Recently, according to people's requirements for safe and congestion-free driving in the public transportation system, the intelligent transportation system (ITS) has been widely concerned. To achieve a safe and time-saving driving experience in ITS, various data sharing methods are proposed to provide traffic information for drivers to perceive their surrounding driving environment. However, the high dynamic characteristic of the vehicular network (VNET) results in a challenging environment for establishing stable communication among vehicles. To face this challenge, a Cellular network-assisted Reliable Traffic-Aware Routing protocol (CRTAR) is proposed in this thesis to provide support for vehicle’s data routing process in a heterogeneous vehicular-cellular network environment. In the method, city-wide traffic information, i.e., traffic density and data transmission density of the road segments, is introduced into vehicle's data routing process to assist the vehicle in selecting the optimal data transmission route to deliver data packets. To further improve the stability of inter-vehicle communication, the link lifetime between vehicles is also considered to select the next forwarder that can establish relatively robust communication. CRTAR takes advantage of the reliability and low-latency features of the communication technology in the cellular network and combines the cellular network with VNET to achieve real-time and reliable Vehicle-to-Infrastructure (V2I) communication. Meanwhile, it realizes the Vehicle-to-Vehicle (V2V) communication by the Dedicated Short Range Communication (DSRC) to mitigate the overload of backbone resources caused by using the cellular network. To be specific, in the method, vehicles can request city-wide traffic information via the cellular network from a cloud service that is connected to the remote data center located in the traffic management agency without latency. According to the real-time traffic information, the source vehicle can execute the data routing process with a global view of the system to calculate the data transmission route that has sufficient transmission resources to the target vehicle. The source vehicle then transmits data to the target via the vehicles in the calculated transmission route. During the forwarding process, vehicles prefer to forward the data packet to the next vehicle with a longer link lifetime. Furthermore, effective backup and recovery strategies are designed for route maintenance. The effectiveness of CRTAR is further verified by conducting simulation experiments.

The General Aviation Airport Choice model is an attempt to model General Aviation (GA) travel patterns in the US in order to provide a means of assessing the impact of General Aviation activities on the National Air Space system. The model will also serve as part of transportation planning tool to help assess the viability of deploying NASA's Small Aircraft Transportation Systems (SATS) aircraft as a competitive mode of transportation for intercity travel. The General Aviation Airport Choice model developed estimates General Aviation (GA) person-trips and number of aircraft operations given trip demand in the form of GA person trips from counties. A pseudo-gravity model is embedded in the model to distribute the inter-county person-trips to a prescribed set of airports in the US. The airport-to-airport person-trips are split into person-trips by three aircraft modes (single, multi and jet engine) using an attractiveness factor based on average occupancy, utilization and a distance distribution factor for each aircraft type and the number of aircraft based at each airport. The person-trips by aircraft type are then converted to aircraft operations using occupancy factors for each aircraft type. The final output from the model are aircraft operations trip-tables by aircraft type between the airports in the model. The GA trips are estimated in order to provide a means of assessing the impact of GA activities on the National Airspace System. The model output may be used to assess the viability of GA aircraft serving as a competitive mode of transportation for intercity travel. / Master of Science

The National Aeronautics and Space Administration (NASA) is leading a research effort to develop a Small Aircraft Transportation System (SATS) that will expand air transportation capabilities to hundreds of underutilized airports in the United States. Most of the research effort addresses the technological development of the small aircraft as well as the systems to manage airspace usage and surface activities at airports. The Federal Aviation Administration (FAA) will also play a major role in the successful implementation of SATS, however, the administration is reluctant to embrace the unproven concept. The purpose of the research presented in this dissertation is to determine if the FAA can pursue a resource management strategy that will support the current radar-based Air Traffic Control (ATC) system as well as a Global Positioning Satellite (GPS)-based ATC system required by the SATS. The research centered around the use of the System Dynamics modeling methodology to determine the future behavior of the principle components of the ATC system over time. The research included a model of the ATC system consisting of people, facilities, equipment, airports, aircraft, the FAA budget, and the Airport and Airways Trust Fund. The model generated system performance behavior used to evaluate three scenarios. The first scenario depicted the base case behavior of the system if the FAA continued its current resource management practices. The second scenario depicted the behavior of the system if the FAA emphasized development of GPS-based ATC systems. The third scenario depicted a combined resource management strategy that supplemented radar systems with GPS systems. The findings of the research were that the FAA must pursue a resource management strategy that primarily funds a radar-based ATC system and directs lesser funding toward a GPS-based supplemental ATC system. The most significant contribution of this research was the insight and understanding gained of how several resource management strategies and the presence of SATS aircraft may impact the future US Air Traffic Control system. / Ph. D.

