Short term traffic flow prediction in heterogeneous condition using artificial neural network

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables. First published online: 16 Oct 201

Unsupervised tracking of time-evolving data streams and an application to short-term urban traffic flow forecasting

I am indebted to many people for their help and support I receive during my Ph.D. study and research at DIBRIS-University of Genoa. First and foremost, I would like to express my sincere thanks to my supervisors Prof.Dr. Masulli, and Prof.Dr. Rovetta for the invaluable guidance, frequent meetings, and discussions, and the encouragement and support on my way of research. I thanks all the members of the DIBRIS for their support and kindness during my 4 years Ph.D. I would like also to acknowledge the contribution of the projects Piattaforma per la mobili\ue0 Urbana con Gestione delle INformazioni da sorgenti eterogenee (PLUG-IN) and COST Action IC1406 High Performance Modelling and Simulation for Big Data Applications (cHiPSet). Last and most importantly, I wish to thanks my family: my wife Shaimaa who stays with me through the joys and pains; my daughter and son whom gives me happiness every-day; and my parents for their constant love and encouragement

A Framework for Mitigating Obsolescence in Military Based Systems

Obsolescence is an unavoidable reality in manufacturing systems and supply chain environments as systems are needed to be sustained for longer and longer periods of time. These extended life cycle products include airplanes, ships, industrial equipment, medical equipment, and military systems. The United States military has coined this issue as Diminishing Manufacturing Sources and Material Shortages (DMSMS). Research shows that the main areas of concern for obsolescence are cost optimization, obsolescence management, system life cycle, design/system refresh planning, architecture/open systems, and end-of-life (EOL) predictions. This effort suggests a need for a more effective management approach to tackling obsolescence with an emphasis on proactive management. The goal of this research was to create an obsolescence management framework for the purpose of managing obsolescence issues with military based systems. This research shows the potential for using machine learning as a life cycle forecasting tool over traditional data mining tools. The results for this small-scale case study show promising results for a larger scale experiment. Another powerful proactive strategy using machine learning is building technology refresh cycles into a system based on obsolescence risk levels. Some key areas of focus for a strong framework are funding for a robust DMSMS team, a robust supply chain, system design that factors in obsolescence risk, and consistent communication with all parties involved. It is imperative to develop an effective and data-driven approach to communicating obsolescence impacts to leadership to ensure successful mitigation of obsolescence issues. Some post-case tools and strategies include utilizing sustainment, production, and technology refresh roadmaps, along with employing data driven metrics to provide key information to leadership and demonstrate value to the customer. This study demonstrates opportunities and challenges for entities dealing with component obsolescence, methods for minimizing the issues that go along with it, and identifies best practices for obsolescence management

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data