Truck platooning is a technology that is expected to become widespread in the coming years. Apart from the numerous benefits that it brings, its potential effects on the overall traffic situation need to be studied further, especially at bottlenecks and ramps. Assuming we can control the platoons from the infrastructure, they can be used as controlled moving bottlenecks, actuating control actions on the rest of the traffic, and potentially improving the throughput of the whole system. In this work, we use a tandem queueing model with moving bottlenecks as a prediction model to calculate control actions for the platoons. We use platoon speeds and formations as control inputs, and design a control law for throughput improvement of a highway section with a stationary bottleneck. By postponing and shaping the inflow to the bottleneck, we are able to avoid capacity drop, which significantly reduces the total time spent of all vehicles. We derived the estimated improvement in throughput that is achieved by applying the proposed control law, and tested it in a simulation study, with multi-class cell transmission model with platoons used as the simulation model, finding that the median delay of all vehicles is reduced by 75.6% compared to the uncontrolled case. Notably, although they are slowed down while actuating control actions, platooned vehicles experience less delay compared to the uncontrolled case, since they avoid going through congestion at the bottleneck. 

Connected and Autonomous Vehicles is a technology that will be disruptive for all layers of traffic control. The Lagrangian, in-the-flow nature of their operation offers untapped new potentials for sensing and actuation, but also presents new fundamental challenges. In order to use these vehicles for traffic state reconstruction and control, we need suitable traffic models, which should be computationally efficient and able to represent complex traffic phenomena. To this end, we propose the Front-tracking Transition System Model, a cell-free modelling approach that can incorporate Lagrangian measurements, and has a structure that yields itself to on-line model learning and control. The model is formulated as a transition system, and based on the front-tracking method for finding entropy solutions to the Lighthill-Whitham-Richards model. We characterize the solution of this model and show that it corresponds to the solution of the underlying PDE traffic model. Algorithms for traffic state reconstruction and model learning are proposed, exploiting the model structure. The model is then used to design a prediction -based control law for stop-and-go wave dissipation using randomly arriving Connected and Autonomous Vehicles. The proposed control framework is able to estimate the traffic state and model, adapt to changes in the traffic dynamics, and achieve a reduction in vehicles' Total Time Spent.

In this paper a platoon-actuated mainstream traffic control is proposed to decongest bottlenecks due to recurrent and nonrecurrent events. Indeed, differently from traditional mainstream control strategies, i.e., control strategies applied with fixed actuators, platoon-actuated control can be applied at any location on the freeway. In this work, the control actions to be communicated to the platoons, i.e., speed and configuration, are defined by means of a predictive control law based on traffic and platoon state detected in an area identified immediately upstream of the bottleneck. The main peculiarity of this scheme is that the size of the controlled area is dynamically adjusted based on the predicted congestion at the bottleneck. This approach keeps the control law computation burden low, while not sacrificing much control performance. Specifically, the number of platoons to be controlled and the time at which the platoons begin to be controlled depend on the size of the controlled area. Simulation results reported in the paper show the effectiveness of the proposed scheme, eliminating from 60% to 80% of the delay incurred from congestion compared with the uncontrolled case, depending on the level of traffic.

Platooning of connected and autonomous vehicles (CAVs) has a significant potential for throughput improvement. However, the interaction between CAVs and non-CAVs may limit the practically attainable improvement due to platooning. To better understand and address this limitation, we introduce a new fluid model of mixed-autonomy traffic flow and use this model to analyze and design platoon coordination strategies. We propose a tandem-link fluid model that considers randomly arriving platoons sharing highway capacity with non-CAVs. We derive verifiable conditions for stability of the fluid model by analyzing an underlying M/D/1 queuing process and establishing a Foster-Lyapunov drift condition for the fluid model. These stability conditions enable a quantitative analysis of highway throughput under various scenarios. The model is useful for designing platoon coordination strategies that maximize throughput and minimize delay. Such coordination strategies are provably optimal in the fluid model and are practically relevant. We also validate our results using standard macroscopic (cell transmission model) and microscopic (simulation for urban mobility) simulation models.