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

Atualmente as dificuldades enfrentadas no deslocamento urbano são consideradas um dos maiores problemas, especialmente nas grandes cidades. O planejamento adequado do sistema de transporte urbano é fundamental para minimizar o tempo e os custos de deslocamento, melhorando a qualidade de vida da população e o próprio funcionamento do ambiente urbano. Esta dissertação parte da premissa de que os sistemas de simulação podem ser usados para estudar diferentes alternativas para melhorar o sistema de transportes, servindo de base para a tomada de decisão de forma a otimizar o deslocamento urbano. Assim, este trabalho apresenta o desenvolvimento de um modelo computacional para simulação do sistema de transporte urbano. O modelo proposto combina características dos modelos mesoscópicos e microscópicos, incluindo o comportamento dos usuários no planejamento da rota. Um framework para o desenvolvimento de aplicações de simulação é descrito e testado com uma implementação usando como cenário o Metropolitano de São Paulo (Metrô), considerando-se dados da pesquisa Origem-Destino para teste e validação do modelo aqui proposto. / Nowadays, the difficulties faced in urban displacement are considered a major problem, especially in big cities. Proper urban transport system planning is essential to minimize travel time and costs, improving quality of life and improving the urban environment. This master thesis starts from the premise that simulation systems can be used to study different alternatives to improve the transport system, so that decision making can be better justified and can optimize the urban displacement. Therefore, this work presents the development of a computational model for urban transportation system simulation. The model proposed targets mesoscopic and microscopic models, including user behaviors of route planning. A framework for development of simulation applications is described, with an implementation using as scenario the Metropolitano of São Paulo (Metrô) for model testing, considering data from Origem-Destino survey for testing and validation of the model here proposed.

Atualmente as dificuldades enfrentadas no deslocamento urbano são consideradas um dos maiores problemas, especialmente nas grandes cidades. O planejamento adequado do sistema de transporte urbano é fundamental para minimizar o tempo e os custos de deslocamento, melhorando a qualidade de vida da população e o próprio funcionamento do ambiente urbano. Esta dissertação parte da premissa de que os sistemas de simulação podem ser usados para estudar diferentes alternativas para melhorar o sistema de transportes, servindo de base para a tomada de decisão de forma a otimizar o deslocamento urbano. Assim, este trabalho apresenta o desenvolvimento de um modelo computacional para simulação do sistema de transporte urbano. O modelo proposto combina características dos modelos mesoscópicos e microscópicos, incluindo o comportamento dos usuários no planejamento da rota. Um framework para o desenvolvimento de aplicações de simulação é descrito e testado com uma implementação usando como cenário o Metropolitano de São Paulo (Metrô), considerando-se dados da pesquisa Origem-Destino para teste e validação do modelo aqui proposto. / Nowadays, the difficulties faced in urban displacement are considered a major problem, especially in big cities. Proper urban transport system planning is essential to minimize travel time and costs, improving quality of life and improving the urban environment. This master thesis starts from the premise that simulation systems can be used to study different alternatives to improve the transport system, so that decision making can be better justified and can optimize the urban displacement. Therefore, this work presents the development of a computational model for urban transportation system simulation. The model proposed targets mesoscopic and microscopic models, including user behaviors of route planning. A framework for development of simulation applications is described, with an implementation using as scenario the Metropolitano of São Paulo (Metrô) for model testing, considering data from Origem-Destino survey for testing and validation of the model here proposed.

