In this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D geometries in multiple transform domains (PCA & LDA) using Minimum Euclidean Distance, Maximum Likelihood and Artificial Neural Networks. Additionally, we demonstrate the ability to fuse separate classifiers from multiple domains via Bayesian Networks to achieve ensemble classification.

Downstream vehicles detect an emergency response vehicle (ERV) through sirens and/or strobe lights. These traditional warning systems do not give any recommendation about how to react, leaving the drivers confused and often adopting unsafe behavior while trying to open a passage for the ERV. In this research, an advanced intra-link emergency assistance system, that leverages the emerging technologies of the connected vehicle environment, is proposed. The proposed system assumes the presence of a centralized system that gathers/disseminates information from/to connected vehicles via vehicle-to-infrastructure (V2I) communications. The major contribution of this dissertation is the intra-link level support provided to ERV as well as non-ERVs. The proposed system provides network-wide assistance as it also considers the routing of ERVs. The core of the system is a mathematical program - a set of equations and inequalities - that generates, based on location and speed data from connected vehicles that are downstream of the ERV, the fastest intra-link ERV movement. It specifies for each connected non-ERV a final assigned position that the vehicle can reach comfortably along the link. The system accommodates partial market penetration levels and is applicable on large transportation link segments with signalized intersections. The system consists of three modules (1) an ERV route generation module, (2) a criticality analysis module and (2) the sequential optimization module. The first module determines the ERV's route (set of links) from the ERV's origin to the desired destination in the network. Based on this selected route, the criticality analysis module scans/filters the connected vehicles of interest and determines whether any of them should be provided with a warning/instruction message. As the ERV is moving towards its destination, new non-ERVs should be notified. When a group of non-ERVs is identified by the criticality analysis module, a sequential optimization module is activated. The proposed system is evaluated using simulation under different combinations of market penetration and congestion levels. Benefits in terms of ERV travel time with an average reduction of 9.09% and in terms of vehicular interactions with an average reduction of 35.46% and 81.38% for ERV/non-ERV and non-ERV/non-ERV interactions respectively are observed at 100% market penetration, when compared to the current practice where vehicles moving to the nearest edge. / Doctor of Philosophy / Downstream vehicles detect an emergency response vehicle (ERV) through sirens and/or strobe lights. These traditional warning systems do not give any recommendations about how to react, leaving the drivers confused and often adopting unsafe behavior while trying to open a passage for the ERV. In this research, an advanced intra-link emergency assistance system, that leverages the emerging technologies of the connected vehicle environment, is proposed. The proposed system assumes the presence of a centralized system that gathers/disseminates information from/to connected vehicles via vehicle-to-infrastructure (V2I) communications. The major contribution of this dissertation is the intra-link level support provided to ERV as well as non-ERVs. The proposed system provides network-wide assistance as it also considers the routing of ERVs. The core of the system is a mathematical program - a set of equations and inequalities - that generates, based on location and speed data from connected vehicles that are downstream of the ERV, the fastest intra-link ERV movement. It specifies for each connected non-ERV a final assigned position that the vehicle can reach comfortably along the link. The system accommodates partial market penetration levels and is applicable on large transportation link segments with signalized intersections. The system consists of three modules (1) an ERV route generation module, (2) a criticality analysis module and (2) the sequential optimization module. The first module determines the ERV’s route (set of links) from the ERV’s origin to the desired destination in the network. Based on this selected route, the criticality analysis module scans/filters the connected vehicles of interest and determines whether any of them should be provided with a warning/instruction message. As the ERV is moving towards its destination, new non-ERVs should be notified. When a group of non-ERVs is identified by the criticality analysis module, a sequential optimization module is activated. The proposed system is evaluated using simulation under different combinations of market penetration and congestion levels. Benefits in terms of ERV travel time with an average reduction of 9.09% and in terms of vehicular interactions with an average reduction of 35.46% and 81.38% for ERV/non-ERV and non-ERV/non-ERV interactions respectively are observed at 100% market penetration, when compared to the current practice where vehicles moving to the nearest edge.

