The purpose of this research is to explore the operational characteristics of a Maglev-based Automated Highway System and how it would interact with freeway operations. The extension of traditional traffic flow phenomenon, including weaving, merging, and stopping distance, into the automated system is looked at. These are also extended into platoon operations and their effect on such properties as gap control and ultimately the capacity of such a system. The ability to incorporate an AHS system into the existing Interstate Highway System is investigated. This includes placing the magways in the right-of-way of the highway system and interfacing the AHS with the existing freeways. A model is developed and run to simulate the assignment of traffic between the freeway and the guideway links. Both operational concepts of user equilibrium and system optimal conditions are explored, and equations are found to estimate the amount of traffic which can be found on the links based on the total traffic volume. / Master of Science

The threat of terrorist incidents is higher than ever before and devastating acts, such as the terrorist attacks on the World Trade Center and the Pentagon, have left many concerns about the possibility of future incidents and their potential impact. Unlike some natural disasters that can be anticipated, terrorist attacks are sudden and unexpected. Even if sometimes we do have partial information about a possible attack, it is generally not known exactly where, when, or how an attack will occur. This lack of information posses great challenges on those responsible for security, specifically, on their ability to respond fast, whenever necessary with flexibility and coordination. The surface transportation system plays a critical role in responding to terrorist attacks or other unpredictable human-caused disasters. In particular, existing Intelligent Transportation Systems (ITS) can be enhanced to improve the ability of the surface transportation system to efficiently respond to emergencies and recover from disasters. This research proposes the development of new information technologies to enhance today's ITS with capabilities to improve the crisis response capabilities of the surface transportation system. The objective of this research is to develop a Smart Traffic Evacuation Management System (STEMS) that responds rapidly and effectively to terrorist threats or other unpredictable disasters, by creating dynamic evacuation plans adaptable to continuously changing traffic conditions based on real-time information. The intellectual merit of this research is that the proposed STEMS will possess capabilities to support both the unexpected and unpredictable aspects of a terrorist attack and the dynamic aspect of the traffic network environment. Studies of related work indicate that STEMS is the first system that automatically generates evacuation plans, given the location and scope of an incident and the current traffic network conditions, and dynamically adjusts the plans based on real-time information received from sensors and other surveillance technologies. Refining the plans to keep them consistent with the current conditions significantly improves evacuation effectiveness. The changes that STEMS can handle range from slow, steady variations in traffic conditions, to more sudden variations caused by secondary accidents or other stochastic factors (e.g., high visibility events that determine a sudden increase in the density of the traffic). Being especially designed to handle evacuation in case of terrorist-caused disasters, STEMS can also handle multiple coordinated attacks targeting some strategic area over a short time frame. These are frequently encountered in terrorist acts as they are intended to create panic and terror. Due to the nature of the proposed work, an important component of this project is the development of a simulation environment to support the design and test of STEMS. Developing analytical patterns for modeling traffic dynamics has been explored in the literature at different levels of resolution and realism. Most of the proposed approaches are either too limited in representing reality, or too complex for handling large networks. The contribution of this work consists of investigating and developing traffic models and evacuation algorithms that overcome both of the above limitations. Two of the greatest impacts of this research in terms of science are as follows. First, the new simulation environment developed for this project provides a test bed to facilitate future work on traffic evacuation systems. Secondly, although the models and algorithms developed for STEMS are targeted towards traffic environments and evacuation, their applicability can be extended to other environments (e.g., building evacuation) and other traffic related problems (e.g., real-time route diversion in case of accidents). One of the broader impacts of this research would be the deployment of STEMS in a real environment. This research provides a fundamental tool for handling emergency evacuation for a full range of unpredictable incidents, regardless of cause, origin and scope. Wider and swifter deployment of STEMS will support Homeland Security in general, and will also enhance the surface transportation system on which so many Homeland Security stakeholders depend.

