Dissertations / Theses on the topic 'Transportation applications'

The Path Flow Estimator (PEE) concept was originally developed to estimate path flows (hence origin-destination flows) and link flows for a whole road network (given some counts at selected roads). It is now further developed as an alternative for modeling different transportation planning applications: (1) a bicycle network analysis tool for non-motorized transportation planning, (2) a multi-class traffic assignment model for freight planning, and (3) a simplified travel demand forecasting framework for small community planning. The first application of the redeveloped PFE is to develop a two-stage bicycle traffic assignment model for estimating/predicting bicycle volumes on a transportation network. The first stage considers key criteria (e.g., distance related attributes, safety related attributes, air quality related attributes etc.) to generate a set of non-dominated (or efficient) paths, while the second stage adopts several traffic assignment methods to determine the flow allocations to the network. This two-stage approach can be used as a stand-alone bicycle traffic assignment to the transportation network given a bicycle origin-destination (O-D) matrix. The second application aims to enhance the realism of traffic assignment models for freight planning by incorporating different modeling considerations into the multi-class traffic assignment problem. These modeling considerations involve developing both model formulation and customized solution algorithm, which in turn involve asymmetric interactions among different vehicle types (i.e., cars versus trucks), a path-size logit (PSL) model (for accounting random perceptions of network conditions with explicit consideration of route overlapping), and various traffic restrictions imposed either individually or together to multiple vehicle types in a transportation network. In the third application, a simplified planning framework is developed to perform planning applications in small communities where limited planning resources hinder the development and application of a full four-step model. Two versions (i.e., base year and future year) of the PFE are proposed to address the specific transportation planning issues and needs of small communities. These new PFE developments for planning applications are tested with different realistic transportation networks. The results suggest that the new PFE applications proposed in this dissertation provide an alternative to the traditional four-step travel demand forecasting model that can be used as a stand-alone application with better modeling capability and fewer resources.

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 227-231).
Due to their magnitude and longevity, transportation investments can determine the long term success or failure of a transportation system. Thus, it is vital for decision-makers to have deep understanding of the alternatives available before they chose to invest. In this thesis, we examine the current state of the practice for transportation investment decisions. We draw upon the literature and this existing state of the practice to develop a new decision aid which we believe is an improvement over existing aids. We then apply this new decision aid to a transportation investment decision facing the East Japan Railway Company (JR East) and draw conclusions about the usefulness of our new tool. Our decision aid, the CLIOSjre Process, is designed to help decision-makers compare multiple alternatives and make an informed transportation investment decision. The process examines the decision from multiple perspectives where each of these perspectives represents one of the priorities of the decision-maker. By considering each priority separately, the CLIOSjre Process provides a detailed understanding of each alternative. The CLIOSjre Process also combines these individual evaluations into a single overall evaluation of each alternative. This overall evaluation provides the decision-maker with an actionable ranking of the alternatives. In combination, these perspective-specific and overall evaluations of each alternative provide a detailed and holistic understanding of the decision facing the decision-maker. Unlike many other decision aids, the CLIOSjre Process accounts for both the multistakeholder nature of transportation investments and the uncertainty inherent to these decisions. The multifaceted nature of the CLIOSjre Process examines each alternative from multiple perspectives. This approach better facilitates negotiation between stakeholders. In addition, the CLIOSjre Process formally identifies and addresses uncertainty in the analysis - the primary source of risk in transportation investment decisions. Thus, the CLIOSjre Process is a unique multicriteria, multistakeholder decision aid which addresses uncertainty. We hope that this thesis provides the reader with a better understanding of the application, challenges, and opportunities of multicriteria multistakeholder decision aids.
by Timothy Patton Doyle.
S.M. in Transportation

In recent years, Intelligent Transportation Systems (ITS) have emerged as an efficient way of enhancing traffic flow, safety and management. These goals are realized by combining various technologies and analyzing the acquired data from vehicles and roadways. Among all ITS technologies, computer vision solutions have the advantages of high flexibility, easy maintenance and high price-performance ratio that make them very popular for transportation surveillance systems. However, computer vision solutions are demanding and challenging due to computational complexity, reliability, efficiency and accuracy among other aspects.   In this thesis, three transportation surveillance systems based on computer vision are presented. These systems are able to interpret the image data and extract the information about the presence, speed and class of vehicles, respectively. The image data in these proposed systems are acquired using Unmanned Aerial Vehicle (UAV) as a non-stationary source and roadside camera as a stationary source. The goal of these works is to enhance the general performance of accuracy and robustness of the systems with variant illumination and traffic conditions.   This is a compilation thesis in systems engineering consisting of three parts. The red thread through each part is a transportation surveillance system. The first part presents a change detection system using aerial images of a cargo port. The extracted information shows how the space is utilized at various times aiming for further management and development of the port. The proposed solution can be used at different viewpoints and illumination levels e.g. at sunset. The method is able to transform the images taken from different viewpoints and match them together. Thereafter, it detects discrepancies between the images using a proposed adaptive local threshold. In the second part, a video-based vehicle's speed estimation system is presented. The measured speeds are essential information for law enforcement and they also provide an estimation of traffic flow at certain points on the road. The system employs several intrusion lines to extract the movement pattern of each vehicle (non-equidistant sampling) as an input feature to the proposed analytical model. In addition, other parameters such as camera sampling rate and distances between intrusion lines are also taken into account to address the uncertainty in the measurements and to obtain the probability density function of the vehicle's speed. In the third part, a vehicle classification system is provided to categorize vehicles into \private car", \light trailer", \lorry or bus" and \heavy trailer". This information can be used by authorities for surveillance and development of the roads. The proposed system consists of multiple fuzzy c-means clusterings using input features of length, width and speed of each vehicle. The system has been constructed by using prior knowledge of traffic regulations regarding each class of vehicle in order to enhance the classification performance.

