Police traffic services, management information system, phase I - synopsis.

National Highway Traffic Safety Administration, Washington, D.C.

Police traffic services, management information system, (phase I). Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Management and evaluation handbook for demonstration projects in traffic safety. 1976.

National Highway Traffic Safety Administration, Washington, D.C.

Traffic mitigation and demand management : summary of national experience and potential applications in New York /

"DOT-I-88-02"--Cover.

Essential air service : changes in subsidy levels, air carrier costs, and passenger traffic : report to the Chairman, Subcommittee on Aviation, Committee on Transportation and Infrastructure, House of Representatives /

"B-284056"--P. 3.

Accelerated traffic tests; summary report: Air Support Command Base, Beltsville, Md., Corpus Christi Municipal Airport, Corpus Christi, Tex., Dothan Municipal Airport, Dothan, Ala., Fargo Municipal Airport, Fargo, N.D., Grenier Field, Manchester, N.H., Lewistown Satellite Airfield, Lewistown, Mont., Natchitoches Municipal Airport, Natchitoches, La. [and] Richmond Army Air Base, Richmond, Va.

At head of title: War Dept., Corps of Engineers, Mississippi River Commission.

Odors, gases and particulate matter from high density poultry management systems as they relate to air pollution : final report /

"April 15, 1969."

Air safety : FAA's Traffic Alert and Collision Avoidance System : briefing report to the honorable J. James Exon, U.S. Senate.

"B-222851"--P. [1]

Traffic-Related PM2.5 Air Pollution and Schools in Proximity to Major Roadways in Shanghai, China

Thesis (Master's)--University of Washington, 2016-06

Cardiovascular implications of exposure to traffic air pollution during exercise

Regular aerobic exercise is recommended by physicians to improve health and longevity. However, individuals exercising in urban regions are often in contact with air pollution, which includes particles and gases associated with respiratory disease and cancer. We describe the recent evidence on the cardiovascular effects of air pollution, and the implications of exercising in polluted environments, with a view to informing clinicians and other health professionals. There is now strong evidence that fine and ultra fine particulate matter present in air pollution increases cardiovascular morbidity and mortality. The main mechanisms of disease appear to be related to an increase in the pathogenic processes associated with atherosclerosis. People exercising in environments pervaded by air contaminants are probably at increased risk, due to an exercise-induced amplification in respiratory uptake, lung deposition and toxicity of inhaled pollutants. We make evidence-based recommendations for minimizing exposure to air-borne toxins while exercising, and suggest that this advice be passed on to patients where appropriate.

Traffic lights management using belief propagation

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Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.