The Flexible Low-cost Automated Scaled Highway (FLASH) laboratory at the Virginia Tech Transportation Institute (VTTI) is one of many facilities dedicated to the field of Intelligent Transportation Systems (ITS). The goal of the FLASH lab is to provide small-scale development and implementation of autonomous control strategies for today's vehicles. The current controller used on the scale vehicles is based solely on the kinematics of the system. This body of work was aimed to develop a dynamic control law to enhance the performance of the existing kinematic controller. This control system is intended to automatically maintain the vehicle's alignment on the road as well as keep the speed of the vehicle constant. Implementation of such systems could conceivably reduce driver fatigue by removing nearly all the burden of the driving process from the driver while on the highway. System dynamics of car-like robots with nonholonomic constraints were employed in this research to create a controller for an autonomous path following vehicle. The application of working kinematic and dynamic models describing car-like robotic systems allowed the development of a nonlinear controller. Simulations of the vehicle and controller were done using MATLAB. Comparisons of the kinematic controller and the dynamic controller presented here were also done. In order to make the simulations model the actual system more closely, measures were taken to approximate actual sensor readings. / Master of Science

Thesis (M.S.)--University of Wyoming, 2009. / Title from PDF title page (viewed on July 22, 2010). Includes bibliographical references (p. 137-139).

This thesis project presents the web service choreography approach used for the composition of web services. It leverages the CHOReVOLUTION platform, a future-oriented and scalable platform, that is used to design and deploy web service choreographies. By using this platform, a use case that falls into the ITS domain is developed. This use case highlights the benefits of the web service choreography when used for the development of ITS applications. The necessary web services are designed and their interactions are defined through a choreography diagram that graphically represents how the services should collaborate together to fulfill a specific goal. By using the choreography diagram as input to the platform and by registering the web services on a web server, the choreography is deployed over the platform. The resulted choreography is tested in terms of services coordination. It is demonstrated that the platform can generate specific components that are interposed between the services and are able to take care of the services coordination for the use case created. Moreover, the execution time required to complete the choreography is measured, analyzed and reported under different conditions. Finally, it is shown that the execution time varies depending on the data that the services have to process and that the processing of huge data sets may lead to high execution times. / Detta examensarbete behandlar hur man med hjälp koreografering av webbtjänster kan komponera webbtjänster. Det använder sig av CHOReVOLUTION plattformen, en framåtblickande och skalbar plattform, som används för att designa och verkställa koreografering av webbtjänster. Med denna plattform skapas ett användningsfall inom ITS-området. Detta fall belyser fördelarna med webbtjänskoreografi i samband med utveckling av ITS- applikationer. De nödvändiga webbtjänsterna designas och deras samspel definieras genom ett diagram för koreografin, som på ett grafiskt vis presenterar hur tjänsterna skall kollaborera för att nå ett specifikt mål. Genom att mata plattformen med data från diagrammet, och genom att registrera webbtjänster på en webbserver, verkställs koreografin. Med resultatet testas koordineringen av tjänsterna. I detta examensarbete visas det att plattformen kan skapa specifika komponenter som interagerar med tjänsterna, samt sköta koordineringen av tjänster som krävs för detta användningsfall. Exekveringstiden mäts, analyseras och rapporteras under flera olika omständigheter. Det demonstreras också att exekveringstiden varierar beroende på den data som tjänsterna måste behandla, och hur behandlingen av mycket stora datamängder kan leda till långa exekveringstider.

The objective of the research described in this dissertation is to improve the travel-time prediction process using machine learning methods for the Advanced Traffic In-formation Systems (ATIS). Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. The increased demand of the traffic flow has motivated the need for development of improved applications and frameworks, which could alleviate the problems arising due to traffic flow, without the need of addition to the roadway infrastructure. In this thesis, the basic building blocks of the travel-time prediction models are discussed, with a review of the significant prior art. The problem of travel-time prediction was addressed by different perspectives in the past. Mainly the data-driven approach and the traffic flow modeling approach are the two main paths adopted viz. a viz. travel-time prediction from the methodology perspective. This dissertation, works towards the im-provement of the data-driven method. The data-driven model, presented in this dissertation, for the travel-time predic-tion on freeways was based on wavelet packet decomposition and support vector regres-sion (WPSVR), which uses the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indi-cate that the wavelet reconstructed coefficients when used as an input to the support vec-tor machine for regression (WPSVR) give better performance (with selected wavelets on-ly), when compared against the support vector regression (without wavelet decomposi-tion). The data used in the model is downloaded from California Department of Trans-portation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5 minute intervals over a distance of 9.13 miles. The results indicate an improvement in accuracy when compared against the classical SVR method. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is present-ed with interchangeable prediction methods along with the details of the Matlab applica-tion used to implement the WPSVR algorithm. The initial results are computed over the set of 42 wavelets. To reduce the compu-tational cost involved in transforming the travel-time data into the set of wavelet packets using all possible mother wavelets available, a methodology of filtering the wavelets is devised, which measures the cross-correlation and redundancy properties of consecutive wavelet transformed values of same frequency band. An alternate configuration of travel-time prediction on freeways using the con-cepts of cloud computation is also presented, which has the ability to interchange the pre-diction modules with an alternate method using the same time-series data. Finally, a graphical user interface is described to connect the Matlab environment with the Caltrans data server for online travel-time prediction using both SVR and WPSVR modules and display the errors and plots of predicted values for both methods. The GUI also has the ability to compute forecast of custom travel-time data in the offline mode.