Planners and decision-makers recognize that non-motorized transportation provides environmental, economic, and public health benefits. Recent technology advances, such as the widespread use of mobile devices and geographic information systems, enable the collection of disaggregate built environment and travel behavior data. To integrate pedestrian planning into transport operations at local and regional scales, it is necessary to develop systems to rank and prioritize zones and corridors for pedestrian infrastructure investment. Best practices for pedestrian planning suggest that jurisdictions prioritize pedestrian projects based on a variety of concerns, such as high pedestrian activity, pedestrian safety, accessibility to transit and mobility for persons with disabilities, children and older adults. Researchers at the Georgia Institute of Technology developed and piloted an automated system to assess the quality of sidewalks, utilizing an Android™ App that collects GPS-enabled video, accelerometer, and gyroscope data. Researchers collected pilot sidewalk data within the City of Atlanta to evaluate the accessibility and walkability of pedestrian facilities. This research proposes a weighted ranking system to prioritize pedestrian projects using App-collected pedestrian facility data collected in the field using a mobile Android application, pedestrian safety indicators, pedestrian activity data and demographic data. The ranking system uses a set of block-level pedestrian potential and deficiency indicators to prioritize planning investments within a subarea of Midtown, Atlanta, Georgia, combining available data sources with app-collected sidewalk width data. The results of these rank-order prioritization analyses indicate that blocks near rail stations and Georgia Institute of Technology/Technology Square should be prioritized for pedestrian investments. However, further refinements are needed to extend the application of this methodology to larger geographic scales. Additionally, this research did not consider the cost constraints of pedestrian project alternatives within the study area. Future availability of comprehensive pedestrian activity and pedestrian network data will enable planners and engineers to prioritize corridors and intersections for pedestrian project implementation.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 70-75).
Smartphones are pervasive, and possess powerful processors, multi-faceted sensing, and multiple radios. However, networked mobile apps still typically use a client-server programming model, sending all shared data queries and uploads through the cellular network, incurring bandwidth consumption and unpredictable latencies. Leveraging the local compute power and device-to-device communications of modern smartphones can mitigate demand on cellular networks and improve response times. This thesis presents two systems towards this vision. First, we present DIPLOMA, which aids developers in achieving this vision by providing a programming layer to easily program a collection of smartphones connected over adhoc wireless. It presents a familiar shared data model to developers, while underneath, it implements a distributed shared memory system that provides coherent relaxed-consistency access to data across different smartphones and addresses the issues that device mobility and unreliable networking pose against consistency and coherence. We evaluated our prototype on 10 Android phones on both 3G (HSPA) and 4G (LTE) networks with a representative location-based photo-sharing service and a synthetic benchmark. We also simulated large scale scenarios up to 160 nodes on the ns-2 network simulator. Compared to a client-server baseline, our system shows response time improvements of 10x over 3G and 2x over 4G. We also observe cellular bandwidth reductions of 96%, comparable energy consumption, and a 95.3% request completion rate with coherent caching. With RoadRunner, we apply our vision to Intelligent Transportation Systems (ITS). RoadRunner implements vehicular congestion control as an in-vehicle smartphone app that judiciously harnesses onboard sensing, local computation, and short-range communications, enabling large-scale traffic congestion control without the need for physical infrastructure, at higher penetration across road networks, and at finer granularity. RoadRunner enforces a quota on the number of cars on a road by requiring vehicles to possess a token for entry. Tokens are circulated and reused among multiple vehicles as they move between regions. We implemented RoadRunner as an Android application, deployed it on 10 vehicles using 4G (LTE), 802.11p DSRC and 802.11n adhoc WiFi, and measured cellular access reductions up to 84%, response time improvements up to 80%, and effectiveness of the system in enforcing congestion control policies. We also simulated large-scale scenarios using actual traffic loop-detector counts from Singapore.
by Jason Hao Gao.
S.M.

Greenhouse gas (GHG) emissions, known as a major cause of climate change, have been emitted by the combustion of fossil fuels over the past few decades. The transportation sector contributes significantly to global GHG emissions. Inspired by the successful implementation of tradable credit schemes (TCSs) in pollution control programs, this dissertation focuses on multi-period TCSs to minimize vehicular emissions. In this scheme, a central authority (CA) allocates travel credits to travelers (credit allocation scheme) and then, charges them to travel on each link (credit charging scheme). Travelers are able to trade credits amongst themselves in the market.

To address the long-term planning goals of the CA, the dissertation proposes the concept of a multi-period TCS framework. This framework enables the CA to achieve steady progress toward system-level goals, i.e., reducing traffic congestion and GHG emissions, over the long-term planning horizon. First, a TCS-based multi-period equilibrium modeling framework is developed to address the planning problem of a CA that seeks to achieve system-level goals by varying the credit supply and the link usage credit charging schemes across the various periods of the planning horizon. Further, the CA seeks stable credit prices across these periods to provide them as information to travelers in an operational context. Based on this information, bank interest rate and their travel needs, travelers determine their actions in terms of the consumption or sale of credits in the current period or the transfer of credits to future periods. It is proved that the credit price volatility is dampened by the ability to transfer credits. Since a TCS is subject to market manipulation and the artificial control of credit price, a transfer fee, which is shown to be an effective instrument to control hoarding among travelers, is proposed.

Using the proposed multi-period TCS framework, the dissertation develops different system optimal (SO) TCS designs, as bi-level models, to derive credit allocation and charging schemes to achieve system-level goals. In the first SO multi- period TCS design, the CA minimizes the vehicular emissions in the upper level over the long-term planning horizon. This enables the CA to plan the trajectory of vehicular emissions during the planning horizon. This trajectory can be used to predetermine the emissions standard for each period to use in the second SO multi-period TCS design, which aims to minimize total system travel time, in the upper level, over the planning horizon. These designs include bounds on increases in travel costs, allowing travelers to better adapt to the TCS implementation. The lower-level models are the equilibrium conditions in which travelers minimize their costs under the obtained multi-period TCS in the upper level.

To enhance realism in capturing the equilibrium conditions under the multi-period TCS, this dissertation factors different travelers’ characteristics and bank interest rates. In making route choices, travelers factor value of time (VOT) and tradeoff credit consumption and travel costs. Hence, travelers’ heterogeneity in terms of VOT is factored. It is shown that if the CA does not factor VOT in SO TCS design, it leads to a socially inequitable policy in practice. Further, the heterogeneity of travelers in terms of perceived future credit prices is factored. Travelers decide to consume or transfer credits in each period based on several factors, including future credit prices. However, due to the uncertainty in traffic network demand/supply forecasts over the long-term horizon, the CA cannot provide an accurate forecast of future credit prices a priori. It is shown that as the difference between travelers’ perceptions of future CPs and the actual CPs set by the CA for each period increases, the effectiveness of the SO TCS design in minimizing total system travel time decreases; this has implications for traffic congestion management.

Fourth, the dissertation investigates the robust design of multi-period TCS to account for travel demand uncertainty and achieve system-level goals. To minimize vehicular emissions, the CA leverages the TCS to promote zero-emissions vehicles (ZEVs), which circumvents the need for current subsidy-based incentive policies. The incentive to shift to ZEVs is fostered by allocating more credits and charging fewer credits to ZEV travelers compared to other travelers. To factor the uncertainty in travel demand forecasts, this research proposes a robust multi-period TCS design that minimizes the worst-case vehicular emissions, i.e. maximum vehicular emissions, under different traffic network demand scenarios. It is shown that the robust TCS design increases reliability in achieving system-level goals, compared to the SO TCS design that does not factor travel demand uncertainty.

Finally, the dissertation analyzes the ability of a TCS to manage morning commute congestion while factoring the market loss aversion of commuters. (Abstract shortened by ProQuest.)