The aim of this study is to establish a nation-wide cost-benefit framework for aviation projection appraisal. This framework is built upon Transportation System Analysis Model developed at Virginia Tech Air Transportation System Model (TSAM). Both supply and demand characteristics and their inter-dependence are investigated. It attempts to solve the absence of supply constraints in aviation demand forecast in the literature. In addition, external costs in term of noise and emission are also considered. A national environmental impact analysis introduced by new generation small aircraft system is conducted. Two case studies are discussed to illustrate the framework. The first one is based on the GPS Wide Area Augmentation System (WAAS) Lower Landing Minima capability. It represents a nation-wide cost-benefit analysis with examination of both supply and demand. System-wide benefit of accessibility improvement and infrastructure cost are scrutinized at the same time. A prioritized set of candidate airports for this technology is provided as a result. The second study focuses on New York area. Benefits brought by DataComm technology are evaluated by multi-iteration simulations. DataComm is projected to reduce entry point intrail and final approach separation. The improvements are modeled at individual airport and New York airspace. Consumer surplus is estimated based on demand and delay relationship using TSAM. / Ph. D.

Traffic congestion has been cited as the most conspicuous problem in traffic management. It has far-reaching economic,social and political effects. Intelligent Transportation Systems (ITS) research and development programs have been assigned the task of developing sophisticated techniques and counter-measures to reduce traffic congestion to manageable levels, and also achieve these objectives using area-wide traffic management methods. During times of traffic congestion, the traffic network in a transient, time-dynamic state, and resembles a dynamic network. In addition, in the context of ITS, the network can accurately detect such transient behavior using traffic sensors, and several other information gathering devices. In conjunction with Operations Research techniques, the time-varying traffic flows can be routed through the network in an optimal manner, based on the feedback from these information sources. Dynamic Traffic Assignment (DTA) methods have been proposed to perform this task. An important step in DTA is the calculation of user-optimal, system-optimal, and multiple optimal routes for assigning traffic. One would also require the calculation of user-optimal paths for vehicle scheduling and dispatching problems. The main objective of this research study is to analyze the effectiveness of time-dependent shortest path (TDSP) algorithms and k-shortest path (k-SP) algorithms as a practical routing tool in such intelligent transportation networks. Similar algorithms have been used to solve routing problems in computer networks. The similarities and differences between computer and ITS road networks are studied. An exhaustive review of TDSP and k-SP algorithms was conducted to classify and determine the best algorithms and implementation procedures available in the literature. A new (heuristic) algorithm (TD-kSP) that calculates multiple optimal paths for dynamic networks is proposed and developed. A complete object-oriented computer program in C++ was written using specialized network representations, node-renumbering schemes and efficient path processing data structures (classes) to implement this algorithm. A software environment where such optimization algorithms can be applied in practice was then developed using object-oriented design methodology. Extensive statistical and regression analysis tests for various random network sizes, densities and other parameters were conducted to determine the computational efficiency of the algorithm. Finally, the algorithm was incorporated within the GIS-based Wide-Area Incident Management Software System (WAIMSS) developed at the Center for Transportation Research, Virginia Tech. The results of these tests are used to obtain the empirical time-complexity of the algorithm. Results indicate that the performance of this algorithm is comparable to the best TDSP algorithms available in the literature, and strongly encourages its possible application in real-time applications. Complete testing of the algorithm requires the use of real-time link flow data. While the use of randomly generated data and delay functions in this study may not significantly affect its computational performance, other measures of effectiveness as a routing tool remains untested. This can be verified only if the algorithm itself becomes a part of the user-behavior feedback loop. A closed loop traffic simulation/ system-dynamics study would be required to perform this task. On the other hand, an open-loop simulation would suffice for vehicle scheduling/dispatching problems. / Master of Science

