We consider a problem of placing route-based filters in a communication network to limit the number of forged address attacks to a prescribed level. Nodes in the network communicate by exchanging packets along arcs, and the originating node embeds the origin and destination addresses within each packet that it sends. In the absence of a validation mechanism, one node can send packets to another node using a forged origin address to launch an attack against that node. Route-based filters can be established at various nodes on the communication network to protect against these attacks. A route-based filter examines each packet arriving at a node, and determines whether or not the origin address could be legitimate, based on the arc on which the packet arrives, the routing information, and possibly the destination. The problem we consider seeks to find a minimum cardinality subset of nodes to filter so that the prescribed level of security is achieved.The primary contributions of this dissertation are as follows. We formulate and discuss the modeling of this filter placement problem as a mixed-integer program. We then show the sensitivity of the optimal number of deployed filters as the required level of security changes, and demonstrate that current vertex cover-based heuristics are ineffective for problems with relaxed security levels. We identify a set of special network topologies on which the filter placement problem is solvable in polynomial time, focusing our attention on the development of a dynamic programming algorithm for solving this problem on tree networks. These results can then in turn be used to derive valid inequalities for an integer programming model of the filter placement problem. Finally, we present heuristic algorithms based on the insights gained from our overall study for solving the problem, and evaluate their performance against the optimal solution provided by our integer programming model.

This dissertation examines the effects of semantic and phonetic radicals on Chinese character decoding by high-intermediate level Chinese as a Foreign Language (CFL) learners. The results of the main study (discussed in Chapter #5) suggest that the CFL learners tested have a well-developed semantic pathway to recognition; however, their phonological pathway is not yet a reliable means of character identification. Semantic radicals that correctly pertain to character meaning facilitated reaction time in semantic categorization tasks (Experiment #1), while radicals that had no immediately interpretable relation to character meaning had a strong inhibitory effect. The relativeaccuracy of phonetic radicals (for predicting the whole-character's pronunciation) did not measurably improve homonym recognition (Experiment #2). Subjects were then tested to determine their default processing modes in Chinese character reading. In a lexical decision task (Experiment #3) wherein semantic radicals or phonological components were blurred to delay recognition, surprisingly, the subjects were significantly slower in identifying pseudo-characters when the phonological component was blurred, indicating that, despite having unreliable phonological pathways to character recognition, the subjects were still utilizing that strategy first. These results were mirrored in a sentence reading task (Experiment #4) wherein a single character had either a blurred semantic radical or phonological component. This tendency to use the less developed pathway is explained as a default means of attempting character recognition as a result of subjects gleaning orthographic information from the densely packed phonological component and as a result of L1 (English) interference predisposing subjects to phonological decoding strategies.Such a study on CFL learner reading processes is an important step towards ameliorating CFL teaching methodologies. For this reason, the author contrasts the data on CFL learners with data taken from similar experiments with native Chinese speakers (in Chapter #6) in order to demonstrate concrete differences in character reading processes which should affect teaching practices between the two groups. The authorconcludes the dissertation by making targeted recommendations for CFL pedagogical practices based upon the results of the study on the effect of character-internal features on reading patterns by non-native readers of Chinese (Chapter #8).

The rapid expansion of low-density suburban areas in North America has led to new travel patterns that require transit services to be more flexible. Flex-Route transit service, which combines fixed-route transit service with elements of demand-responsive transit service, has emerged as a viable transit option to address the travel needs of the residents of these areas. Existing literature in this field, however, is limited and lacks any comprehensive analysis of Flex-Route planning, design and scheduling. This research aims at exploring Flex-Route transit service to provide detailed guidelines for the planning and design of the service, as well as developing a new scheduling system for this type of unique service. Accordingly, the objectives of this research are: assessing the practicality of Flex-Route transit service in serving low-density suburban areas; identifying essential Flex-Route planning steps and design parameters; determining the feasibility and cost of replacing fixed-route transit with Flex-Route service; and developing a Flex-Route-specific dynamic scheduling system that relies on recent developments in computer and communication technologies. In this regard, we develop an analytical model that addresses several design parameters and provide a detailed analysis that includes, among other parameters, finding optimal values for Flex-Route service area and slack time. Furthermore, the analytical model includes a feasibility and cost analysis that estimates the cost incurred by several stakeholders if Flex-Route service is chosen to replace fixed-route service. The core of the scheduling system is a new developed algorithm – the Constrained-Insertion Algorithm- that exploits the powerful search techniques of Constraint Programming. The scheduling system can handle the daily operations of Flex-Route transit services; it accepts daily (or dynamic) inputs and, in minimal time, produces very cost-effective and reliable schedules. Moreover, the scheduling system has the ability to be used as simulation tool to allow transit operators to assess the feasibility and performance of proposed Flex-Route transit services before implementation. The applicability of the analytical model as well as the performance of the scheduling system were subsequently evaluated and validated through process that included testing on a case study in the City of Oakville, Canada.

