Prediction of vehicle activity for emissions estimation under oversaturated conditions along signalized arterials

The traditional methodology for estimating vehicle emissions based on vehicle miles traveled and average speed is not reliable because it does not consider the effects of congestion, control devices, and driving mode (cruise, acceleration, deceleration, and idle). We developed an analytical model to predict vehicle activity on signalized arterials with emphasis on oversaturated traffic conditions. The model depends only on loop detector data and signal settings as inputs and provides estimates of the time spent in each driving mode, which consequently leads to more accurate vehicle emission estimates. The application of the proposed model on a real-world arterial shows that it accurately estimates the time spent and consequently the emissions per driving mode. We also applied the model to evaluate the effectiveness of signal timing optimization in reducing vehicle emissions.