The project team has, on behalf of Autoliv Development AB, analyzed transport flow and logistics in Gothenburg and Shanghai as well as its effects on the society and environment. The project team looked in-depth at different logistics operations, which served as basis for different scenario simulations. Key points of interest in the simulation were traffic safety (including congestion and noise exposure), efficiency, cost and the environmental effect. Due to confidentiality, the original text has been removed. The text above gives a brief overview of different parts of the project.

D.Com. (Transport Economics) / Please refer to full text to view abstract

Ransomware is a type of malware that has gained immense popularity in recent time due to its money extortion techniques. It locks out the user from the system files until the ransom amount is paid. Existing approaches for ransomware detection predominantly focus on system level monitoring, for instance, by tracking the file system characteristics. To date, only a small amount of research has focused on detecting ransomware at the network level, and none of the published proposals have addressed the challenges raised by the fact that an increasing number of ransomware are using encrypted channels for communication with the command and control (C&C) server, mainly, over the HTTPS protocol. Despite the limited amount of ransomware-specific data available in network traffic, network-level detection represents a valuable extension of system-level detection as this would provide early indication of ransomware activities and allow disrupting such activities before serious damage can take place. To address the aforementioned gap, we propose, in the current thesis, a new approach for detecting ransomware in encrypted network traffic that leverages network connection and certificate information and machine learning. We observe that network traffic characteristics can be divided into 3 categories – connection based, encryption based, and certificate based. Based on these characteristics, we explore a feature model that separates effectively ransomware traffic from normal traffic. We study three different classifiers – Random Forest, SVM and Logistic Regression. Experimental evaluation on diversified dataset yields a detection rate of 99.9% and a false positive rate of 0% for random forest, the best performing of the three classifiers. / Graduate

M. Tech. (Department of Civil Engineering and Building, Faculty of Engineering and Technology), Vaal University of Technology / Growth in land development in South Africa resulted in large increase in traffic volumes. A Traffic Impact Assessment (TIA), as a traffic engineering tool, is commonly used to assess the possible effects of a land development project on the transportation and traffic system. During the TIA process, capacity analysis is performed to indicate the measures of effectiveness of the intersection. Intersection capacity analysis in South Africa by engineers is done on the basis of default values of the Peak Hour Factor (PHF) provided by the Highway Capacity Manual (HCM) or limited traffic counts. However, the default value of PHF may be significantly affected by new developments in the neighbourhood of the intersection. This study aimed at investigating the impact land-use change has on the existing intersection PHF, thus predicting values per land-use type. Intersections with traffic counts conducted before and after land-use change in vicinity were selected and investigated. The results showed that change in land-use has an impact on the existing PHF. They also assist in identifying the appropriate intersections to predict the PHF per land-use type. Intersections were identified and analysed, and this led to the development of a design chart showing the predicted PHF per land-use type selected and measures to consider during traffic analysis. Intersection capacity analysis was performed to compare the results using the predicted PHF and the HCM default values. The results showed that traffic flow rate was adjusted by up to 26% when using the default values, 0.92 and 0.95. The results also showed that the default values could overestimate the volume to capacity ratio and the average delay by up to 15% and 35%, respectively. It was then concluded that the use of HCM default values of the PHF for every land-use type will have an effect of the final roadway design results. The computed PHF values for each land-use type were then recommended to be used to ensure fairness and consistency in traffic analysis.

Dissertation for the degree of Master of Science University of the Witwatersrand Johannesburg. April 2013 / Many academic works in the train scheduling environment concentrate on optimizing movements of resources through the physical network. To opti- mize bulk freight lines, algorithms must provide a feasible schedule given the available resources, basic operational constraints and varying demand while ensuring resource allocations that minimise total cost. To be usable the al- gorithm must run within reasonable time limits. This dissertation focuses on the bulk freight train scheduling problem of full loads without track conges- tion but extends to cover operational constraints as well as exible resource allocation and hubs. A problem outline is given wherein the constraints and decision variables are well de ned followed by a review of current literature. An exact formation of the problem is given with benchmarking on small data sets. A genetic algorithm is used to solve for schedules on larger problem data sets. The algorithm was successfully implemented on the 60Mt Coal Line in South Africa which provided notable improvements in e ciencies. Discussion and results are provided.