As the transportation sector fully integrates information technology, transit agencies face decisions that expose them to new technologies, relationships and risks. Accompanying a rise in transit-related web and mobile applications, a set of competing real-time transit data standards from both public and private organizations have emerged. The purpose of this research is to understand the standard-setting processes for these data standards and the forces that move the transit industry towards the widespread adoption of a data standard. This project will analyze through case studies and interviews with members of standard-setting organizations the development of three real-time transit data standards: (1) the development of the General Transit Feed Specification Realtime (GTFS-realtime), (2) the Service Interface for Real Time Information (SIRI), and (3) Transit Communications Interface Profiles (TCIP). The expected outcome of this research is an assessment of federal policy on standards development as well as an analysis of current and future trends in this sector—both technical and institutional. The results will inform federal transit policy and future action in standards-setting and intelligent transportation systems (ITS) requirements, identifying the potential catalysts that will increase the effectiveness of federal- and agency-level programs.

The purpose of this research is to develop a conception of a transportation system called AVCS maglev which is the synergistic combination of two promising concepts, AVCS and Maglev, and to assess its potential as a transportation strategy to cope with the forthcoming challenge of the mobility, safety, environmental protection, and economic growth of the United States. The emphases are put on investigating suitable technological aspects, selecting suitable operational control concepts, assessing economic viability, and determining socioeconomic impacts of the system. Also, the National Development Model (NDM) is developed and analyzed to obtain a deeper understanding of the rational policy formation about the U.S. SOCioeconomic development of the next century, based on the premise that development means improving both quantity of life and quality of life. NDM is organized into six sectors: (1) Industrial Sector, (2) Environmental Sector, (3) Infrastructure Sector, (4) Social Development Sector, (5) Demographic Sector, and (6) Employment Sector. Four policy alternatives are identified, based on the key issues relevant to the future development patterns, and analyzed by computer simulation: (1) Social Development Policy, (2) Industrial Development Policy, (3) Infrastructure Development Policy, and (4) Environmental Protection Policy. / Ph. D.

Tragically, traffic accidents involving drivers, motorcyclists and pedestrians result in thousands of fatalities worldwide each year. For this reason, making improvements to road safety and saving people's lives is an international priority. In recent years, this aim has been supported by Intelligent Transport Systems, offering safety systems and providing an intelligent driving environment. The development of wireless communications and mobile ad hoc networks has led to improvements in intelligent transportation systems heightening these systems' safety. Vehicular ad hoc Networks comprise an important technology; included within intelligent transportation systems, they use dedicated short-range communications to assist vehicles to communicate with one another, or with those roadside units in range. This form of communication can reduce road accidents and provide a safer driving environment. A major challenge has been to design an ideal system to filter relevant contextual information from the surrounding environment, taking into consideration the contributory factors necessary to predict the likelihood of a crash with different levels of severity. Designing an accurate and effective pre-crash system to avoid front and back crashes or mitigate their severity is the most important goal of intelligent transportation systems, as it can save people's lives. Furthermore, in order to improve crash prediction, context-aware systems can be used to collect and analyse contextual information regarding contributory factors. The crash likelihood in this study is considered to operate within an uncertain context, and is defined according to the dynamic interaction between the driver, the vehicle and the environment, meaning it is affected by contributory factors and develops over time. As a crash likelihood is considered to be an uncertain context and develops over time, any usable technology must overcome this uncertainty in order to accurately predict crashes. This thesis presents a context-aware pre-crash collision prediction system, which captures information from the surrounding environment, the driver and other vehicles on the road. It utilises a Dynamic Bayesian Network as a reasoning model to predict crash likelihood and severity level, whether any crash will be fatal, serious, or slight. This is achieved by combining the above mentioned information and performing probabilistic reasoning over time. The thesis introduces novel context aware on-board unit architecture for crash prediction. The architecture is divided into three phases: the physical, the thinking and the application phase; these which represent the three main subsystems of a context-aware system: sensing, reasoning and acting. In the thinking phase, a novel Dynamic Bayesian Network framework is introduced to predict crash likelihood. The framework is able to perform probabilistic reasoning to predict uncertainty, in order to accurately predict a crash. It divides crash severity levels according to the UK department for transport, into fatal, serious and slight. GeNIe version 2.0 software was used to implement and verify the Dynamic Bayesian Network model. This model has been verified using both syntactical and real data provided by the UK department for transport in order to demonstrate the prediction accuracy of the proposed model and to demonstrate the importance of including a large amount of contextual information in the prediction process. The evaluation of the proposed system delivered high-fidelity results, when predicting crashes and their severity. This was judged by inputting different sensor readings and performing several experiments. The findings of this study has helped to predict the probability of a crash at different severity levels, accounting for factors that may be involved in causing a crash, thereby representing a valuable step towards creating a safer traffic network.