The continuing evolution of urban travel patterns and changing policy goals and priorities requires that transportation researchers and practitioners improve their abilities to plan and forecast the demand for travel. Walking and bicycling - the primary forms of active travel - are generating increasing interest for their potential to reduce automobile use, save governmental and consumer costs, and improve personal and social health outcomes. Yet, current transportation planning tools, namely regional travel demand forecasting models, poorly represent these active travel modes, if at all. More broadly, travel models do an incomplete job of representing the decision-making processes involved in travel choices, especially those factors influencing walking and bicycling. In addition to limitations of data and statistical analysis methods, the research upon which modeling tools are based has yet to settle on a comprehensive theory of travel behavior that accounts for complex relationships around a variety of personal, social, and environmental factors. While modeling tools have explained travel primarily through economic theories, contributions from the geography and psychology fields prove promising. A few scholars have attempted to link these travel behavior explanations together, some with a focus on walking and bicycling, but these theories have yet to make a significant impact on travel modeling practice. This thesis presents a unifying interdisciplinary framework for a theory of travel decision-making with applications for travel demand modeling and forecasting and a focus on walking and bicycling. The framework offers a guide for future research examining the complex relationships of activities, built environment factors, demographic and socioeconomic characteristics, attitudes and perceptions, and habit and exploration on individual short-term travel decisions (with considerations of the influence of medium- and long-term travel-related decisions). A key component of the theory is a hierarchy of travel needs hypothesized to be considered by travelers in the course of their decision-making processes. Although developed to account for the factors that particularly influence decisions surrounding walking and bicycling, the framework is postulated to apply to all travel modes and decisions, including frequency, destination, mode, time-of-day, and route. The first section of the thesis reviews theories from the fields of economics, geography, psychology, and travel behavior that have a large influence on the development of the theory of travel decision-making. In the next and largest chapter, the components and relationships in this theory, including the hierarchy of travel needs, are defined and presented with supporting empirical evidence from travel behavior research. This thesis's final section views the theory of travel decision-making through the lens of applicability to travel demand modeling and forecasting. The state of current travel forecasting tools, travel behavior research, data, and analysis methods with respect to each aspect of the theory is reviewed. Research and data needs are identified. In closing, some opportunities for operationalizing the theory in travel demand models and using these transportation planning tools for analyzing walking, cycling, and other policies are hypothesized and discussed. This thesis, and the theory and applications discussed within, contribute to the academic study of travel behavior, the practical modeling of travel demand, and walking and bicycling research and planning.

Le développement du système électronique de puissance dans le transport électrique est poursuivi sous la forme de convertisseurs de puissance à haut rendement impliquant une topologie complexe.Bien que l'analyse et le contrôle d'un tel système soient souvent une tâche difficile en raison de l'environnement haute tension et haut courant, la simulation hardware-in-the-loop (HILs) offre un moyen sûr et rapide d'évaluer la stratégie de contrôle en simulant l'environnement externe du contrôleur dans le système embarqué.Au cours du processus, il y a deux exigences que nous devons relever dans le cadre de temps réel (i) Le processus de calcul doit être terminé avant que l'impulsion de déclenchement suivante de l'horloge en temps réel n'arrive; (ii) La latence dans le simulateur est assez petite pour être ignorée. Les périodes d'échantillonnage et de simulation dans les simulateurs basés sur CPU sont plus de 1 microseconde, il est difficile de prendre en compte l'ensemble des événements des commutateurs dans les systèmes d'entraînement modernes.En revanche, les FPGA (Field Programmable Gate Arrays) fournissent non seulement une vitesse d'échantillonnage rapide mais aussi une alternative viable pour accélérer le simulateur en temps réel. Cependant, la mise en œuvre d’un système électronique de puissance complexe dans les FPGA est l'une des limitations. Ainsi, dans cette thése, nous ferons des recherches sur la simulation en temps réel à base de FPGA avec la tentative de résoudre le problème en résolvant les questions suivantes,1.Comment pourrions-nous partitionner le système électronique de puissance et l'implémenter dans FPGA?2.Comment pouvons-nous tirer parti des fonctionnalités FPGA pour accélérer le processus de résolution de circuit3.Comment pourrions-nous optimiser les performances du FPGA?4.Comment exprimer la caractéristique de commutation non linéaire du système électronique de puissance dans le FPGA?La première question concerne la caractéristique hybride à l'intérieur du système électronique de puissance. Nous avons proposé une nouvelle méthode nodale et un solveur matriciel basé sur la décomposition de Cholesky essayant de garder la topologie du circuit fixe et de traiter chaque élément de commutation et de circuit indépendamment. La deuxième question est celle de savoir comment obtenir des approximations pour toutes sortes d’Équation différentielle (ODE). Nous avons utilisé une série de solveurs ODE parallèles pour accélérer le processus de résolution. La troisième question est d'utiliser des outils de synthèse de haut niveau (HLS) pour optimiser les performances du FPGA. De tels outils sont utilisés pour développer des unités de calcul haute performance pour des applications de simulation en temps réel. Enfin, afin de rechercher l'impact de la caractéristique de commutation non linéaire sur le système électronique de puissance, nous avons proposé un modèle IGBT ultra-rapide avec un temps de calcul en nanosecondes dans le FPGA.Dans l'ensemble, les méthodes présentées contribuent au développement du simulateur en temps réel par FPGA pour le système de transport électrique de trois façons: réduire le temps de calcul des matrices, proposer un solveur ODE parallèle dans le FPGA et optimiser les performances du FPGA
The development of power electronic system in electrical transportation is being pursued in the form of high-efficiency power converters involving complex topology. Although analysis and control of such system is often a difficult task due to the high-voltage and high-current environment, the hardware-in-the-loop simulation (HILs) offers a time-saving and safe way to evaluate the control strategy by simulating the external environment of a controller in the embedded system.During the process, there are two requirements that we have to meet in the context of racing against real-time: (i) the computation process is necessary to the end before the next trigger impulse from the real-time clock arrives (ii) the latency in the simulator is small enough to ignore. The sampling and simulation period in today’s CPU-based HIL simulators can barely go under 1 us, it is hard to take into accounts the entire switch event from PWM (Pulse Width Modulation) in modern power drive systems. In contrast, Field Programmable Gate Arrays (FPGAs) provide not only an ultra-fast sampling speed but also a viable alternative for speeding up the real-time simulator. However, the implementing the complex power electronic system on FPGAs is one of the limitations in real time simulation. Thus, in this these, we will research the FPGA-based real-time simulation with the attempt to solve the following questions,1.How could we partition power electronic system and implement it in FPGA?2.How do we leverage FPGA features to accelerate circuit?3.How could we optimize the performance of FPGA?4.How do we express the nonlinear switch characteristic of power electronic system in the FPGA?The first question is about the hybrid characteristic inside the power electronic system. In the paper, we proposed a novel nodal method and a matrix solver based on Cholesky Decomposition trying to keep the circuit topology fixed and treat each switch and circuit element independently. The second question is one that how to obtain approximations for all kind of ordinary differential equations (ODEs). We utilized a series of parallel ODE solver to accelerate the solving process and deal with the stiff problem. The third question is to use high-level synthesis (HIL) tools to optimize the performance of FPGA. Such tools are employed for developing high-performance computing units, designated hereafter as hardware solvers (HS), for real-time simulation applications. At last, in order to research the impact of nonlinear switch characteristic on the power electronic system, we proposed an ultra-fast IGBT model with a calculation time in nanoseconds in the FPGA.Overall, the presented methods contribute to the development of FPGA-based real-time simulator in three ways: reducing the calculation time of matrix solving process, proposing parallel ODE solver in the FPGA and optimizing the performance of FPGA. Thus, with the FPGA solver we built, the model of power electronic system for electrical transportation can be solved within 50 nanoseconds in high accuracy

The continual development of high-power-density power electronic converters is driven particularly by modern transportation applications like electrical vehicles and more electric aircraft where the space and carrier capability is limited. However, there are several challenges related to transportation applications such as fault tolerance for safety concern, high temperature operation in extreme environments and more strict electromagnetic compatibility requirement. These challenges will increase difficulties for more electrical system adoption in the transportation applications. In this dissertation, comprehensive methodologies including more efficient energy storage solution, better power electronics devices capability, better packaging performance and more compact EMI filter design are analyzed and proposed for the goal of high power density converter design in transportation applications.
Ph. D.