Tam Sai-Wah. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves [103]-104) and index. / Abstracts in English and Chinese. / Abstract --- p.x / 摘要 --- p.xi / Abbreviations --- p.xii / Symbols --- p.xiv / Chapter Part I --- Background / Chapter 1 --- Background on coexistence --- p.2 / Chapter 1.1 --- Data network --- p.2 / Chapter 1.1.1 --- Telephone network vs. data network --- p.2 / Chapter 1.1.2 --- Bandwidth in networks --- p.3 / Chapter 1.2 --- Taxonomy of flows --- p.4 / Chapter 1.3 --- Effect of heterogeneity and proposed solution --- p.4 / Chapter 1.3.1 --- Cause and effect of heterogeneity --- p.4 / Chapter 1.3.2 --- TCP-friendly congestion control as a solution --- p.5 / Chapter 1.3.3 --- Distributed admission control as a solution --- p.6 / Chapter 1.3.4 --- Evaluation methodology and organisation of this thesis --- p.6 / Chapter 2 --- Model of Heterogeneous Flows --- p.8 / Chapter 2.1 --- The network --- p.8 / Chapter 2.2 --- Elastic flows --- p.8 / Chapter 2.3 --- Inelastic flows --- p.10 / Chapter 2.4 --- Stochastic Flows --- p.11 / Chapter 2.5 --- Controls --- p.12 / Chapter 2.5.1 --- Congestion control for elastic flows --- p.12 / Chapter 2.5.2 --- No control for inelastic flows --- p.13 / Chapter 2.5.3 --- Congestion control for inelastic flows --- p.14 / Chapter 2.5.4 --- Admission control for inelastic flows --- p.15 / Chapter 2.5.5 --- Admission control for inelastic flows with continuous assurance --- p.16 / Chapter 2.6 --- Markov chain model of control schemes --- p.17 / Chapter 2.6.1 --- Normalisation --- p.17 / Chapter 2.6.2 --- Control schemes and Markov chains --- p.18 / Chapter Part II --- Evaluation / Chapter 3 --- Stability of network under different controls --- p.29 / Chapter 3.1 --- Stability of queues --- p.29 / Chapter 3.2 --- Stability of the Markov chain models --- p.30 / Chapter 3.2.1 --- Observation of stability from simulation --- p.30 / Chapter 3.3 --- Informal discussion of stability --- p.33 / Chapter 4 --- Bandwidth allocation --- p.35 / Chapter 4.1 --- Aggregated bandwidth --- p.35 / Chapter 4.2 --- Bandwidth per flow --- p.37 / Chapter 5 --- Evaluation based on utility functions --- p.40 / Chapter 5.1 --- Properties of utility function --- p.40 / Chapter 5.1.1 --- Utility for elastic flows --- p.40 / Chapter 5.1.2 --- Utility for inelastic flows --- p.41 / Chapter 5.1.3 --- Utility throughput --- p.41 / Chapter 5.1.4 --- Choice of utility function --- p.43 / Chapter 5.2 --- Degree of elasticity --- p.45 / Chapter 5.3 --- Homogeneous environment --- p.46 / Chapter 5.4 --- Heterogeneous environment --- p.49 / Chapter 5.4.1 --- Comparison for different offered load --- p.50 / Chapter 5.4.2 --- Effect of scaling --- p.52 / Chapter 5.4.3 --- Sensitivity to α and ε --- p.57 / Chapter 6 --- Blocking probability --- p.62 / Chapter 6.1 --- Formulating admission behaviour into PCDSDE --- p.62 / Chapter 6.2 --- Evaluation of the blocking probability --- p.64 / Chapter 6.3 --- Verification by simulation --- p.66 / Chapter 6.3.1 --- Comparison for different offered load --- p.66 / Chapter 6.3.2 --- Effect of scaling --- p.68 / Chapter 6.3.3 --- Sensitivity to α and ε --- p.68 / Chapter 7 --- Population --- p.74 / Chapter 7.1 --- Mean number of inelastic flows --- p.74 / Chapter 7.2 --- Mean number of elastic flows --- p.75 / Chapter 7.2.1 --- Elastic population after scaling --- p.79 / Chapter 7.2.2 --- Effect of aggressiveness --- p.79 / Chapter 7.2.3 --- Effect of α --- p.82 / Chapter Part III --- Conclusion / Chapter 8 --- Conclusion --- p.85 / Chapter 8.1 --- Summary --- p.85 / Chapter 8.2 --- Implication --- p.87 / Chapter 8.3 --- Future Work --- p.88 / Appendices / Chapter A --- Glossary --- p.91 / Chapter B --- Introduction to Poisson counter driven stochastic differential equations --- p.97 / Chapter C --- Simulation --- p.101 / References --- p.103 / Index --- p.105

