The traffic engineering community currently faces the advent of a new generation of Intelligent Transportation Systems (ITS), known as cooperative systems. More specifically, the recent developments of connected and autonomous vehicles, i.e. cooperative vehicles, are expected to cause a societal shift, changing the way people commute on a daily basis and relate to transport in general. The research presented in this dissertation is motivated by the need for proper understanding of the possible inputs of cooperative vehicles in a traffic stream. Beyond legal aspects regarding the introduction of such vehicles and considerations on standardization and harmonization of the communication norms, the research focuses on the use of communication for highway traffic flow homogenization. In particular, the selected approach for the introduction of cooperation inherits from the theory of traffic flow and the recent developments of microscopic traffic models. Cooperation can first be introduced as a form of multi-anticipation, which can either come from drivers' behaviors or from communication. A mathematical framework for investigating the impact of perturbations into a steady-state traffic is proposed for the class of time continuous car-following models. Linear stability analyses are refined for forward and backward multi-anticipation, exploring the underlying importance of considering upstream information. The linear stability analyses for all wavelengths can be deepened by the mean of the graphical root locus analysis, which enables comparisons and design of strategies of cooperation. The positive influence of bilateral cooperation and of added linear control terms are highlighted. Weakly non-linear analyses are also performed, and the equations of solitary waves appearing at the frontier of the instability domain are obtained. A simple condition over the partial derivatives of the dynamical system is found to determine the acceleration regime of the leading edge of the travelling wave. Following these analytical results, one aim is to simulate a realistic traffic thereby reproducing the driving behavior variability. A Next Generation Simulation trajectory dataset is used to calibrate three continuous car-following models. A methodology involving data filtering, robust calibration, parameters estimation and sampling of realistic parameters is detailed, and allows realistic traffic with stop-and-go waves appearances to be replicated. Based on these simulated trajectories, previous analytical results are confirmed, and the growing perturbations are removed for various coverage rates of cooperative vehicles and adequately tuned cooperative strategies. Finally the issue of information reliability is assessed for a mixed fleet of cooperative and non-cooperative vehicles. The modeling choice consists in building a three layers multi-agent framework that enables the following properties to be defined: the physical behavior of vehicles, the communication possibilities, and the trust each vehicle -or agent- has in another vehicle information or in itself. The investigation of trust and communication rules allow the model to deal with high rates of disturbed cooperative vehicles sensors and to learn in real time the quality of the sent and received information. It is demonstrated that appropriate communication and trust rules sensibly increase the robustness of the network to perturbations coming from exchanges of unreliable information.