As Intelligent Transportation Systems (ITS) become more prevalent, there is a need for a system capable of the rigorous evaluation of new ITS strategies for a wide variety of applications. Pre-deployment testing and fine-tuning of the system, performance evaluation, and alternatives analysis are all potential benefits that could be gained through the evaluation of ITS. Simulation, an increasingly popular tool for transportation analysis, would seem an ideal solution to this problem as it allows for the consideration of many scenarios that may be improbable or impossible to observe in the field. Also, simulation provides a framework that allows for the application of rigorous analysis techniques to the output data, providing an accurate and statistically significant conclusion. The difficulty is that many ITS strategies are difficult or impossible to implement in a simulated environment. The rapid nature of technology development and the complicated nature of many ITS solutions are difficult to emulate in simulation models. Furthermore, the emulation of a particular ITS solution is not guaranteed to provide the same result that the physical system would, were it subject to the same inputs. This study seeks to establish a framework for the analysis of advanced ITS applications through the use of Hardware-in-the-Loop Simulation (HILS), which provides a procedure for interfacing simulation models with real-world hardware to conduct analysis. This solution provides the benefits of both advanced ITS evaluation and simulation for powerful and accurate analysis. A framework is established that includes all the steps of the modeling process including construction, validation, calibration, and output analysis. This ensures that the process surrounding the HILS implementation is valid so that the results of the evaluation are accurate and defendable. Finally, a case study of the application of the developed framework to the evaluation, a real-world implementation of an advanced ITS application (SCATS in this case) is considered. The effectiveness of the framework in creating and evaluating a corridor using a simulation model wed to real-world hardware is shown. The results of the analysis show the power of this method when correctly applied and demonstrate where further analysis could expand upon the proposed procedure.

Evident similarities and links between the outcomes of traffic crashes and stranded (or constrained) mobility have been identified and are reported in this research. Generally, a high level of travel activities is an indicator of high crash exposure. However, studies have shown that the highest rates of traffic fatalities occur in low- and middle-income regions, where many citizens experience relatively low levels of motorized travel. This ironic observation reveals serious challenges facing transport mobility systems in the less privileged regions of the world. Studies on traffic crashes and mobility constraints also reveal that they both have individual and regional variations in their occurrence, effects, and severities. Consequently, the outcomes of traffic crashes and constrained mobility are serious public health concerns worldwide.

As public health problems, their study is analogous to the study of diseases and other injuries and thus, suitable for the application of epidemiological techniques. This dissertation therefore explores the use of epidemiological techniques to analyze traffic crashes and mobility/accessibility constraints from a human-centered perspective. The dissertation therefore consists of two major focus areas. The first part of the study applies widely used epidemiology/public health – based statistical tools to analyze traffic crashes with the aim of gaining better understanding of the human-centered causes and factors that influence these causes, and how these ultimately affect the severity of crashes. This part is further divided into two sub-sections. The first sub-section used latent class analysis to identify homogeneous clusters of human-centered crash causal factors and then applied latent class logit and random parameters logit modeling techniques to investigate the effects of these factors on crash outcomes. The second sub-section of the first part of the dissertation applies multilevel regression analysis to understand the effects of driver residential factors on driver behaviors in an attempt to explain the area-based differences in the severity of road crashes across sub-regions. Both studies are necessary to develop potential human-centered mitigations and interventions and for the effective and targeted implementation of those countermeasures. The second part of the study provides an epidemiological framework for addressing mobility/accessibility constraints with a view to diagnosing symptoms, recommending treatment, and even discussing the idea of transmission of constrained mobility among city dwellers. The medical condition, hypomobility, has been used to connote constrained mobility and accessibility for people in urban areas. In transportation and urban studies, hypomobility can result in a diminished ability to engage in economic opportunities and social activities, hence deepening poverty and social exclusion and increasing transport costs, among other negative outcomes. The condition is especially pronounced in poor urban areas in developing countries. The framework proposed in this study is expected to help identify and address barriers to mobility and accessibility in the rapidly growing cities throughout the developing world, with particular applicability to the rapidly developing cities in Sub-Saharan Africa.

Ultimately, this dissertation explores the application of epidemiological techniques to two major transportation problems: traffic safety and constrained mobility. The techniques presented in this dissertation provide policy makers, agencies, and transport professionals with tools for evidence-based policies and effective implementation of appropriate countermeasures.

Number of lanes is a basic roadway attribute that is widely used in many transportation applications. Traditionally, number of lanes is collected and updated through field surveys, which is expensive especially for large coverage areas with a high volume of road segments. One alternative is through manual data extraction from high-resolution aerial images. However, this is feasible only for smaller areas. For large areas that may involve tens of thousands of aerial images and millions of road segments, an automatic extraction is a more feasible approach. This dissertation aims to improve the existing process of extracting number of lanes from aerial images automatically by making improvements in three specific areas: (1) performance of lane model, (2) automatic acquisition of external knowledge, and (3) automatic lane location identification and reliability estimation. In this dissertation, a framework was developed to automatically recognize and extract number of lanes from geo-rectified aerial images. In order to address the external knowledge acquisition problem in this framework, a mapping technique was developed to automatically estimate the approximate pixel locations of road segments and the travel direction of the target roads in aerial images. A lane model was developed based on the typical appearance features of travel lanes in color aerial images. It provides more resistance to “noise” such as presence of vehicle occlusions and sidewalks. Multi-class classification test results based on the K-nearest neighbor, logistic regression, and Support Vector Machine (SVM) classification algorithms showed that the new model provides a high level of prediction accuracy. Two optimization algorithms based on fixed and flexible lane widths, respectively, were then developed to extract number of lanes from the lane model output. The flexible lane-width approach was recommended because it solved the problems of error-tolerant pixel mapping and reliability estimation. The approach was tested using a lane model with two SVM classifiers, i.e., the Polynomial kernel and the Radial Basis Function (RBF) kernel. The results showed that the framework yielded good performance in a general test scenario with mixed types of road segments and another test scenario with heavy plant occlusions.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 1998.
Includes bibliographical references (leaves 94-103).
by Niranjan Krishnan.
S.M.