Traffic congestion is a central problem in transportation systems, especially in urban areas. The rapid development of Connected and Automated Vehicles (CAVs) and new traffic infrastructure technologies provides a promising solution to solve this problem. This work focuses on the safety-critical optimal control of CAVs in autonomous traffic systems. The dissertation starts with the roundabout problem of controlling CAVs travelling through a roundabout so as to jointly minimize their travel time, energy consumption, and centrifugal discomfort while providing speed-dependent safety guarantees. A systematic approach is developed to determine the safety constraints for each CAV dynamically. The joint optimal control and control barrier function (OCBF) controller is applied, where the unconstrained optimal control solution is derived which is subsequently optimally tracked by a real-time controller while guaranteeing the satisfaction of all safety constraints. Secondly, the dissertation deals with the feasibility problem of OCBF. The feasibility problem arises when the control bounds conflict with the Control Barrier Function (CBF) constraints and is solved by adding a single feasibility constraint to the Quadratic Problem (QP) in the OCBF controller to derive the feasibility guaranteed OCBF. The feasibility guaranteed OCBF is applied in the merging control problem which provably guarantees the feasibility of all QPs derived from the OCBF controller. Thirdly, the dissertation deals with the performance loss of OCBF due to the improperly selected reference trajectory which deviates largely from the complete optimal solution especially when the vehicle limitations are tight. A neural network is used to learn the control policy from data retrieved by offline calculation from the complete optimal solutions. Tracking the learnt reference trajectory with CBF outperforms OCBF in simulation experiments. Finally, a hierarchical framework of modular control zones (CZ) is proposed to extend the safety-critical optimal control of CAV from a single CZ to a traffic network. The hierarchical modular CZ framework is developed consisting of a lower-level OCBF controller and a higher-level feedback flow controller to coordinate adjacent CZs which outperforms a direct extension of the OCBF framework to multiple CZs without any flow control in simulation.

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Automotive transport unavoidably raises safety concerns for drivers, passengers, and indeed, all road users alike. Advancements in vehicle safety technologies have come a long way, and have had a major impact on the reduction of road-related accidents and fatalities. However, as the push towards autonomous vehicle systems gains momentum, assumptions must be avoided about the global application of such technologies.This paper proposes an idea for a road safety alert system, which is realized in the form of small-scale prototype, subsequently tested and evaluated to study its theoretical application to real world scenarios. The system is geared towards developing regions of the world where a reduction in road-related accidents and death is needed most. Reviews of various existing and proposed safety systems within the realm of Intelligent Transportation Systems (ITS) are conducted, with a focus on Vehicle-to-Vehicle (V2V) and non-V2V applications, which are compared to and contrasted with our proposal.We hope to foster further discussion and research into suitable technologies and their application, in regions of the world that require a different approach when trying to realistically reduce the consistent destructive trend of accidents and fatalities when humans are still behind the wheel.

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 fuel consumption and operational costs of trucks.  In order to utilize the benefits of truck platooning to the fullest, trucks with different routes in a transportation network need coordination to efficiently meet and form platoons. This thesis addresses platoon coordination when trucks form  platoons at hubs, where some trucks need to wait for others in order to meet, and there is a reward for platooning and a cost for waiting. Three contributions on the topic platoon coordination are presented in this thesis. In the first contribution, we consider platoon coordination among trucks that have pre-defined routes in a network of hubs, and the travel times are either deterministic or stochastic. The trucks are owned by competing transportation companies, and each truck decides on its waiting times at hubs in order to optimize its own operational cost. We consider a group of trucks to form a platoon if it departs from a hub and enters the road at the same time. The strategic interaction among trucks when they coordinate for platooning is modeled by non-cooperative game theory, and the Nash equilibrium is considered as the