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This case study manuscript details the conception and implementation of an artifact that uses floating car data to forecast average speeds on a segment of a Swiss national road. To consider the spatial and temporal dependencies when performing the predictions, the studied segment was modeled as a graph and as a time series problem. Subsequently, to obtain a prediction model, the data collected over a month and augmented to simulate the behavior during summer were used as the input to train a Graph Neural Network. After the evaluation of the results it was concluded that despite the considerable differences between the forecasted values and the reality, it was possible to perform such an implementation with limited data and resources. Moreover, a handful of traffic reporters still considered the results appropriate, and suitable.

The article was presented at the 2023 GISTAM 2023 : 9th International Conference on Geographical Information Systems Theory, Applications and Management and it’s available at the Scitepress Digital Library.

I thank the support of Viasuisse AG in the development of this case study. I had as well the pleasure to work with Ana Lucía Oña and Damian Domura and come up with our first article together.