Active traffic management is a hot topic for addressing issues of highway congestion. It is the use of intelligent transportation systems to provide real time traffic information on highway conditions. In Austin, the segment of Interstate 35 between Riverside Drive and State Highway 71 experiences both congestion and safety issues. This report provides an introduction into the application of active traffic management through the use of a proposed queue warning system in the area. First, select crash data on the region is highlighted to present the safety conditions, particularly the type of collision and crash severity involved. Next, a proposed queue warning system design is described. This includes a description of the equipment used, methodology for system deployment, and expected outcomes. Finally, a computer simulation testing the operational performance of the queue warning system is performed using VISSIM, and the results are reported. This report aims to demonstrate the role that queue warning system and active traffic management may play in addressing metropolitan traffic needs. / text

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.

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The Available Bit Rate (ABR) service class for the Asynchronous Transfer Mode (ATM) protocol was originally designed to manage data traffic. ABR flow control makes no guarantees concerning cell transfer delay or cell delay variation. A closed-loop feedback mechanism is used for traffic management. To use this class of service for video transport, the video source will accept feedback from the network and adapt its source rate based on this status information. The objective of this research is to assess the ability of the ATM ABR service class to deliver Moving Picture Experts Group version 1 (MPEG-1) video. Three approaches to source-level control are compared: (i) arbitrary loss or no control method, (ii) selective discard of MPEG B-pictures, and (iii) selective discard of MPEG B- and P-pictures. Performance is evaluated based on end-to-end delay, congested queue occupancy levels, network utilization, and jitter. A description of the investigation, assumptions, limitations, and results of the simulation study are included. / Master of Science

When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.

Traditionally, space situational awareness (SSA) sensor networks have relied on dynamic programming theory to generate tasking plans which govern how sensors are allocated to observe resident space objects. Deep reinforcement learning (DRL) techniques, with their ability to be trained on simulated environments, which are readily available for the SSA sensor allocation problem, and demonstrated performance in other fields, have potential to exceed performance of deterministic methods. The research presented in this dissertation develops techniques for encoding an SSA environment model to apply DRL to the sensor allocation problem. This dissertation is the compilation of two separate but related studies. The first study compares two alternative invalid action handling techniques, penalization and masking. The second study examines the performance of policies that have forecast state knowledge incorporated in the observation space. / Doctor of Philosophy / Resident space objects (RSOs) are typically tracked by ground-based sensors (telescopes and radar). Determining how to allocate sensors to RSOs is a complex problem traditionally performed by dynamic programming techniques. Deep reinforcement learning (DRL), a subset of machine learning, has demonstrated performance in other fields, and has the potential to exceed performance of traditional techniques. The research presented in this dissertation develops techniques for encoding a space situational awareness environment model to apply DRL to the sensor allocation problem. This dissertation is the compilation of two separate but related studies. The first study compares two alternative invalid action handling techniques, penalization and masking. The second study examines the performance of policies that have forecast state knowledge incorporated in the observation space.

A major traffic-related problem faced by residents is speeding, which not only causes safety concerns, but also noise issues. Traffic calming is a much favoured traffic management tool employed by road controlling authorities to primarily reduce vehicle speed, hence improve community liveability. This research aimed to investigate the impacts of traffic calming on speed, safety and traffic noise. The objectives included developing models for the prediction of speed and noise on traffic-calmed streets, and providing guidance for good design practices. Speeds of individual vehicles as they approached and crossed traffic calming devices were observed in order to identify the behaviour of individual drivers. Results indicated that the speed hump and the raised angled slow point produced the largest speed reductions and least variation in speeds, while mid-block narrowings had no significant speed changes. Inter-device speed was found to be mainly controlled by the separation between devices. 85th percentile speeds at distances from calming devices were 40 – 45 km/h for vertical deflections and 45 – 55 km/h for horizontal deflections. Speeds on approach to speed humps were found to be influenced by the distance available on the approaches, while operating speed at the speed humps were partly influenced by the hump width relative to the road width. There was evidence of safety benefits of traffic calming overall, despite mid-block crashes increasing post-calming. However, there was no association between the traffic calming and the crashes, which appeared to probably be due to other factors, human factors in particular. Noise levels produced by light vehicles across speed humps were in fact lower than on a flat section of road, given their respective mean speeds. At a reference speed of 25 km/h, noise levels produced over the 100 mm hump were 3.6 dBA higher than those produced by the 75 mm hump.

