Este trabalho analisa os impactos do novo Código de Trânsito Brasileiro no trânsito urbano e outros aspectos relacionados com o novo código. Para isso foram realizados estudos nas seguintes cidades: Ribeirão Preto (SP), São Carlos (SP), Araraquara (SP), Curitiba (PR) e Ponta Grossa (PR). São enfocados no estudo os seguintes principais tópicos: segurança viária, obediência dos usuários às leis e regras de trânsito, opinião pública sobre o novo código e sobre o sistema de trânsito e gestão do trânsito. Também é apresentado no trabalho uma análise comparativa dos sistemas de trânsito nas cidades estudadas, utilizando índices apropriados. Essa análise é complementada mediante a comparação com índices relativos a outras cidades. O novo Código de Trânsito Brasileiro tem a aprovação da grande maioria da população e tem apresentado resultados bastante satisfatórios no tocante a redução dos acidentes e das mortes no trânsito urbano. / The impacts of the new Brazillian Traffic Code at the urban traffic and others aspects related to the new code is analysed in this work. The following cities were analysed: Ribeirão Preto (SP), São Carlos (SP), Araraquara (SP), Curitiba (PR) and Ponta Grossa (PR). The main aims focussed are: traffic safety, users obedience to the laws and traffic norms, public opinion about the new code and about traffic system, traffic management, etc. lt also presented in this work a comparative analyses of the Traffic System at the studied cities, for that appropriate index are used. This analyses is complemented using the comparison with relatives indexs from others cities. The New Brazillian Traffic Code has the approval of the greatest number of the population and has presented very satisfactories results about the reduction of accidents and deaths at the urban traffic.

Traffic simulation and signal timing optimization are classified in structure into two main categories: (i) Macroscopic or Microscopic; (ii) Deterministic or Stochastic. Performance of the optimized signal timing derived by any tool is influenced by the methodology used in how calculations are executed in a particular tool. In this study, the performance of the optimal signal timing plans developed by two of the most popular traffic analysis tools, HCS and Tru-Traffic, each of them has its inbuilt objective function(s) to optimize signal timing for intersection, is compared with an ideal and an existing timing plans (base case) for the area of study using the microsimulation software VISSIM. An urban arterial with 29 intersections and high traffic in Fort Lauderdale, Florida serves as the test bed. To eliminate unfair superiority in the results, all experiments were performed under identical geometry and traffic conditions in each tool. Comparison of the optimized plans is conducted on the basis of average delay, average stopped delay, average number of stops, number of vehicles completed trips, latent delay, and latent demand from the simulated vehicle network performance evaluation results in VISSIM. The results indicate that, overall, HCS with its overall delay objective and the Tru-Traffic programs produce signal timing with comparable quality that performed similar to the un-optimized base case for most of the performance measures. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Signal retiming, or signal optimization process, has not changed much over the last few decades. Traditional procedures rely on low-resolution data and a low-fidelity modeling approach. Such developed signal timing plans always require a fine-tuning process for deployed signal plans in field, thus questioning the very benefits of signal optimization. New trends suggest the use of high-resolution data, which are not easily available. At the same time, many improvements could be made if the traditional signal retiming process was modified to include the use of medium-resolution data and high-fidelity modeling. This study covers such an approach, where a traditional retiming procedure is modified to utilize large medium-resolution data sets, high-fidelity simulation models, and powerful stochastic optimization to develop robust signal timing plans. The study covers a 28-intersection urban corridor in Southeastern Florida. Medium-resolution data are used to identify peak-hour, Day-Of-Year (DOY) representative volumes for major seasons. Both low-fidelity and high-fidelity models are developed and calibrated with high precision to match the field signal operations. Then, by using traditional and stochastic optimization tools, signal timing plans are developed and tested in microsimulation. The findings reveal shortcomings of the traditional approach. Signal timing plans developed from medium-resolution data and high-fidelity modeling approach reduce average delay by 5%-26%. Travel times on the corridor are usually reduced by up to 10.5%, and the final solution does not transfer delay on the other neighboring streets (illustrated through latent delay), which is also decreased by 10%-49% when compared with the traditional results. In general, the novel approach has shown a great potential. The next step should be field testing and validation. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Emerging connected and autonomous vehicle technologies (CAV) provide an opportunity to improve highway capacity and reduce adverse impacts of stop-and-go traffic. To realize the potential benefits of CAV technologies, this study provides insightful methodological and managerial tools in microscopic and macroscopic traffic scales. In the macroscopic scale, this dissertation proposes an analytical method to formulate highway capacity for a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles (HVs). The proposed analytical mixed traffic highway capacity model is based on a Markov chain representation of spatial distribution of heterogeneous and stochastic headways. This model captures not only the full spectrum of CAV market penetration rates but also all possible values of CAV platooning intensities that largely affect the spatial distribution of different headway types. Numerical experiments verify that this analytical model accurately quantifies the corresponding mixed traffic capacity at various settings. This analytical model allows for examination of the impact of different CAV technology scenarios on mixed traffic capacity. We identify sufficient and necessary conditions for the mixed traffic capacity to increase (or decrease) with CAV market penetration rate and platooning intensity. These theoretical results caution scholars not to take CAVs as a sure means of increasing highway capacity for granted but rather to quantitatively analyze the actual headway settings before drawing any qualitative conclusion. In the microscopic scale, this study develops innovative control strategies to smooth highway traffic using CAV technologies. First, it formulates a simplified traffic smoothing model for guiding movements of CAVs on a general one-lane highway segment. The proposed simplified model is able to control the overall smoothness of a platoon of CAVs and approximately optimize traffic performance in terms of fuel efficiency and driving comfort. The elegant theoretical properties for the general objective function and the associated constraints provides an efficient analytical algorithm for solving this problem to the exact optimum. Numerical examples reveal that this exact algorithm has an efficient computational performance and a satisfactory solution quality. This trajectory-based traffic smoothing concept is then extended to develop a joint trajectory and signal optimization problem. This problem simultaneously solves the optimal CAV trajectory function shape and the signal timing plan to minimize travel time delay and fuel consumption. The proposed algorithm simplifies the vehicle trajectory and fuel consumption functions that leads to an efficient optimization model that provides exact solutions. Numerical experiments reveal that this algorithm is applicable to any signalized crossing points including intersections and work-zones. Further, the model is tested with various traffic conditions and roadway geometries. These control approaches are then extended to a mixed traffic environment with HVs, connected vehicles (CVs), and CAVs by proposing a CAV-based speed harmonization algorithm. This algorithm develops an innovative traffic prediction model to estimate the real-time status of downstream traffic using traffic sensor data and information provided by CVs and CAVs. With this prediction, the algorithm controls the upstream CAVs so that they smoothly hedge against the backward deceleration waves and gradually merge into the downstream traffic with a reasonable speed. This model addresses the full spectrum of CV and CAV market penetration rates and various traffic conditions. Numerical experiments are performed to assess the algorithm performance with different traffic conditions and CV and CAV market penetration rates. The results show significant improvements in damping traffic oscillations and reducing fuel consumption.

