Above all, comprehensive real-time safety evaluation algorithms were developed for arterials, which would be key components for future real-time safety applications (e.g., real-time crash risk prediction and visualization system) in the context of pro-active traffic management.
Recently, with the development of connected vehicles and mobile sensing technologies, vehicle-based data become much easier to obtain. However, only few studies have investigated the application of this kind of novel data to real-time traffic safety evaluation. This dissertation aims to conduct a series of real-time traffic safety studies by integrating all kinds of available vehicle-based data sources. First, this dissertation developed a deep learning model for identifying vehicle maneuvers using data from smartphone sensors (i.e., accelerometer and gyroscope). The proposed model was robust and suitable for real-time application as it required less processing of smartphone sensor data compared with the existing studies. Besides, a semi-supervised learning algorithm was proposed to make use of the massive unlabeled sensor data. The proposed algorithm could alleviate the cost of data preparation and improve model transferability. Second, trajectory data from 300 buses were used to develop a real-time crash likelihood prediction model for urban arterials. Results from extensive experiments illustrated the feasibility of using novel vehicle trajectory data to predict real-time crash likelihood. Moreover, to improve the model’s performance, data fusion techniques were proposed to integrated trajectory data from various vehicle types. The proposed data fusion techniques significantly improved the accuracy of crash likelihood prediction in terms of sensitivity and false alarm rate. Third, to improve pedestrian and bicycle safety, different vehicle-based surrogate safety measures, such as hard acceleration, hard deceleration, and long stop, were proposed for evaluating pedestrian and bicycle safety using vehicle trajectory data. In summary, the results from this dissertation can be further applied to real-time safety applications (e.g., real-time crash likelihood prediction and visualization system) in the context of proactive traffic management.
Intended to assist agencies responsible for incident management activities on public roadways to improve their programs and operations.Organized into three major sections: Introduction to incident management; organizing, planning, designing and implementing an incident management program; operational and technical approaches to improving the incident management process.
This book presents innovative research and its applications in the development of transportation infrastructure, and discusses the latest trends, challenges and unsolved problems in the field of transport technology. The book also presents a range of solutions to problems faced by the rapidly growing economies of the developing world. Core challenges confronting policymakers in the field of transport technology include traffic congestion, air pollution, traffic fatalities and injuries, and petroleum dependence. At the same time, the increased use of hybrid and electric vehicles is changing consumer needs and behaviors. The solutions discussed in this book will encourage and inspire researchers, industry professionals and policymakers alike to put these methods into practice.
Congestion continues to grow in America¿s urban areas. This report presents details on the 2004 trends, findings and what can be done to address the growing transportation problems. Trend data from 1982 to 2002 for 85 urban areas provides both a local view and a national perspective on the growth and extent of traffic congestion. The 2004 Report provides clear evidence that the time for improvements has arrived. Communicating the congestion levels and the need for improvements is a goal of this report. The decisions about which, and how much, improvement to fund will be made at the local level according to a variety of goals, but there are some broad conclusions that can be drawn from this database. Tables.
Proactive policing, as a strategic approach used by police agencies to prevent crime, is a relatively new phenomenon in the United States. It developed from a crisis in confidence in policing that began to emerge in the 1960s because of social unrest, rising crime rates, and growing skepticism regarding the effectiveness of standard approaches to policing. In response, beginning in the 1980s and 1990s, innovative police practices and policies that took a more proactive approach began to develop. This report uses the term "proactive policing" to refer to all policing strategies that have as one of their goals the prevention or reduction of crime and disorder and that are not reactive in terms of focusing primarily on uncovering ongoing crime or on investigating or responding to crimes once they have occurred. Proactive policing is distinguished from the everyday decisions of police officers to be proactive in specific situations and instead refers to a strategic decision by police agencies to use proactive police responses in a programmatic way to reduce crime. Today, proactive policing strategies are used widely in the United States. They are not isolated programs used by a select group of agencies but rather a set of ideas that have spread across the landscape of policing. Proactive Policing reviews the evidence and discusses the data and methodological gaps on: (1) the effects of different forms of proactive policing on crime; (2) whether they are applied in a discriminatory manner; (3) whether they are being used in a legal fashion; and (4) community reaction. This report offers a comprehensive evaluation of proactive policing that includes not only its crime prevention impacts but also its broader implications for justice and U.S. communities.