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Wong, Chi Hang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 78-80). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview on traffic classification algorithms --- p.2 / Chapter 1.1.1 --- Port based approach --- p.2 / Chapter 1.1.2 --- Payload based approach --- p.2 / Chapter 1.1.3 --- Transport layer information based approach --- p.3 / Chapter 1.2 --- Operating model of traffic classification algorithms --- p.3 / Chapter 1.3 --- Previous related works --- p.4 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Network topology and traffic capturing model --- p.5 / Chapter 2.2 --- Proposed Scheme --- p.6 / Chapter 2.3 --- Analysis on different categories --- p.9 / Chapter 3 --- Objectives --- p.11 / Chapter 3.1 --- Computing Power and Memory --- p.11 / Chapter 3.1.1 --- A rough analysis on the complexity --- p.12 / Chapter 3.2 --- Experiments on the complexity --- p.13 / Chapter 3.2.1 --- Operating Model : batch processing --- p.16 / Chapter 4 --- Computing Power and Memory : parallel processing --- p.22 / Chapter 4.1 --- Goals --- p.22 / Chapter 4.2 --- Parallel processing --- p.23 / Chapter 4.3 --- System Architecture --- p.24 / Chapter 4.4 --- Advantage --- p.26 / Chapter 4.5 --- Practical adjustment --- p.29 / Chapter 4.6 --- The alternative System Architecture --- p.30 / Chapter 5 --- Operating Model : from batch processing to online --- p.34 / Chapter 5.1 --- Goals --- p.34 / Chapter 5.2 --- Proposed model --- p.35 / Chapter 5.3 --- Delay comparasion --- p.35 / Chapter 5.4 --- Performance and accuracy issue --- p.38 / Chapter 5.5 --- Trade off between delay and accuracy --- p.43 / Chapter 6 --- Evaluation --- p.46 / Chapter 6.1 --- Final Prototype --- p.46 / Chapter 6.2 --- Online processing --- p.48 / Chapter 7 --- Others --- p.55 / Chapter 7.1 --- Special cases for network topology --- p.55 / Chapter 7.2 --- Further optimizations for BLINC --- p.56 / Chapter 7.3 --- Study on port-based approach --- p.66 / Chapter 7.4 --- Study on the information used in different algorithms --- p.70 / Chapter 7.5 --- Future works --- p.76 / Chapter 8 --- Conclusion --- p.77 / Bibliography --- p.78

As the number of vehicles increases rapidly, traffic congestion has become a serious problem in a city. Over the past years, a considerable number of studies have been made on traffic light setting. The traffic light setting problem is to investigate how to set the given traffic lights such that the total waiting time of vehicles on the roads is minimized. In this thesis, we use a graph model to represent the traffic network. On this model, some characteristics of the setting problem can be presented and analyzed. We first devise a branch and bound algorithm for obtaining the optimal solution of the traffic light setting problem. In addition, the genetic algorithm (GA), the particle swarm optimization (PSO) and the ant colony optimization (ACO) algorithm are also adopted to get the near optimal solution. Then, to extend this model, we add the assumption that each vehicle can change its direction. By comparing the results of various algorithms, we can study the impact of these algorithms on the traffic light setting problem. In our experiments, we also transform the map of Kaohsiung city into our graph model and test each algorithm on this graph.