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High-speed transmission over contemporary communication networks has drawn many research efforts. Traffic scheduling schemes which play a critical role in managing network transmission have been pervasively studied and widely implemented in various practical communication networks. In a sophisticated communication system, a variety of applications co-exist and require differentiated Quality-of-Service (QoS). Innovative scheduling schemes and hybrid scheduling disciplines which integrate multiple traditional scheduling mechanisms have emerged for QoS differentiation. This study aims to develop novel analytical models for commonly interested scheduling schemes in communication systems under more realistic network traffic and use the models to investigate the issues of design and development of traffic scheduling schemes. In the open literature, it is commonly recognized that network traffic exhibits self-similar nature, which has serious impact on the performance of communication networks and protocols. To have a deep study of self-similar traffic, the real-world traffic datasets are measured and evaluated in this study. The results reveal that selfsimilar traffic is a ubiquitous phenomenon in high-speed communication networks and highlight the importance of the developed analytical models under self-similar traffic. The original analytical models are then developed for the centralized scheduling schemes including the Deficit Round Robin, the hybrid PQGPS which integrates the traditional Priority Queueing (PQ) and Generalized Processor Sharing (GPS) schemes, and the Automatic Repeat reQuest (ARQ) forward error control discipline in the presence of self-similar traffic. Most recently, research on the innovative Cognitive Radio (CR) techniques in wireless networks is popular. However, most of the existing analytical models still employ the traditional Poisson traffic to examine the performance of CR involved systems. In addition, few studies have been reported for estimating the residual service left by primary users. Instead, extensive existing studies use an ON/OFF source to model the residual service regardless of the primary traffic. In this thesis, a PQ theory is adopted to investigate and model the possible service left by selfsimilar primary traffic and derive the queue length distribution of individual secondary users under the distributed spectrum random access protocol.

This study is an analysis of the traffic situation for pedestrians and bicyclist, unprotected road users, in Accra’s Central Business District. A Swedish method, Calm streets, is used to identify conflicts in the mixed traffic situation. The findings reveal a large amount of conflicts between unprotected road users and motor vehicles. These conflicts cause congestions which have negative implications on the environment, health and economy. In addition an assessment of the quality of and access to pedestrian and bicycle facilities were conducted. This assessment indicates that the quality and access to the facilities are in general low, especially for the disabled, elderly and children. A larger Traffic Network Analysis and a Cost Benefit Analysis are needed to address these problems for stakeholders and decision makers.

<p> </p><p>This study is an analysis of the traffic situation for pedestrians and bicyclist, unprotected road users, in Accra’s Central Business District. A Swedish method, Calm streets, is used to identify conflicts in the mixed traffic situation. The findings reveal a large amount of conflicts between unprotected road users and motor vehicles. These conflicts cause congestions which have negative implications on the environment, health and economy.</p><p>In addition an assessment of the quality of and access to pedestrian and bicycle facilities were conducted. This assessment indicates that the quality and access to the facilities are in general low, especially for the disabled, elderly and children. A larger Traffic Network Analysis and a Cost Benefit Analysis are needed to address these problems for stakeholders and decision makers.</p><p> </p>

