Organizational, DS, GS, and depot maintenance manual : public address set AN/UIH-5.

"November 1964."

Organizational maintenance repair parts and special tools lists for public address sets AN/UIH-2 (NSN 5830-00-503-1079) and AN/UIH-2A (NSN 5830-00-503-1079).

Includes index.

Reproducing equipment MC-364-D and public address set AN/UIH-2 : organizational maintenance, second echelon.

"January 1959."

Organizational, direct support, and general support maintenance repair parts and special tools list ... for public address set AN/PIQ-5A (NSN 5830-00-985-9033).

"August 1976."

Operator and organizational maintenance manual : public address set AN/UIH-4.

"10 September 1963."

The New York City double deck bus demonstration project : an evaluation /

Final report; May 1978.

Transit technology evaluation : a literature capsule.

"November 1981"--Cover.

Automotive manufacturer risk analysis: meeting the automotive fuel economy standard. Final report.

National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.

The behavioral impacts of flexible working hours : final report /

"Service and methods demonstration."

United States LPPSD technical information exchange document.

"The United States ... as part of the NATO/CCMS Air Pollution Pilot Study, introduced the Low Pollution Power Systems Development (LPPSD) program."

Truck transport of hazardous chemicals : phosphorus pentasulfide.

Shipping list no.: 97-0029-P.

Recreational transit service to the California Santa Monica Mountains : final report /

Performed by Crain and Associates, Inc. under contract to the Transportation Systems Center.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

A study of the differences among African -American, Hispanic, and Anglo women on the perceived barriers and strategies to career advancement in public school administration

Women have been traditionally excluded from the ranks of principals and district administrators in public school systems throughout the country. Traditionally, Anglo women have been more successful than African American and Hispanic women in breaking down the barriers that impede their ascension to the top. The purpose of this study was to ascertain how African American, Hispanic, and Anglo higher-level female administrators perceive the barriers that hinder their progress, the effects of the barriers, and the strategies to overcome the barriers. ^ Two hundred, sixty female administrators employed with Miami-Dade County Public Schools and serving in the role of principal or higher were mailed a questionnaire consisting of 49 questions centering on personal and professional characteristics, perceptions of barriers, perceived effects of barriers, and strategies to overcome the barriers. One hundred, seventy-five questionnaires were returned. To analyze the respondents' personal and professional characteristics, cross tabulations were conducted on the demographic information and on the strategies. ANOVA was conducted on the barriers and the effects of the barriers by ethnic groups. Tukey's test for post-hoc comparisons was utilized to identify groups with means significantly divergent from those of other ethnicities. ^ The data revealed that Hispanic female higher-level administrators who returned the questionnaire were more likely to be married and have children as compared to Anglo and African American female administrators. When addressing the barriers to career success, African American females had a higher mean score on 14 of the 17 barriers to career success as compared to the other ethnic groups. Hispanic female administrators proved to be more successful in utilizing the strategies to overcome career barriers. The strategy, forming a ���New Girl Network,��� was the least utilized with 79 of the respondents reporting that they had never used it. ^ It is concluded that there is strong need for female administrators to network, mentor, and support one another. Also, it is imperative that the success of particular groups in certain areas is shared with others. ^

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.