Travel time reliability is deemed as one of the most important factors affecting travelers’ route choice decisions. However, existing practices mostly consider average travel time only. This dissertation establishes a methodology framework to overcome such limitation. Semi-standard deviation is first proposed as the measure of reliability to quantify the risk under uncertain conditions on the network. This measure only accounts for travel times that exceed certain pre-specified benchmark, which offers a better behavioral interpretation and theoretical foundation than some currently used measures such as standard deviation and the probability of on-time arrival. Two path finding models are then developed by integrating both average travel time and semi-standard deviation. The single objective model tries to minimize the weighted sum of average travel time and semi-standard deviation, while the multi-objective model treats them as separate objectives and seeks to minimize them simultaneously. The multi-objective formulation is preferred to the single objective model, because it eliminates the need for prior knowledge of reliability ratios. It offers an additional benefit of providing multiple attractive paths for traveler’s further decision making. The sampling based approach using archived travel time data is applied to derive the path semi-standard deviation. The approach provides a nice workaround to the problem that there is no exact solution to analytically derive the measure. Through this process, the correlation structure can be implicitly accounted for while simultaneously avoiding the complicated link travel time distribution fitting and convolution process. Furthermore, the metaheuristic algorithm and stochastic dominance based approach are adapted to solve the proposed models. Both approaches address the issue where classical shortest path algorithms are not applicable due to non-additive semi-standard deviation. However, the stochastic dominance based approach is preferred because it is more computationally efficient and can always find the true optimal paths. In addition to semi-standard deviation, on-time arrival probability and scheduling delay measures are also investigated. Although these three measures share similar mathematical structures, they exhibit different behaviors in response to large deviations from the pre-specified travel time benchmark. Theoretical connections between these measures and the first three stochastic dominance rules are also established. This enables us to incorporate on-time arrival probability and scheduling delay measures into the methodology framework as well.

Traffic state estimation (TSE) aims to estimate the time-varying traffic characteristics (such as flow rate, flow speed, flow density, and occurrence of incidents) of all roads in traffic networks, provided with limited observations in sparse time and locations. TSE is critical to transportation planning, operation and infrastructure design. In this new era of “big data”, massive volumes of sensing data from a variety of source (such as cell phones, GPS, probe vehicles, and inductive loops, etc.) enable TSE in an efficient, timely and accurate manner. This research develops a Bayesian-based theoretical framework, along with statistical inference algorithms, to (1) capture the complex flow patterns in the urban traffic network consisting both highways and arterials; (2) incorporate heterogeneous data sources into the process of TSE; (3) enable both estimation and perdition of traffic states; and (4) demonstrate the scalability to large-scale urban traffic networks. To achieve those goals, a Hierarchical Bayesian probabilistic model is proposed to capture spatio-temporal traffic states. The propagation of traffic states are encapsulated through mesoscopic network flow models (namely the Link Queue Model) and equilibrated fundamental diagrams. Traffic states in the Hierarchical Bayesian model are inferred using the Expectation-Maximization Extended Kalman Filter (EM-EKF). To better estimate and predict states, infrastructure supply is also estimated as part of the TSE process. It is done by adopting a series of algorithms to translate Twitter data into traffic incident information. Finally, the proposed EM-EKF algorithm is implemented and examined on the road networks in Washington DC. The results show that the proposed methods can handle large-scale traffic state estimation, while achieving superior results comparing to traditional temporal and spatial smoothing methods.

For applications that rely on User-Generated Content (UGC), there is a need to find what may motivate the applications user base to consistently contribute with quality content. One category of such applications is Intelligent Transportation Systems (ITS) traffic applications, which serve a specific goal; providing useful traffic-oriented content. By implementing useful incentives into Intelligent Transportation System traffic applications, the applications can better serve their purposes, and at the same time, improve their user's experience. Incentives are intrinsic or extrinsic, i.e., the motivation comes from internal- or external stimuli, which can motivate users in different ways and produce different incentive outcomes. To find the most useful incentives, and gain a better understanding of how to best stimulate active application participation, the research question addressed by this thesis is: Which incentives are useful for increasing quality content in the field of ITS traffic applications? The main method employed to address the research question was a survey. The survey was carried out to investigate what people thought was motivating in ITS traffic applications. In addition to the survey, an interview with the project manager of a Swedish ITS traffic application was done. Previous research concludes that the gain and the incentive for people or organizations hosting UGC are apparent, but the gain for the creators is not as clearly recognized and varies in which area the content is created. The findings of this study showed, from a user perspective, an interest in helping others and monetary gain, as potential incentives for implementation. The authors concluded that intrinsic inclined incentives should work better in-line with the goal of functionality and user long-term engagement, which the authors believe would be preferable for UGC based ITS traffic applications. These findings will be useful for understanding the optimal way to increase motivation for adequate quality UGC in ITS traffic applications.

