Impact of Traffic Management on Black Carbon Emissions: a Microsimulation Study

Mascia, Margherita; Hu, Simon; Han, Ke; North, Robin; Van Poppel, Martine; Theunis, Jan; Beckx, Carolien; Litzenberger, Martin · 2016 · Crossref

DOI: 10.1007/s11067-016-9326-x

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Summary

This study investigates the effectiveness of Intelligent Transport Systems (ITS), specifically traffic signal control (TSC) and variable message signs (VMS), in reducing traffic congestion and associated emissions of CO₂, NOₓ, and black carbon (BC). Motivated by the significant health and climate impacts of BC, which is primarily emitted during vehicle acceleration and braking, the research aims to quantify how smoothing traffic flow can mitigate these pollutants. The study addresses a gap in existing literature, which has largely focused on isolated junctions or incident management rather than the combined impact of TSC and VMS on recurring urban congestion and BC emissions. The researchers employed a microsimulation approach using the S-Paramics software for traffic modeling and the AIRE model for emission estimation, applied to a real-world road network in West Glasgow. The methodology involved calibrating a base model for the morning peak period (08:00–09:00) using loop detector data and manual counts, ensuring an 85% resemblance to observed traffic flows. The study simulated three ITS interventions: a new TSC plan optimizing green time distribution at a key junction, VMS directing drivers to alternative routes with compliance rates of 10%, 20%, and 30%, and a combination of both. To account for demand variability, five boundary conditions representing different traffic inflows were tested, with each scenario run 25 times to ensure statistical significance. Results indicate that these traffic management tools can reduce network-wide travel delay by up to 6% and BC emissions by up to 3%. However, the benefits are highly sensitive to dynamic demand profiles. Under conditions with increased demand from the north and west, the interventions significantly improved traffic flow and reduced emissions across junctions, corridors, and the network. Conversely, when demand increased from the south and west, the interventions sometimes worsened local traffic conditions and increased emissions due to congestion buildup. The study also found that while reduced delay generally correlates with lower emissions, fleet composition plays a critical role, as heavy goods vehicles contribute significantly to BC regardless of traffic conditions. The findings suggest that while ITS actions can effectively reduce emissions and delay, their success depends heavily on real-time monitoring of traffic demand profiles. The study highlights the importance of a decision support system that evaluates current inflows before activating specific ITS measures to avoid unintended negative impacts. This research provides managerial insights for traffic operators, demonstrating that environmental goals can be integrated into traffic network control, provided that interventions are tailored to specific demand scenarios and compliance behaviors.

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• traffic density