Framtidens fordon kommer vara väldigt annorlunda jämfört med dagens fordon. Stor del av förändringen kommer ske med hjälp av IoT. Världen kommer bli oerhört uppkopplat, sensorer kommer kunna ta fram data som de flesta av oss inte ens visste fanns. Mer data betyder även mer problem. Enorma mängder data kommer genereras och distribueras av framtidens IoT-enheter och denna data behöver analyseras och lagras på effektiva sätt med hjälp av Big data principer. Fog computing är en utveckling av Cloud tekniken som föreslås som en lösning på många av de problem IoT lider utav. Är tradionella lagringsmöjligheter och analyseringsverktyg tillräckliga för den enorma volymen data som kommer produceras eller krävs det nya tekniker för att stödja utvecklingen? Denna studie kommer försöka besvara frågeställningen: ”Vilka problem och möjligheter får utvecklingen av Fog computing i personbilar för konsumenter?” Frågeställningen besvaras genom en systematisk litteraturstudie. Den systematiska litteraturstudien syfte är identifiera och tolka tidigare litteratur och forskning. Analys av materialet har skett med hjälp av öppen kodning som har använts för att sortera och kategorisera data. Resultat visar att tekniker som IoT, Big data och Fog computing är väldigt integrerade i varandra. I framtidens fordon kommer det finns mycket IoTenheter som producerar enorma mängder data. Fog computing kommer bli en effektiv lösning för att hantera de mängder data från IoT-enheterna med låg fördröjning. Möjligheterna blir nya applikationer och system som hjälper till med att förbättra säkerheten i trafiken, miljön och information om bilens tillstånd. Det finns flera risker och problem som behöver lösas innan en fullskalig version kan börja användas, risker som autentisering av data, integriteten för användaren samt bestämma vilken mobilitetsmodell som är effektivast. / Future vehicles will be very different from today's vehicles. Much of the change will be done using the IoT. The world will be very connected, sensors will be able to access data that most of us did not even know existed. More data also means more problems. Enormous amounts of data will be generated and distributed by the future's IoT devices, and this data needs to be analyzed and stored efficiently using Big data Principles. Fog computing is a development of Cloud technology that is suggested as a solution to many of the problems IoT suffer from. Are traditional storage and analysis tools sufficient for the huge volume of data that will be produced or are new technologies needed to support development? This study will try to answer the question: "What problems and opportunities does the development of Fog computing in passenger cars have for consumers?" The question is answered by a systematic literature study. The objective of the systematic literature study is to identify and interpret previous literature and research. Analysis of the material has been done by using open coding where coding has been used to sort and categorize data. Results show that technologies like IoT, Big data and Fog computing are very integrated in each other. In the future vehicles there will be a lot of IoT devices that produce huge amounts of data. Fog computing will be an effective solution for managing the amount of data from IoT devices with a low latency. The possibilities will create new applications and systems that help improve traffic safety, the environment and information about the car's state and condition. There are several risks and problems that need to be resolved before a full-scale version can be used, such as data authentication, user integrity, and deciding on the most efficient mobility model.

The road based transportation operations are growing rapidly, but the current infrastructure cannot sustain the entire growth. At the same time vehicle utilisation and fill rates are low. Improved efficiency of the operations is a necessary way forward for road based transportation. Parallel to this, heavy vehicle producers are currently improving the efficiency with services accompanying the product that are focused on the driver and the vehicle performance. However, the data from connected vehicles required for these services only entail a small amount of the operational data generated by connected vehicles. The case study aims to answer how to use connected vehicle operational data in order to suggest value adding services in a dynamic road distribution system. The applied methodology is an inductive study with an explanatory approach to map the current and future service offerings of the case company. This knowledge is combined with an exploratory approach with interviews of transport planners and theories of Lean and fleet management. Primarily, it is concluded that the perspective of operational data requires widening. Considering not only driver and vehicle operations but rather the entire transport operation of a company. It is also concluded that value creation with operational data is possible during three phases of fleet management. First, if knowledge about order data is accessible, the planning of transportations can be improved using route optimisation and operations research. Secondly, it is possible to create value during the execution phase, throughless manual supervision and communication by transport planners. Lastly, both the currently used operational data and further data usage can contribute to a better understanding of the performance of a fleet operation and facilitate for continuous improvements during an evaluation phase.

Asset managers continue to prepare physical infrastructure investments needed to accommodate the emerging technologies, namely vehicle connectivity, electrification, and automation. The provision of new infrastructure and modification of existing infrastructure is expected to incur a significant amount of capital investment. Secondly, with increasing EV and CAV operations, the revenues typically earned from vehicle registrations and fuel tax are expected to change due to changing demand for vehicle ownership and amount of travel, respectively. This research estimated (i) the changes in highway expenditures in an era of ECAV operations, (ii) the net change in highway revenues that can be expected to arise from ECAV operations, and (iii) the changes in user equity across the highway user groups (vehicle classes). In assessing the changes in highway expenditures, the research developed a model to predict the cost of highway infrastructure stewardship based on current and/ or future system usage. <div><br></div><div>The results of the research reveal that CAVs are expected to significantly change the travel patterns, leading to increased system usage which in turn results in increased wear and tear on highway infrastructure. This, with the need for new infrastructure to support and accommodate the new technologies is expected to result in increased highway expenditure. At the same time, CAVs are expected to have significantly improved fuel economy as compared to their human driven counterparts, leading to a decrease in fuel consumption per vehicle, resulting in reduced fuel revenues. Furthermore, the prominence of EVs is expected to exacerbate this problem. This thesis proposed a revision to the current user fee structure to address these impacts. This revision contains two major parts designed to address the system efficiency and equity in the near and long term. For the near term, this thesis recommended a variable tax scheme under which each vehicle class pays a different fuel tax rate. This ensures that both equity and system efficiency are improved during the transition to ECAV. In the long term, this thesis recommended supplementing the fuel tax with a distance based VMT tax, applicable to electric vehicles.<br></div>

