Impact of Optimal Selection of Merging Position on Fuel Consumption at Highway On-Ramps

Abstract

In recent years, there have been research efforts to develop intelligent control approaches for connected and automated vehicles (CAVs) at merging highways to reduce traffic jams and the consumption of fuel. This paper addresses the problem of optimal coordination of CAVs at highway on-ramps to reduce fuel consumption. To formulate the problem of online optimal vehicle coordination, two optimization functions (acceleration and jerk) are analyzed to obtain a closed-form solution of each vehicle at a highway on-ramp. Real-world field traffic data from an on-ramp were used as the input of the optimization algorithm to validate the optimal fuel consumption of three different merging behaviors by considering different merging positions along the acceleration lane. The different merging behaviors considered were the merging vehicle entering the highway after the leader and follower vehicles, between the leader and follower vehicles, and in front of the leader and follower vehicles. To illustrate the performance of the proposed method, simulation results were given to find the optimal merging behavior with the least fuel consumption. The results showed that the optimal control functions improved fuel consumption by 55% compared with the base scenario.