solution concept when the trucks make their decisions at the beginning of their journeys. In case of stochastic travel times, we also develop feedback-based solutions wherein trucks repeatedly update their decisions. We show in a simulation study of the Swedish transportation network that the feedback-based solutions achieve platooning rates up to 60 %. In the second contribution, we propose models for sharing the platooning profit among platoon members. The platooning benefit is not equal for all trucks in a platoon; typically, the lead truck benefits less than its followers. The incentive for transportation companies to cooperate in platooning may be low unless the profit is shared. We formulate platoon coordination games based on profit-sharing models, and in a simulation of a single hub, the outcomes of the platoon coordination games are evaluated. The evaluation shows that the total profit achieved when the trucks aim to maximize their own profits, but the platooning benefit is evened out among platoon members, is nearly as high as when each truck aims to maximize the total profit in the platooning system.  In the last contribution, we study a problem where trucks arrive to a hub according to a stochastic arrival process. The trucks do not share a priori information about their arrivals; this may be sensitive information to share with others. A coordinator decides, based on the statistical distribution of arrivals, when to release the trucks at the hub in the form of a platoon. Under the assumption that the arrivals are independent and identically distributed, we show that it is optimal to release the trucks at the hub when the number of trucks exceeds a certain threshold. This contribution shows that simple and dynamic coordination approaches can obtain a high profit from platooning, even under high uncertainty and limited a priori 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 koordineras för att effektivt mötas och bilda konvojer. Den här avhandlingen behandlar koordinering av lastbilar som kan bilda konvojer på transporthubbar, där vissa lastbilar måste vänta på andra lastbilar för att bilda konvojer, och det finns en belöning för konvojkörning och en kostnad för att vänta. Tre bidrag som behandlar konvojkoordinering presenteras i den här avhandlingen. Det första bidraget behandlar koordinering av lastbilar med förutbestämda rutter i ett transportnätverk med deterministiska eller stokastiska restider. Lastbilarna ägs av konkurrerande transportföretag, och varje lastbil beslutar om sina väntetider på hubbarna längs med sin rutt för att optimera sin driftskostnad. Vi antar att lastbilar bildar en konvoj om de avgår från en hubb och kör in på vägen samtidigt. Den strategiska interaktionen mellan lastbilar när de koordinerar för konvojbildning modelleras med icke-kooperativ spelteori, och vi betraktar Nashjämvikt som lösningskoncept när lastbilarna beslutar om sina väntetider i början av sina resor. I fallet med stokastiska restider utvecklar vi även lösningar där lastbilarna tillåts uppdatera sina väntetider längs med sina resor. I en simuleringsstudie över det svenska transportnätverket visas att när lastbilarna tillåts uppdatera sina väntetider uppnås en konjovkörningsgrad på 60%. I det andra bidraget utreds modeller för att dela på vinsten från konvojkörning. Fördelarna med konvojkörning är inte lika för alla lastbilar i en konvoj; vanligtvis är fördelen större för följarlastbilarna än för ledarlastbilen. Således kan incitamenten för transportföretag att samarbeta i form av konvojkörning vara låga om inte vinsterna från konvojkörning delas. Baserat på vinstdelningsmodeller formulerar vi konvojkoordineringsspel. I en simulering av en transporthubb utvärderar vi utfallet från konvojkoordinationsspelen. Det visar sig att den totala vinsten som uppnås när lastbilarna försöker maximera sina egna vinster, men vinsten från konvojkörning jämnas ut helt bland konvojmedlemmar, är nästan lika hög som när varje lastbil försöker att maximera den totala vinsten i systemet. I det sista bidraget studeras ett koordineringsproblem där lastbilar anländer till en transporthubb enligt en stokastisk ankomstprocess. Lastbilarna delar inte förhandsinformation om sina ankomster; detta kan vara känslig information att dela. En koordinator bestämmer, baserat på den statistiska sannolikhetsfördelningen av ankomster, när lastbilarna på transporthubben ska släppas iväg i form av en konvoj. Under antagandet att ankomsterna är statistiskt oberoende och likafördelade, visar vi att det är optimalt att släppa iväg lastbilarna från transporthubben i form av en konvoj när antalet lastbilar överskrider en viss tröskel. Detta bidrag visar att enkla och dynamiska koordineringsmetoder kan erhålla en hög vinst från konvojkörning, även under hög osäkerhet och begränsad förhandsinformation. / <p>Länk till den offentliga granskningen tillkännages via: https://www.kth.se/profile/alexjoha</p><p>QC 20200609</p>