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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.
The Mobility Analysis Section of the CDOT Division of Transportation Development (DTD) developed this study to determine whether the cluster count method developed by CDOT is statistically reliable for estimating vehicle classification on urban roadways with average daily traffic volumes exceeding 15,000 vehicles per day. Specifically, CDOT needed to assess whether or not the percentages of vehicles in the 13 FHWA vehicle classifications estimated by the cluster count method differ significantly from expected percentages obtained by 24-hour counts. Since vehicle classification is expensive to perform by manual observation over long periods of time, a statistically reliable method of estimating vehicle type percentages on urban roadways using a less time-consuming method is desirable. The study team utilized the chi-square statistical test to evaluate the similarity between vehicle classifications collected using the cluster count method and 24-hour vehicle counts collected using other data collection methods. Vehicle classification data were collected at 12 sites around Denver, Colorado that represented different roadway classes. The statistical tests between the data collected using the cluster count method and the 24-hour counts revealed that the current cluster count method varied beyond an acceptable statistical similarity to the 24-hour counts. Upon reaching this conclusion, the study panel simulated various changes to the short duration count methodology in an effort to identify the greatest improvement in statistical accuracy. As a result of this study, the recommended short duration vehicle classification methodology requires vehicle counts to be performed for 15 minutes every hour for a 24-hour period. This method exhibits strong statistical similarity to the 24-hour classification counts for all roadway classes and study sites included in this analysis. This collection method is statistically accurate, easy for field personnel to understand and collect, and is about onethird of the cost of a manual 24-hour count. The Mobility Analysis Section of DTD has developed a guidebook on the recommended short duration count methodology that will be available to CDOT staff, data collectors, consultants, and other public agencies. This guidebook outlines how to collect the short duration classification data, process and manage the data, and perform quality control checks.