Site Verification of Weigh-in-Motion Traffic and TIRTL Classification Data

Abstract

unrealistic to predict the unclassified vehicles in terms of traffic volume and lane number at this time. Overall, vehicles under Categories 1, 2, 3, and 4 account respectively for 35%, 5%, 48%, and 12% of the total unclassified vehicles. Under clear weather conditions, the TIRTL vehicle counts agreed very well with the manual and video vehicle counts, respectively. When vehicles are classified into four categories, such as two-axle, fourtire vehicles, single unit trucks, single-trailer trucks, and multi-trailer trucks, the accuracy of vehicle classification for Category 1, including all two-axle, four-tire vehicles, was better than that for tractor trailers. WIM vehicle counts were utilized to validate the performance of TIRTL system. Unlike WIM system, the number of unclassified vehicles by TIRTL was very small.

High‐resolution mapping of motor vehicle carbon dioxide emissions

“…Using results from Li et al . [], we estimate ~15% uncertainty when traffic counts from WIM are compared with those obtained from video counting. After 2007 (Figure b), many locations saw decreases in emissions in excess of 2% yr −1 .…”

“…A 2% yr À1 detection limit applies to this analysis, because when compounded over the period from 2000 to 2007, this matches the uncertainty in traffic counts from weigh-in-motion detectors. Using results from Li et al [2010], we estimate~15% uncertainty when traffic counts from WIM are compared with those obtained from video counting. After 2007 (Figure 7b), many locations saw decreases in emissions in excess of 2% yr À1 .…”