This article investigates the use of a model-based neural network for the traffic reconstruction problem using noisy measurements coming from Probe Vehicles (PV). The traffic state is assumed to be the density only, modeled by a partial differential equation. There exist various methods for reconstructing the density in that case. However, none of them perform well with noise and very few deal with lagrangian measurements. This paper introduces a method that can reduce the processes of identification, reconstruction, prediction, and noise rejection into a single optimization problem. Numerical simulations, based either on a macroscopic or a microscopic model, show good performance for a moderate computational burden. Copyright

Traffic congestion is a constantly growing problem, with a wide array of negative effects on the society, from wasted time and productivity to elevated air pollution and reduction of safety. The introduction of connected, autonomous vehicles enables a new, Lagrangian paradigm for sensing andcontrolling the traffic, by directly using connected vehicles inside the traffic flow, as opposed to the classical, Eulerian paradigm, which relies on stationary equipment on the road. By using control methods specifically tailored to the Lagrangian paradigm, we are able to influence the traffic flow even if the penetration rate of connected vehicle is low. This allows us to answer one of the central impending questions of the traffic control using emerging technologies: How can we influence the overall traffic by using only a smallportion of vehicles that we can control directly?

Traffic phenomena such as moving bottlenecks and stop-and-go waves are particularly pertinent to Lagrangian traffic control, and therefore need to be captured in traffic models. In this thesis we introduce the influence of these phenomena into the cell transmission model, multi-class cell transmission model, and tandem queueing model. We also propose a transition system model based on front tracking, which captures the relevant phenomena, and show under which conditions it corresponds to the Lighthill-Whitham-Richards model. Moving bottlenecks are introduced as a moving zone in which a reduced flux function describes the traffic flow, and their influence on the surrounding traffic is given by solving the Riemann problems at the flux function boundaries. Stop-and-go waves are introduced by constraining the wave speed of rarefaction, resulting in constant stop-and-go wave propagation speed and discharging flow lower than the road capacity, which is consistent with the empirical observations.

We use the proposed traffic models to design control laws that address three problems: platoon merging coordination, congestion reduction, and traffic state reconstruction. We study the case when two trucks are closing the distance and merging into a platoon on a public road, and propose an optimal control algorithm which accounts for the mutual influence between the trucks and the surrounding traffic. The proposed control law minimizes the total fuel consumption of the trucks, and improves the reliability of platooning. Then, we consider two forms of the congestion reduction problem: stationary bottleneck decongestion, and stop-and-go wave dissipation. In both cases, connected vehicles are used as moving bottlenecks to restrict the traffic flow enough to let the congestion dissipate. By applying these control laws, the throughput of the road is increased and the total travel time of all vehiclesis reduced. Finally, we generalize the stop-and-go wave dissipation problem by dropping the assumption that the full traffic state is known, and instead propose traffic state reconstruction algorithms which use local measurements originating from the connected vehicles. We show that the proposed control laws can also be implemented using the reconstructed traffic state. In this case, as the number of available connected vehicles increases, the control performance approaches the full-information control case.

Trafikstockning är ett ständigt växande problem, med ett brett utbud av negativa effekter på samhället, från bortkastad tid och produktivitet till ökade mängd luftföroreningar och minskning av säkerhet. Införandet av uppkopplade, autonoma fordon möjliggör ett nytt, Lagrangianskt paradigm för att styra och mäta trafiken, genom att direkt använda uppkopplade fordon inuti trafikflödet, i motsats till det klasiska, Euleriska paradigmet, som är beroende på stillastående utrustning på vägen. Genom att använda kontrollmetoder som är anpassad för Lagrangian-paradigmet kan vi påverka trafikflödet även om marknadsintrång av uppkopplade fordon är låg. Detta gör det möjligt för oss att besvara en av de centrala överhängande frågorna om trafikkontrollen med framväxande teknik: Hur kan vi påverka den totala trafiksituationen genomatt direkt kontrollera en liten del av fordonen?

Vissa trafikfenomen som rörliga flaskhalsar och stop-and-go-vågor är särskilt relevanta för Lagrangian trafikstyrning, och måste därför modelleras. I denna avhandling introducerar vi påverkan av dessa fenomen i cellöverföringsmodellen, flerklasscellöverföringsmodellen, och tandemkömodellen. Vi föreslår även en övergångssystemmodell baserad på front-tracking-metoden, som beskriver relevanta fenomen, och visar under vilka förhållanden den motsvarar Lighthill-Whitham-Richards-modellen. Rörliga flaskhalsar introduceras som en rörlig zon där en reducerad flödesfunktion beskriver trafikflödet, och deras inflytande på trafiken beräknas genom att lösa Riemann-problemen vid flödesfunktioners gränser. Stop-and-go-vågor introduceras genom att begränsa sällsynthetens våghastighet, som resulterar i konstant stop-and-go-vågenshastighet och utflöde som är lägre än vägkapaciteten, vilket överensstämmermed de empiriska observationerna.