Connected and automated vehicles (CAVs) will behave fundamentally differently than human drivers. In mixed traffic, this could lead to inefficiencies and safety-critical situations since neither human drivers nor CAVs will be able to fully anticipate or predict surrounding traffic dynamics. Thus, some researchers proposed to separate CAVs from conventional vehicles by dedicating exclusive lanes to them. However, the separation of road infrastructure can negatively impact the system’s capacity. While the effects of CAV lanes were addressed for freeways, their deployment in urban settings is not yet fully understood. This paper systematically analyzes the effects of CAV-lanes in an urban setting accounting for the corresponding complexities. We employ microscopic traffic simulation to model traffic flow dynamics in a detailed manner and to be able to consider a wide array of supply-related characteristics. These concern intersection geometry, public transport operation, traffic signal control, and traffic management. Our study contributes to the existing literature by revealing the potential of CAV lanes in an urban setting while accounting for the behavioral and topological complexities. The results of this study can support decision-makers in the design of future urban transportation systems and to prepare cities for the upcoming era of automation in traffic.

Connected and automated vehicles (CVs and AVs, respectively) are rapidly emerging paradigms aiming to deploy and develop transportation systems that enable automated driving and data exchange among vehicles, infrastructure, and mobile devices to improve mobility, enhance safety, and reduce the adverse environmental impacts of transportation systems. Based on these premises, the focus of this research is to quantify the potential benefits of CVs and AVs to provide insight into how these technologies will impact road users and network performance. To assess the traffic operational performance of CVs, a connectivity-based modeling framework was developed based on traffic microsimulation for a real network in the city of Toronto. Then the effects of real-time routing guidance and advisory warning messages were studied for CVs. In addition, the impact of rerouting of non-connected vehicles (non-CVs) in response to various sources of information, such as mobile apps, GPS or VMS, was considered and evaluated. The results demonstrate the potential of such systems to improve mobility, enhance safety, and reduce greenhouse gas emissions (GHGs) at the network-wide level presented for different CVs market penetration. Additionally, the practical application of CVs in travel time estimation and its relationship with the number and location of roadside equipment (RSE) along freeways was investigated. A methodology was developed for determining the optimal number and location of roadside equipment (RSE) for reducing travel time estimation error in a connected vehicle environment. A simulation testbed that includes CVs was developed and implemented in the microsimulation model for Toronto 400-series highway network. The results reveal that the suggested methodology is capable of optimizing the number and location of RSEs in a connected vehicle environment. The optimization results indicate that the accuracy of travel time estimates is primarily dependent on the location of RSEs and less dependent on the total density of RSEs. In addition to CVs, the potential capacity increase of highways as a function of AVs market penetration was also studied and estimated. AVs are classified into Cooperative and Autonomous AVs. While Autonomous AVs rely only to their detection technology to sense their surroundings, Cooperative AVs, can also benefit from direct communication between vehicles and infrastructure. Cooperative car-following and lane-changing models were developed in a microsimulation model to enable AVs to maintain safe following and merging gaps. This study shows that cooperative AVs can adopt shorter gap than autonomous AVs and consequently, can significantly improve the lane capacity of highways. The achievable capacity increase for autonomous AVs appears highly insensitive to the market penetration, namely, the capacity remains within a narrow range of 2,046 to 2,238 vph irrespective of market penetration. The results of this research provide practitioners and decision-makers with knowledge regarding the potential capacity benefits of AVs with respect to market penetration and fleet conversion. / Thesis / Doctor of Philosophy (PhD)