Master of Science
Department of Civil Engineering
Yacoub M. Najjar
Reliable and economical design of Portland Cement Concrete (PCC) pavement structural systems relies on various factors, among which is the proper characterization of the expected permeability response of the concrete mixes. Permeability is a highly important factor which strongly relates the durability of concrete structures and pavement systems to changing environmental conditions. One of the most common environmental attacks which cause the deterioration of concrete structures is the corrosion of reinforcing steel due to chloride penetration. On an annual basis, corrosion-related structural repairs typically cost millions of dollars. This durability problem has gotten widespread interest in recent years due to its incidence rate and the associated high repair costs. For this reason, material characterization is one of the best methods to reduce repair costs. To properly characterize the permeability response of PCC pavement structure, the Kansas Department of Transportation (KDOT) generally runs the Rapid Chloride Permeability test to determine the resistance of concrete to penetration of chloride ions as well as the Boil test to determine the percent voids in hardened concrete. Rapid Chloride test typically measures the number of coulombs passing through a concrete sample over a period of six hours at a concrete age of 7, 28, and 56 days. Boil Test measures the volume of permeable pore space of the concrete sample over a period of five hours at a concrete age of 7, 28, and 56 days. In this research, backpropagation Artificial Neural Network (ANN)-based and Regression-based permeability response prediction models for Rapid Chloride and Boil tests are developed by using the databases provided by KDOT in order to reduce or eliminate the duration of the testing period. Moreover, another set of ANN- and Regression-based permeability prediction models, based on mix-design parameters, are developed using datasets obtained from the literature. The backpropagation ANN learning technique proved to be an efficient methodology to produce a relatively accurate permeability response prediction models. Comparison of the prediction accuracy of the developed ANN models and regression models proved that ANN models have outperformed their counterpart regression-based models. Overall, it can be inferred that the developed ANN-Based permeability prediction models are effective and applicable in characterizing the permeability response of concrete mixes used in transportation applications.

This thesis concerns the mathematical modeling of transportation systems for improved decision support and analysis of transportation-related problems. The main purpose of this thesis is to develop and evaluate models and methods that exploit link flows. Link flows are straightforward to obtain by measurements or estimation methods and are commonly used to describe the traffic state. The models and methods used in this thesis apply mathematical optimization techniques, computer simulations, and probabilistic methods to gain insights into the transportation network under study and provide benefits for both traffic managers and road users.  First, we present an optimization model for allocating charging stations in a transportation network to serve owners of electric vehicles. The model utilizes a probabilistic route selection process to detect locations through which vehicles may pass. It also considers the limited driving range of electric vehicles. The iterative solution procedure finds the minimal number of minimal charging stations and their locations, which provides a lower bound of charging stations to cover each of the considered routes. Second, we present a case study, in which we argue that stationary and mobile measurement devices possess complementary characteristics. In that study, we investigate how speed cameras and probe vehicles can be used in conjunction with each other for the collection of detailed traffic data. The results show that the share of successfully observed and identified vehicles can be significantly improved by using both stationary and mobile measurement devices. Third, we present a simulation model with the intent of finding the most probable underlying routes based on hourly link flows. The model utilizes Dijkstra's algorithm to find the shortest paths and uses a straightforward statistical test procedure to find the most significant routes in the network based on replicated movements of trucks. Finally, we investigate the possibility to study how the traffic flow in one location reflects the flows in the surrounding area. The statistical basis of the proposed model is built upon measured link flows to study the dispersion of aggregate traffic flows in nodes. By considering the alternative ways vehicles can travel between locations, the model is able to determine the expected link flow that originates from a node in a nearby region. The results of the thesis show that the link flows, which are basic descriptors of the road segments in a transportation network, can be used to study a broad range of problems in transportation.

In recent years, the importance of incorporating uncertainty into planning models for logistics and transportation systems has been widely recognized in the Operations Research and transportation science communities. Maritime transportation, as a major mode of transport in the world, is subject to a wide range of disruptions at the strategic, tactical and operational levels. This thesis is mainly concerned with the development of robustness planning strategies that can mitigate the effects of some major types of disruptions for an important class of optimization problems in the shipping industry. Such problems arise in the creation and negotiation of long-term delivery contracts with customers who require on-time deliveries of high-value goods throughout the year. In this thesis, we consider the disruptions that can increase travel times between ports and ultimately affect one or more scheduled deliveries to the customers. Computational results show that our integrated solution procedure and robustness planning strategies can generate delivery plans that are both economical as well as robust against uncertain disruptions.

An analysis of crash data shows that the number of fatal school bus related crashes has remained nearly constant over the past ten years, despite an increase in available safety-improving technology. One of the main concerns related to school bus safety is the issue of illegally passing a stopped school bus. To improve safety around stopped school buses, this dissertation presents a Concept of Operations for a connected vehicle application to improve safety around stopped school buses using Dedicated Short Range Communication. The focus of this application is to increase awareness of stopped school buses or bus stops that are obscured from the driver's view. An on-road naturalistic driving experiment evaluated driver response to an in-vehicle message to warn drivers that they were approaching a school bus that was stopped around a curve. The dissertation concludes by using microsimulation to evaluate the impact of implementing specialized speed control algorithms to reduce vehicle speeds near bus stops along high speed roads. The simulation evaluated the effect of the reduce speed zone on travel time and emissions when the system was considered as a pre-timed speed limit and also when the system was modeled as a connected vehicle system.
Ph. D.

With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is one of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and prediction of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of these stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. ?? Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. ?? A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. ?? Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector data is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2000.
Includes bibliographical references (leaves 139-141).
In this thesis, we develop methods for the following problems: the representation of discrete-time dynamic data, and the computation of fastest paths in continuous-time dynamic networks. We apply these methods for the following application problems: storage and communication of discrete-time dynamic transportation network data, and computation of fastest paths in traffic networks with signalized intersections. These problems are at the heart of realtime management of transportation networks equipped with information technologies. We propose a representation (called the bit-stream representation) method for nondecreasing discrete-time dynamic functions as a stream of 0 and 1 bits. We show that this representation is 12 times less memory consuming than the classical representation for such data, where the function value at each time-instant is stored as an L-bit integer. We exploit this representation to efficiently store and represent travel-time data in discrete-time dynamic transportation networks. Since the bit-stream representation requires lesser memory space, it also leads to lesser communication-time requirements for applications involving communication of such data. We adapt a classical dynamic one-to-all fastest path to work on bit-streams and show that this leads to savings of up to 16-times in over-all communication and computation times. This holds the potential to impact the development of efficient high performance computer implementations of dynamic shortest path algorithms in time-dependent networks. We model travel-times in dynamic networks using piece-wise linear functions. We consider the one-to-all fastest path problem in a class of continuous-time dynamic networks. We present two algorithms: Algorithm OR, that is based on a conceptual algorithm known in the literature; and Algorithm IOT-C, that is developed in this thesis. We implement the two algorithms, and show that Algorithm IOT-C outperforms Algorithm OR by a factor of two. We study the application problem of computing fastest paths in traffic networks with signalized intersections. We use a piece-wise linear link travel-time dynamic network model to address this problem, and demonstrate that this model is more accurate than discrete-time models proposed in the literature. Some of the implemented algorithms are applied to solve variants of the one-to-all fastest path problem in traffic networks with signalized intersections, and study the computational performance of these implementations.
by Vinay M. Yadappanavar.
S.M.

