Thesis (Ph. D.)-- University of California, Berkeley, 2004. / Includes bibliographical references (leaves 46-47). Also available online via the University of California eLibrary website (http://repositories.cdlib.org/).

Air traffic congestion problems in many areas of the world are well known and have been highly publicised in recent years. This airspace dilemma, which results in delays and other undesirable knock-on effects, is escalating at a phenomenal rate and requires immediate attention. Correspondingly, there is concern about safety standards in some worldwide airspace regions. In addition, it is imperative that the significant projected growth in air transport movements over the next two decades is accommodated. Thus, there is an urgent need to solve the current airspace problems and plan in a responsible manner to meet forecast demand. Solutions to these predicaments have been developed and are encompassed under the auspices of the term 'future air navigation systems'. The systems include technologies and procedures that merge to optimise the potential of airport and airspace resources so that the capacity, flexibility and safety of these resources are maximised, while delays and their operating costs are minimised. Future air navigation systems use automated communications. navigation and surveillance technologies to provide enhanced air traffic management through continuous information on aircraft positions and intention, so that reductions in separation are possible without compromising safety. However, confusion exists regarding what technologies and procedures constitute these future air navigation systems. Additionally, their current worldwide integration status is not as advanced as it should be and, in fact, remains largely unknown. Indeed, their successful introduction is far from guaranteed at present. Therefore, this research addresses these requirements by evaluating and improving implementation of tile systems on a global basis. Ultimately, this thesis provides a comprehensive analysis that discovers what systems are pertinent and whether or where they have been applied to date, in addition to developing and validating a framework strategy for improved introduction of the future air navigation systems around the world.

No description available.

Traffic congestion can incur serious costs on society, and reducing these costs is of high importance. This paper assesses the feasibility of a price change or other demand shift on the toll roads in Southern California in alleviating traffic on the public freeways, using California State Road 73 and Interstate 5 as a case study. State Road 73 is found to have the ability to take on additional traffic density, which would increase the flow for all drivers on both Interstate 5 and State Road 73. Alternatives to traditional tolls are also offered as potential solutions to congestion in Southern California.

In large urbanized cities, a major problem that affects the economy and health of all citizens is vehicular congestion. This is because the traffic light cycles are not adequate. In the present study, we seek to optimize traffic light cycles based on simulation, in order to improve vehicle flow. For this, the PTV Vissim 9.0 software was used as a simulator and the Synchro 10.0 software to determine the initial optimal traffic light cycle. Through several runs and having as variables the length of queues, delay times and the average speed, the optimal traffic light cycle could be found for the study area. The results obtained reflect a 14% reduction in delay times and 10% in queue lengths. On the other hand, the average vehicle speed increased by 10.56%. All this represents an improvement in the service level of the study intersections.

This paper examines the effect the Gothenburg congestion tax, which was implemented in 2013, has had on the usage of public transportation. The aim of the policy is to reduce congestion, emissions and to finance public transportation infrastructure. The analysis contains panel data over the years 2010 - 2016 in order to measure the changes for the corresponding period of time. This research will contribute to existing research with a new approach since the instrument for controlling congestion is recently implemented, and the empirics are underdeveloped within this field. The difference in difference analysis is executed to isolate the impact of the introduced congestion tax. Gothenburg act as a treatment region, and Växjö act as a control region. Växjö is used as a control region due to limitations in the collection of data; differences between the selected regions are present and are essential to recognize. Significant differences are found in the population, controlled by a generated ratio between monthly commutes and population. Parallel trends are analyzed to increase the validity of the approach and estimated to be held constant during this period. Results show increased usage of public transportation in Gothenburg by approximately five trips per citizen monthly, due to the implementation of the congestion tax. Citizens in Gothenburg avoid getting additional taxied by correcting their type of transit.

Traffic congestion (TC) is a complex issue having an adverse impact on the environment, business operations and health. Many cities are taking action to curb it. Corporations have increasingly engaged in corporate social responsibility (CSR) actions. Using corporations headquartered in the top-rated traffic congested cities in the United States, this study examines the relationship between TC and CSR. The quantitative research employed a general linear model with two datasets, traffic speed data and CSRHub ratings. The speed data was used to calculate travel time index (TTI), a measure of TC. Using Atlanta BeltLine Inc. as a case study, a phenomenological thematic approach was utilized to assess stakeholders’ viewpoints of congestion mitigation efforts in Atlanta, GA. This study adds to research on CSR by examining the effects that CSR actions have on a specific local event, e.g., TC. In addition, research reflecting on the impact of CSR on TC has not been conducted. This study aims to fill this gap. Of the four areas of CSR studied in the quantitative phase, the community, environment, and governance ratings are significantly related to TTI, with community and environment having an inverse relationship to TTI. The qualitative study showed that stakeholders struggle with TC, and that the relationship between CSR and TC is not obvious to them. This quantitative study was conducted on eighteen top-rated congested cities. Further study on other major congested cities may shed more light on CSR and TC. A future qualitative analysis can explore the viewpoint of city government. Findings in this study are expected to be a leverage point for public-private TC mitigation and to inform policies that incorporate TC reduction as a CSR indicator. Although the quantitative analysis showed that a relationship exists between CSR and TC, the literature and DOT reports revealed increased and continuous congestion in these cities. The case study of the ABI project in the qualitative research indicated that TC is an area where CSR can have a major local impact. Some corporate respondents acknowledged that TC has a business cost, however no serious steps are taken to tackle TC.