This thesis focuses on shared use lane facilities and road user understanding. Shared use lanes (sharrows) are a common solution for road facilities that are too narrow to accommodate a full bicycle lane and where the local jurisdiction wants to reduce dooring crashes. In recent years, engineers have focused on incorporating sustainable transportation into new or reconstructed infrastructure improvements. Bicycle transportation connectivity, as an example, is essential to efficient bicycle commuting. Sharrows pavement marking is a well-engineered design but without education road users do not use it properly resulting in an increased risk of being in a crash. / Graduation date: 2012

Cooperative traffic safety applications such as lane change or overtaking assistance have the potential to reduce the number of road fatalities. Many emerging traffic safety applications are based on IEEE 802.11p and periodic position messages, so-called cooperative awareness messages (CAM) being broadcasted by all vehicles. In Europe, ETSI defines a periodic report rate of 2 Hz for CAMs. Although a high report rate is the key to early hazard detection, the 2 Hz rate has been chosen to avoid congestion in settings where the vehicle density is high, e.g., on major highways and in urban scenarios. However, on rural roads with a limited number of communicating vehicles, a report rate of 2 Hz leads to unnecessary delay in cooperative awareness. By adapting the CAM report rate depending on the specific application and road traffic density, and by making use of the priority levels provided by the 802.11p quality of service mechanism, we show that hazards can be detected earlier and the available bandwidth is used more efficiently, while not overexploiting the network resources. / <p>©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.</p><p></p><p>Category number CFP11VTF-ART; Code 87844</p>

When a traffic signals malfunction monitoring unit detects a problem with a traffic signal such as the simultaneous display of green indications to conflicting movements or loss of power to some signal heads, the signal is automatically placed into flash mode as a safety precaution. Signals can have either red/red malfunction flash mode or yellow/red malfunction flash mode, and the mode cannot change by time of day or day of week. This study analyzed traffic operation at 34 instances of yellow/red malfunction flash and 9 instances of red/red malfunction flash in the Atlanta, Georgia area. Many of these instances were during high volume periods. A high level of driver confusion exists at malfunction flash intersections. The rate at which through major street drivers (i.e. those facing a flashing yellow signal) stopped exceeded 75 percent at some yellow/red flash intersections. This creates a safety hazard for other major street drivers who are not expecting vehicles to stop, and for minor street drivers who cannot tell what type of control is being presented to cross traffic or do not understand that vehicles are not required to stop when approaching a flashing yellow indication. Furthermore, high stopping rates at a flashing yellow signal eliminate many of the operational benefits that yellow/red flash is assumed to have over red/red flash. Based on the findings of this study, the use of red/red flash should be the primary flash mode and possibly used exclusively. Requiring all vehicles to stop will improve safety conditions and not have large operational impacts at intersections where a majority of major street vehicles are already stopping at a flashing yellow signal. Yellow/red flash may be an acceptable malfunction flash mode at the intersection of a very large street and a very small street, but additional measures would be required at these intersections to address potential driver confusion.