Thesis (MScEng)--Stellenbosch University, 2001. / ENGLISH ABSTRACT: With the increased focus on the use of innovative and state-of-the-art technology in Intelligent Transport Systems (ITS), the need for more accurate and more detailed road traffic flow data has become apparent. Data obtained from vehicle detector loops, which merely act as vehicle presence sensors, is neither reliable nor accurate enough anymore. This type of sensor poses the problem that it has to be inserted into the road surface; temporarily obstructing traffic flows, and has to be replaced after pavement reconstruction. One of the solutions to this problem is to develop a traffic surveillance system that uses video image processing. In cities where Intelligent Transport Systems are used extensively, roadways are monitored through Closed Circuit Television Cameras (CCTV) that are closely watched by traffic control centre personnel. These cameras are mounted on posts on the roadside. These cameras can serve a dual purpose, being used for both human monitoring and as inputs to Video Image Processing Systems. In this study some of the digital image processing techniques that could be used in a traffic surveillance system were investigated. This report leads the reader through the various steps in the processing of a scene by a traffic surveillance system based on feature tracking, and discusses the pitfalls and problems that are experienced. The tracker was tested using three image sequences and the results are presented in the final chapter of this report. / AFRIKAANSE OPSOMMING: Met die toenemende fokus op die gebruik van innoverende oplossings en gevorderde tegnologie in Intelligente Vervoerstelsels, het die noodsaaklikheid van akkurater en meer gedetailleerde padverkeer vloeidata duidelik geword. Data wat verkry word d.m.v. voertuig deteksie lusse, wat alleenlik voertuig teenwoordigheid/afwesigheid meet, is nie meer akkuraat of betroubaar genoeg nie. Hierdie tipe sensors het egter die nadeel dat dit in die plaveisel ingesny moet word, dus vloei tydelik kan belemmer, en moet vervang word elke keer as plaveisel rekonstruksie gedoen word. Een van die oplossings vir hierdie probleem is om 'n verkeers waarnemingstelsel te ontwikkel wat van videobeeldverwerking gebruik maak. In stede waar van uitgebreide intelligente verkeerstelsels gebruik gemaak word, word paaie gemonitor d.m.v. geslote baan televisiekameras wat op pale langs die paaie aangebring is. Personeellede van die verkeers beheer sentrum hou dan die inkomende televisiebeelde dop. Hierdie kameras kan 'n dubelle rol vervul deurdat dit vir beide menslike waarneming en as invoer in 'n video-beeldverwerking stelsel gebruik kan word. In hierdie studie was verskeie digitale beeldverwerking tegnieke wat gebruik kan word in 'n verkeers waarnemingstelsel ondersoek. Hierdie verslag lei die leser deur die verskeie stappe in die verwerking van 'n toneel deur 'n verkeers waarneming stelsel wat gebaseer is op die volg van kenmerke. Die verslag beskryf ook die slaggate en probleme wat ondervind word. Die voertuig volger was getoets deur van drie reekse beelde gebruik te maak en die resultate word weergegee in die finale hoodfstuk van hierdie verslag.