Effects of traffic signal coordination on noise and air pollutant emissions

De Coensel, Bert; Can, Arnaud; Degraeuwe, Bart; De Vlieger, I; Botteldooren, Dick · 2012 · OpenAlex-citations

DOI: 10.1016/j.envsoft.2012.02.009

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Summary

This study investigates the environmental impact of traffic signal coordination, specifically addressing the lack of scientific data regarding how synchronized traffic lights affect noise and air pollutant emissions. While green waves are widely implemented to reduce travel times, their influence on emissions is often assumed rather than empirically verified. The authors aim to quantify these effects by analyzing the relationship between traffic intensity, signal timing parameters, and the emission of carbon dioxide, nitrogen oxides, particulate matter, and noise. The research employs a computational methodology combining the microscopic traffic simulation model Paramics with the Imagine noise emission model and the VERSIT+ air pollutant emission model. The experimental design simulates a simplified urban arterial road with five signalized intersections spaced 200 meters apart, operating under a 50 km/h speed limit. The study evaluates 3,360 unique scenarios by varying traffic demand (50 to 2,000 vehicles/hour), cycle time (30 to 90 seconds), and green split (0.5 to 0.8). Three signal coordination schemes are compared: a "green wave" (vehicles stop only at the first light), a "red wave" (vehicles stop at every light), and a desynchronized scheme. The models are calibrated to represent the average emissions of the Dutch light-duty vehicle fleet. The results indicate that traffic signal coordination significantly influences air pollutant emissions but has a more complex effect on noise. Under optimal conditions, implementing a green wave reduced air pollutant emissions by 10% to 40%, depending on traffic flow and signal timing. However, noise outcomes varied by location: sound pressure levels decreased by up to 1 dB(A) near traffic signals due to reduced idling and acceleration, but increased by up to 1.5 dB(A) between intersections due to higher average vehicle speeds. The analysis identified traffic intensity and green split as the most influential factors on emissions, whereas cycle time had no significant impact. The study concludes that while green waves offer substantial benefits for air quality, their noise reduction benefits are localized and may be offset by increased noise in inter-section areas. The findings highlight the need for a combined approach to traffic management that considers both air quality and noise, rather than optimizing solely for travel time. The computational framework provides a validated tool for evaluating environmental policies, demonstrating that microscopic simulation coupled with instantaneous emission models can effectively predict the environmental consequences of traffic signal strategies across a wide range of operational conditions.

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