This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease.

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 5,000 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 15 times higher monetary profit and increases the CO2 emission reductions from 0.4% to 5.5%. 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.

Truck platooning is a well-studied technology that has the potential to reduce both the environmental impact and operational costs of trucks. The technology has matured over the last 20 years, and the commercial rollout of platooning is approaching. Cooperation across carriers is essential for the viability of platooning; otherwise, many platooning opportunities are lost. We first present a cross-carrier platooning system architecture in which many carriers cooperate in forming platoons through a platoon-hailing service. Then, we present a cross-carrier platoon coordination approach in which each carrier optimizes its platooning plans according to the predicted plans of other carriers. A profit-sharing mechanism to even out the platooning profit in each platoon is embedded in the platoon coordination approach. Finally, a simulation study over the Swedish road network is performed to evaluate the potential of platooning under realistic conditions. The simulation study shows that the energy consumption of trucks in Sweden can be reduced by 5.4% due to platooning and that cooperation across carriers is essential to achieve significant platooning benefits.

The emerging commercial rollout of heavy-duty vehicle platooning necessitates the development of efficient platoon coordination solutions. The commercial vehicle fleet consists of vehicles owned by different transportation companies with different objectives. To capture their strategic behavior, we study platoon coordination that aims to maximize profits for individual vehicles. The interaction among vehicles is modeled as a non-cooperative game. In our cyber-physical system, we consider a large number of vehicles with fixed routes in a transportation network that can wait at hubs along their routes to form platoons. Each vehicle aims to maximize its utility function, which includes a reward for platooning and a cost for waiting. We propose open-loop coordination solutions when the vehicles decide on their waiting times at the beginning of their trips and do not update their decisions during their trips. It is shown that the corresponding game admits at least one Nash equilibrium. We also propose feedback solutions in which the vehicles are allowed to update their decisions along their routes. In a simulation study over the Swedish road network, we compare the proposed platoon coordination solutions and evaluate the benefits of non-cooperative platooning at a societal scale.

We model a platooning system including trucks and a third-party service provider that performs platoon coordination, distributes the platooning profit within platoons, and charges the trucks in exchange for its services. This paper studies one class of pricing rules, where the third-party service provider keeps part of the platooning profit each time a platoon is formed. Furthermore, we propose a platoon coordination solution based on distributed model predictive control in which the pricing rule is integrated. To evaluate the effect of the pricing on the platooning system, we perform a simulation over the Swedish road network. The simulation shows that the platooning rate and profit highly depend on the pricing. This suggests that pricing needs to be set carefully to obtain a satisfactory platooning system in the future.

Truck drivers are required to stop and rest with a certain regularity according to the driving and rest time regulations, also called Hours-of-Service (HoS) regulations. This paper studies the problem of optimally forming platoons when considering realistic HoS regulations. In our problem, trucks have fixed routes in a transportation network and can wait at hubs along their routes to form platoons with others while fulfilling the driving and rest time constraints. We propose a distributed decision-making scheme where each truck controls its waiting times at hubs based on the predicted schedules of others. The decoupling of trucks' decision-makings contributes to an approximate dynamic programming approach for platoon coordination under HoS regulations. Finally, we perform a simulation over the Swedish road network with one thousand trucks to evaluate the achieved platooning benefits under the HoS regulations in the European Union (EU). The simulation results show that, on average, trucks drive in platoons for 37 % of their routes if each truck is allowed to be delayed for 5 % of its total travel time. If trucks are not allowed to be delayed, they drive in platoons for 12 % of their routes.

The need for sustainable transportation solutions is urgent as the demand for mobility of goods and people is expected to multiply in the upcoming decades. One promising solution is truck platooning, which shows great potential in reducing the energy consumption and operational costs of trucks. To utilize the benefits of truck platooning to the fullest, trucks with different schedules and routes in a road network need coordination to form platoons. This thesis addresses platoon coordination when trucks can wait at hubs to form platoons. We assume there is a reward for driving in a platoon and a cost for waiting at a hub, and the objective is to maximize the overall profit. We focus on coordinating trucks from different carriers, which is important considering that many platoon opportunities are lost if only trucks from the same carrier form platoons.

