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.

Nationwide surveys of departments of transportation, metropolitan planning organizations, and classification vendors/producers were conducted to determine the state of practice on equipment and methodologies used to determine truck vehicle miles traveled (VMT). The current Illinois Department of Transportation (IDOT) methodology was evaluated and it was found that it overestimated truck VMT for multi-unit trucks on all eight functional classes except on the minor urban arterials. The average overestimation was 11.5% and it varied from -10% to +44%. The current method overestimated truck VMT for single-unit trucks in five and underestimated in three functional classes. The under/over estimation ranged from -6% to +35%, but the average value was close to zero. To calculate truck VMT more accurately, this study proposed two different methods based on average truck percentage (ATP) and average section length (ASL). In the ATP method, truck VMT is calculated by multiplying the ATP for a group of roadway sections by the total VMT of that group. The ATP method should be used when the ATP and the total VMT by volume groups are available. In the ASL method, the total truck volume for the sampled sections is multiplied by the ASL. The ASL method should be used when the information required for ATP is not available or not reliable. Sample size influences the accuracy of truck VMT estimation and the decision on sample size must consider the error level that is acceptable. This study looked at the likely error for different sample sizes and recommended using 8% to 16% of the number of roadway sections. The sections should be distributed among the volume groups. Recently, IDOT collects vehicle classification data for three categories at about 10,000 sections, biennially. It is recommended to evaluate the truck VMT calculation using recent data.