Researchers at Rutgers University–New Brunswick conducted a study at a high-traffic intersection in a Jersey Shore town and discovered that the installation of a bike lane along the road approaching the convergence resulted in reduced driving speeds.
This “traffic calming” effect could significantly improve road safety by reducing the risk and severity of crashes, as speeding has been identified as a contributing factor in a majority of traffic accidents.
“We are giving you more evidence that bike lanes save lives,” said Hannah Younes, a lead author of the study and a postdoctoral research associate at the Alan M. Voorhees Transportation Center in the Rutgers Bloustein School of Planning and Public Policy. “And it’s not only cyclists’ lives that could be saved. It’s more than that – drivers and pedestrians as well.”
The Rutgers team, comprised of experts from the Bloustein School, the Department of Civil and Environmental Engineering in the Rutgers School of Engineering, and the Department of Computer Science in the Rutgers School of Arts and Sciences, directed their efforts toward Cookman and Asbury Avenues in Asbury Park, N.J. Cookman, a local two-lane road, intersects with Asbury Avenue, a road that directly leads to the city’s popular Atlantic Ocean beaches.
Drivers often make a legal right-turn-on-red at the intersection’s traffic light when heading to the beach, but many fail to come to a complete stop first, creating hazardous conditions for pedestrians and cyclists crossing at the corner.
To tackle this issue, the research team established a temporary bike lane on Cookman and Asbury Avenues, marked with orange road cones, and surveyed random bike and electric scooter riders using the temporary bike lane to gauge their attitudes towards bike lanes. The survey indicated that most people surveyed expressed a positive view of bike lanes.
In order to assess the impact of the bike lane on traffic speeds, the researchers utilized computer vision techniques to categorize the speed and path of over 9,000 motor vehicles. Computer vision is a branch of AI that focuses on computers’ ability to interpret and analyze visual data from the environment. The researchers gathered data before and after the establishment of the bike lane for comparison.
Their findings revealed that the presence of the designated bike lane had a significant effect: a 28% decrease in average maximum speeds and a 21% reduction in average speeds for vehicles making right turns. For vehicles traveling straight without turning, a smaller reduction in speed of 8% was observed. Furthermore, vehicles moving perpendicular to the bike lane did not exhibit reduced speeds.
The use of cones to mark the bike lanes as a clearly defined space was more effective in reducing speeds compared to lanes that were solely painted. The lanes that were solely painted were associated with a smaller reduction in speed, ranging between 11% and 15%, but this reduction was only observed for vehicles making right turns.
Younes suggested that drivers may reduce speed when encountering a bike lane marked with cones due to the narrower driving lane requiring heightened concentration. He emphasized that it is easier for drivers to notice cones or planters than painted lines on the road surface.
With the increasing number of pedestrian fatalities, research like this could inform the development of new traffic policies or the re-evaluation of existing ones, contributing to efforts aimed at eliminating all road-related fatalities and serious injuries. For instance, cities across the nation are adopting comprehensive road safety initiatives like Vision Zero.
In 2021, there were 7,388 pedestrian deaths, marking a 13% increase from the previous year and constituting 17% of all crash fatalities, according to the Insurance Institute for Highway Safety.
Journal reference:
- Hannah Younes, Clinton Andrews, Robert B. Noland, Jiahao Xia, Song Wen, Wenwen Zhang, Dimitri Metaxas, Leigh Ann Von Hagen, Jie Gong. The Traffic Calming Effect of Delineated Bicycle Lanes. Journal of Urban Mobility, 2024; DOI: 10.1016/j.urbmob.2024.100071
