 Automated detection of entry and exit nodes in traffic networks of irregular shapeSimon Plakolb, Christian Hofer, Georg Jäger and Manfred Füllsack International Journal of Computational Economics and Econometrics, 2021, vol. 11, issue 2, 143-160 Abstract: We devise an algorithm that can automatically identify entry and exit nodes of an arbitrary traffic network. It is applicable even if the network is of irregular shape, which is the case for many cities. Additionally, the method can calculate the nodes' attractiveness to commuters. This technique is then used to improve a traffic model, so that it is no longer dependent on expert knowledge and manual steps and can thus be used to analyse arbitrary traffic systems. Evaluation of the algorithm is performed twofold: the positions of the identified entry nodes are compared to existing traffic data. A more in-depth analysis uses the traffic model to simulate a city in two ways: once with hand-picked entry nodes and once with automatically detected ones. The evaluation shows that the simulation yields a good match to the real world data, substantiating the claim that the algorithm can fully replace a manual identification process. Keywords: traffic modelling; network analysis; commuting; automated detection; entry nodes; exit nodes; traffic simulation; mobility behaviour; agent-based model; road usage; congestion. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:11:y:2021:i:2:p:143-160 Access Statistics for this article More articles in International Journal of Computational Economics and Econometrics from Inderscience Enterprises Ltd |
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