Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013

Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

A Hybrid Multi-Criteria Analysis Model for Solving the Facility Location–Allocation Problem: a Case Study of Infectious Waste Disposal

Choosing locations for infectious waste disposal (IWD) is one of the most significant issues in hazardous waste management due to the risk imposed on the environment and human life. This risk can be the result of an undesirable location of IWD facilities. In this study a hybrid multi-criteria analysis (Hybrid MCA) model for solving the facility location–allocation (FLA) problem for IWD was developed by combining two objectives: total cost minimization and weight maximization. Based on an actual case of forty-seven hospitals and three candidate municipalities in the northeastern region of Thailand, first, the Fuzzy AHP and Fuzzy TOPSIS techniques were integrated to determine the closeness of the coefficient weights of each candidate municipality. After that, these weights were converted to weighting factors and then these factors were taken into the objective function of the FLA model. The results showed that the Hybrid MCA model can help decision makers to locate disposal centers, hospitals and incinerator size simultaneously. Besides that the model can be extended by incorporating additional selection criteria/objectives. Therefore, it is believed that it can also be useful for addressing other complex problems

Methodology for the development of a new Sustainable Infrastructure Rating System for Developing Countries (SIRSDEC)

The improvement of infrastructures in developing countries has become a priority for the most advanced economies, which have founded a broad range of international development organizations to undertake infrastructure projects worldwide. Infrastructure is the key driver that can accelerate the balance among the economic, social and environmental aspects forming the Triple Bottom Line (TBL) in these countries. Given the lack of appropriate tools to ensure the achievement of this goal, this paper describes the methodology conceived for the development of a Sustainable Infrastructure Rating System (SIRSDEC) aimed at promoting the design, construction and operation of sustainable infrastructure projects in these geographical areas. SIRSDEC is structured into a hierarchical decision-making tree consisting of three levels of elements (requirements, criteria and indicators) selected to assess infrastructure systems according to sustainability principles. The methodology on which SIRSDEC is based combines the action of two multi-criteria decision-making methods (MCDM) such as the Analytical Hierarchy Process (AHP) and the Integrated Value Model for Sustainable Assessment (MIVES). AHP is proposed to weight the elements forming the decision-making tree after processing the opinions provided by a group of international experts regarding the importance of requirements, criteria and indicators, whilst MIVES is suggested to value infrastructure projects according to their contribution to the TBL. The article emphasizes the added value provided by the combination of AHP and MIVES in the design of an ad-hoc rating system aimed at fostering the implementation of sustainable infrastructure projects in developing countries