Abstract
Research in traffic assignment relies largely on experimentation. The outcomes of experiments using different traffic assignment methods on different road network scenarios may vary greatly. It is difficult for the reader to assess the quality of the results without prior knowledge about the difficulty of the road network. In the current work, we propose an approximate metric to characterise the difficulty of network scenarios which takes travellers’ origins and destinations as well as link capacities into account rather than relying on the size of the network. As this metric considers number of overlapping routes between the origins and destinations of the travelles, a higher number in the metric would indicate a higher possibility of congestion in the road network scenario.
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Galib, S., Moser, I. (2013). A Framework for the Evaluation of Methods for Road Traffic Assignment. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_20
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DOI: https://doi.org/10.1007/978-3-319-03680-9_20
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