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.

Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to queue spillbacks, which disrupt traffic flows across the entire network and increase congestion. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This manuscript presents a technology-agnostic adaptive real-time server based approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide timeliness guarantee bounds by analyzing the travel time delays, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed with robots mimicking human driving behaviors. Compared to the pre-timed traffic control and an adaptive scheduling based traffic control, our algorithm is able to avoid spillbacks under highly dynamic traffic conditions and improve the average crossing delay in most cases by 10--50 %. / Master of Science / Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to congestion that blocks other intersection upstream, which disrupt traffic flows across the entire network. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This work presents a technologyagnostic adaptive approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide theoretical bounds to guarantee the performance of our approach in terms of the travel delays that may incur on the vehicles in the system, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed which uses robots to mimic human driving behaviour in an urban environment. Comparisons with widely deployed and state-of-the-art traffic control techniques show that our approach is able to minimize spillbacks as well as improve on the average crossing delay in most cases.

Collision avoidance is treated as a game of two players with opposing desiderata. In the application to automated car-like vehicles, we will use a differential game in order to model and assess a worst-case analysis. The end result will be an almost analytic representation of a boundary between a "safe" set and a "unsafe" set. We will generalize the research in [27] to non-identical players and begin the setup of the boundary construction. Then we will consider the advantages and disadvantages of manipulation of the cost function through the solution and control techniques. In particular, we introduce a possible way to incorporate a secondary objective such as sticking to a straight path. We also look a hybrid technique to reduce steering when the opposing player is out of the reach of the vehicle; i.e., is out of the "unsafe" set and less extreme maneuvers may be desired. We first look at a terminal cost formulation and through retrograde techniques may shape this boundary between the "safe" and "unsafe" set. We would like this research, or part thereof, to be assessed and simulated on a simulation vehicle such as that used in the Flexible Low-cost Automated Scaled Highway (FLASH) at the Virginia Tech Transportation Institute (VTTI). In preparation, we briefly look at the sensor demands from this game-theoretic approach. / Master of Science

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

While increasing number of vehicles on urban roadways create uncontrolled congestion, connectivity among vehicles, traffic lights and other road-side units provide abundant data that paves avenues for novel smart traffic control mechanisms to mitigate traffic congestion and delays. However, increasingly complex vehicular applications have outpaced the computational capabilities of on-board processing units, therefore requiring novel offloading schemes onto additional resources located by the road-side. Adding connectivity and other computational resources on legacy traffic infrastructure may also introduce security vulnerabilities. To ensure that the timeliness and resource constraints of the vehicles using the roadways as well as the applications being deployed on the traffic infrastructure are met, the transportation systems needs to be more predictable. This dissertation discusses three areas that focus on improving the predictability and performance of the connected traffic infrastructure. Firstly, a holistic traffic control strategy is presented that ensures predictable traffic flow by minimizing traffic delays, accounting for unexpected traffic conditions and ensuring timely emergency vehicle traversal through an urban road network. Secondly, a vehicular edge resource management strategy is discussed that incorporates connected traffic lights data to meet timeliness requirements of the vehicular applications. Finally, security vulnerabilities in existing traffic controllers are studied and countermeasures are provided to ensure predictable traffic flow while thwarting attacks on the traffic infrastructure. / Doctor of Philosophy / Exponentially increasing vehicles especially in urban areas create pollution, delays and uncontrolled traffic congestion. However, improved traffic infrastructure brings connectivity among the vehicles, traffic lights, road-side detectors and other equipment, which can be leveraged to design new and advanced traffic control techniques. The initial work in this dissertation provides a traffic control technique that (i) reduces traffic wait times for the vehicles in urban areas, (ii) ensures safe and quick movements of emergency vehicles even through crowded areas, and (iii) ensures that the traffic keeps moving even under unexpected lane closures or roadblocks. As technology advances, connected vehicles are becoming increasingly automated. This allows the car manufacturers to design novel in-vehicle features where the passengers can now stream media-rich content, play augmented reality (AR)-based games and/or get high definition information about the surroundings on their car's display, while the car is driven through the urban traffic. This is made possible by providing additional computing resources along the road-side that the vehicles can utilize wirelessly to ensure passenger's comfort and improved experience of in-vehicle features. In this dissertation, a technique is provided to manage the computational resources which will allow vehicles (and its passengers) to use multiple features simultaneously. As the traffic infrastructure becomes increasingly inter-connected, it also allows malicious actors to exploit vulnerabilities such as modifying traffic lights, interfering with road-side sensors, etc. This can lead to increased traffic wait times and eventually bring down the traffic network. In the final work, one such vulnerability in traffic infrastructure is studied and mitigating measures are provided so that the traffic keeps moving even when an attack is detected. In all, this dissertation aims to improve safety, security and overall experience of the drivers, passengers and the pedestrians using the connected traffic infrastructure.

Intelligent transportation systems data collected on drivers who presumably participated in a study of cognitive mapping and way-finding were evaluated with two basic procedures for data coding, including analysis of video data based on the occurrence or non-occurrence of a) critical behaviors during consecutive 15 second intervals of a driving trial, and b) the safe alternative when a safe behavior opportunity was available. Methods of data coding were assessed for practical use, reliability, and sensitivity to variation in driving style. A factor analysis of at-risk driving behaviors identified a cluster of correlated driving behaviors that appeared to share a common characteristic identified as aggressive/impatient driving. The relationship between personality and driving style was also assessed. That is, analysis of the demographics and personality variables associated with the occurrence of at-risk driving behaviors revealed that driver Age and Type A personality characteristics were significant predictors of vehicle speed and following distance to the preceding vehicle. Results are discussed with regard to implications for safe driving interventions and problem behavior theory. / Ph. D.

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.