A safe and efficient highway infrastructure is a critical component and a valuable asset in terms of its monetary value, as well as supporting the way of life and economic activities of the people it serves. In North America, performing maintenance, repair, and expansion of an aging highway infrastructure to a target level of performance while dealing with ever-increasing traffic demands creates a significant challenge in terms of road user safety and mobility. Much of the current highway infrastructure was built several decades ago and it is therefore requiring increasing levels of maintenance and rehabilitation. <p>The cost of delays resulting from traffic congestion induced by work zones is estimated to be more than $6 billion per year. Work zone related traffic fatalities exceed more than 1000 lost lives per year in North America. Work zone related fatalities account for approximately 2.8 percent of highway fatalities in United States and 1.3 percent in Canada. While overall fatal crash rates have been steadily decreasing in both Canada and United States, work zone related fatalities have not been decreasing. <p>Smart Work Zones are an emerging technology designed to improve the safety and mobility within work zones on highways. Smart Work Zones employ various technologies to monitor current traffic conditions and provide relevant information to road managers and road users on current traffic flow conditions and automatically provide guidance to motorists for safer and more efficient navigation of the work zone. <p>This research examined the effects of a Smart Work Zone deployment by modeling traffic flow with and without a Smart Work Zone at the case study site in North Carolina to provide inputs into a performance analysis framework. The quantification of benefits and costs related to the deployment of a Smart Work Zone was developed in a probabilistic analysis framework model. The performance was quantified in economic terms of expected benefit cost ratio and net value realized from the deployment of a Smart Work Zone. The model considers the cost of deployment and potential savings in terms of motorist safety (fatal and injury crash reduction) as well as improvements in traveler mobility including reductions in user delays, vehicle operating costs, and emissions.<p>The model output is a risk profile that provides a range of expected values and associated probabilities of occurrence to quantify the expected benefits while also taking into consideration the uncertainty of the most sensitive input variables. The uncertainty of input variables determined to be the most sensitive were those associated with the amount of user delay and the valuation of user delay. The next most sensitive inputs are those associated with the cost of deploying and operating the Smart Work Zone system. <p>The model developed in this research concurs with the approach and analysis used in other models for the analysis of transportation projects. The model developed in this research provides a tool that can be used for decision making regarding the deployment of a Smart Work Zone and comparison with other transportation project alternatives. The model employs a user definable approach that enables it to be adapted to the specific conditions of a diverse range of field state conditions and has the ability to interface with several traffic flow models. <p>When applied to a case study project on Interstate 95 in North Carolina, the model was found to be capable of providing useful and relevant results that correlated to observed performance. The case study represented one of many operating scenarios on the project, and is not necessarily representative of all the field state conditions occurring over the period of the entire deployment. <p>The model results included a sensitivity analysis that identified the sensitivity of the outcome to uncertainty in the input values and a risk analysis that quantified the uncertainty of the predictions. The findings indicated that, at a 95 percent confidence level, the expected benefit / cost ratio of deploying a Smart Work Zone system was between 1.2 and 11.9 and the net value was between $10,000 and $225,000 per month of operation. Approximately 94 percent of the expected benefits were from savings in user delay and the remainder from savings due to improved safety, reduced emissions, and reduced vehicle operating costs. The results indicate that when applied under appropriate conditions, Smart Work Zones have the potential to provide significant benefits to road users. Under heavily congested conditions, the diversion of even a small amount of traffic to a more efficient route can provide sizable travel time improvements for all traffic.<p>In summary, the model developed in this research was specifically developed to apply to Smart Work Zones, but in its general form could also be applied to other work zone traffic management applications. In the case study the model was applied to a single rural work zone, but the framework could be extended for an integrated analysis of multiple work zones and network analysis in an urban setting. The research provides a fundamental framework and model for the analysis of Smart Work Zones and a method to determine the sensitivity of the uncertainty of input values. The research also identifies areas for continued examination of the effects of Smart Work Zone deployment and the prediction of expected benefits.