Construction ¡§traffic control system of high-speed road network Northern traffic information management and coordination command center systems engineering" Vendor, RPTI, due to execute his right to stop, causing to others under construction engineering progress into delay or even halt, Furthermore, causing irreparable damage to the public interest. This action will also affect either the progress to be procrastinating, or failure of the entire project of construction plan. In a word, it stalls off the development of the ITS industrial. This paper aimed at studying the case manufacturers suffer administrative relief and derivative right to stop financial crises, and finally to the brink of bankruptcy. Stopped right disputing caused by uncertain legal concept of cognitive difference. Once the vendor has been suspended for the execution of the contract likely to have non-compliance or abuse of the fact that compliance; legal system face the compliance phase of the case as a stop right to the subject, but also derivative is easy to mediation and complaint competing, as well as administrative litigation court rejected status; complaint Review lengthy process to consider the judgment as if the provisions of the appeal decision has resulted in the responsible entity procurement operations distress. The vendor will be published in the Government Procurement Gazette by the responsible organization. As the result, it might be more difficulty to maintain the business; moreover, the published not only announce to loss of right of government bidding; even worse, will affect the company¡¦s Goodwill and survival of business. The Suspended punishment of the Government Procurement Law system, whether is appropriateness, and the further influencing factors of overall competitiveness. Made by MICHAEL PORTER¡¦s diamond theory, "stop right" under the "other external factors opportunities (opportunity and Government policy) "An analysis of the State ITS industrial competitiveness. Diamond combination of the four factors is well and available, but opportunities exist does not necessarily help to enhance the competitiveness of industry, but counterproductive injury, will allow businesses or companies collapse, or even disappear. As the ITS industry as the government established policy of the Department of National Infrastructure, the opportunity this factor evolved out of the results of the company, the enterprise, the whole industry chain and even the country's competitiveness will be immeasurable. Therefore, this article will discuss actual cases of the case manufacturer¡¦s discussion. RPTI is to the brink of bankruptcy, forced to dissolve, engineers, technicians. Subsequently, a large number of employees left, the company¡¦s core values (the process of information technology and intelligent transportation systems technology and system integration capabilities) gone, the competitiveness of the domestic intelligent transportation industry will thus setback

Recurrent congestion in urban traffic networks, especially on arterials, is a growing problem. Non-recurrent congestion, mainly due to incidents, only aggravates the problem. Any solution requires monitoring of the network, for which many developing countries, such as Turkey, do not have the traditional surveillance systems on arterials mainly due to high costs. An alternative solution is the utilization of Global Positioning System (GPS) technology, which is increasingly used in traffic monitoring. It is easy and cheap to obtain the GPS track information,even in real-time, from a probe-vehicle or a fleet of vehicles / and spatial variation of speed and travel time of the vehicle(s) in a network can be determined. GPS-based data, especially with only one probe-vehicle, would not provide information on the concurrent states of upstream and downstream traffic, needed to define the state of traffic in a network. To overcome this obstacle, a methodology based on statistical analysis of archival traffic conditions obtained through different sources is proposed to analyze traffic fluctuations and identify daily traffic pattern. As a result, bottleneck and resulting queues can be detected on a corridor. Thus, it enables detection of recurrent congestion and queues that may result from incidents. The proposed methodology is tested on a corridor the roadway between METU and Kizilay of in&ouml / n&uuml / Boulevard. The results show that the methodology can effectively identify bottleneck locations on the corridor and also an incident observed during the data collection is detected correctly by the proposed algorithm.

Tez (Yüksek Lisans) - Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Yapı Eğitimi Anabilim Dalı, 2009. / Kaynakça var.

Thesis (Ph. D.)--University of California, Riverside, 2009. / Includes abstract. Title from first page of PDF file (viewed January 27, 2010). Includes bibliographical references (p. 82-87). Issued in print and online. Available via ProQuest Digital Dissertations.

M. Tech. Electrical Engineering. / Discusses the performance of CRs that can be hampered by the environment, modulation schemes and how they can be selected based on the current environment i.e link adaptation, bandwidth efficient schemes and those that are prone to noise, formulate a set of decisions and actions based on the knowledge about the current environment and its effects on propagation and how to use a game theoretic approach for fair use of the spectrum.