Magister Philosophiae - MPhil / There are many reasons why Cape Town is the most congested city in South Africa besides there just being “too many cars on the roads”. This paper explores Cape Town’s transport system and spatial policies and how it impacts traffic congestion as traffic congestion serves as a primary indicator of poor spatial planning and development. A desktop study was conducted with secondary data being utilized. The City of Cape Town is aware of the issue of traffic congestion and the impact that it has on commuters, especially those with little financial means. There are numerous policies and frameworks in place to combat the issue of traffic congestion, but corruption and the politicization of spatial planning hinders the resolution of the problem.

Wi-Fi (IEEE 802.11) is currently one of the primary media to access the Internet. Guaranteeing the availability of Wi-Fi networks is essential to numerous online activities, such as e-commerce, video streaming, and IoT services. Attacks on availability are generally referred to as Denial-of-Service (DoS) attacks. While there exists signif- icant literature on DoS attacks against Wi-Fi networks, most of the existing attacks are localized in nature, i.e., the attacker must be in the vicinity of the victim. The purpose of this dissertation is to investigate the feasibility of mounting global DoS attacks on Wi-Fi networks and develop effective counter-measures. First, the dissertation unveils the existence of a vulnerability at the MAC layer of Wi-Fi, which allows an adversary to remotely launch a Denial-of-Service (DoS) attack that propagates both in time and space. This vulnerability stems from a coupling effect induced by hidden nodes. Cascading DoS attacks can congest an entire network and do not require the adversary to violate any protocol. The dissertation demonstrates the feasibility of such attacks through experiments with real Wi-Fi cards, extensive ns-3 simulations, and theoretical analysis. The simulations show the attack is effective both in networks operating under fixed and varying bit rates, as well as ad hoc and infrastructure modes. To gain insight into the root-causes of the attack, the network is modeled as a dynamical system and its limiting behavior is analyzed. The model predicts that a phase transition (and hence a cascading attack) is possible when the retry limit parameter of Wi-Fi is greater or equal to 7. Next, the dissertation identifies a vulnerability at the physical layer of Wi-Fi that allows an adversary to launch cascading attacks with weak interferers. This vulnerability is induced by the state machine’s logic used for processing incoming packets. In contrast to the previous attack, this attack is effective even when interference caused by hidden nodes do not corrupt every packet transmission. The attack forces Wi-Fi rate adaptation algorithms to operate at a low bit rate and significantly degrades network performance, such as communication reliability and throughput. Finally, the dissertation proposes, analyzes, and simulates a method to prevent such attacks from occurring. The key idea is to optimize the duration of packet transmissions. To achieve this goal, it is essential to properly model the impact of MAC overhead, and in particular MAC timing parameters. A new theoretical model is thus proposed, which relates the utilization of neighboring pairs of nodes using a sequence of iterative equations and uses fixed point techniques to study the limiting behavior of the sequence. The analysis shows how to optimally set the packet duration so that, on the one hand, cascading DoS attacks are avoided and, on the other hand, throughput is maximized. The analytical results are validated by extensive ns-3 simulations. A key insight obtained from the analysis and simulations is that IEEE 802.11 networks with relatively large MAC overhead are less susceptible to cascading DoS attacks than networks with smaller MAC overhead.

The purpose of this dissertation is to develop models and solution approaches to identify the critical hierarchies of railroad and surface transportation network infrastructures, and to facilitate re-routing options that will be necessary for traffic management decision makings in the event of a disaster. We focus on building mathematical models for routing/re-routing of traffic considering the congestion effects which are obvious in the disrupted networks due to disaster. Based on these models, the critical hierarchies of infrastructures are determined. For railroad, we develop two different models: the first one considers ‘unit’ train re-routing and the other one considers Train Design approach. For intermodal system, the optimization model facilitates optimal re-routing of traffic using three surface transportation modes: highway, railway and waterway, considering the congestion characteristics of each mode. For the first model of railroad routing, the optimization model optimally routes unit trains based on a minimum cost network flow formulation with nonlinear objective function. The nonlinear objective function is approximated with a piece-wise linear function to make the model computationally tractable. The second model, known as Train Design optimization, is a highly combinatorial and complex optimization problem. The developed model’s computational complexity suggests us to use heuristic solution procedures. We develop a special heuristic algorithm to route the traffic in the congested network. In this heuristic procedure, we divide the problem into two sub-problems (SPs): SP-1 is termed as Block-to-Train Assignment (BTA) problem, and SP-2 is termed as Train Routing (TR) problem. BTA problem provides a feasible solution that includes the minimum number of required trains with the pick-up and drop-off points of the blocks carried by these trains, and TR problem ensures the optimal routing of these trains. Similar to railroad, an optimization model is developed for optimal routing/rerouting of traffic using the intermodal network. It is a mixed integer programming (MIP) problem, which is not practical to solve for real-world problem instances within reasonable amount of time. Linear relaxation to this model provides a very good lower bound closer to optimal solution. Therefore, we implemented our case-study for a realworld intermodal transportation system of five U.S. states.