Intersections and merging roadways are the primary sources of bottlenecks, note Jackeline Rios-Torre and Andreas A. Malikopoulos in their paper. In 2014, congestion caused people in urban areas to spend 6.9 billion hours more on the road and to purchase an extra 3.1 billion gallons of fuel—a total cost estimated at $160 billion.

Connected and automated vehicles (CAVs) can provide shorter gaps between vehicles and faster responses while improving highway capacity. Several efforts reported in the literature have aimed at enhancing our understanding of the potential benefits of connected vehicle technologies.

… Although previous research reported in the literature has aimed at enhancing our understanding of coordinating vehicles either at intersections, or merging roadways, deriving online an optimal closed-form solution for vehicle coordination in terms of fuel consumption still remains a challenging control problem. This paper has two main objectives: (1) to formulate the problem of optimal vehicle coordination at merging roadways in terms of fuel consumption under the hard constraint of collision avoidance and (2) to derive online a closed-form solution in a centralized fashion.

—Rios-Torre and Malikopoulos

For the study, they used a simple model of merging roads with connected and automated vehicles controlled by a centralized controller. Rios-Torres and Malikopoulos. Click to enlarge.

To address the problem of optimal coordination, they formulated the problem as an unconstrained optimal control problem and we applied Hamiltonian analysis to derive an analytical, closed-form solution.

To validate the effectiveness of the efficiency of our analytical solution, they simulated a merging scenario in MATLAB. The length of the control and merging zones is L = 400 m and S = 30 m. They assumed that each vehicle travels at a constant speed of 13.4 m/s (30 mph) before entering the control zone.

When a vehicle reaches the control zone then the centralized controller designates its acceleration/deceleration until the vehicle exits the merging zone.

They considered four cases:

1. Coordination of 4 vehicles, 2 for each road. The purpose of this scenario is to validate that the controller will coordinate each vehicle to enter the merging zone only after the previous vehicle has already left/
2. Coordination of 30 vehicles, 15 for each road.
3. Coordination of 30 vehicles assuming the vehicles on the secondary road reach the control zone at a lower speed of 11.2 m/s (25 mph).
4. Coordination of 30 vehicles that enter the control zone with 29 m/s (65 mph).

The solutions were compared to a baseline scenario where it was assumed that the vehicles on the main road have the right-of-way—i.e., the vehicles on the secondary road have to come to a full stop before entering the merging zone.

They found that the cumulative fuel consumption is higher in the baseline case compared to the case studies 2 and 3 where the vehicles are coordinated through the centralized controller. Optimal vehicle coordination improves overall fuel consumption by 52.7% for the case study 2, and 48.1% for the case study 3 compared to the baseline scenario. The total travel time is also improved by 7.1%, and 13.5%, respectively.

This research was supported in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory and in part by the Department of Energy’s SMART Mobility initiative.

Resources

• J. Rios-Torres; A. A. Malikopoulos (2016) “Automated and Cooperative Vehicle Merging at Highway On-Ramps,” IEEE Transactions on Intelligent Transportation Systems doi: 10.1109/TITS.2016.2587582