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Cars on a busy highway.
A row of cars down a congested highway is a common nuisance for drivers. | Image: Getty Images

The thought of having the transportation industry taken over by self-driving cars can seem more like a futuristic vision than a reality. While there is still a technological gap between traditional vehicles and autonomous vehicles that needs to be bridged, ongoing research continues to push toward a future of autonomy.

 In anticipation of an autonomous future, researchers in the Zachry Department of Civil Engineering at Texas A&M University, in collaboration with Purdue University, conducted a study on the impacts of connected vehicle technology on networkwide traffic flow and fuel consumption. Connected vehicles have equipped internet access and can wirelessly communicate inside and outside of the vehicle, much like Cadillac’s “vehicle-2-vehicle” safety technology in their recent models. The study revealed that in connected vehicles, drivers can have access to traffic data and travel suggestions, allowing them to detour and cause traffic congestion to decrease, which would also lower emission levels and create a positive environmental impact.

The research team is comprised of Dr. Mark Burris, the Herbert D. Kelleher Professor in the department; Dr. Alireza Talebpour, an assistant professor in the department; Arezoo Samimi, a civil engineering graduate student; and Dr. Kumares Sinha, a distinguished civil engineering professor from Purdue. The study was presented at the 2018 American Society of Civil Engineers International Conference on Transportation and Development and received the best paper award.

“We are at a very transformative phase (in the transportation industry) and are at the lower end of the spectrum in terms of autonomy,” said Sinha. “The success of connected vehicles depends on if more people are willing to be connected; then it will bring more benefits and progress.”

The researchers developed a simulation model of a traffic network encompassing two congested roads in El Paso, Texas. These roads were chosen for this study not only because they are highly congested, but also because they encompass a nonattainment area, meaning that any project implemented in the area would improve air quality, or at least have no negative effects. In addition, the researchers developed an early warning congestion system as a result of the study that would function as a more interactive and intelligent Google Maps system.

“We are working on the theoretical component of the technology and what we have is a simulation algorithm of a road network that we tried to make as realistic as possible to be able to consider the scenarios and see how it works,” said Samimi.

To do this, they considered two cases of nonoccurring and recurring congestion and developed incident scenarios to see what the effects of that would be. Congestion areas behave differently in a nonoccurring, rural city like Amarillo or a larger, metropolitan area like Houston. After the driver receives a warning of congestion ahead, the driver is presented with options for re-routing away from the congested area. Options for re-routing included the shortest travel time for the driver and travel routes based on fuel efficiency.   

“The mileage might affect the fuel consumption but it’s also important how the drivers are traveling on those lanes,” said Samimi. “If you have a lot of congestion and there is a lot of stopping and going on those lanes then the fuel consumption (and congestion) will be higher.” 

Samimi played a large role in simulating the data into a viable model. She said that because the algorithms in the model were new and innovative, there were necessary adjustments that needed to be made to the computing system for the model. 

“It took a lot of time to just simulate it and if there were any small problems, I would have to restart it all over again,” said Samimi. “The high-performance computing center at Texas A&M University helped us a lot with this.” 

The researchers wanted to take their simulation a step further and implement a unique, realistic component into their study. Instead of assuming that all drivers would accept their suggested rerouting options, they used market penetration techniques to measure the theoretical, random amount of people who would and wouldn’t accept the suggested routes provided to them by the vehicle. To further develop an accurate representation, the team also conducted a recent survey of 2,000 people to see the likelihood of accepting the rerouting suggestions and will use this to further develop their research. 

“Not everyone is going to accept the recommendation of the suggested route and we modeled this to compare what happens in a connected vehicle environment where quite a few travelers do change their route versus a non-connected environment where only (a) few people change their route,” said Burris.

Samimi said that this research has the potential to help transportation planners and policymakers determine how to get the best results from connected vehicle technology. Since the research is still theoretical, there would need to be a vast market of people wanting vehicles to be connected in order for the benefit to be fully realized, according to Samimi. 

“Just because people have the technology, that does not mean it will be used as expected,” said Sinha. “We did a nationwide survey that provided the underlying probabilities and that is the added beauty of this paper.”

The Hagler Institute for Advanced Study at Texas A&M funded this research project and was essential in securing collaborations with Sinha.