Abstract Manager

Abstract

This study was conducted to identify optimal driving behaviour for connected and automated vehicles (CAV) that can reduce traffic congestion and GHG emissions. The study evaluated 12 different scenarios for the city of Ottawa and employed traffic simulations at the meso scale to assess traffic performance, and correlation models to estimate vehicular GHG emissions. The study evaluated four different driving behaviours: driver-operated vehicles (DOVs), Cautious CAVs, Normal CAVs, and Aggressive CAVs. The city-wide model was simulated with traffic volumes equal to 80%, 100% and 120% of peak-hour traffic forecasted for the year 2031. CAVs with aggressive driving behaviour showed the greatest potential to enhance traffic performance and reduce GHG emissions under all traffic demand levels. In terms of traffic characteristics, CAVs with the aggressive driving behaviour obtained the largest reduction in travel time compared to DOVs for all traffic flow levels. The reduction in travel time increased with an increase in traffic demand, indicating a more pronounced effect under higher levels of congestion.

Assessing the impacts of CAVs in a simulated environment presents itself as a viable precursor to real-world testing, and results may provide insight to help guide policy development. The results of this Transport Canada funded study aim to encourage regulatory bodies to adopt effective CAV-related policies that can enhance traffic performance and reduce GHG emissions.