Research and development of solid polymer fuel cell (SPFC) systems for the transportation and stationary power generation industries has evolved rapidly over the last decade. This growth has been due to the ever-increasing demand for a cleaner and more efficient technology in these industries. To compete with the existing technology, SPFC systems have to be highly efficient at both full and partial loads, environmentally friendly (in terms of emissions and noise) and competitively priced. For many applications, SPFCs have the potential to deliver a system that can fulfil these criteria. However, a number of system design issues have to be addressed in order to provide a well-integrated and optimised system, which is a practical alternative to conventional modes of energy conversion.

In formulating discrete optimization problems, it is not only important to have a correct mathematical model, but to have a well structured model that can be solved effectively. Two important characteristics of a general integer or mixed-integer program are its size (the number of constraints and variables in the problem), and its strength or tightness (a measure of how well it approximates the convex hull of feasible solutions). In designing model formulations, it is critical to ensure a proper balance between compactness of the representation and the tightness of its linear relaxation, in order to enhance its solvability. In this dissertation, we consider these issues pertaining to the modeling of mixed-integer 0-1 programming problems in general, as well as in the context of several specific real-world applications, including a telecommunications network design problem and an airspace management problem. We first consider the Reformulation-Linearization Technique (RLT) of Sherali and Adams and explore the generation of reduced first-level representations for mixed-integer 0-1 programs that tend to retain the strength of the full first-level linear programming relaxation. The motivation for this study is provided by the computational success of the first-level RLT representation (in full or partial form) experienced by several researchers working on various classes of problems. We show that there exists a first-level representation having only about half the RLT constraints that yields the same lower bound value via its relaxation. Accordingly, we attempt to a priori predict the form of this representation and identify many special cases for which this prediction is accurate. However, using various counter-examples, we show that this prediction as well as several variants of it are not accurate in general, even for the case of a single binary variable. Since the full first-level relaxation produces the convex hull representation for the case of a single binary variable, we investigate whether this is the case with respect to the reduced first-level relaxation as well, and show similarly that it holds true only for some special cases. Empirical results on the prediction capability of the reduced, versus the full, first-level representation demonstrate a high level of prediction accuracy on a set of random as well as practical, standard test problems. Next, we focus on a useful modeling concept that is frequently ignored while formulating discrete optimization problems. Very often, there exists a natural symmetry inherent in the problem itself that, if propagated to the model, can hopelessly mire a branch-and-bound solver by burdening it to explore and eliminate such alternative symmetric solutions. We discuss three applications where such a symmetry arises. For each case, we identify the indistinguishable objects in the model which create the problem symmetry, and show how imposing certain decision hierarchies within the model significantly enhances its solvability. These hierarchies render an otherwise virtually intractable formulation computationally viable using commercial software. For the first problem, we consider a problem of minimizing the maximum dosage of noise to which workers are exposed while working on a set of machines. We next examine a problem of minimizing the cost of acquiring and utilizing machines designed to cool large facilities or buildings, subject to minimum operational requirements. For each of these applications, we generate realistic test beds of problems. The decision hierarchies allow all previously intractable problems to be solved relatively quickly, and dramatically decrease the required computational time for all other problems. For the third problem, we investigate a network design problem arising in the context of deploying synchronous optical networks (SONET) using a unidirectional path switched ring architecture, a standard of transmission using optical fiber technology. Given several rings of this type, the problem is to find a placement of nodes to possibly multiple rings, and to determine what portion of demand traffic between node pairs spanned by each ring should be allocated to that ring. The constraints require that the demand traffic between each node pair should be satisfiable given the ring capacities, and that no more than a specified maximum number of nodes should be assigned to each ring. The objective function is to minimize the total number of node-to-ring assignments, and hence, the capital investment in add-drop multiplexer equipments. We formulate the problem as a mixed-integer programming model, and propose several alternative modeling techniques designed to improve the mathematical representation of this problem. We then develop various classes of valid inequalities for the problem along with suitable separation procedures for tightening the representation of the model, and accordingly, prescribe an algorithmic approach that coordinates tailored routines with a commercial solver (CPLEX). We also propose a heuristic procedure which enhances the solvability of the problem and provides bounds within 5-13% of the optimal solution. Promising computational results that exhibit the viability of the overall approach and that lend insights into various modeling and algorithmic constructs are presented. Following this we turn our attention to the modeling and analysis of several issues related to airspace management. Currently, commercial aircraft are routed along certain defined airspace corridors, where safe minimum separation distances between aircraft may be routinely enforced. However, this mode of operation does not fully utilize the available airspace resources, and may prove to be inadequate under future National Airspace (NAS) scenarios involving new concepts such as Free-Flight. This mode of operation is further compounded by the projected significant increase in commercial air traffic. (Free-Flight is a paradigm of aircraft operations which permits the selection of more cost-effective routes for flights rather than simple traversals between designated way-points, from various origins to different destinations.) We begin our study of Air Traffic Management (ATM) by first developing an Airspace Sector Occupancy Model (AOM) that identifies the occupancies of flights within three dimensional (possibly nonconvex) regions of space called sectors. The proposed iterative procedure effectively traces each flight's progress through nonconvex sector modules which comprise the sectors. Next, we develop an Aircraft Encounter Model (AEM), which uses the information obtained from AOM to efficiently characterize the number and nature of blind-conflicts (i.e., conflicts under no avoidance or resolution maneuvers) resulting from a selected mix of flight-plans. Besides identifying the existence of a conflict, AEM also provides useful information on the severity of the conflict, and its geometry, such as the faces across which an intruder enters and exits the protective shell or envelope of another aircraft, the duration of intrusion, its relative heading, and the point of closest approach. For purposes of evaluation and assessment, we also develop an aggregate metric that provides an overall assessment of the conflicts in terms of their individual severity and resolution difficulty. We apply these models to real data provided by the Federal Aviation Administration (FAA) for evaluating several Free-Flight scenarios under wind-optimized and cruise-climb conditions. We digress at this point to consider a more general collision detection problem that frequently arises in the field of robotics. Given a set of bodies with their initial positions and trajectories, we wish to identify the first collision that occurs between any two bodies, or to determine that none exists. For the case of bodies having linear trajectories, we construct a convex hull representation of the integer programming model of Selim and Almohamad, and exhibit the relative effectiveness of solving this problem via the resultant linear program. We also extend this analysis to model a situation in which bodies move along piecewise linear trajectories, possibly rotating at the end of each linear translation. For this case, we again compare an integer programming approach with its linear programming convex hull representation, and exhibit the relative effectiveness of solving a sequence of problems based on applying the latter construct to each time segment. Returning to Air Traffic Management, another future difficulty in airspace resource utilization stems from a projected increase in commercial space traffic, due to the advent of Reusable Launch Vehicle (RLV) technology. Currently, each shuttle launch cordons off a large region of Special Use Airspace (SUA) in which no commercial aircraft are permitted to enter for the specified duration. Of concern to airspace planners is the expense of routinely disrupting air traffic, resulting in circuitous diversions and delays, while enforcing such SUA restrictions. To provide a tool for tactical and planning purposes in such a context within the framework of a coordinated decision making process between the FAA and commercial airlines, we develop an Airspace Planning Model (APM). Given a set of flights for a particular time horizon, along with (possibly several) alternative flight-plans for each flight that are based on delays and diversions due to special-use airspace (SUA) restrictions prompted by launches at spaceports or weather considerations, this model prescribes a set of flight-plans to be implemented. The model formulation seeks to minimize a delay and fuel cost based objective function, subject to the constraints that each flight is assigned one of the designated flight-plans, and that the resulting set of flight-plans satisfies certain specified workload, safety, and equity criteria. These requirements ensure that the workload for air-traffic controllers in each sector is held under a permissible limit, that any potential conflicts which may occur are routinely resolvable, and that the various airlines involved derive equitable levels of benefits from the overall implemented schedule. In order to solve the resulting 0-1 mixed-integer programming problem more effectively using commercial software (CPLEX-MIP), we explore the use of various facetial cutting planes and reformulation techniques designed to more closely approximate the convex hull of feasible solutions to the problem. We also prescribe a heuristic procedure which is demonstrated to provide solutions to the problem that are either optimal or are within 0.01% of optimality. Computational results are reported on several scenarios based on actual flight data obtained from the Federal Aviation Administration (FAA) in order to demonstrate the efficacy of the proposed approach for air traffic management (ATM) purposes. In addition to the evaluation of these various models, we exhibit the usefulness of this airspace planning model as a strategic planning tool for the FAA by exploring the sensitivity of the solution provided by the model to changes both in the radius of the SUA formulated around the spaceport, and in the duration of the launch-window during which the SUA is activated.
Ph. D.

