The road traffic along with other key infrastructure sectors such as telecommunication, power, etc. has an important role in economic and technological growth of one country. Traffic engineers and analysts are responsible for solving a diversity of traffic problems, such as traffic data acquisition and evaluation. In response to the need to improve traffic operation, researchers implement advanced technologies and integration of systems and data, and develop state-of-the-art applications. This thesis introduces three novel web applications with an aim to offer traffic operators, managers, and analysts’ possibility to monitor the congestion, and analyze incidents and signal performance measures. They offer more detailed analysis providing users with insights from different levels and perspectives. The benefit of providing these visualization tools is more efficient estimation of the performance of local networks, thus facilitating the decision making process in case of emergency events. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

M. Tech. (Information Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology / Traffic congestion is a concern within the main arteries that link Johannesburg to Pretoria. In this study Matlab function Randperm is used to generate random vehicle speeds on a simulated highway. Randperm is used to mimic vehicle speed sensors capturing vehicle traffic on the highway. Java sockets are used to send vehicle speed to the Road Traffic Control Centre (RTCC)-database server through a wireless medium. The RTCC-database server uses MySQL to store vehicle speed data. The domain controller with active directory together with a certificate server is used to manage and provide security access control to network resources. The wireless link used by speed sensors to transmit vehicle speed data is protected using PEAP with EAP-TLS which employs the use of digital certificates during authentication. A java database connectivity driver is used to retrieve data from MySQL and a multilayer perceptron (MLP) model is used to predict future traffic status on the highway being monitored i.e. next 5 minutes from previous 5 minutes captured data. A dataset of 402 instances was divided as follows: 66 percent training data was used to train the MLP model, 15 percent data used during validation and the remaining 19 percent was used to test the trained MLP model. An excel spreadsheet was used to introduce novel (19 percent data not used during training) data to the trained MLP model to predict. Assuming that the spreadsheet data represent captured highway vehicle data for the last 5 minutes, the model showed 100 percent accuracy in predicting the four classes: congested, out congested, into congested and normal traffic flow. Predicted traffic status is displayed for the motorist on the highway to know. Ability of the proposed model to continuously capture the traffic pattern on the highway (monitor) helps in redirecting (controlling) the highway traffic during periods of congestion. Implementation of this project will definitely decrease traffic congestion across main arteries of Johannesburg. Pollution normally experienced when cars idle for a long time during congestion will be reduced by free highway traffic flow. Frequent servicing of motor vehicles will no longer be required by the motorists. Furthermore the economy of Gauteng and South Africa as a whole will benefit due to increase in production. Consumers will also benefit in obtaining competitive prices from organizations that depend on haulage services.

As transportation policies are changing to encourage alternative modes of transportation to reduce congestion problems and air quality impacts, more planning organizations are considering or implementing activity-based travel demand models to forecast future travel patterns. The proclivity towards operating activity-based models is the capability to model disaggregate travel data to better understand the model results that are generated with respect to the latest transportation policy implementations. This thesis first examines the differences between the two major modeling techniques used in the United States and then describes the assessment tool that was developed to recommend whether a region should convert to the advanced modeling procedures. This tool consists of parameters that were decided upon based on their known linkages to the advantages of activity-based models.

