Investigating the impacts of traffic signal timing on the urban traffic-related particulate matters (PMs): A case study in Xi'an, China

Characterizing the relationship between signal timing and particulate matter (PM) is important for a sustainable traffic signal control and accurate exposure assessment at emission hotspot locations. The impact of traffic signal timing and meteorological variables on size-resolved PM in the 0.25-10 mu m range at the signalized intersection was investigated. The PMs, particle number concentrations (PNCs), and particle mass concentrations (PMCs) in the given size distribution were obtained at the signalized intersection in Xi'an. A comprehensive analysis method involving statistical analysis, regression analysis, simulation was used. The results demonstrated that the fine particles (PMC0.25-2.5 and PNC0.25-2.5) have a positive relationship with wind speed and vehicles per cycle, and a size ranges of PM, ranging from 2.5 to 10 mu m, was greatly affected by those factors. Moreover, the regression findings via multivariate multiple linear regression (MMLR) showed that the coefficients of independent variables (temperature, and delay) were statistically significant, which indicated that those factors enormously affect fine particles. In addition, the simulation results from the CAL3QHC indicated the benefit of pollutant reduction (PM2.5 reduced by an average of 5.3%) becomes more significant as signal timing optimization. This study serves as a demonstration of the abilities of reasonable signal timing to improve air quality and make better at the service level of the intersection.

Automated summary

This study investigated the impact of signal timing and meteorological variables on different size ranges of PM at a signalized intersection, revealing a positive relationship between fine particles and wind speed as well as vehicles per cycle. Regression analysis indicated that temperature and delay have significant effects on fine particles. Moreover, simulation results demonstrated the potential reduction of pollutant levels with signal timing optimization.