Abstract
Heavy-duty diesel trucks are a significant source of carbon dioxide, oxides of nitrogen, and particulate matter emissions. The construction of accurate emission inventories of these trucks requires a proper characterization of their speed because emission rates vary substantially by speed. However, data regarding truck speed have been very limited to date. This paper presents a statistical method for estimating truck traffic speed that takes advantage of the existing traffic monitoring systems. With traffic data from these systems examined, it was found that truck traffic speed can be effectively estimated on the basis of the knowledge of the overall traffic speed alone. The relationships between the two variables are shown to be very strong, with the coefficient of determination of the regression equations being .98 or higher. All regression coefficients are found to be statistically significant at the 5% alpha level. In addition, the validation results of the estimated truck traffic speed show a good estimation performance of the regression equations, where most of the average absolute errors are within the range of 2 to 4 mph. On the basis of the developed statistical relationship, regional truck activity, in terms of vehicle miles traveled versus speed distribution, on Southern California freeways was estimated and used to construct the associated running exhaust emission inventories. The resulting emission inventories show that using the heavy-duty diesel truck-specific speed distribution rather than the overall speed distribution reduces the estimates of oxides of nitrogen emissions by 4% and particulate matter emissions by 26%.
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