Chen, Liang. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 164-175). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Lau Kwok Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 66-67). / Abstracts in English and Chinese. / Acknowledgement --- p.1 / Abstract --- p.2 / 摘要 --- p.3 / Chapter Chapter 1 --- Introduction --- p.6 / Chapter Chapter 2 --- Background and Related Work --- p.9 / Chapter 2.1 --- Application-Layer Bandwidth Differentiation --- p.9 / Chapter 2.2 --- Related Work --- p.14 / Chapter 2.2.1 --- Bandwidth Differentation --- p.14 / Chapter 2.2.2 --- Shared Congestion Management --- p.15 / Chapter 2.2.3 --- Flow Partition --- p.16 / Chapter Chapter 3 --- VPS Protocol Architecture --- p.17 / Chapter 3.1 --- Virtual and Actual Flows --- p.18 / Chapter 3.2 --- VPS Controller --- p.21 / Chapter Chapter 4 --- ACK Translation --- p.25 / Chapter 4.1 --- Fast Retransmit and Fast Recovery --- p.27 / Chapter 4.2 --- Timeout --- p.30 / Chapter 4.3 --- Packet and ACK Reordering --- p.33 / Chapter 4.4 --- False Duplicate ACK Suppression --- p.35 / Chapter 4.5 --- Maxburst --- p.37 / Chapter 4.6 --- Memory Overhead and Computation Complexity --- p.38 / Chapter Chapter 5 --- Bandwidth Differentiation --- p.41 / Chapter 5.1 --- Distribution of Virtual Packets --- p.41 / Chapter 5.2 --- Temporary Suspension of Actual Flows --- p.43 / Chapter 5.3 --- Receive Window Limit --- p.44 / Chapter 5.4 --- Limited Data Transmission --- p.44 / Chapter Chapter 6 --- Performance Evaluatoin --- p.45 / Chapter 6.1 --- Performance Metric --- p.45 / Chapter 6.2 --- Simulation Setup --- p.46 / Chapter 6.3 --- Performance over Different Time Scales --- p.47 / Chapter 6.4 --- Performance over Different Bottleneck Bandwidth --- p.53 / Chapter 6.5 --- Performance over Different Application-specified Ratios --- p.54 / Chapter 6.6 --- Performance over Different Number of Flows --- p.57 / Chapter 6.7 --- Heterogeneous Receivers --- p.60 / Chapter Chapter 7 --- Conclusions and Future Work --- p.65 / Bibliography --- p.66

Ng Ping-chung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 71-72). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview and Motivation --- p.1 / Chapter 1.2 --- Background of Offered Load Control --- p.2 / Chapter 1.3 --- Background of Stability Control --- p.3 / Chapter 1.4 --- Organization of the Thesis --- p.4 / Chapter Chapter 2 --- Performance Problems and Solutions --- p.6 / Chapter 2.1 --- Simulation Set-up --- p.6 / Chapter 2.2 --- High Packet-Drop Rate --- p.7 / Chapter 2.3 --- Re-routing Instability --- p.8 / Chapter 2.3.1 --- Hidden-Node Problem --- p.8 / Chapter 2.3.2 --- Ineffectiveness of Solving Hidden-Node Problem with RTS/CTS …… --- p.9 / Chapter 2.4 --- Solutions to High-Packet Loss Rate and Re-routing Instability --- p.10 / Chapter 2.4.1 --- Link-Failure Re-routing --- p.11 / Chapter 2.4.2 --- Controlling Offered Load --- p.13 / Chapter 2.5 --- Verification of Simulation Results with Real-life Experimental Measurements --- p.14 / Chapter Chapter 3 --- Offered Load Control --- p.16 / Chapter 3.1 --- Capacity Limited by the Hidden-node and Exposed-node Problems --- p.16 / Chapter 3.1.1 --- Signal Capture --- p.18 / Chapter 3.1.2 --- Analysis of Vulnerable Period induced by Hidden Nodes --- p.20 / Chapter 3.1.3 --- Analysis of Vulnerable Period induced by Exposed Nodes --- p.21 / Chapter 3.1.4 --- Sustainable Throughput --- p.22 / Chapter 3.2 --- Capacity Limited by Carrier Sensing Property --- p.23 / Chapter 3.3 --- Numerical Results --- p.26 / Chapter 3.4 --- General Throughput Analysis of a Single Multi-hop Traffic Flow --- p.29 / Chapter 3.5 --- Throughput Analysis on Topologies with Variable Distances between Successive Nodes --- p.31 / Chapter Chapter 4 --- Discussions of Other Special Cases --- p.33 / Chapter 4.1 --- A Carrier-sensing Limited Example --- p.33 / Chapter 4.2 --- A Practical Solution to Improve Throughput --- p.34 / Chapter Chapter 5 --- Achieving Fairness in Other Network Topologies --- p.36 / Chapter 5.1 --- Lattice Topology --- p.36 / Chapter Chapter 6 --- Stability Control --- p.39 / Chapter 6.1 --- Ad-hoc routing protocols --- p.39 / Chapter 6.2 --- Proposed scheme --- p.40 / Chapter 6.2.1 --- Original AODV --- p.41 / Chapter 6.2.2 --- AODV with Proposed Scheme --- p.42 / Chapter 6.2.2.1 --- A Single Flow in a Single Chain of Nodes --- p.43 / Chapter 6.2.2.2 --- Real-break Case --- p.44 / Chapter 6.3 --- Improvements --- p.45 / Chapter Chapter 7 --- Impacts of Data Transmission Rate and Payload Size --- p.48 / Chapter 7.1 --- Signal Capture --- p.48 / Chapter 7.2 --- Vulnerable region --- p.50 / Chapter Chapter 8 --- Performance Enhancements in Multiple Flows --- p.53 / Chapter 8.1 --- Impacts of Re-routing Instability in Two Flow Topology --- p.53 / Chapter 8.2 --- Impacts of Vulnerable Periods in Multiple Flow Topologies --- p.55 / Chapter 8.2.1 --- The Vulnerable Period induced by Individual Hidden-terminal Flow --- p.57 / Chapter 8.2.2 --- The Number of Hidden-terminal Flows --- p.58 / Chapter 8.2.3 --- Correlation between Hidden-terminal Flows --- p.60 / Chapter Chapter 9 --- Conclusion --- p.63 / Chapter Appendix A: --- General Throughput Analysis of a Single Multi-hop Traffic Flow --- p.67 / Chapter A.l --- Capacity Limited by Hidden-node and Exposed-Node --- p.67 / Chapter A.1.1 --- Sustainable Throughput --- p.68 / Chapter A.2 --- Capacity Limited by Carrier Sensing Property --- p.68 / Bibliography --- p.71