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

Optimizing Energy Savings for a Fleet of Commercial Autonomous Vehicles via Centralized and Decentralized Platooning Decisions

Thanks to advanced technologies like Connected and Autonomous Vehicles, platooning is becoming more and more useful as a method to potentially increase road capacity and reduce energy consumption. While there are many studies in the literature reporting significant fuel and energy savings as a result of platooning, these studies are ignoring the extra energy required to maintain vehicles in close formation referred to as string stability. Also, there are other factors many of the current studies are not considering such as the position of a vehicle in a platoon, the background traffic that may complicate the process of forming platoons, and the vehicle type. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this study, we develop a simulation-optimization framework to tackle this challenge. The simulation model simulates real traffic conditions for individual vehicles and platoons. Additionally, the simulation model implements platoon forming decisions obtained from an optimization model. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. Our optimization model determines vehicle-to-platoon assignments given the locations, speed, and acceleration of vehicles and platoons. Particularly, we concentrate two different optimization models. One is a centralized model to make platooning decisions with aim to maximize potential energy savings system-wide. On the other hand, a decentralized model utilizing a competition game is developed to make decisions for individual vehicle energy saving purpose. In addition to the simulation- optimization framework, an accurate energy consumption model is developed, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. In addition to the centralized and decentralized decision making models, we track a single platoon to observe the energy consumption for this one platoon under different parameters in order to better understand the factors that impact energy savings. Our results show that a system-wide savings of about 3% in centralized model, and 1.5% in decentralized model can be realized over 100 miles when platoons are formed strategically. Comparison between two models also confirm, as expected, that the centralized model forms better platoons in terms of energy savings

