Connected vehicle is emerging as a solution to exacerbating congestion problems in urban areas.It is important to understand the impacts of connected vehicle on network and travel behavior of road users.The main objective of this paper is to evaluate the impact of connected vehicle on the mode choice and mobility of transportation networks.An iterative methodology was used in this paper where demands for various modes were modified based on the changes in travel time between each origin-destination(OD) pair caused by introduction of connected vehicle.Then a traffic assignment was performed in a microsimulation model,which was able to accurately simulate vehicle-to-vehicle communication.It is assumed that vehicles are equipped with a dynamic route guidance technology to choose their own route using real-time traffic information obtained through communication.The travel times obtained from the micro-simulation model were compared with a base scenario with no connected vehicle.The methodology was tested for a portion of Downtown Toronto,Ontario,Canada.In order to quantify changes in mode share with changes in travel time associated with each OD pair,mode choice models were developed for auto,transit,cycling and pedestrians using data mainly from the Transportation Tomorrow Survey.The impact of connected vehicle on mode choice was evaluated for different market penetrations of connected vehicle.The results of this study show that average travel times for the whole auto mode will generally increase,with the largest increase from connected vehicles.This causes an overall move away from the auto mode for high market penetrations if a dynamic route guidance algorithm is implemented. Connected vehicle is emerging as a solution to exacerbating congestion problems in urban areas. It is important to understand the impacts of vehicle on network and travel behavior of road users. The main objective of this paper is to evaluate the impact of connected vehicle on the mode choice and mobility of transportation networks. An iterative methodology was used in this paper where demands for various modes were modified based on the changes in travel time between each origin-destination (OD) pair caused by introduction of connected vehicle. was performed in a microsimulation model, which was able to accurately simulate vehicle-to-vehicle communication. It is assumed that vehicles are equipped with a dynamic route guidance technology to choose their own route using real-time traffic information obtained through communication. travel times obtained from the micro-simulation model were compared with a base scenario with no connected vehicle. method logy was tested for a portion of Downtown Toronto, Ontario, Canada. Order to quantify changes in mode share with changes in travel time associated with each OD pair, mode choice models were developed for auto, transit, cycling and pedestrians using data mainly from the Transportation of Tomorrow Survey. The impact of connected vehicle on mode choice was evaluated for different market penetrations of connected vehicle. The results of this study show that average travel times for the whole auto mode will generally increase, with the largest increase from connected vehicles. This causes an overall move away from the auto mode for high market penetrations if a dynamic route guidance algorithm is implemented.