Le développement du transport partout dans le monde a fourni un grand nombre d'avantages pour de nombreux aspects de la vie humaine. Les systèmes de transport intelligents (ITS) sont des applications avancées qui visent à rendre les réseaux de transport plus sûrs, plus pratiques et plus intelligents. Selon leurs usages, ils peuvent être classés en deux types d'applications ITS, qui sont des applications de sûreté et des applications non-sûreté. Le réseau de véhicules ad hoc (VANET) est un élément clé des systèmes ITS, car il permet la communication entre les unités de transport. Ces communications prennent en charge différentes applications ITS avec différentes propriétés. Parmi les deux types d'applications, nous nous intéressons aux applications de sûreté qui ont des contraintes de qualité de service et des contraintes de sécurité plus strictes. Selon le scénario considéré et l'application de sûreté donnée, les informations échangées entre les véhicules doivent être diffusé localement dans une communication à un seul saut et / ou également notifiées aux véhicules à large dimension. L'objectif principal de cette thèse est d'améliorer les performances des applications de sûreté en termes de qualité de service et de sécurité, à la fois dans une communication à un saut et dans une communication multi-sauts. Nous nous intéressons à la fiabilité, la connectivité et le déni de service (DoS). Nous étudions et proposons des solutions techniques provenant de couches inférieures (Physique, Liaison et Réseaux) qui jouent un rôle fondamental dans l'atténuation des défis créés par la nature de l'environnement des véhicules. Tout d'abord, nous introduisons une nouvelle méthode efficace pour fiabiliser la radiodiffusion. Dans notre système, les messages de sécurité sont rediffusés lorsque l'expéditeur est sollicité. Cela augmente le pourcentage de véhicules qui reçoivent les messages alors que le nombre de messages dupliqués reste limité. En second lieu, en tenant compte de la fragmentation du réseau, nous étudions des solutions qui permettent de pallier la déconnexion temporaire du réseau pour apporter l'information de sécurité aux destinataires. Basé sur les propriétés sociales des réseaux de véhicules, nous proposons un protocole de transfert basé sur des relations sociales pour relayer la communication entre les véhicules et des points d'intérêt qui fournissent des services de sécurité avec des contraintes de temps plus souples, telles que la recherche et le sauvetage. Troisièmement, nous étudions l'attaque de brouillage, une sorte d'attaques DoS, qui est cruciale pour les applications de sûreté et qui et facilement réalisable au niveau des couches inférieures. Nous modélisons l'attaque de brouillage afin d'étudier la dégradation causée par l'attaque sur les performances du réseau. La dégradation à un certain niveau dans les performances du réseau est une indication de présence d'attaques de brouillage dans le réseau; donc les résultats de cette analyse nous permettent de déterminer les seuils de performance du réseau pour distinguer entre les scénarios normaux et les scénarios attaqués. Toutefois, selon cette analyse, le procédé utilisant la dégradation comme une indication pour détecter une attaque de brouillage est impossible pour des applications temps réel. Par conséquent, nous proposons des nouvelles méthodes afin de détecter les attaques de brouillage temps réel. Nos méthodes permettent la détection en temps réel avec une grande précision, non seulement chez le moniteur central mais aussi au niveau de chaque véhicule. Par conséquent, les véhicules sont avertis sur l'attaque assez tôt pour récupérer la communication et réagir à ces attaques
The development of transportation all over the world has been providing a lot of benefits for many aspects of human life. Intelligent Transportation Systems (ITS) are advanced applications that aim to make the transport networks safer, more convenient and smarter. According to their usages, they can be classified into two types of ITS applications, which are safety applications and non-safety applications. Vehicular ad hoc network (VANET) is a key component of ITS since it enables communications among transportation units. These communications support different ITS applications with various properties. Between two types of applications, we are interested in safety applications which have tighter quality and security constraints. Depending on an applied scenario of a given safety application, the exchanged information among vehicles must be broadcast locally within one-hop communication and/or also be notified to vehicles in large range. The main objective of this thesis is to improve the performance of safety applications in term of the quality of service and security, in both one-hop communication and multi-hop communication. We focus on reliability, connectivity and Denial of Services (DoS) attack. We study and propose technical solutions coming from lower layers (Physical, MAC and network layers) which play a fundamental role in mitigation to challenges created by the nature of the vehicular environment. Firstly, we introduce a reliable scheme to achieve the reliability for broadcasting. In our scheme, the safety messages are rebroadcast when the sender is solicited. This increases the percentage of vehicles receiving the messages while duplicated messages are limited. Secondly, with consideration of the fragmentation of the network, we study solutions that overcome the temporary disconnection in the network to bring the safety information to the recipients. Based on the social properties of vehicular networks, we propose a social-based forwarding protocol to support the communication between vehicles to points of interest that provide safety services with looser time constraints, such as search and rescue. Thirdly, we investigate jamming attack, a kind of DoS attacks, which is crucial for safety applications because of the adequate condition of the attack at the lower layers. We model jamming attack on broadcasting in order to study the degradation caused by the attack on network performance. The degradation at a certain level in network performance is an indication of a jamming attack presence in the network; therefore results from this analysis will allow us to determine network performance thresholds to distinguish between normal and attacked scenarios. However, according to our analysis, the method using the degradation as an indication to detect a jamming attack is not feasible for real-time applications. Hence, we propose methods to detect jamming attacks in real-time. Our methods allow real-time detection with high accuracy, not only at the central monitor but also at each vehicle. Therefore, vehicles are noticed about the attack soon enough to recover the communication and react to these attacks

Thesis (M.S. in Systems Technology (Space Systems Operations)) Naval Postgraduate School, September 1994.
Thesis advisor(s): Philip A. Durkee. "September 1994." Bibliography: p. 52-53. Also available online.

