Platoon formation maximization through centralized routing and departure time coordination
Vadim Sokolov, Jeffrey Larson, Todd Munson, Josh Auld, Dominik Karbowski
TL;DR
The paper addresses maximizing fuel savings from platooning by centrally coordinating routes and departure times for platoon-capable vehicles. It couples platoon coordination with the vehicle routing problem in a mixed-integer program, solving on a grid network and contrasting with ad hoc platooning via the POLARIS simulator using an LWR-type traffic model and Newell's discretization. The optimization uses variables $f_{v,i,j}$ and $q_{v,w,i,j}$ with edge costs $C_{i,j}$, a platooning gain factor $\eta$, and waiting costs $\epsilon_v t_v$, yielding the objective $ \min \sum_{v,i,j} C_{i,j} \left( f_{v,i,j} - \eta \sum_{w} q_{v,w,i,j} ight) + \epsilon_v t_v$ and a pruning approach to keep it tractable. Case studies show coordinated planning can substantially increase miles driven in platoons, but the economic viability depends on system-level benefits such as congestion relief or toll incentives, as modeled with certain parameters.
Abstract
Platooning allows vehicles to travel with small intervehicle distance in a coordinated fashion thanks to vehicle-to-vehicle connectivity. When applied at a larger scale, platooning will create significant opportunities for energy savings due to reduced aerodynamic drag, as well as increased road capacity and congestion reduction resulting from shorter vehicle headways. However, these potential savings are maximized if platooning-capable vehicles spend most of their travel time within platoons. Ad hoc platoon formation may not ensure a high rate of platoon driving. In this paper we consider the problem of central coordination of platooning-capable vehicles. By coordinating their routes and departure times, we can maximize the fuel savings afforded by platooning vehicles. The resulting problem is a combinatorial optimization problem that considers the platoon coordination and vehicle routing problems simultaneously. We demonstrate our methodology by evaluating the benefits of a coordinated solution and comparing it with the uncoordinated case when platoons form only in an ad hoc manner. We compare the coordinated and uncoordinated scenarios on a grid network with different assumptions about demand and the time vehicles are willing to wait.