Air Traffic Management (ATM) aims at ensuring safe and efficient movement of aircraft in the airspace. The National Airspace System is currently undergoing a comprehensive overhaul known as NextGen. With the predicted growth of air transportation, providing traffic flow managers with the tools to support decision making is essential. These tools should aid in accommodating the air traffic throughput increase, while limiting controller workload and ensuring high safety levels. In the National Airspace System (NAS), the goal of en-route Traffic Flow Management (TFM) is to balance air traffic demand against available airspace capacity, in order to ensure a safe and expeditious flow of aircraft, both under nominal and perturbed conditions. The objective of this thesis is to develop a better understanding of how to analyze, model and simulate air traffic in a given airspace, under both nominal and degraded conditions. First, a new framework for en-route Traffic Flow Management and Airspace Health Monitoring is developed. It is based on a data-driven approach for air traffic flow modeling using historical data. This large-scale 3D flow network of the Cleveland center airspace provides valuable insight on airspace complexity. A linear formulation for optimizing en-route Air Traffic is proposed. It takes into account a controller taskload model based on flow geometry, in order to estimate airspace capacity. The simulations run demonstrate the importance of sector constraints and traffic demand patterns in estimating the throughput of an airspace. To analyze airspace degradation, weather blockage maps based on vertically integrated liquid (VIL) are incorporated in the model, representing weather perturbations on the same data set used to compute the flows. Comparing the weather blockages and the network model of the airspace provides means of quantifying airspace degradation. Simulations under perturbed conditions are then run according to different objectives. The results of the simulations are compared with the data from these specific days, to identify the advantages and drawbacks of the present model.

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (p. 245-249).

Researchers in multiple areas have shown that characterizing and accounting for the uncertainty inherent in decision support models is critical for developing more efficient planning and operational strategies. This is particularly applicable for the transportation engineering domain as most strategic decisions involve a significant investment of money and resources across multiple stakeholders and has a considerable impact on the society. Moreover, most inputs to transportation models such as travel demand depend on a number of social, economic and political factors and cannot be predicted with certainty. Therefore, in recent times there has been an increasing emphasis being placed on identifying and quantifying this uncertainty and developing models which account for the same. This dissertation contributes to the growing body of literature in tackling uncertainty in transportation models by developing methodologies which address the uncertainty in input parameters in traffic assignment models. One of the primary sources of uncertainty in traffic assignment models is uncertainty in origin destination demand. This uncertainty can be classified into long term and short term demand uncertainty. Accounting for long term demand uncertainty is vital when traffic assignment models are used to make planning decisions like where to add capacity. This dissertation quantifies the impact of long term demand uncertainty by assigning multi-variate probability distributions to the demand. In order to arrive at accurate estimates of the expected future system performance, several statistical sampling techniques are then compared through extensive numerical testing to determine the most "efficient" sampling techniques for network assignment models. Two applications of assignment models, network design and network pricing are studied to illustrate the importance of considering long term demand uncertainty in transportation networks. Short term demand uncertainty such as the day-to-day variation in demand affect traffic assignment models when used to make operational decisions like tolling. This dissertation presents a novel new definition of equilibrium when the short term demand is assumed to follow a probability distribution. Various properties of the equilibrium such as existence, uniqueness and presence of a mathematical programming formulation are investigated. Apart from demand uncertainty, operating capacity in real world networks can also vary from day to day depending on various factors like weather conditions and incidents. With increasing deployment of Intelligent Transportation Systems, users get information about the impact of capacity or the state of the roads through various dissemination devices like dynamic message signs. This dissertation presents a new equilibrium formulation termed user equilibrium with recourse to model information provision and capacity uncertainty, where users learn the state or capacity of the link when they arrive at the upstream node of that link. Depending on the information received about the state of the upstream links, users make different route choice decisions. In this work, the capacity of the links in the network is assumed to follow a discrete probability distribution. A mathematical programming formulation of the user equilibrium with recourse model is presented along with solution algorithm. This model can be extended to analytically model network flows under information provision where the arcs have different cost functional form depending on the state of the arc. The corresponding system optimal with recourse model is also presented where the objective is minimize the total system cost. The network design problem where users are routed according to the user equilibrium with recourse principle is studied. The focus of this study is to show that planning decisions for networks users have access to information is significantly different from the no-information scenario. / text