Platooning connected and autonomous vehicles (CAVs) provide significant
benefits in terms of traffic efficiency and fuel economy. However, most
existing platooning systems assume the availability of pre-determined plans,
which is not feasible in real-time scenarios. In this paper, we address this
issue in time-dependent networks by formulating a Markov decision process at
each junction, aiming to minimize travel time and fuel consumption. Initially,
we analyze coordinated platooning without routing to explore the cooperation
among controllers on an identical path. We propose two novel approaches based
on approximate dynamic programming, offering suboptimal control in the context
of a stochastic finite horizon problem. The results demonstrate the superiority
of the approximation in the policy space. Furthermore, we investigate
platooning in a network setting, where speed profiles and routes are determined
simultaneously. To simplify the problem, we decouple the action space by
prioritizing routing decisions based on travel time estimation. We subsequently
employ the aforementioned policy approximation to determine speed profiles,
considering essential parameters such as travel times. Our simulation results
in SUMO indicate that our method yields better performance than conventional
approaches, leading to potential travel cost savings of up to 40%.
Additionally, we evaluate the resilience of our approach in dynamically
changing networks, affirming its ability to maintain efficient platooning
operations