Comparative field evaluation of the signal control strategy TUC in three traffic networks.

This paper describes how the recently developed network-wide real-time signal control strategy TUC has been implemented in three traffic networks with quite different traffic and control infrastructure characteristics: Chania, Greece (23 junctions); Southampton, U.K. (53 junctions); and Munich, Germany (25 junctions), where it has been compared to the respective resident real-time signal control strategies TASS, SCOOT and BALANCE. After a short outline of TUC, the paper describes the three application networks; the application, demonstration and evaluation conditions; as well as the comparative evaluation results. The main conclusions drawn from this high-effort inter-European undertaking is that TUC is an easy-to-implement, inter-operable, low-cost real-time signal control strategy whose performance, after limited fine-tuning, proved to be better or, at least, similar to the ones achieved by long-standing strategies that were in most cases very well fine-tuned over the years in the specific networks.

Defining the Resolution of a Network for Transportation Analyses: a New Method to Improve Transportation Planning Decisions

Travel demand models are important tools used in the analysis of transportation plans, projects, and policies. The modeling results are useful for transportation planners making transportation decisions and for policy makers developing transportation policies. Defining the level of detail (i.e., the number of roads) of the transport network in consistency with the travel demand model’s zone system is crucial to the accuracy of modeling results. However, travel demand modelers have not had tools to determine how much detail is needed in a transport network for a travel demand model. This dissertation seeks to fill this knowledge gap by (1) providing methodology to define an appropriate level of detail for a transport network in a given travel demand model; (2) implementing this methodology in a travel demand model in the Baltimore area; and (3) identifying how this methodology improves the modeling accuracy. All analyses identify the spatial resolution of the transport network has great impacts on the modeling results. For example, when compared to the observed traffic data, a very detailed network underestimates traffic congestion in the Baltimore area, while a network developed by this dissertation provides a more accurate modeling result of the traffic conditions. Through the evaluation of the impacts a new transportation project has on both networks, the differences in their analysis results point out the importance of having an appropriate level of network detail for making improved planning decisions. The results corroborate a suggested guideline concerning the development of a transport network in consistency with the travel demand model’s zone system. To conclude this dissertation, limitations are identified in data sources and methodology, based on which a plan of future studies is laid out.

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.

Geothermal district heating networks: modelling novel operational strategies incorporating heat storage

The value of integrating a heat storage into a geothermal district heating system has been investigated. The behaviour of the system under a novel operational strategy has been simulated focusing on the energetic, economic and environmental effects of the new strategy of incorporation of the heat storage within the system. A typical geothermal district heating system consists of several production wells, a system of pipelines for the transportation of the hot water to end-users, one or more re-injection wells and peak-up devices (usually fossil-fuel boilers). Traditionally in these systems, the production wells change their production rate throughout the day according to heat demand, and if their maximum capacity is exceeded the peak-up devices are used to meet the balance of the heat demand. In this study, it is proposed to maintain a constant geothermal production and add heat storage into the network. Subsequently, hot water will be stored when heat demand is lower than the production and the stored hot water will be released into the system to cover the peak demands (or part of these). It is not intended to totally phase-out the peak-up devices, but to decrease their use, as these will often be installed anyway for back-up purposes. Both the integration of a heat storage in such a system as well as the novel operational strategy are the main novelties of this thesis. A robust algorithm for the sizing of these systems has been developed. The main inputs are the geothermal production data, the heat demand data throughout one year or more and the topology of the installation. The outputs are the sizing of the whole system, including the necessary number of production wells, the size of the heat storage and the dimensions of the pipelines amongst others. The results provide several useful insights into the initial design considerations for these systems, emphasizing particularly the importance of heat losses. Simulations are carried out for three different cases of sizing of the installation (small, medium and large) to examine the influence of system scale. In the second phase of work, two algorithms are developed which study in detail the operation of the installation throughout a random day and a whole year, respectively. The first algorithm can be a potentially powerful tool for the operators of the installation, who can know a priori how to operate the installation on a random day given the heat demand. The second algorithm is used to obtain the amount of electricity used by the pumps as well as the amount of fuel used by the peak-up boilers over a whole year. These comprise the main operational costs of the installation and are among the main inputs of the third part of the study. In the third part of the study, an integrated energetic, economic and environmental analysis of the studied installation is carried out together with a comparison with the traditional case. The results show that by implementing heat storage under the novel operational strategy, heat is generated more cheaply as all the financial indices improve, more geothermal energy is utilised and less fuel is used in the peak-up boilers, with subsequent environmental benefits, when compared to the traditional case. Furthermore, it is shown that the most attractive case of sizing is the large one, although the addition of the heat storage most greatly impacts the medium case of sizing. In other words, the geothermal component of the installation should be sized as large as possible. This analysis indicates that the proposed solution is beneficial from energetic, economic, and environmental perspectives. Therefore, it can be stated that the aim of this study is achieved in its full potential. Furthermore, the new models for the sizing, operation and economic/energetic/environmental analyses of these kind of systems can be used with few adaptations for real cases, making the practical applicability of this study evident. Having this study as a starting point, further work could include the integration of these systems with end-user demands, further analysis of component parts of the installation (such as the heat exchangers) and the integration of a heat pump to maximise utilisation of geothermal energy.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Turbocharging Networks in the Public Sector

Networks have come to occupy a key position in the strategic armoury of the government, business and community sectors and now have impact on a broad array of policy and management arenas. An emphasis on relationships, trust and mutuality mean that networks function on a different operating logic to the conventional processes of government and business. It is therefore important that organizational members of networks are able to adopt the skills and culture necessary to operate successfully under these distinctive kinds of arrangements. Because networks function from a different operational logic to traditional bureaucracies, public sector organizations may experience difficulties in adapting to networked arrangements. Networks are formed to address a variety of social problems or meet capability gaps within organizations. As such they are often under pressure to quickly produce measurable outcomes and need to form rapidly and come to full operation quickly. This paper presents a theoretical exploration of how diverse types of networks are required for different management and policy situations and draws on a set of public sector case studies to understand/demonstrate how these various types of networked arrangements may be ‘turbo-charged’ so that they more quickly adopt the characteristics necessary to deliver required outcomes.