The concept of smart cities, which may have seemed distant and unachievable not very long ago, is becoming reality today. Behind the concept of a smart city lies a vast intelligence, namely the Internet of Things. As the population in urban areas grows, the development of smart parking solutions are becoming more relevant and crucial than ever. One of the biggest concerns regarding todays’ way of parking is congestion. Drivers’ are cruising around in search of a parking space which is both time consuming and causes big congestion. In this thesis project, a comparative analysis was conducted between two parking operators in Sweden, EasyPark and LinPark. A qualitative study in the forms of interviews and a literature study was conducted to further investigate and analyse technologies and methods used in smart parking today. The problems that this thesis focuses on are congestion due to poor parking management as well as the smartphone dependency regarding the management of a parking space. Highlighted in this report are key technologies used to track the parking occupancy status, the impact connected vehicles might have on the smart parking industry, as well as how smart parking solutions could help to decrease congestion. The results of this thesis project showed that there are several solutions that could significantly decrease traffic congestion. By aiding the drivers to a parking space, or allowing the driver to pre-book a parking space, the efficiency with regards to time and congestion could be improved. Furthermore, the establishment of electrical and connected vehicles seem to have a crucial impact on the smart parking industry. Parking applications need to adjust their services to be compatible with both types of vehicles. Even though there seem to be many new technologies available and developed, they may not be able to be implemented in the near future. That is why it is important for parking applications to provide a sufficient service with the technology available today. With regards to this, a feature addition was suggested where users are able to filter their search for certain parking spaces, such as filtering by the cheapest or closest parking space. Lastly, a feature for connected vehicles was suggested where the car is able to detect and track which parking operators are operating certain parking spaces. / Begreppet smarta städer, som må ha verkat ouppnåeligt för inte så länge sedan, blir verklighet idag. Bakom begreppet “smart cities” ligger en stor intelligens, nämligen “Internet of Things”, sakernas internet. När befolkningen i städer växer, blir utvecklingen av smarta parkeringslösningar mer relevant och avgörande än någonsin. Ett av de största bekymret för dagens parkeringssätt är trafikstockningar. Förarna åker runt på jakt efter en parkeringsplats vilket både är tidskrävande och bidrar till ytterligare trafikstockningar. I detta examensarbete genomfördes en komparativ analys mellan två parkeringsoperatörer i Sverige, EasyPark och LinPark. En kvalitativ studie i form av intervjuer och en litteraturstudie genomfördes för att ytterligare undersöka och analysera tekniker och metoder som används inom smart parkering idag. De viktigaste problemen som denna avhandling fokuserar på är trängsel på grund av ineffektiva parkeringsåtgärder samt smartphoneberoendet gällande hanteringen av en parkeringsplats. I denna rapport framhålls viktiga teknologier som används för att spåra status för parkeringsplatser, den påverkan anslutna fordon kan ha på smart parking industrin, samt hur smarta parkeringslösningar kan bidra till att minska trängseln. Resultaten av detta examensarbete visade att det finns flera lösningar som kan minska trafikstockningar avsevärt. Genom att guida förare till en parkeringsplats, eller genom att låta föraren förhandsboka en parkeringsplats, kan effektiviteten när det gäller tid och trängsel förbättras. Dessutom verkar etablering av elektriska och anslutna fordon ha en avgörande inverkan på smart parking industrin. Parkeringsapplikationer måste anpassa sina tjänster så att de är kompatibla med alla olika typer av fordon. Även om det verkar finnas många nya teknologier tillgängliga och utvecklade, är de inte implementerbara inom en snar framtid. Det är därför det är viktigt för parkeringsapplikationer att tillhandahålla en tillräckligt effektiv tjänst med den teknik som finns tillgänglig idag. Ett funktionstillägg föreslogs, där användare kan filtrera sin sökning efter vissa typer av parkeringsplatser, till exempel filtrering efter den billigaste eller närmaste parkeringsplatsen. Slutligen föreslogs en funktion för anslutna fordon där bilen kan upptäcka och spåra vilka parkeringsoperatörer som driver vissa parkeringsplatser.

