Forecast based traffic signal coordination using congestion modelling and real-time data

This dissertation focusses on the implementation of a Real-Time Simulation-Based Signal Coordination module for arterial traffic, as proof of concept for the potential of integrating a new generation of advanced heuristic optimisation tools into Real-Time Traffic Management Systems. The endeavour represents an attempt to address a number of shortcomings observed in most currently marketed on-line signal setting solutions and provide better adaptive signal timings. It is unprecedented in its use of a Genetic Algorithm coupled with Continuous Dynamic Traffic Assignment as solution evaluation method, only made possible by the recently presented parallelisation strategies for the underlying algorithms. Within a fully functional traffic modelling and management framework, the optimiser is developed independently, leaving ample space for future adaptations and extensions, while relying on the best available technology to provide it fast and realistic solution evaluation based on reliable real-time supply and demand data. The optimiser can in fact operate on high quality network models that are well calibrated and always up-to-date with real-world road conditions; rely on robust, multi-source network wide traffic data, rather than being attached to single detectors; manage area coordination using an external simulation engine, rather than a na¨ıve flow propagation model that overlooks crucial traffic dynamics; and even incorporate real-time traffic forecast to account for transient phenomena in the near future to act as a feedback controller. Results clearly confirm the efficacy of the proposed method, by which it is possible to obtain relevant and consistent corridor performance improvements with respect to widely known arterial bandwidth maximisation techniques under a range of different traffic conditions. The computational efforts involved are already manageable for realistic real-world applications, and future extensions of the presented approach to more complex problems seem within reach thanks to the load distribution strategies already envisioned and prepared for in the context of this work

Real-Time Forecasting/Control of Water Resource Systems; Selected Papers from an IIASA Workshop, October 18-21,1976

When water resource systems are not under control, the consequences can be devastating. In the United States alone, flood damage cost approximately $1.5 billion annually. These losses can be avoided by building more reservoirs to hold the flood waters, but such construction is very expensive, especially because reservoirs have already been built on the best sites. A better and less expensive alternative is the development of more effective management methods for existing water resource systems, which commonly waste approximately 20 percent of their capacities through mismanagement. Statistical models first appeared in hydrology at the beginning of the 1970s. Hydrologists began to use the techniques of time series analysis and system identification in their models, which seemed to give better results than the earlier, deterministic simulation models. In addition, real-time control of water resources was being developed at the practical level and on-line measurements of rainfall and runoff from a catchment were becoming available. The conceptual models then in use could not take advantage of measurements from the catchment, but on-line measurements now allow an operator to anticipate flood waters upstream or a water shortage downstream. This book contains selected papers from a workshop devoted to the consolidation of international research on statistically estimated models for real-time forecasting and control of water resource systems. The book is divided into three parts. The first part presents several methods of forecasting for water resource systems: distributed lag models, maximum likelihood identification, nonlinear catchment models, Kalman filtering, and self-tuning predictors. The papers in the second part present methods for controlling stream quality and stream flow, and the third part describes forecasting in the United States, the United Kingdom, and Poland

Demand Reduction and Responsive Strategies for Underground Mining

This thesis presents a demand reduction and responsive strategy for underground mining operations. The thesis starts with a literature review and background research on global energy, coal mining and the energy related issues that the mining industry face everyday. The thesis then goes on to discuss underground mine electrical power systems, data acquisition, load profiling, priority ranking, load shedding and demand side management in mining. Other areas presented in this thesis are existing energy reduction techniques, including: high efficiency motors, motor speed reduction and low energy lighting. During the thesis a data acquisition system was designed and installed at a UK Coal colliery and integrated into the mines existing supervisory control and data acquisition (SCADA) system. Design and installation problems were overcome with the construction of a test meter and lab installation and testing. A detailed explanation of the system design and installation along with the data analysis of the data from the installed system. A comprehensive load profile and load characterisation system was developed by the author. The load profiling system is comprehensive allows the definition of any type of load profile. These load profiles are fixed, variable and transient load types. The loads output and electrical demand are all taken into consideration. The load characterisation system developed is also very comprehensive. The LC MATRIX is used with the load profiles and the load characteristics to define off-line schedules. A set of unique real-time decision algorithms are also developed by the author to operate the off-line schedules within the desired objective function. MATLAB Simulation is used to developed and test the systems. Results from these test are presented. Application of the developed load profiling and scheduling systems are applied to the data collected from the mine, the results of this and the cost savings are also presented

