A computer model was constructed that allowed two vessels involved in a possible collision situation to take collision avoidance action following the "International Regulations for Preventing Collisions at Sea". The mariners’ actions were modelled by the concepts of the domain and the RDRR (Range to Domain/Range-rate). The domain was used to determine if a vessel was threatening and the RDRR to determine the time at which a vessel should give-way to a threatening target. Each vessel in the simulation had four domains corresponding to the type of encounter in which the vessel was involved. Values for the time at which a vessel manoeuvres and the domain radii were determined from an analysis of high quality cine films of the radar at H.M. Coastguard at St. Margaret's Bay, Dover. Information was also taken from simulator exercises set up on the Polytechnic radar simulator. The two ship encounter was then developed to become the multi-ship encounter and eventually was able to model over 400 vessels over a two day period through a computer representation of the Dover Strait. A further development included a computer graphical representation of a radar simulator running in real-time, and which allowed a mariner to navigate one of the vessels using computer control. A validation of the computer model was undertaken by comparing the simulated results with those observed from the cine films. Following the validation several examples of the computer model being used as a decision support system were included.

With the recent widespread deployment of intelligent transportation systems (ITS) in North America there is an abundance of data on traffic systems and thus an opportunity to use these data in the calibration of microscopic traffic simulation models. Even though ITS data have been utilized to some extent in the calibration of microscopic traffic simulation models, efforts have focused on improving the quality of the calibration based on aggregate form of ITS data rather than disaggregate data. In addition, researchers have focused on identifying the parameters associated with car-following and lane-changing behavior models and their impacts on overall calibration performance. Therefore, the estimation of the Origin-Destination (OD) matrix has been considered as a preliminary step rather than as a stage that can be included in the calibration process. This research develops a methodology to calibrate the OD matrix jointly with model behavior parameters using a bi-level calibration framework. The upper level seeks to identify the best model parameters using a genetic algorithm (GA). In this level, a statistically based calibration objective function is introduced to account for disaggregate form of ITS data in the calibration of microscopic traffic simulation models and, thus, accurately replicate dynamics of observed traffic conditions. Specifically, the Kolmogorov-Smirnov test is used to measure the "consistency" between the observed and simulated travel time distributions. The calibration of the OD matrix is performed in the lower level, where observed and simulated travel times are incorporated into the OD estimator for the calibration of the OD matrix. The interdependent relationship between travel time information and the OD matrix is formulated using a Extended Kalman filter (EKF) algorithm, which is selected to quantify the nonlinear dependence of the simulation results (travel time) on the OD matrix. The two test sites are from an urban arterial and a freeway in Houston, Texas. The VISSIM model was used to evaluate the proposed methodologies. It was found that that the accuracy of the calibration can be improved by using disaggregated data and by considering both driver behavior parameters and demand.

No-notice evacuations of metropolitan areas can place significant demands on transportation infrastructure. In preparation, emergency managers and transportation engineers study potential demands and many create evacuation traffic management plans. The findings from a St. Louis Metro East evacuation study revealed some problematic areas of the transportation network. At these locations the traffic backed up during a simulated evacuation, caused a significant amount of delay, and increased the evacuation clearance time. An emerging paradigm called Connected Vehicle (CV) technology can provide real-time communication between vehicles in a traffic stream. The objectives of this research were to evaluate the impacts of CVs on evacuation from a downtown metropolitan area. The microsimulation software VISSIM was used to model the roadway network and the evacuation traffic. The model was built, calibrated and validated for studying the performance of traffic during the evacuation. This model helped researchers to find the time required to evacuate people in this area for different disaster scenarios. Because it is unlikely that vehicles equipped with CV technologies will become commonplace soon, the researcher tested different levels of deployment, also known as penetration rate. This study included penetration rates from 0 to 30 percent CVs; evaluating the average speed, average and total delays. The findings suggest significant reductions in total delays when CVs reached a penetration rate of 30 percent or greater. Results showed that the presence of CVs at a penetration rate of 30 percent could reduce the overall traffic delay by 60 percent over the evacuation period. A sensitivity analysis was conducted and the finding showed that a 10 percent increase in the penetration rate will significantly improve traffic flow. The findings of this study suggest that the communication capabilities of CVs can reduce delays and improve the traffic flow rate during a no-notice evacuation. Additionally, the benefits could be greater for evacuations with higher volumes, evacuations that last longer, and evacuations with higher proportions of CVs in the vehicle stream.

