325 research outputs found
Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming
Platooning connected and autonomous vehicles (CAVs) can improve traffic and
fuel efficiency. However, scalable platooning operations require junction-level
coordination, which has not been well studied. In this paper, we study the
coordination of vehicle platooning at highway junctions. We consider a setting
where CAVs randomly arrive at a highway junction according to a general renewal
process. When a CAV approaches the junction, a system operator determines
whether the CAV will merge into the platoon ahead according to the positions
and speeds of the CAV and the platoon. We formulate a Markov decision process
to minimize the discounted cumulative travel cost, i.e. fuel consumption plus
travel delay, over an infinite time horizon. We show that the optimal policy is
threshold-based: the CAV will merge with the platoon if and only if the
difference between the CAV's and the platoon's predicted times of arrival at
the junction is less than a constant threshold. We also propose two
ready-to-implement algorithms to derive the optimal policy. Comparison with the
classical value iteration algorithm implies that our approach explicitly
incorporating the characteristics of the optimal policy is significantly more
efficient in terms of computation. Importantly, we show that the optimal policy
under Poisson arrivals can be obtained by solving a system of integral
equations. We also validate our results in simulation with Real-time Strategy
(RTS) using real traffic data. The simulation results indicate that the
proposed method yields better performance compared with the conventional
method
Planning of Truck Platoons: a Literature Review and Directions for Future Research
A truck platoon is a set of virtually linked trucks that drive closely behind one another using automated driving technology. Benefits of truck platooning include cost savings, reduced emissions, and more efficient utilization of road capacity. To fully reap these benefits in the initial phases requires careful planning of platoons based on trucks’ itineraries and time schedules. This paper provides a framework to classify various new transportation planning problems that arise in truck platooning, surveys relevant operations research models for these problems in the literature and identifies directions for future research
Hub-Based Platoon Formation: Optimal Release Policies and Approximate Solutions
This paper studies the optimal hub-based platoon formation at hubs along a
highway under decentralized, distributed, and centralized policies. Hubs are
locations along highways where trucks can wait for other trucks to form
platoons. A coordinator at each hub decides the departure time of trucks, and
the released trucks from the hub will form platoons. The problem is cast as an
optimization problem where the objective is to maximize the platooning reward.
We first show that the optimal release policy in the decentralized case, where
the hubs do not exchange information, is to release all trucks at the hub when
the number of trucks exceeds a threshold computed by dynamic programming. We
develop efficient approximate release policies for the dependent arrival case
using this result. To study the value of information exchange among hubs on
platoon formation, we next study the distributed and centralized platoon
formation policies which require information exchange among hubs. To this end,
we develop receding horizon solutions for the distributed and centralized
platoon formation at hubs using the dynamic programming technique. Finally, we
perform a simulation study over three hubs in northern Sweden. The profits of
the decentralized policies are shown to be approximately 3.5% lower than the
distributed policy and 8% lower than the centralized release policy. This
observation suggests that decentralized policies are prominent solutions for
hub-based platooning as they do not require information exchange among hubs and
can achieve a similar performance compared with distributed and centralized
policies.Comment: Accepted for T-ITS 202
Large-Scale Multi-Fleet Platoon Coordination: A Dynamic Programming Approach
Truck platooning is a promising technology that enables trucks to travel in
formations with small inter-vehicle distances for improved aerodynamics and
fuel economy. The real-world transportation system includes a vast number of
trucks owned by different fleet owners, for example, carriers. To fully exploit
the benefits of platooning, efficient dispatching strategies that facilitate
the platoon formations across fleets are required. This paper presents a
distributed framework for addressing multi-fleet platoon coordination in large
transportation networks, where each truck has a fixed route and aims to
maximize its own fleet's platooning profit by scheduling its waiting times at
hubs. The waiting time scheduling problem of individual trucks is formulated as
a distributed optimal control problem with continuous decision space and a
reward function that takes non-zero values only at discrete points. By suitably
discretizing the decision and state spaces, we show that the problem can be
solved exactly by dynamic programming, without loss of optimality. Finally, a
realistic simulation study is conducted over the Swedish road network with
trucks to evaluate the profit and efficiency of the approach. The
simulation study shows that, compared to single-fleet platooning, multi-fleet
platooning provided by our method achieves around times higher monetary
profit and increases the CO emission reductions from to . In
addition, it shows that the developed approach can be carried out in real-time
and thus is suitable for platoon coordination in large transportation systems.Comment: IEEE Transactions on Intelligent Transportation Systems, accepte
Designing and Scheduling Cost-Efficient Tours by Using the Concept of Truck Platooning
Truck Platooning is a promising new technology to reduce the fuel consumption by around 15% via the exploitation of a preceding and digitally connected truck’s slipstream. However, the cost-efficient coordination of such platoons under consideration of mandatory EU driving time restrictions turns out to be a highly complex task. For this purpose, we provide a comprehensive literature review and formulate the exact EU-Truck Platooning Problem (EU-TPP) as an Integer Linear Program (ILP) which also features a hypothetical task-relieving effect for following drivers in a convoy. In order to increase the computational efficiency, we introduce an auxiliary constraint and two hierarchical planning-based matheuristic approaches: the Shortest Path Heuristic (SPH) and the Platoon Routing Heuristic (PRH). Besides a qualitative sensitivity analysis, we perform an extensive numerical study to investigate the impact of different critical influence factors on platooning, being of major political and economic interest. Our experiments with the EU-TPP suggest remarkable fuel cost savings of up to 10.83% without a 50% task relief, while its inclusion leads to additional personnel cost savings of up to even 31.86% at best with maximally 12 trucks to be coordinated in a recreated part of the European highway network. Moreover, we prove our matheuristics’ highly favorable character in terms of solution quality and processing time.
Keywords: autonomous transport; Truck Platooning; driving time and rest periods; cost-efficient routing & scheduling; computational efficiency
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