Incentive-based Platoon Formation: Optimizing the Personal Benefit for Drivers

TL;DR

This paper introduces a novel platoon formation algorithm for passenger cars that maximizes individual driver benefits by combining fuel and time costs into a personal monetary metric, outperforming existing methods.

Contribution

The paper proposes a new metric for personal benefits and a platoon formation algorithm that forms platoons only when beneficial for individual drivers, improving upon previous similarity-based methods.

Findings

Outperforms traditional ACC and similarity-based platooning methods.

Balances fuel savings and travel time effectively.

Works independently of traffic conditions and driver time valuation.

Abstract

Platooning or cooperative adaptive cruise control (CACC) has been investigated for decades, but debate about its lasting impact is still ongoing. While the benefits of platooning and the formation of platoons are well understood for trucks, they are less clear for passenger cars, which have a higher heterogeneity in trips and drivers' preferences. Most importantly, it remains unclear how to form platoons of passenger cars in order to optimize the personal benefit for the individual driver. To this end, in this paper, we propose a novel platoon formation algorithm that optimizes the personal benefit for drivers of individual passenger cars. For computing vehicle-to-platoon assignments, the algorithm utilizes a new metric that we propose to evaluate the personal benefits of various driving systems, including platooning. By combining fuel and travel time costs into a single monetary value, drivers can estimate overall trip costs according to a personal monetary value for time spent. This provides an intuitive way for drivers to understand and compare the benefits of driving systems like human driving, adaptive cruise control (ACC), and, of course, platooning. Unlike previous similarity-based methods, our proposed algorithm forms platoons only when beneficial for the driver, rather than solely for platooning. We demonstrate the new metric for the total trip cost in a numerical analysis and explain its interpretation. Results of a large-scale simulation study demonstrate that our proposed platoon formation algorithm outperforms normal ACC as well as previous similarity-based platooning approaches by balancing fuel savings and travel time, independent of traffic and drivers' time cost.