Description

This technical report details the mathematical specification, engine physics, and algorithmic implementation of the vehicle exhaust emissions model developed for the Highway Development and Management (HDM-4) system under the ISOHDM initiative. The model bridges the gap between traditional macroscopic highway evaluation tools and microscopic vehicle emission models by utilizing vehicle power demand, engine rotational speed, fuel consumption, and instantaneous traffic dynamics to predict exhaust emissions under varied operating conditions. Emissions of carbon dioxide, carbon monoxide, unburned hydrocarbons, oxides of nitrogen, and particulate matter are calculated for diverse vehicle categories, ranging from light passenger vehicles to heavy multi-axle freight trucks. A central feature of the report is the inclusion of acceleration noise algorithms—measuring speed variability caused by traffic congestion, roadside friction, and vertical or horizontal geometry—to accurately capture transient engine operations during high-power acceleration phases that significantly elevate pollutant generation rates. Fuel composition parameters, fuel sulfur content, engine thermal efficiency, catalytic converter degradation factors, and ambient altitude/temperature corrections are fully incorporated into the predictive equations. The resulting exhaust model enables transportation planners to assess the environmental footprint of alternative highway maintenance standards, road alignment improvements, and traffic management interventions, providing reliable data for national emissions inventories and sustainable transport policy formulation. Overall, the report emphasizes rigorous empirical validation, standardized data acquisition protocols, and modular integration into the broader Highway Development and Management analytical engine. By combining physical principles with statistical calibration across international trial sites, this working paper provides a robust theoretical and practical baseline for highway authorities to evaluate long-term