905 resultados para Transit Vehicle Passengers.


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Urban Mass Transportation Administration, Washington, D.C.

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Federal Transit Administration, Washington, D.C.

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"DOT-T-89-07"--P. [4] of cover.

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Federal Transit Administration, Washington, D.C.

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Texas Department of Transportation, Austin

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Federal Transit Administration, Washington, D.C.

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Urban Mass Transportation Administration, Washington, D.C.

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Transportation Department, Office of University Research, Washington, D.C.

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Lesões fatais em crianças causadas por acidentes de trânsito representam um problema em muitos países. Este estudo analisou a taxa de mortalidade em crianças passageiras de automóveis menores de 10 anos de idade no Brasil, entre 1997 e 2005. Para isso, o número de mortes foi obtido diretamente no banco de dados do Sistema de Informação sobre Mortalidade (SIM) e os dados da população são projeções intercensitárias a partir censo demográfico do Instituto Brasileiro de Geografia e Estatística (IBGE) disponíveis pelo site do DATASUS. Foram calculadas, para os triênios compreendidos no período em estudo, as taxas de mortalidade por acidente de trânsito entre crianças passageiras de automóveis segundo faixa etária (menor que 1 ano, 1 a 4 e 5 a 9) e região geográfica. Os resultados mostraram taxas de mortalidade de 5,68, 7,32 e 6,78 (por 1.000.000), respectivamente, para os períodos 1997-1999, 2000-2002 e 2003-2005 para todo o Brasil. Crianças menores de 1 ano de idade apresentam taxa de mortalidade de 10,18 (por 1,000,000), maior que as observadas para as outras faixas etárias. Para o período 1997-2005, as maiores taxas foram observadas nas regiões Centro-Oeste e Sul, representando, respectivamente, 13,88 e 11,47 (por 1.000.000). Tais resultados mostram a situação de risco da criança em relação a acidentes de trânsito como passageiras de automóveis e contribuem para a elaboração de campanhas educativas de prevenção de lesões.

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Trains in Latin America and the Caribbean mainly serve as a means of mass transit, bearing passengers along local and suburban routes of cities and transporting freight beyond. Non-urban passenger trains almost disappeared during the last few decades of the twentieth century. In the new emerging markets, however, demand is based on the train itself or the scenery en route rather than a wish to arrive at a given station as in the past. The new tourist trains, which are often well-restored historical engines, are expensive to operate and their special characteristics make it difficult to integrate them with mass transit railway services. However, some may be profitable when run privately and others may have a social justification, based on the boost they can provide to economic development in the often isolated and relatively depressed areas where they tend to operate.

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California Department of Transportation, Sacramento

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Transportation Department, Office of University Research, Washington, D.C.

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Urban Mass Transportation Administration, Washington, D.C.

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Federal Transit Administration, Washington, D.C.

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An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^