Traffic is a problem across the globe, reaching perniciously into cities and communities nearly everywhere. The United States (US) has its share of traffic problems; of the ten cities with the highest traffic delay times in 2022, four were in the US. While nearly everyone living in the US has likely experienced traffic congestion of some kind, some cities are notoriously worse than others. In New York City (NYC), traffic congestion has been a problem as far back as 1913, when Fifth Avenue was so traffic-clogged it could take 40 minutes to go 23 blocks. Today, of the 25 most congested traffic corridors in the US, three are in NYC. One of these runs through the South Bronx, an environmental justice neighborhood we highlight in this dissertation. Traffic congestion is a source of air pollution (traffic-related air pollution, or TRAP) and noise, and it can result in property damage, injuries, and fatalities from collisions with other vehicles, pedestrians, or those using other forms of transportation. Both traffic congestion and TRAP have been associated with numerous negative health outcomes. For example, TRAP is associated with respiratory, cardiovascular, neurological, and pregnancy outcomes, including asthma exacerbation, incident childhood and adult asthma, reduced lung function, atherosclerosis, hypertension, stroke, myocardial infarction, cardiovascular-related mortality, cognitive decline, neurodevelopmental outcomes, pregnancy loss, term low birth weight, and small for gestational age birth. In general across the US, communities of color and higher-poverty neighborhoods face greater exposure and health burden from traffic. Throughout this dissertation, we study traffic congestion and TRAP through two lenses: (1) environmental justice; and (2) policy. Additionally, we assess the cardiovascular health impacts of TRAP. In Chapter 1, we provide background on the problem of traffic, focusing on NYC and the South Bronx. In Chapter 2, we present a case study from the South Bronx, where a new trucking-intensive warehouse was opened in 2018. In this study, we quantified the increase in vehicles and trucks following the opening of the warehouse and estimated the resulting increases in black carbon (BC) and noise. We discuss the injustice in the methods used to assess the environmental impact of the warehouse, the warehouse’s siting in a predominantly Black and Lantinx community already overburdened with trucking-intensive industries, and the desire of the community to instead use the land for a community park. In Chapter 3, we present a study quantifying how traffic congestion in NYC changed during the COVID-19 pandemic. We assess how NY on Pause, the state’s stay-at-home order, impacted traffic congestion by comparing the magnitude of traffic decreases in environmentally burdened or systematically disadvantaged neighborhoods to the magnitude of decreases in less burdened and more advantaged neighborhoods. We discuss the implications of these results for upcoming traffic policies in NYC, such as congestion pricing. In Chapter 4, we present a study evaluating diurnal changes in TRAP in NYC during NY on Pause. We discuss the implications of these results for congestion pricing, including the potential timing of TRAP decreases. In Chapter 5, we present an epidemiologic study of TRAP and myocardial infarction (MI) in New York State, identifying hazard windows of exposure in a study period where the mean nitrogen dioxide (NO₂) concentration was substantially lower than the hourly national standard. We discuss implications for the NO₂ National Ambient Air Quality Standards (NAAQS) and suggest that the current standard may be insufficient to protect population cardiovascular health. Finally, in Chapter 6, we conclude with a discussion of recommended research directions and policy considerations.

Safety and congestion are ever present and increasingly severe transportation problems in urban areas throughout the nation and world. These phenomena can have wide-ranging consequences relating to safety, the economy, and the environment. Adverse weather conditions represent another significant challenge to safety and mobility on highways. Oregon is not immune from either of these global issues. Oregon Route (OR) 217, to the southwest of the downtown Portland, is one of the worst freeways for congestion in the state and is also subject to the Pacific Northwest's frequently inclement and unpredictable climate. High crash rates, severe recurrent bottlenecks and highly unreliable travel times continuously plague the corridor, making it a major headache for the thousands of commuters using it every day. In an effort to more effectively combat both congestion and adverse weather, transportation officials all over the world have been turning to increasingly technological strategies like Active Traffic Management (ATM). This can come in many forms, but among the most common are variable speed limit (VSL) systems which use real-time data to compute and display appropriate reduced speeds during congestion and/or adverse weather. After numerous studies and deliberations, Oregon Department of Transportation (ODOT) selected Oregon Route (OR) 217 as one of the first locations in the state to be implemented with an advisory VSL system, and that system began operation in the summer of 2014. This thesis seeks to evaluate the effectiveness of this VSL system through the first eight months of its operation through an in-depth and wide-ranging "before and after" analysis. Analysis of traffic flow and safety data for OR 217 from before the VSL system was implemented made clear some of the most prevalent issues which convinced ODOT to pursue VSL. Using those issues as a basis, a framework of seven specific evaluation questions relating to both performance and safety, as well as both congestion and adverse weather, was established to guide the "before and after" comparisons. Hypotheses, and measures of effectiveness for each question were developed, and data were obtained from a diverse array of sources including freeway detectors, ODOT's incident database, and the National Oceanic and Atmospheric Administration (NOAA). The results of the various "before and after" comparisons performed as a part of this thesis indicate that conditions have changed on OR 217 in a number of ways since the VSL system was activated. Many, but not all, of the findings were consistent with the initial hypotheses and with the findings from other VSL studies in the literature. Certain locations along the corridor have seen significant declines in speed variability, supporting the common notion that VSL systems have a harmonizing effect on traffic flow. Crash rates have not decreased, but crashes have become less frequent in the immediate vicinity of VSL signs. Flow distribution between adjacent lanes has been more even since VSL implementation during midday hours and the evening peak, and travel time reliability has seen widespread improvement in three of the corridor's four primary travel lanes during those same times. The drops in flow that generally occur upstream of bottlenecks once they form have had diminished magnitudes, while the drops in flow downstream of the same bottlenecks have grown. Finally, the increase in travel times that is usually brought about by adverse weather has been smaller since VSL implementation, while the decline in travel time reliability has largely disappeared.