for an exemplary dataset is provided on GitHub.
Highlights
IntroductionOpenStreetMap (OSM) road data are frequently used
Finding the fastest path in a road network is a common task that needs to be solved in various applications such as transportation, route planning, or disaster risk managing.For routing applications, OpenStreetMap (OSM) road data are frequently used
The predicted speed information, can be used in routing applications. This intention naturally leads to a general but intriguing question: Can purely data-driven machine learning (ML) approaches predict the average speed of rural road segments when trained on generic inhomogeneous OSM road network data? To address this overall question, we develop a ML estimation framework following a typical ML processing pipeline and investigate the underlying input data
Summary
OpenStreetMap (OSM) road data are frequently used. The main reason for this is that OSM is one of the best-known volunteered geographic information projects and features free data available worldwide and real-time updates [5,6]. Most routing applications ask for the link travel time as a parameter since information about the road network is crucial. According to Stanojevic et al [8], the link travel time is the average time that it takes a vehicle to pass a road segment. The average speed information together with the length of a road segment can be used to calculate the respective road segment’s link travel time. We consider a road segment similar to an edge of the road network in a topological representation of the network
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