<div><p>Predictive modeling allows us to analyze historical patterns to forecast future events. When the data available for this analysis is imbalanced or skewed, many challenges arise. The lack of sensitivity towards the class with less data available hinders the sought-after predictive capabilities of the model. These imbalanced datasets are found across many different fields, including medical imaging, insurance claims and financial frauds. The objective of this thesis is to identify the challenges, and means to assess, the application of machine learning to transportation data that is imbalanced and using only one independent variable. </p><p>Airlines undergo a decision-making process on air route addition or deletion in order to adjust the services offered with respect to demand and cost, amongst other criteria. This process greatly affects the topology of the network, and results in a continuously evolving Air Traffic Network (ATN). Organizations like the Federal Aviation Administration (FAA) are interested in the network transformation and the influence airlines have as stakeholders. For this reason, they attempt to model the criteria used by airlines to modify routes. The goal is to be able to predict trends and dependencies observed in the network evolution, by understanding the relation between the number of passengers per flight leg as the single independent variable and the airline’s decision to keep or eliminate that route (the dependent variable). Research to date has used optimization-based methods and machine learning algorithms to model airlines’ decision-making process on air route addition and deletion, but these studies demonstrate less than a 50% accuracy. </p><p>In particular, two machine learning (ML) algorithms are examined: Sparse Gaussian Classification (SGC) and Deep Neural Networks (DNN). SGC is the extension of Gaussian Process Classification models to large datasets. These models use Gaussian Processes (GPs), which are proven to perform well in binary classification problems. DNN uses multiple layers of probabilities between the input and output layers. It is one of the most popular ML algorithms currently in use, so the results obtained using SGC were compared to the DNN model. </p><p>At a first glance, these two models appear to perform equally, giving a high accuracy output of 97.77%. However, post-processing the results using a simple Bayes classifier and using the appropriate metrics for measuring the performance of models trained with imbalanced datasets reveals otherwise. The results in both SGC and DNN provided predictions with a 1% of precision and 20% of recall with an score of 0.02 and an AUC (Area Under the Curve) of 0.38 and 0.31 respectively. The low score indicates the classifier is not performing accurately, and the AUC value confirms the inability of the models to differentiate between the classes. This is probably due to the existing interaction and competition of the airlines in the market, which is not captured by the models. Interestingly enough, the behavior of both models is very different across the range of threshold values. The SGC model captured more effectively the low confidence in these results. In order to validate the model, a stratified K-fold cross-validation model was run. </p>The future application of Gaussian Processes in model-building for decision-making will depend on a clear understanding of its limitations and the imbalanced datasets used in the process, the central purpose of this thesis. Future steps in this investigation include further analysis of the training data as well as the exploration of variable-optimization algorithms. The tuning process of the SGC model could be improved by utilizing optimal hyperparameters and inducing inputs.<br></div><div><div><br></div></div>

A key attribute of resilience, robustness serves as a predictor of infrastructure system performance under disruptions, thus informing proactive infrastructure risk management. A literature review indicated that previous studies did not consider some key factors that can influence the robustness of Air Transportation Infrastructure Networks (ATIN) and thus their (system-level cascade) systemic risk management processes. In this respect, the current study first assesses existing and then develops a new methodology to quantify the robustness of ATIN. Specifically, based on integrating travel time and flight frequency, the study develops alternative best route and link weight approaches to assess key ATIN robustness measures and relevant operating cost losses (OCL). In order to demonstrate the practical use of the developed methodology, the robustness and the associated OCL of the Canadian Domestic Air Traffic Network are evaluated under random failures (i.e., disruptive events that occur randomly) and targeted threats (i.e., disruptive events that occur deliberately). The analysis results show that the network robustness is influenced by the utilized evaluation approach, especially after 20% of the network components become nonoperational. Overall, the methodology developed within this study is expected to provide ATIN policymakers with the means to quantify the network robustness and OCL, and thus enable ATIN resilience-guided proactive risk management in the face of natural or anthropogenic hazard realizations. / Thesis / Master of Applied Science (MASc)

<p>This thesis investigates the second-best toll pricing and capacity expansion problems when stated as mathematical programs with equilibrium constraints (MPEC). Three main questions are rised: First, whether conventional descent methods give sufficiently good solutions, or whether global solution methods are to prefer. Second, how the performance of the considered solution methods scale with network size. Third, how a discretized stochastic mathematical program with equilibrium constraints (SMPEC) formulation of a stochastic network design problem can be practically solved. An attempt to answer these questions is done through a series ofnumerical experiments.</p><p>The traffic system is modeled using the Wardrop’s principle for user behavior, separable cost functions of BPR- and TU71-type. Also elastic demand is considered for some problem instances.</p><p>Two already developed method approaches are considered: implicit programming and a cutting constraint algorithm. For the implicit programming approach, several methods—both local and global—are applied and for the traffic assignment problem an implementation of the disaggregate simplicial decomposition (DSD) method is used. Regarding the first question concerning local and global methods, our results don’t give a clear answer.</p><p>The results from numerical experiments of both approaches on networks of different sizes shows that the implicit programming approach has potential to solve large-scale problems, while the cutting constraint algorithm scales worse with network size.</p><p>Also for the stochastic extension of the network design problem, the numerical experiments indicate that implicit programming is a good approach to the problem.</p><p>Further, a number of theorems providing sufficient conditions for strong regularity of the traffic assignment solution mapping for OD connectors and BPR cost functions are given.</p>

