Network-Wide Link Flow Estimation Through Probe Vehicle Data Supported Count Propagation.

Richard Brunauer, Stefan Henneberger and Karl Rehrl (2017): Network-Wide Link Flow Estimation Through Probe Vehicle Data Supported Count Propagation. In: Proc. of the 20th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2017), Yokohama, Japan. Ausgezeichnet mit dem Best Paper Award.

Der präsentierte Algorithmus verteilt lokal an einem Straßenquerschnitt gemessene Verkehrsstärken (KFZ/h) mit Hilfe von Propagierungsregelen im gesamten Straßennetz. Es soll so ein ganzheitliches Bild des (aktuellen) Verkehrszustandes generiert werden. Die Verteilungsregeln basieren auf statistischen Analysen von historischen Floating Car Daten (FCD) und können tageszeit- und wochentagspezifisch sein.

Abstract:
Network-wide dynamic link flow estimation is one of the challenging questions in transportation research. Most of the previous approaches rely on static or dynamic OD matrices. The proposed data-driven approach tackles the problem of link flow estimation as a local network propagation problem between cross-section measurement sites. Distinct propagation rules consider time-dependent travel speeds, turning fractions at intersections and vehicle gain-loss ratios between links. The rules are derived from recorded vehicle paths originating from a probe vehicle data (PVD) system including data from thousands of vehicles from several different fleets. The proposed approach introduces an algorithm with dedicated propagation rules and measures for evaluating propagation quality. Our approach is evaluated using Austria’s nation-wide road network (including freeways, urban and rural roads) with approximately 224,443 links and 16,566 intersections as well as traffic count data from 664 cross-section measurement sites. Results show the general applicability of the approach, but also reveal several challenging situations, which have to be treated with suitable propagation strategies.

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