Many transportation systems used today are costly, slow, fragmented, and dangerous. This paper explores the inefficiencies and negative impacts associated with our current transportation systems. Simple to technologically advanced solutions are explored along with how to integrate these methods for all users in a sustainable fashion. The vision proposes a blend of scientific method, technological advancement, and common sense which is environmentally aware and integrated for all users by using the Dutch Regional and Sustainable Traffic Management Process. / text

published_or_final_version / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

published_or_final_version / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

Public transit is considered a cost-effective alternative to mitigate the effects of traffic gridlock through the implementation of innovative service designs, and deploying new smart systems for operations control and traveller information. Public transport planners use transit assignment models to predict passenger loads and levels of service. Existing transit assignment approaches have limitations in evaluating the effects of information technologies, since they are neither sensitive to the types of information that may be provided to travellers nor to the traveller’s response to that information. Moreover, they are not adequate for evaluating the impacts of Intelligent Transportation Systems (ITS) deployments on service reliability, which in turn affect passengers’ behaviour. This dissertation presents an innovative transit assignment framework, namely the MIcrosimulation Learning-based Approach to TRansit ASsignment – MILATRAS. MILATRAS uses learning and adaptation to represent the dynamic feedback of passengers’ trip choices and their adaptation to service performance. Individual passengers adjust their behaviour (i.e. trip choices) according to their experience with the transit system performance. MILATRAS introduces the concept of ‘mental model’ to maintain and distinguish between the individual’s experience with service performance and the information provided about system conditions. A dynamic transit path choice model is developed using concepts of Markovian Decision Process (MDP) and Reinforcement Learning (RL). It addresses the departure time and path choices with and without information provision. A parameter-calibration procedure using a generic optimization technique (Genetic Algorithms) is also proposed. A proof-of-concept prototype has been implemented; it investigates the impact of different traveller information provision scenarios on departure time and path choices, and network performance. A large-scale application, including parameter calibration, is conducted for the Toronto Transit Commission (TTC) network. MILATRAS implements a microsimulation, stochastic (nonequilibrium-based) approach for modelling within-day and day-to-day variations in the transit assignment process, where aggregate travel patterns can be extracted from individual choices. MILATRAS addresses many limitations of existing transit assignment models by exploiting methodologies already established in the areas of traffic assignment and travel behaviour modeling. Such approaches include the microsimulation of transportation systems, learning-based algorithms for modelling travel behaviour, agent-based representation for travellers, and the adoption of Geographical Information Systems (GIS). This thesis presents a significant step towards the advancement of the modelling for the transit assignment problem by providing a detailed operational specification for an integrated dynamic modelling framework – MILATRAS.

Public transit is considered a cost-effective alternative to mitigate the effects of traffic gridlock through the implementation of innovative service designs, and deploying new smart systems for operations control and traveller information. Public transport planners use transit assignment models to predict passenger loads and levels of service. Existing transit assignment approaches have limitations in evaluating the effects of information technologies, since they are neither sensitive to the types of information that may be provided to travellers nor to the traveller’s response to that information. Moreover, they are not adequate for evaluating the impacts of Intelligent Transportation Systems (ITS) deployments on service reliability, which in turn affect passengers’ behaviour. This dissertation presents an innovative transit assignment framework, namely the MIcrosimulation Learning-based Approach to TRansit ASsignment – MILATRAS. MILATRAS uses learning and adaptation to represent the dynamic feedback of passengers’ trip choices and their adaptation to service performance. Individual passengers adjust their behaviour (i.e. trip choices) according to their experience with the transit system performance. MILATRAS introduces the concept of ‘mental model’ to maintain and distinguish between the individual’s experience with service performance and the information provided about system conditions. A dynamic transit path choice model is developed using concepts of Markovian Decision Process (MDP) and Reinforcement Learning (RL). It addresses the departure time and path choices with and without information provision. A parameter-calibration procedure using a generic optimization technique (Genetic Algorithms) is also proposed. A proof-of-concept prototype has been implemented; it investigates the impact of different traveller information provision scenarios on departure time and path choices, and network performance. A large-scale application, including parameter calibration, is conducted for the Toronto Transit Commission (TTC) network. MILATRAS implements a microsimulation, stochastic (nonequilibrium-based) approach for modelling within-day and day-to-day variations in the transit assignment process, where aggregate travel patterns can be extracted from individual choices. MILATRAS addresses many limitations of existing transit assignment models by exploiting methodologies already established in the areas of traffic assignment and travel behaviour modeling. Such approaches include the microsimulation of transportation systems, learning-based algorithms for modelling travel behaviour, agent-based representation for travellers, and the adoption of Geographical Information Systems (GIS). This thesis presents a significant step towards the advancement of the modelling for the transit assignment problem by providing a detailed operational specification for an integrated dynamic modelling framework – MILATRAS.