The rapid expansion of low-density suburban areas in North America has led to new travel patterns that require transit services to be more flexible. Flex-Route transit service, which combines fixed-route transit service with elements of demand-responsive transit service, has emerged as a viable transit option to address the travel needs of the residents of these areas. Existing literature in this field, however, is limited and lacks any comprehensive analysis of Flex-Route planning, design and scheduling. This research aims at exploring Flex-Route transit service to provide detailed guidelines for the planning and design of the service, as well as developing a new scheduling system for this type of unique service. Accordingly, the objectives of this research are: assessing the practicality of Flex-Route transit service in serving low-density suburban areas; identifying essential Flex-Route planning steps and design parameters; determining the feasibility and cost of replacing fixed-route transit with Flex-Route service; and developing a Flex-Route-specific dynamic scheduling system that relies on recent developments in computer and communication technologies. In this regard, we develop an analytical model that addresses several design parameters and provide a detailed analysis that includes, among other parameters, finding optimal values for Flex-Route service area and slack time. Furthermore, the analytical model includes a feasibility and cost analysis that estimates the cost incurred by several stakeholders if Flex-Route service is chosen to replace fixed-route service. The core of the scheduling system is a new developed algorithm – the Constrained-Insertion Algorithm- that exploits the powerful search techniques of Constraint Programming. The scheduling system can handle the daily operations of Flex-Route transit services; it accepts daily (or dynamic) inputs and, in minimal time, produces very cost-effective and reliable schedules. Moreover, the scheduling system has the ability to be used as simulation tool to allow transit operators to assess the feasibility and performance of proposed Flex-Route transit services before implementation. The applicability of the analytical model as well as the performance of the scheduling system were subsequently evaluated and validated through process that included testing on a case study in the City of Oakville, Canada.

Providing unambiguous, succinct descriptions of routes for pedestrians to follow is very challenging. Route descriptions vary according to many things, such as route length and complexity, availability of easily identifiable landmarks, and personal preferences. It is well known that the inclusion of a variety of landmarks facilitates route following – either at key decision points, or as a confirmatory cue. Many of the existing solutions, however, behave like car navigation systems and do not include references to such landmarks. The broader ambition of this research is the automatic generation of route descriptions that cater specifically to the needs of the pedestrian. More specifically this research describes empirical evidence gathered to identify the information requirements for an automated pedestrian navigation system. The results of three experiments helped to identify the criteria that govern the relative saliency of features of interest within an urban environment. There are a large variety of features of interest (together with their descriptions) that can be used as directional aids within route descriptions (for example buildings, statues, monuments, hills, and roads). A set of variables were developed in order to measure the saliency of the different classes of features. The experiments revealed that the most important measures of saliency included name, size, age, and colour. This empirical work formed the basis of the development of a pedestrian navigation system that incorporated the automatic identification of features of interest using the City of Edinburgh as the study area. Additionally the system supported the calculation of the saliency of a feature of interest, the development of an intervisibility model for the route to be navigated to determine the best feature of interest to use at each decision point along the route. Finally, the pedestrian navigation system was evaluated against route descriptions gathered from a random set of individuals to see how efficiently the system reflected the more natural and richer route description that people typically generate. This work shows that modelling features of interest is the key to the automatic generation of route descriptions that can be readily understood and followed by pedestrians.

