MS in Civil Engineering (M.S.C.E.)
Degree Granting Department
Civil and Environmental Engineering
Robert L. Bertini, Ph.D.
Xiaopeng Li, Ph.D.
Seckin Ozkul, Ph.D.
Data Visualization, Freight, Inductive Loop Detectors, ITS, PORTAL
There is an increasing demand for application of Intelligent Transportation Systems (ITS) in order to make highways safer and sustainable. Collecting and analyzing traffic stream data are the most important parameters in transportation engineering in enhancing our understanding of traffic congestion and mobility. Classification of the vehicles using traffic data is one of the most essential parameters for traffic management. Of particular interest are heavy vehicles which impact traffic mobility due to their lack of maneuverability and slower speeds. The impact of heavy vehicles on the traffic stream results in congestion and reduction of road efficiency. In this paper, length-based vehicle count and speed data were analyzed and interpreted using one week's data from Interstate 5 (I-5) in the Portland, Oregon (OR) region of the United States (US). I-5 was chosen due to its prominent role in promoting North-South freight movement between Canada and Mexico and its vicinity to the Port of Portland. The objective of this analysis was to find better visualization techniques for the length-based traffic count and speed data. In total, 13,901,793 out of 56,146,138 20-second records were analyzed. The vehicles were classified into two categories. Those that were 20 feet or less were considered as passenger vehicles and those above 20 feet were considered as heavy vehicles. The data consisted of approximately 25% heavy vehicles. Results showed the merit of applying more disaggregate data (5-min polar, and radar plots) for better visualization as against hourly, and 15-min plots in order to capture sudden changes in average speed, heavy vehicle volume, and heavy vehicle percentage.
Scholar Commons Citation
Yuksel, Eren, "Heavy Vehicle Classification Analysis Using Length-Based Vehicle Count and Speed Data" (2018). Graduate Theses and Dissertations.