In the first contributions of the thesis, we propose coordination solutions where carriers aim to maximize their own profits through cross-carrier platoon cooperation. We propose an architecture of a platoon-hailing service that stores reported platooning plans of carriers and, based on these, informs carriers about the platoons their trucks can join when they make platooning decisions. A realistic simulation study shows that the cross-carrier platooning system can achieve energy savings of 3.0% and 5.4% when 20% and 100% of the trucks are coordinated, respectively. A non-cooperative game is then formulated to model the strategic interaction among trucks with individual objectives when they coordinate for platooning and make decisions at the beginning of their journeys. The existence of at least one Nash equilibrium is shown. In the case of stochastic travel times,  feedback-based solutions are developed wherein trucks repeatedly update their equilibrium decisions. A simulation study with stochastic travel times shows that the feedback-based solutions achieve platooning rates only $5\%$ lower than a solution where the travel times are known. We also explore Pareto-improving coordination guaranteeing each carrier is better off coopering with others, and models for distributing the profit within platoons.

In the last contributions of the thesis, we study the problem of optimally releasing trucks at hubs when arriving according to a stochastic process, and a priori information about truck arrivals is inaccessible; this may be sensitive information to share with others. First, we study the release problem at hubs in a hub-corridor where the objective is to maximize the profit over time. The optimality of threshold-based release policies is shown under the assumption that arrivals are independent or that arrivals are dependent due to the releasing behavior at the preceding hub in the corridor. Then, we study the release problem at a single hub where the aim is to maximize the profit of trucks currently at the hub. This is realistic if trucks are only willing to wait at the hub if they can increase their own profits. Stopping time theory is used to show the optimality of a  threshold-based release policy when arrivals are independent and identically distributed. These contributions show that simple coordination approaches can achieve high profits from platooning, even under limited information. 

Under de kommande decennierna förväntas efterfrågan på transport av varor och passagerare mångfaldigas, vilket innebär att behovet av hållbara transportlösningar är brådskande. En lovande lösning är konvojkörning, som visar stor potential att minska bränsleförbrukningen och driftskostnaderna för lastbilar. För att utnyttja fördelarna med konvojkörning till fullo behöver lastbilar med olika scheman och rutter koordineras. Den här avhandlingen behandlar koordinering av lastbilar som kan bilda konvojer på transporthubbar, där lastbilar kan vänta på andra lastbilar för att bilda konvojer. Vi antar att det finns en vinst av konvojkörning och en kostnad för att vänta. Vi fokuserar på koordinering av lastbilar från olika åkerier, vilket är viktigt med tanke på att många konvojkörningsmöjligheter går förlorade om bara lastbilar från samma åkeri bildar konvojer.

I avhandlingens första bidrag föreslår vi koordineringslösningar där åkerier strävar efter att maximera sina egen vinster från konvojkörning genom samarbete med andra. Vi föreslår en arkitektur för en tjänst som lagrar åkeriers rapporterade konvojkörningsplaner och, utifrån dessa, informerar åkerier om vilka konvojer deras lastbilar kan ansluta sig till när de fattar konvojkörningsbeslut. En realistisk simuleringsstudie visar att konvojkörningsystemet kan uppnå energibesparingar på 3.0% och 5.4% när 20% respektive 100% av lastbilarna koordineras. Ett icke-kooperativt spel formuleras sedan för att modellera den strategiska interaktionen mellan lastbilar med individuella mål när de koordinerar för konvojkörning och fattar beslut i början av sina resor. Existensen av minst en Nashjämviktslösning visas. När restiderna är stokastiska utvecklas även lösningar där lastbilarna tillåts uppdatera sina beslut längs med sina resor. I en simuleringsstudie visas att när lastbilarna tillåts uppdatera sina väntetider uppnås en konjovkörningsgrad på endast 5% lägre än i en lösning där restiderna är kända. Vi utforskar också Pareto-förbättrande koordinering som garanterar att varje åkeri tjänar på att sammarbeta med andra, och modeller för att fördela vinsten inom konvojer.