Vi använder de föreslagna trafikmodellerna för att utforma kontrolllagar som hanterar tre problem: koordinering av fordonstågsammanfogning, minskning av trafikstockningar och uppskattning av trafiktillstånd. Vi studerar fallet när två lastbilar närmar sig varandra och sammanfogar till en fordonståg på allmän väg, och föreslår en optimal kontrollalgoritm som tar hänsyn till interaktionen mellan lastbilarna och den omgivande trafiken. Den föreslagna kontrolllagen minimerar den totala bränsleförbrukningen för lastbilarna och förbättrar pålitligheten av fordonstågskörning. Sedan granskar vi två former av problem med minskning av trafikstockningar: stationär flaskhalsavlastning och stop-and-go-vågskingring. I båda fallen används uppkopplade fordon som rörliga flaskhalsar för att begränsa trafikflödet så att trängseln upplösas. Genom att tillämpa dessa kontrolllagar ökar vägens genomströmning och den totala restiden för alla fordon minskas. Slutligen, generaliserar vi stop-and-go-vågskingringsproblem genom att släppa antagandet att hela trafiktillståndet är känt, och istället föreslå trafiktillståndsuppskattningsalgoritmer som använder lokala mätningar från de uppkopplade fordonen. Vi visar att de föreslagna kontrolllagarna kan även implementeras med hjälp av det uppskattade traffiktillståndet. I detta fall, när antalet tillgängliga uppkoppladefordon ökar, blir kontrollprestationer nästan lika bra som när det fullständiga traffiktillståndet är känt.

Cell-based approximations of PDE traffic models are widely used for traffic prediction and control. However, in order to represent the traffic state with good resolution, cell-based models often require a short cell length, which results in a very large number of states. We propose a new transition system traffic model, based on the front tracking method for solving the LWR PDE model. Assuming piecewise-linear flux function and piecewise-constant initial conditions, this model gives an exact solution. Furthermore, it is easier to extend, has fewer states and, although its dynamics are intrinsically hybrid, is faster to simulate than an equivalent cell-based approximation. The model is extended to enable handling moving bottlenecks as well as probabilistic traffic breakdowns and capacity drops at static bottlenecks. A control strategy that utilizes controlled moving bottlenecks for bottleneck decongestion is described and tested in simulation. It is shown that we are able to keep the static bottleneck in free flow by creating controlled moving bottlenecks at specific instances along on the road, and using them to regulate the incoming traffic flow. 

In this paper we present a numerical study on control and observation of traffic flow using Lagrangian measurements and actuators. We investigate the effect of some basic control and observation schemes using probe and actuated vehicles within the flow. The aim is to show the effect of the state reconstruction on the efficiency of the control, compared to the case using full information about the traffic. The effectiveness of the proposed state reconstruction and control algorithms is demonstrated in simulations. They show that control using the reconstructed state approaches the full-information control when the gap between the connected vehicles is not too large, reducing the delay by more than 60% when the gap between the sensor vehicles is 1.25 km on average, compared to a delay reduction of almost 80% in the full-information control case. Moreover, we propose a simple scheme for selecting which vehicles to use as sensors, in order to reduce the communication burden. Numerical simulations demonstrate that with this triggering mechanism, the delay is reduced by around 65%, compared to a reduction of 72% if all connected vehicles are communicating at all times.

Vehicle platooning has been attracting attention recently because of its ability to improve road capacity, safety and fuel efficiency. Vehicles communicate using Vehicle-to- Vehicle (V2V) wireless communication, making their status (acceleration, position, etc.) available to other vehicles. Shock waves, i.e. zones of reduced traffic speed that propagate upstream, are a well known emergent traffic phenomenon. Since vehicles entering such a zone need to decelerate sharply, shock waves cause a deterioration of fuel economy, driving comfort, and safety. While typically caused by bad driving behavior, recent studies have shown that it is possible to diminish or dissipate shock waves by applying certain good driving behavioral patterns. In this work, we use the information about the traffic situation to adapt the reference speed profile of the platoon we control, in order to mitigate the effect of a shock wave coming from downstream. The platoon leader receives the velocity of the vehicles downstream of the platoon and distance gap between them using V2V communication and it computes the shock wave speed. We show that by doing this we reduce the fuel consumption of the vehicles in the platoon, and improve the traffic situation by helping dissipate the shock wave. We validate our results using microscopic models with the help of a toolchain composed of Matlab, and the SUMO traffic simulator.

Traffic congestion is a constantly growing problem, with a wide array ofnegative effects on the society, from wasted time and productivity to elevated air pollution and increased number of accidents. Classical traffic control methods have long been successfully employed to alleviate congestion, improving the traffic situation of many cities and highways. However, traffic control is not universally employed, because of the necessity of installing additional equipment and instating new legislation. 

