Nearly 56% of nitrogen oxides (NOx) emissions, an important ozone precursor, are produced by mobile sources in the United States. Transportation and air quality managers at the state and regional levels have the responsibility of developing and evaluating Transportation Control Measures (TCMs) to improve regional air quality. Signal synchronization is considered to be an effective TCM to reduce corridor congestion and benefit air quality.
This research was conducted to determine the impacts of signal synchronization on real world, on-road emissions. Key objectives were:
- To evaluate the effect of signal synchronization on vehicular emissions on a selected corridor using field data collection.
- To determine whether signal synchronization significantly reduces emissions in terms of g/mile during peak and off peak travel times using statistical tests. I
- To develop an aggregate model, or corridor-level model, to predict emissions.
- To develop a disaggregate model to predict correlation between instantaneous NOx emissions and other parameters.
A portable instrument, Horiba OBS-1300, was used to measure on-road tailpipe emissions of NOx on a second-by-second basis during actual driving conditions. Emissions from a light-duty 2000 Chevy Astro van were measured before and after synchronization of 16 signals on Great Southwest Parkway, Grand Prairie, Texas.
Signal synchronization significantly improved traffic flow by increasing run time and average speed, and by decreasing control delay, number of stops and stop time. However, in most cases, there was no statistically significant change in NOx emissions after signal retiming. If more runs had been conducted, a statistically significant difference would have been more likely to be observed. Also, on a corridor with greater traffic volume than Great Southwest, signal synchronization would likely have a significant impact on NOx emissions because for a corridor with more traffic, the level of service is likely to be lower before and improve significantly as a result of signal synchronization.
Thus, on-board data demonstrate the importance of real-world conditions and help develop more accurate traffic and air quality management policies.