Best Paper Award for Traffic Flow Determination System

2017-11-06:

Researchers Richard Brunauer, Stefan Henneberger and Karl Rehrl of Salzburg Research received the Best Paper Award for the paper “Network-Wide Link Flow Estimation Through Probe Vehicle Data Supported Count Propagation”. The paper was presented in October at the IEEE ITSC 2017: International Conference on Intelligent Transportation Systems in Yokohama, Japan.

Best Paper Award Yokohama Japan

The presented algorithm distributes traffic volumes (vehicle/h) measured locally at a road cross section with the help of propagation rules throughout the road network. It should be generated as a holistic picture of the (current) traffic condition. The distribution rules are based on statistical analysis of historical floating car data (FCD) and can be specific to daytime and weekday.

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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