I avhandlingens sista bidrag studerar vi problemet med att optimalt släppa iväg lastbilar vid hubbar där lastbilar ankommer enligt en stokastisk process, och förhandsinformation om ankomsterna är otillgänglig; detta kan vara känslig information att dela med andra. Först studerar vi ivägsläppningsproblemet vid hubbar som ligger i en kedja och målet är att maximera vinsten över tid. Optimaliteten hos tröskelregler för att släppa iväg lastbilar visas under antagandet att ankomsterna är oberoende eller att ankomster är beroende pågrund av ivägsläppningsbeteendet vid den föregående hubben i kedjan. Sedan studerar vi ivägsläppningsproblemet vid en hubb där målet är att maximera vinsten för lastbilar som för närvarande befinner sig vid hubben. Detta är realistiskt om lastbilar bara är villiga att vänta vid hubben om de kan öka sin egen vinst. Stopptidsteori används för att visa optimaliteten hos en tröskelregel när ankomster är oberoende och identiskt fördelade. Dessa bidrag visar att enkla koordineringslösningar kan uppnå höga vinster, även under begränsad information.

We study the strategic interaction among vehicles in a non-cooperative platoon coordination game. Vehicles have predefined routes in a transportation network with a set of hubs where vehicles can wait for other vehicles to form platoons. Vehicles decide on their waiting times at hubs and the utility function of each vehicle includes both the benefit from platooning and the cost of waiting. We show that the platoon coordination game is a potential game when the travel times are either deterministic or stochastic, and the vehicles decide on their waiting times at the beginning of their journeys. We also propose two feedback solutions for the coordination problem when the travel times are stochastic and vehicles are allowed to update their strategies along their routes. The solutions are evaluated in a simulation study over the Swedish road network. It is shown that uncertainty in travel times affects the total benefit of platooning drastically and the benefit from platooning in the system increases significantly when utilizing feedback solutions.

This paper considers the problem of hub-based platoon coordination for a large-scale transport system, where trucks have individual utility functions to optimize. An event-triggered distributed model predictive control method is proposed to solve the optimal scheduling of waiting times at hubs for individual trucks. In this distributed framework, trucks are allowed to decide their waiting times independently and only limited information is shared between trucks. Both the predicted reward gained from platooning and the predicted cost for waiting at hubs are included in each truck's utility function. The performance of the coordination method is demonstrated in a simulation with one hundred trucks over the Swedish road network.

This paper studies a multi-fleet platoon coordination system in transport networks that deploy hubs to form trucks into platoons. The trucks belong to different fleets that are interested in increasing their profits by platooning across fleets. The profit of each fleet incorporates platooning rewards and costs for waiting at hubs. Each truck has a fixed route and a waiting time budget to spend at the hubs along its route. To ensure that all fleets are willing to participate in the system, we develop a cross-fleet Pareto-improving coordination strategy that guarantees higher fleet profits than a coordination strategy without cross-fleet platoons. By leveraging multiple hubs for platoon formation, the coordination strategy can be implemented in a real-time and distributed fashion while largely reducing the amount of travel information to be shared for system-wide coordination. We evaluate the proposed strategy in a simulation study over the Swedish transportation network. The cross-fleet platooning strategy significantly improves fleets' profits compared with single-fleet platooning, especially the profits from smaller fleets. The cross-fleet platooning strategy also shows strong competitiveness in terms of the system-wide profit compared to the case when a system planner optimizes all fleets' total profit.