ORCID Profile
0000-0002-8885-8858
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Publisher: Elsevier BV
Date: 2010
Publisher: Elsevier BV
Date: 2011
Publisher: IEEE
Date: 10-2008
Publisher: Elsevier BV
Date: 10-2005
Publisher: Elsevier BV
Date: 06-2012
Publisher: SAGE Publications
Date: 2008
DOI: 10.3141/2071-03
Abstract: Incidents on freeways cause large delays for road users. These delays depend largely on the capacity at the incident location, which is determined by the drivers’ behavior at the accident location. Few empirical facts are available on traffic operations during an incident. This paper presents high-quality videos of the traffic flow around two accidents recorded from a helicopter. From the collected images, traffic counts have been performed at the exact location of the incident. This has two advantages. First, the capacity at the bottleneck per lane could be estimated. Second, truck counts could be converted to passenger car units at the location of the bottleneck. Counts show that the (outflow) capacity of the remaining lanes is about 50% lower than the (free-flow) capacity of the same number of lanes. This means that the road capacity in the opposite direction is reduced by half by the rubbernecking effect. The capacity of the road in the direction of the accident is reduced by more than half because not all lanes are in use. The images provide information on the causes for the capacity reduction. A leader accelerates and the follower accelerates a short time later. The average time between these two accelerations is estimated at about 3 s, but the video also shows a large spread of these times. The results can be used to assess consequences of incidents, in an analytical way and in macroscopic or microscopic traffic simulators.
Publisher: IEEE
Date: 09-2010
Publisher: Elsevier BV
Date: 2009
Publisher: SAGE Publications
Date: 2009
DOI: 10.3141/2129-07
Abstract: Video data are being used more often to study traffic operations. However, extracting vehicle trajectories from video by current methods is a difficult process, typically resulting in many errors. The process requires extensive labor to correct the trajectories manually. This paper proposes a method to process video data from traffic operations. Instead of detecting a vehicle in each picture of the video separately, the video data are transformed so that the trajectories of the vehicles (their position over time) become visible in a single image. In this single image, the trajectories can be found by detecting lines. The difference from other methods is that trajectories rather than vehicles are detected. Trajectory (line) detection is more robust than vehicle (rectangle) detection with this method, about 95% of the trajectories are detected correctly and, more important, the segments of each trajectory are much longer compared with results from other methods in the literature. Also, the detection is a quick process because only a single image is required to be analyzed. For a data set 5 min long, transforming costs several minutes, and automatically detecting and tracking costs 40 to 50 min per lane. Manual correction is then necessary, which costs about 10 min per lane. In contrast, with a different method the total processing time for analyzing traffic operations costs about 1 week for all lanes together.
Location: Netherlands
No related grants have been discovered for Henk van Zuylen.