Thesis (M.S.)--Missouri University of Science and Technology, 2009.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed January 22, 2009) Includes bibliographical references (p. 53-55).

The continuous operation of civil and marine structures is essential for maintaining the flow of people and goods. However, structures are exposed to extreme or progressive events during their service time. The uncertainties associated with the occurrence and the magnitude of extreme events (e.g. flooding and scour) may change, leading to unprecedented loading conditions, while the progressive events (e.g. corrosion and fatigue) may jeopardize the structural capacity to resist loads. In order to maintain or improve the structural capacity, repair and maintenance actions need to be applied to structures. However, the determination of these actions may be challenging for decision makers due to (a) limited financial resources to be allocated for a group of structures, (b) uncertainties associated with current structure conditions and future loading conditions, and (c) various decision-making factors (e.g. reliability threshold, decision time, and risk attitude). In order to address these issues, the focus of the research in this dissertation is to enhance the development of management strategies with the application in (a) management of bridge networks under hydraulic events and climate changes, (b) service life extension of ships considering financial feasibility and decision-making factors, and (c) determination of reliability threshold in the decision-making process. The management of bridge networks involves the quantification of regional hazards imposed on the network, performance assessment of structures, and consequence evaluation of potential bridge failure. Regional hazards such as floods may be affected by the changes in the intensity of precipitation due to anticipated climate changes. These hazards may cause extensive damage to bridges, and failure may cause significant costs to bridge managers and result in inconvenience on the daily traffic commute. This research focuses on enhancing the assessment and management of bridges networks vulnerable to regional hydraulic events and climate changes. The integration of transportation network analysis, which reflects the redistribution of traffic flow in the event of bridge failure, is shown to be essential when determining the risk level of bridges. Furthermore, this work includes proposed methodologies for determining optimal management strategies that account for the connection between global climate predictions and regional hydrologic conditions. The crux of determining management strategies, especially for extending ship service lives, is to ensure an adequate safety margin within and beyond the design life. In addition to the loading effect and structural capacity, the safety margin of ships is related to the deterioration acting on the structure. During ship operation, in-service condition surveys are conducted on ship details to assess structural conditions and to inform maintenance actions. This research focuses on the integration of condition surveys of ship details, as well as the timing of conducting surveys, to improve the service life extension for ship structures. While decision makers strive to maintain the safe operation of ships, they should also identify the management strategy that can deliver the best return given the limited budget. This research, from the perspective of cost-effectiveness and profitability, proposes optimization frameworks to clarify the financially feasible life expectancy of different management strategies as well as identify the optimal duration of extended service life for different categories of commercial ships. The last focus of this research emanates from the reliability threshold when determining management strategies. In addition to facilitating decision-making on the management of civil and marine structures, the reliability threshold in terms of target reliability index has been extensively used in design guidelines to ensure adequate safety margin for structures. The level of safety is typically related to the failure mode and severity of failure consequences (e.g. number of potential fatalities). Driven by the emerging application of unmanned ships where there are fewer or no crew members on board, this research specifically focuses on the integration of different acceptance criteria for human safety into the determination of the target reliability index.

With climate change arising as an important issue in the 21th century, many states have been working diligently to develop climate action plans with the hopes of reducing greenhouse gas emissions and stop climate change from occurring. According to scientists' theories, however, many places across the globe are already feeling the effects of a changing climate and must therefore switch their focus from mitigation to adaptation. In the United States, there has been a focus on how climate change will impact one of the most vulnerable parts of the country, the transportation infrastructure. Many countries have already begun adapting their transportation infrastructure to climate change including the United States. This thesis focuses on how states are adapting to climate change by analyzing strategies, frameworks, and reports released by these states in order to document where they stand in regards to adaptation of the transportation network. The states that are adapting their transportation infrastructure are Washington, Oregon, California, Hawaii, Alaska, Florida, North Carolina, Maryland, Delaware, Pennsylvania, Michigan, Connecticut, Massachusetts, Vermont, and Maine. There is also a brief summary of how Canada and the United Kingdom are preparing for climate change with an analysis of frameworks and strategies used to adapt their transportation infrastructure. The ultimate goal of this thesis is to provide engineers and policymakers with evidence that several states are implementing adaptation into transportation projects and provide a variety of strategies for them to use in their own state. Specifically, this report provides applications of adaptation for Georgia to use, so that they can begin the lengthy process of adapting their transportation infrastructure to climate change.

With the technological advancement in computer science, Geographic Information Science (GIScience), and transportation, more and more complex path finding queries including category based queries are proposed and studied across diverse disciplines. A category based query, such as Optimal Sequenced Routing (OSR) queries and Trip Planning Queries (TPQ), asks for a minimum-cost path that traverses a set of categories with or without a predefined order in a graph. Due to the extensive computing time required to process these complex queries in a large scale environment, efficient algorithms are highly desirable whenever processing time is a consideration. In Artificial Intelligence (AI), a best first search is an informed heuristic path finding algorithm that uses domain knowledge as heuristics to expedite the search process. Traditional best first searches are single-variate in terms of the number of variables to describe a state, and thus not appropriate to process these queries in a graph. In this dissertation, 1) two new types of category based queries, Category Sequence Traversal Query (CSTQ) and Optimal Sequence Traversal Query (OSTQ), are proposed; 2) the existing single-variate best first searches are extended to multivariate best first searches in terms of the state specified, and a class of new concepts--state graph, sub state graph, sub state graph space, local heuristic, local admissibility, local consistency, global heuristic, global admissibility, and global consistency--is introduced into best first searches; 3) two bivariate best first search algorithms, C* and O*, are developed to process CSTQ and OSTQ in a graph, respectively; 4) for each of C* and O*, theorems on optimality and optimal efficiency in a sub state graph space are developed and identified; 5) a family of algorithms including C*-P, C-Dijkstra, O*-MST, O*-SCDMST, O*- Dijkstra, and O*-Greedy is identified, and case studies are performed on path finding in transportation networks, and/or fully connected graphs, either directed or undirected; and 6) O*- SCDMST is adopted to efficiently retrieve optimal solutions for OSTQ using network distance metric in a large transportation network.
Ph. D.

Local agencies must comply with environmental justice regulation and as such, it is important that they possess practical tools to identify target populations and assess impacts of projects, programs, and policies on these populations. These tools are not readily available or fully developed to evaluate impacts on a regional level, especially when the impacts are benefits rather than burdens. This issue comes into play when accessibility is assessed. This analysis measures accessibility for an environmental justice evaluation using spatial statistical clusters and cumulative opportunity. It shows that the majority of schools, libraries and local transit lines are within areas with high concentrations of target populations, however, park space is limited in these areas. Alternative approaches for environmental justice assessments of regional outcomes such as accessibility provide opportunities for MPOs to gain a greater understanding of the regional impacts of transportation improvements as well as more accurately comply with the spirit of environmental justice regulations.