This paper describes a systematic bottleneck identification and analysis for Oregon Highway 217 in the Portland, Oregon metropolitan region. Bottleneck studies can reveal key information about freeway operations and traffic flow characteristics. They also provide relevant and useful tools when teaching the fundamentals of traffic operations. This study uses 20-second aggregated data collected from eleven mainline and eight on-ramp inductive loop detectors that form part of the regional intelligent transportation systems (ITS) network. The data is archived by the Oregon DOT in the Portland Archival Listing (PORTAL), a unique online database developed, maintained and housed at Portland State University. This study relies on the relationship between vehicle flows and occupancies to produce definitive proof of bottleneck activation during the morning peak period in the southbound travel lanes. Oblique plots comparing changes in flow and occupancy reveal time segments with stationary conditions and times at which transitions between congested and uncongested traffic states occur. An analysis of the data produces quantitative measurements of wave propagation speeds and queue discharge flows. Several days over a period of three months in 2006 are analyzed to confirm the presence of recurrent congestion and to demonstrate reproducibility. A comparison is made between recurrent congestion observed at the bottleneck site with non-recurrent congestion (due to an incident) observed at the same location on a different day. This research may contribute to additional analyses of this corridor including an ongoing ramp-metering study currently being conducted along the 217 corridor, and other analyses of Portland area freeways.
Diagnosing a Freeway Bottleneck in Portland, Oregon (USA) Using Archived Sensor Data
Sixth International Conference of Traffic and Transportation Studies Congress (ICTTS) ; 2008 ; Nanning, China
17.07.2008
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Techniques for Validating an Automatic Bottleneck Detection Tool Using Archived Freeway Sensor Data
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