Effectively modeling the acceleration behavior of vehicles is an important consideration in a variety of transportation engineering applications. The acceleration profiles of vehicles are important in the geometric design of roadways and are used to model vehicle behavior in simulation software packages. The acceleration profile of the vehicle is also a critical parameter in fuel consumption and emissions models. This paper develops and validates a vehicle dynamics model to predict the maximum acceleration rates of passenger vehicles. The model is shown to be superior to other similar models in that it accurately predicts speed and acceleration profiles in all domains and for a variety of vehicle types. The paper also modifies the model by introducing a reduction factor, which enables the model to predict the typical acceleration patterns for different driver types. The reduction factors for the driving population are shown to follow a normal distribution with a mean of 0.60 and a standard deviation of 0.08. The paper also provides new data sets containing maximum and typical acceleration profiles for thirteen different vehicles and twenty different drivers. / Master of Science

<p>A simulation model for traffic loading analysis is needed to aid the designers of landmobile radio communication systems. In the past, our system designers have relied on analytical solutions to perform traffic loading analysis. As our communication system evolved and changed from a voice-only system, to a voice and data system, it became apparent that the assumptions needed for an accurate Erlang C prediction were no longer valid. Thus, the need for a simulation model was realized in order to better predict the radio system's capacity to support a given traffic load.</p> <p> This paper describes the process by which the simulation model was designed, developed, and validated. The simulation modeling system was developed using systems engineering methodologies. This document describes the need for a traffic analysis tool and the development process followed, from conceptualization through test and evaluation. An analysis package is also presented to show the verification and validation of the simulation model. Recommendations and a summary conclude the discussion.</p> / Master of Science

Traffic Cop is a game where the player become a traffic police officer trainee and direct the traffic to reduce the car accident rate in MA. The game is a top-down view traffic simulation and the player need to respond to the violations correctly. The game is aimed at new drivers ages from 16-24 and the purpose is to change the drivers' attitude about safe driving. Player will learn what they should stay focused on while driving and what the consequences may be when they choose to violate the traffic signal.

Recent advances in the optimization of fixed time traffic signals have demonstrated a move towards the use of genetic algorithm optimization with traffic network performance evaluated via stochastic microscopic simulation models. This dissertation examines methods for improved optimization. Several modified versions of the genetic algorithm and alternative genetic operators were evaluated on test networks. A traffic simulation model was developed for assessment purposes. Application of the CHC search algorithm with real crossover and mutation operators were found to offer improved optimization efficiency over the standard genetic algorithm with binary genetic operators. Computing resources are best utilized by using a single replication of the traffic simulation model with common random numbers for fitness evaluations. Combining the improvements, delay reductions between 13%-32% were obtained over the standard approaches. A coding scheme allowing for complete optimization of signal phasing is proposed and a statistical model for comparing genetic algorithm optimization efficiency on stochastic functions is also introduced. Alternative delay measurements, amendments to genetic operators and modifications to the CHC algorithm are also suggested.

