Self-Driving Cars Can Slow Down Traffic, Study Finds

A new study has revealed that while "connected" vehicles exchanging data wirelessly can significantly enhance travel time at intersections, self-driving cars without such connectivity might actually slow down traffic. Surprisingly, the reason behind this phenomenon is safety.

Simulating Traffic Scenarios

Ali Hajbabaie, associate professor of civil, construction, and environmental engineering at North Carolina State University and the study's first author, explained the dual objectives of automated vehicles: improving passenger safety and reducing travel time.

While previous research has highlighted the safety benefits of self-driving cars, Hajbabaie's computational modeling study suggests that to truly improve travel time, vehicles need to communicate with each other and with traffic-control systems at intersections.

The researchers utilized a computational model to simulate various traffic scenarios, taking into account four types of vehicles: human-driven vehicles (HVs), connected vehicles (CVs) driven by humans that exchange data with other connected vehicles and traffic light control systems, automated vehicles (AVs), and connected automated vehicles (CAVs).

Hajbabaie pointed out that AVs, due to their programming, tend to operate more cautiously than human drivers, prioritizing safety. On the other hand, CVs and CAVs receive information about upcoming traffic lights and adjust their speeds accordingly, leading to smoother movements with fewer stops compared to HVs and AVs.

Numerous simulations were conducted, which examined how different combinations of HVs, AVs, CVs, and CAVs impacted travel time through intersections.

The study highlighted that a higher percentage of CVs and CAVs increased intersection capacity, allowing more vehicles to move through intersections faster. It also reduced the number of vehicles waiting at red lights on average.

Slow Travel Times

Interestingly, the study revealed that higher proportions of AVs lacking connectivity could lead to slower travel times at intersections. It is attributed to the conservative driving behavior programmed into AVs to prevent collisions.

The findings underscore the importance of incorporating connectivity not only within vehicles but also in traffic-control systems.

"However, we found that higher percentages of AVs-which are not connected-actually slows travel times through intersections," Hajbabaie said in a statement.

"This is because those AVs are programmed to drive conservatively in order to reduce the risk of collisions. Our findings underscore the importance of incorporating connectivity into both vehicles and traffic-control systems," Hajbabaie added.

Hajbabaie acknowledged the study's limitations are based on computational models but underscored their significance in identifying potential challenges before real-world implementation.

Real-world tests involving mixed fleets of vehicles under various conditions can be costly and pose safety concerns. Therefore, computational modeling helps unveil issues that can inform future developments to ensure efficiency and safety in autonomous vehicle integration.

"Field tests involving human drivers can also raise safety concerns, making these modeling studies particularly important; we want to identify potential problems now, and not when real lives are at stake," said Hajbabaie.

The study's findings were published in Transportation Research Record: Journal of the Transportation Research Board.