The introduction of connected, autonomous vehicles offers new opportunities for sensing and controlling the traffic. The data that most of the vehicles nowadays provide are already widely used to measure the traffic conditions. It is natural to consider how vehicles could also be used as actuators, driving them in a specific way so that they affect the traffic positively. However, many of the currently considered strategies for congestion reduction using autonomous vehicles rely on having a high penetration rate, which is not likely to be the case in the near future. This raises the question: How can we influence the overall traffic by using only a small portion of vehicles that we have direct control over? There are two problems in particular that this thesis considers, congestion wave dissipation and avoidance, and platoon catch-up coordination.

First, we study how to dissipate congestion waves by use of a directly controlled vehicle acting as a moving bottleneck. Traffic data can help predict disturbances and constraints that the vehicle will face, and the individual vehicles can be actuated to improve the overall traffic situation. We extend the classical cell transmission model to include the influence of a moving bottleneck, and then use this model to devise a control strategy for an actuator vehicle. By employing such control, we are able to homogenize the traffic without significantly reducing throughput. Under realistic conditions, it is shown that the average total variation of traffic density can be reduced over 5%, while the total travel time increases only 1%.

Second, we study how to predict and control vehicles catching up in order to form a platoon, while driving in highway traffic. The influences of road grade and background traffic are examined and vehicles attempting to form a platoon are modelled as moving bottlenecks. Using this model, we are able to predict how much the vehicles might be delayed because of congestion and adjust the plan accordingly, calculating the optimal platoon catch-up speeds for the vehicles by minimizing their energy consumption. This leads to a reduction of energy cost of up to 0.5% compared to the case when we ignore the traffic conditions. More importantly, we are able to predict when attemptingto form a platoon will result in no energy savings, with approximately 80% accuracy.

Trafikstockning är ett ständigt växande problem, med ett brett utbud av negativa effekter på samhället, från bortkastad tid och produktivitet till ökade mängd luftföroreningar och antal olyckor. Klassiska metoder för trafik kontroll har länge använts framgångsrikt för att lindra detta problem, med förbättrad trafiksituation för många städer och motorvägar. Trafik kontrollen är emellertid inte universellt tillämpad eftersom den är beroende av ytterligare utrustning och ny lagstiftning som behover instaleras och införas.

Införandet av uppkopplade, autonoma fordon medför nya möjligheter att mäta och kontrollera trafiken. Data som de flesta fordon tillhandahållar redan idag används allmänt för att mäta trafikförhållandena. Det är naturligt att överväga hur fordon också skulle kunna användas som ställdon, genom att driva dem på ett visst sätt så att de påverkar trafiken positivt. Men många av dagens strategierna för trängselnedsättning med hjälp av autonoma fordon är beroende av att de tillämpas av en stor del av fordonen, vilket sannoliktinte kommer att bli fallet inom en snar framtid. Det väcker frågan: Hur kan vi påverka den totala trafiksituationen genom att kontrollera en liten del avfordonen? Det finns två problem specifika problem som den här avhandlingentar hänsyn till, trängselvågsavledning och –undvikande samt koordinering av fordonståg av lastbilar.

I det första problemet studerar vi hur vi kan skingra trängselvågor med hjälp av ett direktstyrt fordon som fungerar som en rörlig flaskhals. Trafikdatakan hjälpa till att förutsäga störningar och begränsningar som fordonet kommer att stöta på, och de enskilda fordonen kan styras för att förbättra den totala trafiksituation. Vi utvidgar den klassiska cellöverföringsmodellenför att inkludera påverkan av en rörlig flaskhals och använder sedan denna modell för att utforma en kontrollstrategi för ett styrbart fordon. Genom att använda sådan styrning kan vi homogenisera trafiken utan att avsevärt minska genomströmningen. Under realistiska förhållanden visar vi att den genomsnittliga totala variationen i trafiktäthet kan minskas med över 5%, medan den totala körtiden ökar med endast 1%.

I det andra problemet studerar vi hur vi kan förutsäga och styra fordonens hastighetsprofiler vid formering av fordonståg under körning i motorvägstrafik. Påverkan av väglutning och motorvägstrafik undersöks, och fordon som försöker bilda en fordonståg modelleras som rörliga flaskhalsar. Med denna modell kan vi förutsäga förseningar på grund av trängsel och justera planen i enlighet med dessa, samt beräkna de optimala hastigheterna för fordonengenom att minimera energiförbrukningen. Detta leder till en minskning av energikostnaden på upp till 0,5% i jämförelse med fallet när vi ignorerar trafikförhållandena. Ännu viktigare är att vi kan vi förutsäga när försök att bildaett fordonståg kommer att resultera i utebliven energibesparing, med ungefär 80% noggrannhet.