TRB's Commercial Truck and Bus Safety Synthesis Program (CTBSSP) Synthesis 4: Individual Differences and the "High-Risk" Commercial Driver explores individual differences among commercial drivers, particularly as these differences relate to the "high-risk" commercial driver. The synthesis identifies factors relating to commercial vehicle crash risk and assesses ways that the high-risk driver can be targeted by various safety programs and practices, at both fleet- and industry-wide levels.
TRB's Commercial Truck and Bus Safety Synthesis Program (CTBSSP) Synthesis 1: Effective Commercial Truck and Bus Safety Management Techniques focuses on the problems commercial truck and bus fleet managers confront, and the methods that are available to address problems in the areas of driver and vehicle safety, and more.
"TRB's Commercial Truck and Bus Safety Synthesis Program (CTBSSP) Synthesis 21: Driver Selection Tests and Measurement synthesizes information on the use of tests, measurements, and other assessment methods used by commercial truck and bus companies in the driver selection process. The report also identifies and describes driver selection methods and instruments and their potential usefulness in predicting driver crash risk."--Publisher's description.
"TRB's Hazardous Materials Cooperative Research Program (HMCRP) Report 7: Role of Human Factors in Preventing Cargo Tank Truck Rollovers analyzes the causes of the major driver factors contributing to cargo tank truck rollovers and offers safety, management, and communication practices that can be used to help potentially minimize or eliminate driver errors in cargo tank truck operations. The report focuses on three areas of practice--rollover-specific driver training and safety programs, the use of behavior management techniques, and the use of fitness-for-duty management practices--that could have long-lasting benefits for motor carriers of all sizes across the tank truck industry."--Publisher's description.
There are approximately 4,000 fatalities in crashes involving trucks and buses in the United States each year. Though estimates are wide-ranging, possibly 10 to 20 percent of these crashes might have involved fatigued drivers. The stresses associated with their particular jobs (irregular schedules, etc.) and the lifestyle that many truck and bus drivers lead, puts them at substantial risk for insufficient sleep and for developing short- and long-term health problems. Commercial Motor Vehicle Driver Fatigue, Long-Term Health and Highway Safety assesses the state of knowledge about the relationship of such factors as hours of driving, hours on duty, and periods of rest to the fatigue experienced by truck and bus drivers while driving and the implications for the safe operation of their vehicles. This report evaluates the relationship of these factors to drivers' health over the longer term, and identifies improvements in data and research methods that can lead to better understanding in both areas.
Analytics for the public sector involves the application of operations research and statistical techniques to solve various problems existing outside of the private sector. The use of analytics for the public sector results in more efficient and effective services for the clients and users of these systems. Analytics, Operations, and Strategic Decision Making in the Public Sector is an essential reference source that discusses analytics applications in various public sector organizations, and addresses the difficulties associated with the design and operation of these systems including multiple conflicting objectives, uncertainties and resulting risk, ill-structured nature, combinatorial design aspects, and scale. Featuring research on topics such as analytical modeling techniques, data mining, and statistical analysis, this book is ideally designed for academicians, educators, researchers, students, and public sector professionals including those in local, state, and federal governments; criminal justice systems; healthcare; energy and natural resources; waste management; emergency response; and the military.
This report identifies potential improvements in terms of more effective safety and environmental regulation for trucks, backed by better systems of enforcement, and identifies opportunities for greater efficiency and higher productivity.
The development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradigms such as cloud computing and Internet of Things (IoT). The book serves as a reference for researchers and engineers in domains of advanced computation, optimization, and data mining for smart civil infrastructure condition assessment, dynamic visualization, intelligent transportation systems (ITS), cyber-physical systems, and smart construction technologies. The chapters are presented in a hands-on manner to facilitate researchers in tackling applications. Arguably, data analytics technologies play a key role in tackling the challenge of creating smart cities. Data analytics applications involve collecting, integrating, and preparing time- and space-dependent data produced by sensors, complex engineered systems, and physical assets, followed by developing and testing analytical models to verify the accuracy of results. This book covers this multidisciplinary field and examines multiple paradigms such as machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. The book explores new territory by discussing the cutting-edge concept of Big Data analytics for interpreting massive amounts of data in smart city applications.
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on Transportation, Housing and Urban Development, and Related Agencies
