Knowledge-based models for adaptive traffic management systems

This paper describes a general approach for real time traffic management support using knowledge based models. Recognizing that human intervention is usually required to apply the current automatic traffic control systems, it is argued that there is a need for an additional intelligent layer to help operators to understand traffic problems and to make the best choice of strategic control actions that modify the assumption framework of the existing systems.

Optimizing PV use through active demand side management

With recent technological developments within the field of power conditioning and the progressive decrease of incentives for PV electricity in grid-connected markets, new operation modes for PV systems should be explored beyond the traditional maximization of PV electri city feed-in. An example can be found in the domestic sector, where the use of modern PV hybrid systems combin ed with efficient electrical appliances and demand side management strategies can significantly enhance the PV value for the user. This paper presents an active demand side management system able to displace the consumer’s load curve in response to local (PV hybrid system, user) and external conditions (external grid). In this way, th e consumer becomes an “active consumer” that can also cooperate with others and the grid, increasing even more the PV value for the electrical system.

Development of the infrastructure for a multi-agent traffic management system simulation

This document contains detailed description of the design and the implementation of a multi-agent application controlling traffic lights in a city together with a system for simulating traffic and testing. The goal of this thesis is to design and build a simplified intelligent and distributed solution to the problem with the traffic in the big cities following different good practices in order to allow future refining of the model of the real world. The problem of the traffic in the big cities is still a problem that cannot be solved. Not only is the increasing number of cars a reason for the traffic jams, but also the way the traffic is organized. Usually, the intersections with traffic lights are replaced by roundabouts or interchanges to increase the number of cars that can cross the intersection in certain time. But still there are places where the infrastructure cannot be changed and the traffic light semaphores are the only way to control the car flows. In real life, the traffic lights have a predefined plan for change or they receive information from a centralized system when and how they have to change. But what if the traffic lights can cooperate and decide on their own when and how to change? Using this problem, the purpose of the thesis is to explore different agent-based software engineering approaches to design and build a non-conventional distributed system. From the software engineering point of view, the goal of the thesis is to apply the knowledge and use the skills, acquired during the various courses of the master program in Software Engineering, while solving a practical and complex problem such as the traffic in the cities.

A web-based application for the management and evaluation of tutoring requests in PBL-based massive laboratories

One important steps in a successful project-based-learning methodology (PBL) is the process of providing the students with a convenient feedback that allows them to keep on developing their projects or to improve them. However, this task is more difficult in massive courses, especially when the project deadline is close. Besides, the continuous evaluation methodology makes necessary to find ways to objectively and continuously measure students' performance without increasing excessively instructors' work load. In order to alleviate these problems, we have developed a web service that allows students to request personal tutoring assistance during the laboratory sessions by specifying the kind of problem they have and the person who could help them to solve it. This service provides tools for the staff to manage the laboratory, for performing continuous evaluation for all students and for the student collaborators, and to prioritize tutoring according to the progress of the student's project. Additionally, the application provides objective metrics which can be used at the end of the subject during the evaluation process in order to support some students' final scores. Different usability statistics and the results of a subjective evaluation with more than 330 students confirm the success of the proposed application.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Nutrient Load Reduction: Challenges facing the Chesapeake 2000 Agreement

The Chesapeake Bay is the largest estuary in the United States supporting a complex ecosystem that sustains many habitats and the organisms that depend on them. The bay also supports economic, recreational, and cultural activities to over 16 million people residing in the watershed. Changes within the watershed have caused excessive levels of nutrients, mainly nitrogen and phosphorous, to pollute the bay. The Chesapeake Bay Program, guided by a complex agreement, was created to address these and other issues and oversee the restoration of the bay. The most recent version of this agreement, the Chesapeake 2000, declares its continued commitment to restore the bay with over 100 goals to be met by the year 2010. Reports show that although intensive efforts have been made to promote nutrient reduction, very little reduction has actually resulted. This project described these efforts. The final results reveal obstacles affecting progress, shortcomings to current approaches and possible solutions for future implementation.

Integrating intelligent transportation systems within the planning process: an interim handbook.

Federal Highway Administration, Office of Traffic Management and Intelligent Systems Applications, Washington, D.C.

Responsive multimodal transportation management strategies and IVHS. Final report.

Federal Highway Administration, Office of Safety and Traffic Operations Research and Development, McLean, Va.

A comparable systems analysis: comparing automated highway systems to air traffic management. Final report.

Federal Highway Administration, Washington, D.C.

Review of and preliminary guidelines for integrating transit into transportation management centers. Final report.

Transportation Systems Center, Cambridge, Mass.

National Program Plan for Intelligent Vehicle-Highway Systems (IVHS).

Mode of access: Internet.

Department of Transportation's Intelligent transportation systems (ITS) projects book.

Mode of access: Internet.

Smart highways: challenges facing DOT's intelligent vehicle highway systems program.

Mode of access: Internet.

Developing freeway and incident management systems using the national ITS architecture.

Performing organizations: Mitretek Systems and PB Farradyne, Inc.

Intelligent transportation systems benefits: 2001 update.

Transportation Department, Joint Program Office for Intelligent Transportation Systems, Washington, D.C.