Apesar da expressiva quantidade de trabalhos relacionados aos veículos autônomos, poucos são aqueles encontrados sobre a coexistência de veículos autônomos e convencionais no sistema viário. O comportamento dos veículos autônomos é abordado majoritariamente visando analisar a tecnologia envolvida para a comunicação entre os veículos ou a segurança destes dispositivos. Desta forma, o objetivo da pesquisa é avaliar o impacto, do ponto de vista logístico, no comportamento do tráfego em vias com veículos convencionais e autônomos simultaneamente. Através de simulação computacional utilizando ferramentas tradicionais e de código aberto, este trabalho analisa, como a inclusão crescente de veículos autônomos em algumas vias da malha viária da cidade de São Paulo afeta o comportamento dinâmico dos veículos. Cenários de tráfego foram modelados e considerados neste estudo, e sobre eles se concluiu, que a simples inserção de veículos autônomos no sistema não reflete na melhoria uniforme do tráfego, ou seja, algumas rotas, dependendo da sua extensão, volume de veículos e número de semáforos podem sofrer melhorias pouco representativas com relação à velocidade média, tempo de espera em fila e tempo médio de viagem. / Despite the abundance of research related to autonomous vehicles, few papers are found on the coexistence of autonomous and conventional. The behavior of the autonomous vehicles is often approached to analyze aspects such as the technology involved for the communication between the vehicles or the safety of these devices. This research has the objective of evaluating the impact, from the logistic point of view, of the traffic behavior in routes with conventional and autonomous vehicles simultaneously. Through computer simulation using traditional and open source tools, this paper analyzes the dynamic behavior of vehicles, as the growing inclusion of autonomous vehicles in some of the streets of the city of São Paulo. Traffic scenarios were modeled and considered in this study, and it was concluded that the simple insertion of autonomous vehicles into the system does not reflect the uniform improvement of traffic, ie some routes, depending on their extension, traffic volume and number of traffic lights may undergo minor improvements in relation to the average speed, waiting time in queue and average travel time.

The City of Fredericton is the capital of New Brunswick, located in eastern Canada. Right-turn-on-red (RTOR) is a general practice at any traffic intersection in this maritime province. Many collisions between pedestrians and vehicles have been recorded at signalized intersections in the downtown area of the city. Due to the number of collisions, the City of Fredericton was interested in investigating how a restriction against RTOR could affect vehicular traffic. The purpose and goal of this project was to develop a calibrated traffic model of the downtown area of Fredericton that could be used for simulation studies. Two main changes were investigated: 1) a restriction against RTOR for each of eleven key intersections in the downtown area, and 2) a restriction against left-turns at one selected intersection. The traffic simulation model was also used for analyzing how factors such as pedestrian volumes, lane channelization, and turning proportions affect the changes in traffic conditions due to permitting, or prohibiting, right-turn-on-red. The traffic simulation model was created in the TSIS/CORSIM software. Several different scenarios were generated for analysis. The results of the simulation show that the traffic conditions in the whole downtown area will be affected when introducing a restriction against RTOR. Certain intersections show a relatively high change while others show no significant change at all. Several different factors were seen to affect the number of RTOR that could be performed at an intersection. One main factor was lane channelization. With a shared lane, the proportion of right-turning vehicles at the intersection was found to highly affect how many RTOR can be performed. Pedestrian volumes prove to be a third factor affecting the number of RTOR at an intersection. Overall results demonstrate that there are only a few intersections where it is suitable for the City of Fredericton to implement a restriction against RTOR.

This thesis develops adaptive simulation control techniques that differentiate between competing system configurations. Here, a system is a real world environment under analysis. In this context, proposed modifications to a system denoted by different configurations are evaluated using large-scale hybrid simulation. Adaptive control techniques, using ranking and selection methods, compare the relative worth of competing configurations and use these comparisons to control the number of required simulation observations. Adaptive techniques necessitate embedded statistical computations suitable for the variety of data found in detailed simulations, including hybrid and agent-based simulations. These embedded statistical computations apply efficient sampling methods to collect data from simulations running on a network of workstations. The National Airspace System provides a test case for the application of these techniques to the analysis and design of complex systems, implemented here in the Reconfigurable Flight Simulator, a large-scale hybrid simulation. Implications of these techniques for the use of simulation as a design activity are also presented.