Modeling of stochastic passenger car velocity processes for the purpose of pollutant emission inventory

Bebkiewicz, Katarzyna; Grzelak, Paulina Luiza; Chłopek, Zdzisław; Biernat, Krzysztof · 2026 · Crossref

DOI: 10.19206/ce-218133

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Summary

This study investigates the feasibility of using stochastic processes to model passenger car velocity for the purpose of creating accurate road transport pollutant emission inventories. The research is motivated by the uncertainty inherent in real driving conditions, where engine operating states—and consequently fuel consumption and emissions—fluctuate due to variable vehicle speeds. By treating vehicle velocity as a stochastic process, the authors aim to provide a more realistic framework for emissions inventorying than static or deterministic models. The methodology involved empirical studies of passenger car velocities under four distinct traffic conditions: cities in traffic congestion (C), cities outside congestion (U), outside cities (R), and highways/expressways (H). For each condition, more than 20 velocity realizations were recorded at a frequency of 1 Hz. These signals were processed using a second-order Savitzky-Golay filter to reduce noise, and four representative realizations were selected for each model based on zero-dimensional statistical characteristics. Emissions and fuel consumption for the 2020 passenger car category were then determined using INFRAS simulation software, analyzing carbon monoxide, hydrocarbons, nitrogen oxides, particulate matter, and carbon dioxide. The results indicate that the stochastic velocity models exhibit significant similarity and good repeatability across implementations within the same traffic condition. Statistical analysis showed that average velocity uniformity was highest in urban traffic outside congestion and lowest on highways. Crucially, the study found that fuel consumption and pollutant emissions were not highly sensitive to the specific realizations of the stochastic velocity processes. The coefficients of variation for emissions and fuel consumption remained low across all traffic models, demonstrating that while velocity varies, the resulting emission outputs are stable and predictable. The authors conclude that stochastic processes are a viable and effective tool for modeling road vehicle velocity in emissions inventories. This approach allows for the collection of both vehicle characteristics and their statistical features, providing a robust basis for regulatory compliance and environmental assessment. The study recommends extending this methodology to other vehicle categories, such as heavy-duty trucks and buses, and suggests future research incorporating Real Driving Emissions (RDE) testing with Portable Emissions Measurement Systems to further validate these stochastic models.

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• speed choice