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When performing a transient test on a heavy-duty engine as outlined in the Code of Federal Regulations (CFR), defined regression values of engine speed, torque and power must meet specific tolerances for the test to be considered valid. Regression of actual engine feedback data against target points from a schedule defined from an engine map is performed using the method of least squares to determine the slope, intercept, coefficient of regression and standard error of the estimate. To minimize the biasing effects of time lag between actual and schedule data, shifting of the data in the time domain prior to analysis and certain point deletions are permitted. There are presently no regression criteria available for heavy duty chassis testing. This leaves facilities performing these chassis tests with no suitable guidelines to validate individual tests. This study applies the regression analysis used in engine testing to chassis testing and examines the difficulties encountered.

Yard hostlers are tractors (switchers) used to move containers at ports and storage facilities. While many speed-time driving cycles for assessing emissions and performance from heavy-duty vehicles exist, a driving cycle representative of yard hostler activity at Port of Long Beach, CA was not available. Activity data were collected from in-use yard hostlers as they performed ship loading/unloading, rail loading/unloading and other yard routines, primarily moving containers on trailers or carts. The activity data were then used to develop four speed-time driving cycles with durations of 1200 seconds, representing light and heavy ship activities and light and heavy load rail activities. These cycles were constructed using actual speed-time data collected during activity logging and the cycles created were statistically comparable to each subset of activity data.

Transit agencies across the United States operate bus fleets primarily powered by diesel, natural gas, and hybrid drive systems. Passenger loading affects the power demanded from the engine, which in turn affects distance-specific emissions and fuel consumption. Analysis shows that the nature of bus activity, taking into account the idle time, tire rolling resistance, wind drag, and acceleration energy, influences the way in which passenger load impacts emissions. Emissions performance and fuel consumption from diesel and natural gas powered buses were characterized by the West Virginia University (WVU) Transportable Emissions Testing Laboratory. A comparison matrix for all three bus technologies included three common driving cycles (the Braunschweig Cycle, the OCTA Cycle, and the ADEME-RATP Paris Cycle). Each bus was tested at three different passenger loading conditions (empty weight, half weight, and full weight).