Distributed, decentralised and compensational mechanisms for platoon formation

Verkehrsprobleme nehmen mit der weltweiten Urbanisierung und der Zunahme der Anzahl der Fahrzeuge pro Kopf zu. Platoons, eine Formation von eng hintereinander fahrenden Fahrzeugen, stellen sich als mögliche Lösung dar, da bestehende Forschungen darauf hinweisen, dass sie zu einer besseren Straßenauslastung beitragen, den Kraftstoffverbrauch und die Emissionen reduzieren und Engpässe schneller entlasten können. Rund um das Thema Platooning gibt es viele Aspekte zu erforschen: Sicherheit, Stabilität, Kommunikation, Steuerung und Betrieb, die allesamt notwendig sind, um den Einsatz von Platooning im Alltagsverkehr näher zu bringen. Während in allen genannten Bereichen bereits umfangreiche Forschungen durchgeführt wurden, gibt es bisher nur wenige Arbeiten, die sich mit der logischen Gruppierung von Fahrzeugen in Platoons beschäftigen. Daher befasst sich diese Arbeit mit dem noch wenig erforschten Problem der Platoonbildung, wobei sich die vorhandenen Beispiele mit auf Autobahnen fahrenden Lastkraftwagen beschäftigen. Diese Fälle befinden sich auf der strategischen und taktischen Ebene der Planung, da sie von einem großen Zeithorizont profitieren und die Gruppierung entsprechend optimiert werden kann. Die hier vorgestellten Ansätze befinden sich hingegen auf der operativen Ebene, indem Fahrzeuge aufgrund der verteilten und dezentralen Natur dieser Ansätze spontan und organisch gruppiert und gesteuert werden. Dadurch entstehen sogenannte opportunistische Platoons, die aufgrund ihrer Flexibilität eine vielversprechende Voraussetzung für alle Netzwerkarte bieten könnten. Insofern werden in dieser Arbeit zwei neuartige Algorithmen zur Bildung von Platoons vorgestellt: ein verteilter Ansatz, der von klassischen Routing-Problemen abgeleitet wurde, und ein ergänzender dezentraler kompensatorischer Ansatz. Letzteres nutzt automatisierte Verhandlungen, um es den Fahrzeugen zu erleichtern, sich auf der Basis eines monetären Austausches in einem Platoon zu organisieren. In Anbetracht der Tatsache, dass alle Verkehrsteilnehmer über eine Reihe von Präferenzen, Einschränkungen und Zielen verfügen, muss das vorgeschlagene System sicherstellen, dass jede angebotene Lösung für die einzelnen Fahrzeuge akzeptabel und vorteilhaft ist und den möglichen Aufwand, die Kosten und die Opfer überwiegt. Dies wird erreicht, indem den Platooning-Fahrzeugen eine Form von Anreiz geboten wird, im Sinne von entweder Kostensenkung oder Ampelpriorisierung. Um die vorgeschlagenen Algorithmen zu testen, wurde eine Verkehrssimulation unter Verwendung realer Netzwerke mit realistischer Verkehrsnachfrage entwickelt. Die Verkehrsteilnehmer wurden in Agenten umgewandelt und mit der notwendigen Funktionalität ausgestattet, um Platoons zu bilden und innerhalb dieser zu operieren. Die Anwendbarkeit und Eignung beider Ansätze wurde zusammen mit verschiedenen anderen Aspekten untersucht, die den Betrieb von Platoons betreffen, wie Größe, Verkehrszustand, Netzwerkpositionierung und Anreizmethoden. Die Ergebnisse zeigen, dass die vorgeschlagenen Mechanismen die Bildung von spontanen Platoons ermöglichen. Darüber hinaus profitierten die teilnehmenden Fahrzeuge mit dem auf verteilter Optimierung basierenden Ansatz und unter Verwendung kostensenkender Anreize unabhängig von der Platoon-Größe, dem Verkehrszustand und der Positionierung, mit Nutzenverbesserungen von 20% bis über 50% im Vergleich zur untersuchten Baseline. Bei zeitbasierten Anreizen waren die Ergebnisse uneinheitlich, wobei sich der Nutzen einiger Fahrzeuge verbesserte, bei einigen keine Veränderung eintrat und bei anderen eine Verschlechterung zu verzeichnen war. Daher wird die Verwendung solcher Anreize aufgrund ihrer mangelnden Pareto-Effizienz nicht empfohlen. Der kompensatorische und vollständig dezentralisierte Ansatz weißt einige Vorteile auf, aber die daraus resultierende Verbesserung war insgesamt vernachlässigbar. Die vorgestellten Mechanismen stellen einen neuartigen Ansatz zur Bildung von Platoons dar und geben einen aussagekräftigen Einblick in die Mechanik und Anwendbarkeit von Platoons. Dies schafft die Voraussetzungen für zukünftige Erweiterungen in der Planung, Konzeption und Implementierung effektiverer Infrastrukturen und Verkehrssysteme.Traffic problems have been on the rise corresponding with the increase in worldwide urbanisation and the number of vehicles per capita. Platoons, which are a formation of vehicles travelling close together, present themselves as a possible solution, as existing research indicates that they can contribute to better road usage, reduce fuel consumption and emissions and decongest bottlenecks faster. There are many aspects to be explored pertaining to the topic of platooning: safety, stability, communication, controllers and operations, all of which are necessary to bring platoons closer to use in everyday traffic. While extensive research has already made substantial strides in all the aforementioned fields, there is so far little work on the logical grouping of vehicles in platoons. Therefore, this work addresses the platoon formation problem, which has not been heavily researched, with existing examples being focused on large, freight vehicles travelling on highways. These cases find themselves on the strategic and tactical level of planning since they benefit from a large time horizon and the grouping can be optimised accordingly. The approaches presented here, however, are on the operational level, grouping and routing vehicles spontaneously and organically thanks to their distributed and decentralised nature. This creates so-called opportunistic platoons which could provide a promising premise for all networks given their flexibility. To this extent, this thesis presents two novel platoon forming algorithms: a distributed approach derived from classical routing problems, and a supplementary decentralised compensational approach. The latter uses automated negotiation to facilitate vehicles organising themselves in a platoon based on monetary exchanges. Considering that all traffic participants have a set of preferences, limitations and goals, the proposed system must ensure that any solution provided is acceptable and beneficial for the individual vehicles, outweighing any potential effort, cost and sacrifices. This is achieved by offering platooning vehicles some form of incentivisation, either cost reductions or traffic light prioritisation. To test the proposed algorithms, a traffic simulation was developed using real networks with realistic traffic demand. The traffic participants were transformed into agents and given the necessary functionality to build platoons and operate within them. The applicability and suitability of both approaches were investigated along with several other aspects pertaining to platoon operations such as size, traffic state, network positioning and incentivisation methods. The results indicate that the mechanisms proposed allow for spontaneous platoons to be created. Moreover, with the distributed optimisation-based approach and using cost-reducing incentives, participating vehicles benefited regardless of the platoon size, traffic state and positioning, with utility improvements ranging from 20% to over 50% compared to the studied baseline. For time-based incentives the results were mixed, with the utility of some vehicles improving, some seeing no change and for others, deteriorating. Therefore, the usage of such incentives would not be recommended due to their lack of Pareto-efficiency. The compensational and completely decentralised approach shows some benefits, but the resulting improvement was overall negligible. The presented mechanisms are a novel approach to platoon formation and provide meaningful insight into the mechanics and applicability of platoons. This sets the stage for future expansions into planning, designing and implementing more effective infrastructures and traffic systems