This thesis investigates the second-best toll pricing and capacity expansion problems when stated as mathematical programs with equilibrium constraints (MPEC). Three main questions are rised: First, whether conventional descent methods give sufficiently good solutions, or whether global solution methods are to prefer. Second, how the performance of the considered solution methods scale with network size. Third, how a discretized stochastic mathematical program with equilibrium constraints (SMPEC) formulation of a stochastic network design problem can be practically solved. An attempt to answer these questions is done through a series ofnumerical experiments. The traffic system is modeled using the Wardrop’s principle for user behavior, separable cost functions of BPR- and TU71-type. Also elastic demand is considered for some problem instances. Two already developed method approaches are considered: implicit programming and a cutting constraint algorithm. For the implicit programming approach, several methods—both local and global—are applied and for the traffic assignment problem an implementation of the disaggregate simplicial decomposition (DSD) method is used. Regarding the first question concerning local and global methods, our results don’t give a clear answer. The results from numerical experiments of both approaches on networks of different sizes shows that the implicit programming approach has potential to solve large-scale problems, while the cutting constraint algorithm scales worse with network size. Also for the stochastic extension of the network design problem, the numerical experiments indicate that implicit programming is a good approach to the problem. Further, a number of theorems providing sufficient conditions for strong regularity of the traffic assignment solution mapping for OD connectors and BPR cost functions are given.

In recent years there has been an exponential growth in Internet traffic resulting in increased network bandwidth requirements which, in turn, has led to stringent processing requirements on network layer devices like routers. Present backbone routers on OC 48 links (2.5Gbps) have to process four million minimum-sized packets per second. Further, the functionality supported in the network devices is also on the increase leading to programmable processors, such as Intel's IXP, Motorola's C5 and IBM's.NP. These processors support multiple processors and multiple threads to exploit packet-level-parallelism inherent in network workloads. This thesis studies the performance of network processors. We develop a Petri Net model for a commercial network processors (Intel IXP 2400,2850) for three different applications viz., IPv4 forwarding, Network Address Translation and IP security protocols. A salient feature of the Petri net model is its ability to model the application, architecture and their interaction in great detail. The model is validated using the intel proprietary tool (SDK 3.51 for IXP architecture) over a range of configurations. Our Performance evaluation results indicate that 1. The IXP processor is able to support a throughput of 2.5 Gbps for all modeled applications. 2. Packet buffer memory (DRAM) is the bottleneck resource in a network proces sor and even multithreading is ineffective beyond a total of 16 threads in case of header processing applications and beyond 32 threads for payload processing applications. Since DRAM is the bottleneck resource we explore the benefits of increasing the DRAM banks and other software schemes like offloading the packet header to SRAM. The second part of the thesis studies the impact of parallel processing in network processor on packet reordering and retransmission. Our results indicate that the concurrent processing of packets in a network processor and buffer allocation schemes in TFIFO leads to a significant packet reordering, (61%), on a 10-hop network (with packet sizes of 64 B) which in turn leads to a 76% retransmission under the TCP fast-restransmission algorithm. We explore different transmit buffer allocation schemes namely, contiguous, strided, local, and global for transmit buffer which reduces the packet retransmission to 24%. Our performance results also indicate that limiting the number of microengines can reduce the extent of packet reordering while providing the same throughput. We propose an alternative scheme, Packetsort, which guarantees complete packet ordering while achieving a throughput of 2.5 Gbps. Further, we observe that Packetsort outperforms, by up to 35%, the in-built schemes in the IXP processor namely, Inter Thread Signaling (ITS) and Asynchronous Insert and Synchronous Remove (AISR). The final part of this thesis investigates the performance of the network processor in a bursty traffic scenario. We model bursty traffic using a Pareto distribution. We consider a parallel and pipelined buffering schemes and their impact on packet drop under bursty traffic. Our results indicate that the pipelined buffering scheme outperforms the parallel scheme.

Description and evaluation of a traffic network of Pardubice in all transport sectors (air, water, railway, road and cycling) and its comparison with a network of Hradec Králové. Integration of both transport systems. Suggestions for improvement of traffic infrastructure and transport integration. Optimalization of connection of chosen urban neighbourhood with a town centre by urban mass transportation.