The goal of this research is to develop a method of objectively comparing and optimizing the performance of demand-responsive transportation systems in heterogeneous environments. Demand-responsive transportation refers to modes of transportation that do not follow fixed routes or schedules, including taxis, paratransit, deviated-route services, ride sharing as well as other modes. Heterogeneous environments are transportation environments in which streets do not follow regular patterns, passenger behavior is difficult to model, and transit schedules and layouts are non-uniform. An example of a typical heterogeneous environment is a modern suburb with non-linear streets, low pedestrian activity, and infrequent or sparse transit service. The motivation for this research is to determine if demand-responsive transportation can be used to improve customer satisfaction and reduce operating costs in suburban and low-density urban areas where fixed-route transportation may be inefficient. This research extends existing comparison and optimization techniques that are designed to work in homogeneous environments. Homogeneous environments refer to transportation systems where the streets follow regular and repeating patterns, passengers are evenly distributed throughout the system, and the transit system is easily modeled. The performance of systems with these characteristics can be approximated with closed-form analytical expressions representing passenger travel times, vehicle distances traveled, and other performance indicators. However, in the low-density urban areas studied in this research, the street patterns and transit schedules are irregular and passenger behavior is difficult to model. In these areas, analytical solutions cannot be found. Instead, this research develops a simulation-based approach to compare and optimize performance in these heterogeneous environments. Using widely-available route-planning tools, open-source transit schedules, and detailed passenger data, it is possible to simulate the behavior of transit vehicles and passengers to such an exacting degree that analytical solutions are not needed. A major technical contribution of this research is the development of a demand-responsive transportation simulator to analyze performance of demand-responsive systems in heterogeneous environments. The simulator combines several open-source tools for route planning with a custom-built demand-responsive vehicle and passenger-itinerary optimizer to simulate individual vehicles and passengers within a large system. With knowledge of the street network, the transit schedule, passenger locations, and trip request times, the simulator will output exact passenger transit times, passenger travel distances, vehicle travel distance, and other performance indicators for a particular transportation setup in a given area. The simulator is used to develop a method of comparing various demand-responsive and fixed-route systems. By predefining a set of performance indicators, such as passenger travel time and operating cost, the simulator can be used to ascertain the performance of a wide array of transportation systems. Comparing the weighted cost of each type of system permits a transportation engineer or planner to determine what type of system will provide the best results in a given area. The simulator is extended to assist in optimization of the demand-responsive transportation system layout. A key problem that needs to be solved when implementing a demand-responsive system is to determine the size, shape, and location of the demand-responsive coverage areas, i.e., the areas in which passengers are eligible for demand-responsive transportation. Using a particle swarm optimization algorithm and the simulation-based comparison technique, the optimal size and shape for a demand-responsive coverage area can be determined. The efficacy of the comparison and optimization techniques is demonstrated within the city of Atlanta, GA. It is shown that for certain areas of the city of Atlanta, demand-responsive transportation is more efficient than the currently implemented fixed-route system. Depending on the objective of the transportation planner, passenger satisfaction as well as operating costs can be improved by implementing a demand-responsive system in certain low-density areas. The techniques introduced in this research, and the simulation tool developed to implement those techniques, provide a repeatable, accurate, and objective method with which to optimize and compare demand-responsive transportation systems in heterogeneous environments.

Behavioral patterns prediction in the context of Vehicular Ad hoc Networks (VANETs)has been receiving increasing attention due to enabling on-demand, intelligent traffic analysis and response to real-time traffic issues. One of these patterns, sequential patterns, are a type of behavioral patterns that describe the occurence of events in a timely-ordered fashion. In the context of VANETs, these events are defined as an ordered list of road segments traversed by vehicles during their trips from a starting point to their final intended destination, forming a vehicular path. Due to their predictable nature, undertaken vehicular paths can be exploited to extract the paths that are considered frequent. From the extracted frequent paths through data mining, the probability that a vehicular path will take a certain direction is obtained. However, in order to achieve this, samples of vehicular paths need to be initially collected over periods of time in order to be data-mined accordingly. In this thesis, a new set of formal definitions depicting vehicular paths as sequential patterns is described. Also, five novel communication schemes have been designed and implemented under a simulated environment to collect vehicular paths; such schemes are classified under two categories: Road Side Unit-Triggered (RSU-Triggered) and Vehicle-Triggered. After collection, extracted frequent paths are obtained through data mining, and the probability of these frequent paths is measured. In order to evaluate the e ciency and e ectiveness of the proposed schemes, extensive experimental analysis has been realized. From the results, two of the Vehicle-Triggered schemes, VTB-FP and VTRD-FP, have improved the vehicular path collection operation in terms of communication cost and latency over others. In terms of reliability, the Vehicle-Triggered schemes achieved a higher success rate than the RSU-Triggered scheme. Finally, frequent vehicular movement patterns have been effectively extracted from the collected vehicular paths according to a user-de ned threshold and the confidence of generated movement rules have been measured. From the analysis, it was clear that the user-de ned threshold needs to be set accordingly in order to not discard important vehicular movement patterns.

Thesis (Ph.D.)--Case Western Reserve University, 2003. / Includes bibliographical references (leaves 215-219).

Thesis(M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (p.116-119).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.