Facilitating Cooperative Truck Platooning for Energy Savings: Path Planning, Platoon Formation and Benefit Redistribution

Enabled by the connected and automated vehicle (CAV) technology, cooperative truck platooning that offers promising energy savings is likely to be implemented soon. However, as the trucking industry operates in a highly granular manner so that the trucks usually vary in their operation schedules, vehicle types and configurations, it is inevitable that 1) the spontaneous platooning over a spatial network is rare, 2) the total fuel savings vary from platoon to platoon, and 3) the benefit achieved within a platoon differs from position to position, e.g., the lead vehicle always achieves the least fuel-saving. Consequently, trucks from different owners may not have the opportunities to platoon with others if no path coordination is performed. Even if they happen to do so, they may tend to change positions in the formed platoons to achieve greater benefits, yielding behaviorally unstable platoons with less energy savings and more disruptions to traffic flows. This thesis proposes a hierarchical modeling framework to explicate the necessitated strategies that facilitate cooperative truck platooning. An empirical study is first conducted to scrutinize the energy-saving potentials of the U.S. national freight network. By comparing the performance under scheduled platooning and ad-hoc platooning, the author shows that the platooning opportunities can be greatly improved by careful path planning, thereby yielding substantial energy savings. For trucks assembled on the same path and can to platoon together, the second part of the thesis investigates the optimal platoon formation that maximizes total platooning utility and benefits redistribution mechanisms that address the behavioral instability issue. Both centralized and decentralized approaches are proposed. In particular, the decentralized approach employs a dynamic process where individual trucks or formed platoons are assumed to act as rational agents. The agents decide whether to form a larger, better platoon considering their own utilities under the pre-defined benefit reallocation mechanisms. Depending on whether the trucks are single-brand or multi-brand, whether there is a complete information setting or incomplete information setting, three mechanisms, auction, bilateral trade model, and one-sided matching are proposed. The centralized approach yields a near-optimal solution for the whole system and is more computationally efficient than conventional algorithms. The decentralized approach is stable, more flexible, and computational efficient while maintaining acceptable degrees of optimality. The mechanisms proposed can apply to not only under the truck platooning scenario but also other forms of shared mobility.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163047/1/xtsun_1.pd

A Study of Truck Platooning Incentives Using a Congestion Game

We introduce an atomic congestion game with two types of agents, cars and trucks, to model the traffic flow on a road over various time intervals of the day. Cars maximize their utility by finding a trade-off between the time they choose to use the road, the average velocity of the flow at that time, and the dynamic congestion tax that they pay for using the road. In addition to these terms, the trucks have an incentive for using the road at the same time as their peers because they have platooning capabilities, which allow them to save fuel. The dynamics and equilibria of this game-theoretic model for the interaction between car traffic and truck platooning incentives are investigated. We use traffic data from Stockholm to validate parts of the modeling assumptions and extract reasonable parameters for the simulations. We use joint strategy fictitious play and average strategy fictitious play to learn a pure strategy Nash equilibrium of this game. We perform a comprehensive simulation study to understand the influence of various factors, such as the drivers' value of time and the percentage of the trucks that are equipped with platooning devices, on the properties of the Nash equilibrium.Comment: Updated Introduction; Improved Literature Revie

