Grant: Discovery Grants Program - Individual

This research program proposes modeling tools that integrate connected and autonomous vehicles (i.e., automated vehicles) into conventional traffic streams. The proposed models control the interactions between conventional and automated vehicles on uninterrupted flow facilities. The goal is for such models to be used by transportation analysts to assess the sustainability of the next generation transportation systems. The research program is divided into three stages. The first stage will extend previously developed mixed traffic flow models into a unified methodology to capture vehicle interactions in mixed traffic. This methodology will be suitable to assess traffic conditions (i.e., delay, safety, emissions) on highways with various proportions of automated and conventional vehicles. Specific control algorithms will be developed to model mixed traffic considering different driving maneuvers (e.g., free-flow and passing maneuvers lane selection, access and egress to/from highway ramps, etc.). The developed methodology has to be integrated into the evaluation methods of traffic operations. Thus, the existing highway capacity methodology has to be adjusted to account for the effects of automated vehicles into mixed traffic flows. Next generation transportation systems are expected to contribute to the sustainability of transportation development, hence the need to define accurate modeling tools that can assist decision makers in promoting appropriate transportation management policies. The second stage of the research program will develop a methodology to evaluate the three-fold benefits of automated vehicles (i.e., potential to reduce delay due to traffic congestion, to improve safety at locations with increased vehicular conflicts, and to ameliorate the environmental impact by reducing vehicle's GHG emissions). This will be done by calibrating the parameters of the control algorithms of automated vehicles, while considering various ratios between automated and conventional vehicles in mixed traffic flows. Traffic data available from other projects will be complemented by data collection through the available smart traffic controllers. In the third stage, the research team will validate the developed models by assessing the effects of mixed traffic flows on the specific case of reserved-lanes facilities (e.g., bus lanes, HOV lanes, etc.). Different ratios of automated vehicles will be considered for the analysis of traffic operations, safety and environment. Hence, a trade-off will be identified between gradual development of the next generation transportation infrastructures versus waiting a longer period until a critical mass of automated vehicles are penetrating the market. This program considers the current EDI guidelines for recruiting and training students in the newly emerging ITS area of automated vehicles, therefore addressing the current shortage of expertise in both the public and private sectors.