According to a recent study, the presence of “connected” automobiles, or those that wirelessly communicate data, can speed up traffic through crossings. The study finds that autonomous vehicles traveling apart from one another can actually slow down traffic at crossings. Safety is the main cause of this disparity.
The appeal of automated vehicles, according to Ali Hajbabaie, an associate professor of civil, construction, and environmental engineering at North Carolina State University, is in enhancing passenger safety as well as cutting down on journey time.
While prior research has shown that automated vehicles can improve safety, this new study using computational modeling implies that more than just an increase in automated vehicles may be required to speed up travel times.
The study uses a simulation model to simulate traffic conditions and takes into account a variety of vehicles, including connected, automated, and human-driven vehicles. Connected automated vehicles (CAVs) exchange information with other connected vehicles and traffic control systems.
Due to their programming, automated cars are renowned to drive attentively and put safety first. Contrarily, CVs and CAVs smooth out movement and reduce stops by adjusting their speeds in response to incoming traffic signal data. To determine how different vehicle combinations affect travel time at intersections, the study examined 57 traffic simulations.
The report emphasises that more CVs and CAVs boost intersection capacity, allowing more vehicles to move through more quickly and with fewer vehicles stopping at red lights.
Surprisingly, a growing proportion of autonomous vehicles (AVs), which drive on their own, may result in slower travel times at junctions because of their cautious driving algorithms designed to reduce collisions.
The study underlines the importance of including connection in traffic control systems as well as in automobiles in order to successfully improve both safety and travel time.
The research, despite being carried out using a computational model, is vital in detecting potential problems that may occur with autonomous vehicles and offers insights that may one day save lives.
The research paper was published in Journal of the Transportation Research Board: Transportation Research Record.