Are we getting vehicle emissions estimation right?
Simulation is a valuable prediction tool to evaluate the impact of climate change interventions prior to disruptive implementations that may yield unintended and unwanted consequences. Advances in agent-based simulation allow us to estimate the dynamic emissions produced by vehicles at a fine resolu...
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Veröffentlicht in: | Transportation research. Part D, Transport and environment Transport and environment, 2022-11, Vol.112, p.103477, Article 103477 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Simulation is a valuable prediction tool to evaluate the impact of climate change interventions prior to disruptive implementations that may yield unintended and unwanted consequences. Advances in agent-based simulation allow us to estimate the dynamic emissions produced by vehicles at a fine resolution, with each vehicle modelled individually. The scalability of these models allow decision-makers to evaluate large-scale scenarios. But how good are these models? In this paper, the authors apply a state-of-the-art emissions model in the Multi Agent Transport Simulation (MATSim) framework to simulate individual vehicle emissions. Simulation results are compared with real driving emissions tests, using a portable emissions measurement device on a light and heavy vehicle. Quantifying the gap between macro-level simulation and actual emissions, the paper contributes by showing that pollutants are significantly underestimated, especially for heavy vehicles. This confirms prior micro-scale studies and magnifies the detrimental impact of using uncorrected aggregated models to inform environmental policy affecting transport.
•Quantifying the gap in emission estimates on large-scale traffic simulations.•City-scale activity-based transport model coupled with vehicle-specific emissions.•Variation in real driving emissions is much larger than in randomised simulations.•Macro-level models underestimate heavy vehicle emissions more than light vehicles. |
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ISSN: | 1361-9209 1879-2340 |
DOI: | 10.1016/j.trd.2022.103477 |