Performing Short-Term Travel Time Prediction on Arterials

As urban centers become larger and more densely developed, their roadway networks tend to experience more severe congestion for longer periods of the day and increasingly unreliable travel times. Proactive traffic management (PTM) strategies such as proactive traffic signal control systems and advanced traveler information systems provide the potential to cost effectively improve road network operations. However, these proactive management strategies require an ability to accurately predict near-future traffic conditions. Traffic conditions can be described using a variety of measures of performance and travel time is one of the most valued by both travelers and transportation system managers. Consequently, there exists a large body of literature dedicated to methods for performing travel time prediction. The majority of the existing body of research on travel time prediction has focused on freeway travel time prediction using fixed point sensor data. Predicting travel times on signalized arterials is more challenging than on freeways mainly as a result of the higher variation of travel times in these environments. For both freeways and arterial environments, making predictions in real-time is more challenging than performing off-line predictions, mainly because of data availability issues that arise for real-time applications. Recently, Bluetooth detectors have been utilized for collecting both spatial (i.e. travel time) and fixed point (e.g. number of detections) data. Bluetooth detectors have surpassed most of the conventional travel time measuring techniques in three main capacities: (i) direct measurement of travel time, (ii) continuous collection of travel times provides large samples, and (iii) anonymous detection. Beside these advantages, there are also caveats when using these detectors: (i) the Bluetooth obtained data include different sources of outliers and measurement errors that should be filtered out before the data are used in any travel time analysis and (ii) there is an inherent time lag in acquiring Bluetooth travel times (due to the matching of the detections at the upstream and downstream sensors) that should be carefully handled in real-time applications. In this thesis, (1) the magnitude of Bluetooth travel time measurement error has been examined through a simulation framework; (2) a real-time proactive outlier detection algorithm, which is suitable for filtering out data anomalies in Bluetooth obtained travel times, has been proposed; (3) the performance of the existing real-time outlier detection algorithms has been evaluated using both field data and simulation data; and (4) two different data-driven methodologies, that are appropriate for real-time applications, have been developed to predict near future travel times on arterials using data obtained from Bluetooth detectors. The results of this research demonstrate that (1) although the mean Bluetooth travel time measurement error is sufficiently close to zero across all the examined traffic conditions, for some situations the 95% confidence interval of the mentioned error approaches 35% of the true mean travel time; (2) the proposed proactive filtering algorithm appropriately detects the Bluetooth travel time outliers in real time and outperforms the existing data-driven filtering techniques; (3) the performance of different outlier detection algorithms can be objectively quantified under different conditions using the developed simulation framework; (4) the proposed prediction approaches significantly improved the accuracy of travel time predictions for 5-minutre prediction horizon. The daily mean absolute relative errors are improved by 18% to 24% for the proposed k-NN model and 8% to 14% for the proposed Markov model; (5) prevailing arterial traffic state and its transition through the course of the day can be adequately modeled using data obtained from Bluetooth technology

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Simulation study on electrical resistance tomography using metal wall for bubble detection

Industrial process pipelines are mostly known to be constructed from metal which is a conducting material. Bubbles or gas detection are crucial in facilitating the bubble columns performance. By employing the Electrical Resistance Tomography (ERT) technique, a simulation study using COMSOL has been conducted to investigate the effect of excitation strategy, bubble sizes and locations towards the metal wall system. As for the current excitation strategy, conducting boundary protocol has to be applied when it comes to metallic vessel to overcome the grounding effect. Bubbles with a greater size than 2 mm and especially the one that is located near the wall boundary are much easier to detect. Further potential improvements to the current design and image reconstruction of the ERT system are desirable to improve the detection of small and centred bubble

The EnTrak system : supporting energy action planning via the Internet

Recent energy policy is designed to foster better energy efficiency and assist with the deployment of clean energy systems, especially those derived from renewable energy sources. To attain the envisaged targets will require action at all levels and effective collaboration between disparate groups (e.g. policy makers, developers, local authorities, energy managers, building designers, consumers etc) impacting on energy and environment. To support such actions and collaborations, an Internet-enabled energy information system called 'EnTrak' was developed. The aim was to provide decision-makers with information on energy demands, supplies and impacts by sector, time, fuel type and so on, in support of energy action plan formulation and enactment. This paper describes the system structure and capabilities of the EnTrak system

Temperature Regulation in Multicore Processors Using Adjustable-Gain Integral Controllers

This paper considers the problem of temperature regulation in multicore processors by dynamic voltage-frequency scaling. We propose a feedback law that is based on an integral controller with adjustable gain, designed for fast tracking convergence in the face of model uncertainties, time-varying plants, and tight computing-timing constraints. Moreover, unlike prior works we consider a nonlinear, time-varying plant model that trades off precision for simple and efficient on-line computations. Cycle-level, full system simulator implementation and evaluation illustrates fast and accurate tracking of given temperature reference values, and compares favorably with fixed-gain controllers.Comment: 8 pages, 6 figures, IEEE Conference on Control Applications 2015, Accepted Versio