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

Gemischt-autonome Flotten in der urbanen Logistik

We consider a city logistics application in which a service provider seeks a repeatable plan to transport commodities from distribution centers to satellites. The service provider uses a mixed autonomous fleet that is composed of autonomous vehicles and manually operated vehicles. The autonomous vehicles are only able to travel independently on feasible streets of the heterogeneous infrastructure but elsewhere need to be pulled by manually operated vehicles in platoons. We introduce the service network design problem with mixed autonomous fleets to determine a tactical plan that minimizes the total costs over a medium-term time horizon. The tactical plan determines the size and mix of the fleet, schedules transportation services, and decides on the routing or outsourcing of commodities. We model this problem as an integer program on a time-expanded network and study the impact of different problem characteristics on the solutions. To precisely depict the synchronization requirements of the problem, the time-expanded networks need to consider narrow time intervals. Thus, we develop an exact solution approach based on the dynamic discretization discovery scheme that refines partially time-expanded networks containing only a fraction of the nodes and arcs of the fully time-expanded network. Further methodological contributions of this work include the introduction of valid inequalities, two enhancements that exploit linear relaxations, and a heuristic search space restriction. Computational experiments show that all evaluated variants of the solution approach outperform a commercial solver. For transferring a tactical plan to an operational solution that minimizes the transshipment effort on a given day, we present a post-processing technique that specifically assigns commodities to vehicles and vehicles to platoons. Finally, we solve a case study on a real-world based network resembling the city of Braunschweig, Germany. Analyzing the tactical and operational solutions, we assess the value of using a mixed autonomous fleet and derive practical implications.Wir betrachten eine Anwendung der urbanen Logistik, bei der ein Dienstleister einen wiederholbaren Plan für den Gütertransport von Distributionszentren zu Satelliten anstrebt. Dafür setzt der Dienstleister eine gemischt-autonome Flotte ein, die sich aus autonomen Fahrzeugen und manuell gesteuerten Fahrzeugen zusammensetzt. Die autonomen Fahrzeuge können nur auf bestimmten Straßen der heterogenen Infrastruktur selbstständig fahren, außerhalb dieser müssen sie von manuell gesteuerten Fahrzeugen mittels Platooning gezogen werden. Wir führen das „service network design problem with mixed autonomous fleets“ ein, um einen taktischen Plan zu ermitteln, der die Gesamtkosten über einen mittelfristigen Zeithorizont minimiert. Der taktische Plan bestimmt die Größe und Zusammensetzung der Flotte, legt die Transportdienste fest und entscheidet über das Routing oder das Outsourcing von Gütern. Wir modellieren dieses Problem als ganzzahliges Programm auf einem zeiterweiterten Netzwerk und untersuchen die Auswirkungen verschiedener Problemeigenschaften auf die Lösungen. Um die Synchronisationsanforderungen des Problems präzise darzustellen, müssen die zeiterweiterten Netzwerke kleine Zeitintervalle berücksichtigen. Daher entwickeln wir einen exakten Lösungsansatz, der auf dem Schema des „dynamic discretization discovery“ basiert und partiell zeiterweiterte Netzwerke entwickelt, die nur einen Teil der Knoten und Kanten des vollständig zeiterweiterten Netzwerks enthalten. Weitere methodische Beiträge dieser Dissertation umfassen die Einführung von Valid Inequalities, zweier Erweiterungen, die lineare Relaxationen verwenden, und einer heuristischen Suchraumbegrenzung. Experimente zeigen, dass alle evaluierten Varianten des Lösungsansatzes einen kommerziellen Solver übertreffen. Um einen taktischen Plan in eine operative Lösung zu überführen, die die Umladevorgänge an einem bestimmten Tag minimiert, stellen wir eine Post-Processing-Methode vor, mit der Güter zu Fahrzeugen und Fahrzeuge zu Platoons eindeutig zugeordnet werden. Schließlich lösen wir eine Fallstudie auf einem realitätsnahen Netzwerk, das der Stadt Braunschweig nachempfunden ist. Anhand der taktischen und operativen Lösungen bewerten wir den Nutzen einer gemischt-autonomen Flotte und leiten Implikationen für die Praxis ab