Reservation-based traffic control is a revolutionary intersection management system which involves the communication of autonomous vehicles and an intersection to request space-time trajectories through the intersection. Although previous studies have found congestion and throughput benefits of reservation-based control that surpass signalized control, other studies have found negative impacts at peak travel times. The main purpose of this paper is to find and characterize favorable mixed configurations of reservation-based controls and signalized controls in a large city network which minimize total system travel times. As this optimization problem is bi-level and challenging, three different methods are proposed to heuristically find effective mixed configurations. The first method is an intersection ranking method that uses simulation to assign a score to each intersection in a network based on localized potential benefit to system travel time under reservation control and then ranks all intersections accordingly. The second is another ranking method; however, it uses linear regression to predict an intersection’s localized score. Finally, a genetic algorithm is presented that iteratively approaches high-performing network configurations yielding minimal system travel times. The methods were tested on the downtown Austin network and configurations found that are less than half controlled by reservation intersections that improve travel times beyond an all-reservation controlled network. Overall, the results show that the genetic algorithm finds the best performing configurations, with the initial score-assigning ranking method performing similarly but much more efficiently. It was finally find that favorable reservation placement is in consecutive chains along highly trafficked corridors.
A distribution strategy using a two-step optimization to maximize blood services considering stochastic travel times
