Self-driving cars could slow down traffic, study finds
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Using computer simulations, researchers have found that automated vehicles could slow down travel time through intersections compared to connected vehicles.
A team at North Carolina State University ran a series of simulations to investigate the impact of the widespread use of autonomous vehicles in cities.
The researchers found that although connected vehicles – which share data with each other wirelessly – significantly improve travel time through intersections, automated vehicles’ focus on safety leads them to slow down when not connected to other cars.
“There are two significant reasons that people are interested in automated vehicles – improving passenger safety and reducing travel time,” said Ali Hajbabaie, associate professor of civil, construction and environmental engineering at North Carolina State University.
“There is a lot of research showing that automated vehicles can improve safety. But our research here – which relies on computational modelling – suggests that if we want to also improve travel time, an increase in automated vehicles isn’t enough; we need vehicles that are capable of communicating with each other and with the traffic-control systems that manage traffic flow at intersections.”
For the study, the researchers used a computational model that simulates traffic conditions.
The researchers accounted for four types of vehicles: human-driven vehicles (HVs); connected vehicles (CVs), which are driven by humans but share information with other connected vehicles and with the control system that manages traffic lights; automated vehicles (AVs); and connected automated vehicles (CAVs).
“Because of their programming, AVs are assumed to move more cautiously compared to human drivers,” Hajbabaie said. “Their safety stems, in part, from being programmed to drive conservatively. CVs and CAVs are designed to receive information about the future state of traffic lights and adjust their speeds to avoid stopping at intersections.
“As a result, the movement of CVs and CAVs is expected to be smoother – and have a lower number of stops – than HVs and AVs.”
The researchers ran 57 traffic simulations to assess the impact of a host of variables on travel time through an intersection. For example, the researchers looked at how traffic would be affected by various combinations of HVs, AVs, CVs and CAVs.
One clear takeaway was that the higher the percentage of CVs and CAVs, the greater the intersection capacity. This meant there were, on average, fewer vehicles sitting in line at a red light. In contrast, the higher the percentages of AVs – which are not connected – the slower the travel times through intersections became.
“This is because those AVs are programmed to drive conservatively in order to reduce the risk of collisions,” said Hajbabaie. “Our findings underscore the importance of incorporating connectivity into both vehicles and traffic-control systems.”
The team did explain that the use of a computational model instead of real-life situations for the study did limit its findings. However, they stressed the usefulness of computing tools to identify potential problems so that they could be solved before real lives are at stake.
In January, the Department for Transport (DfT) published traffic projections for England and Wales, which showed that delays may rise by up to 85 per cent from 2025 to 2060 once self-driving vehicles reach the mainstream.
According to a Thatcham Research 2022 survey, 73 per cent of UK motorists recognise the potential benefits of emerging automated driving technology. The majority identified improved safety as the main benefit of the technology (21 per cent), followed by improving mobility for elderly and disabled people (14 per cent) and reducing pollution (8 per cent).
The North Carolina State University researchers’ findings were published in the journal Transportation Research Record.
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