The macroscopic measure of traffic stream density is crucial in advanced traffic management systems. However, measuring the traffic stream density in the field is difficult since it is a spatial measurement. In this dissertation, several estimation approaches are developed to estimate the traffic stream density on signalized approaches using connected vehicle (CV) data. First, the dissertation introduces a novel variable estimation interval that allows for higher estimation precision, as the updating time interval always contains a fixed number of CVs. After that, the dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the traffic stream density using CV data only. The proposed model-driven approaches are evaluated using empirical and simulated data, the former of which were collected along a signalized approach in downtown Blacksburg, VA. Results indicate that density estimates produced by the linear KF approach are the most accurate. A sensitivity of the estimation approaches to various factors including the level of market penetration (LMP) of CVs, the initial conditions, the number of particles in the PF approach, traffic demand levels, traffic signal control methods, and vehicle length is presented. Results show that the accuracy of the density estimate increases as the LMP increases. The KF is the least sensitive to the initial traffic density estimate, while the PF is the most sensitive to the initial traffic density estimate. The results also demonstrate that the proposed estimation approaches work better at higher demand levels given that more CVs exist for the same LMP scenario. For traffic signal control methods, the results demonstrate a higher estimation accuracy for fixed traffic signal timings at low traffic demand levels, while the estimation accuracy is better when the adaptive phase split optimizer is activated for high traffic demand levels. The dissertation also investigates the sensitivity of the KF estimation approach to vehicle length, demonstrating that the presence of longer vehicles (e.g. trucks) in the traffic link reduces the estimation accuracy. Data-driven approaches are also developed to estimate the traffic stream density, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The data-driven approaches also utilize solely CV data. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Lastly, the dissertation compares the performance of the model-driven and the data-driven approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the large amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the linear KF approach is highly recommended in the application of traffic density estimation due to its simplicity and applicability in the field. / Doctor of Philosophy / Estimating the number of vehicles (vehicle counts) on a road segment is crucial in advanced traffic management systems. However, measuring the number of vehicles on a road segment in the field is difficult because of the need for installing multiple detection sensors in that road segment. In this dissertation, several estimation approaches are developed to estimate the number of vehicles on signalized roadways using connected vehicle (CV) data. The CV is defined as the vehicle that can share its instantaneous location every time t. The dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the number of vehicles using CV data only. The proposed model-driven approaches are evaluated using real and simulated data, the former of which were collected along a signalized roadway in downtown Blacksburg, VA. Results indicate that the number of vehicles produced by the linear KF approach is the most accurate. The results also show that the KF approach is the least sensitive approach to the initial conditions. Machine learning approaches are also developed to estimate the number of vehicles, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The machine learning approaches also use CV data only. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Finally, the dissertation compares the performance of the model-driven and the machine learning approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the huge amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the KF approach is highly recommended in the application of vehicle count estimation due to its simplicity and applicability in the field.

This thesis project presents a traffic micro simulation study that investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves simulations for a mixed traffic environment of conventional and automated vehicles both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is also accessed for its potential to improve traffic efficiency in a full market penetration of connected automated vehicles with enhanced capabilities such as having shorter time head ways.

This thesis project investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves traffic microscopic simulations for a mixed traffic environment of conventional and automated vehicles (AVs) both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is accessed for its potential to improve traffic efficiency in a full market penetration of connected AVs with absolute compliance. The project also aims to determine the possible outcome resulting from enhancing the AVs capabilities such as implementing short time headways between vehicles in the future.  The best traffic performance results achieved by operating GLOSA goes for connected AVs with the lowest simulated time headway (0.3 sec). The waiting time reduction reaches 95% and trip delay lessens to 88 %.