978 resultados para Urban Mass Transit.


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The number of pedestrian victims at Australian and foreign level crossings has remained stable over the past decade and it continues to be a significant problem. To examine the factors contributing to pedestrians’ unsafe crossing behaviours, direct observations were conducted at three black spot urban level crossings in Brisbane for a total of 45 h during morning and afternoon peak. In total, 129 pedestrians transgressed the active controls. More transgressions were observed at the crossings located in more populated suburbs in close proximity to large shopping centres and school zones, whereas the smallest number of transgressions were observed at the least populated locations. In addition to characteristics associated with the larger socio-economic area, the patterns of transgression could be associated with the properties of the existing safety equipment and the design of each level crossing (i.e. location of the platforms, number of rail tracks). Indeed, the largest number of crossed unoccupied but “at risk” rail tracks (where a train could have passed), was observed at the crossing with the least transgressions. Contrary to previous findings, younger adults were the most frequent transgressors. School children and elderly were most likely to transgress in groups. Potential directions for future research and more effective measures are discussed.

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The paper presents an innovative approach to modelling the causal relationships of human errors in rail crack incidents (RCI) from a managerial perspective. A Bayesian belief network is developed to model RCI by considering the human errors of designers, manufactures, operators and maintainers (DMOM) and the causal relationships involved. A set of dependent variables whose combinations express the relevant functions performed by each DMOM participant is used to model the causal relationships. A total of 14 RCI on Hong Kong’s mass transit railway (MTR) from 2008 to 2011 are used to illustrate the application of the model. Bayesian inference is used to conduct an importance analysis to assess the impact of the participants’ errors. Sensitivity analysis is then employed to gauge the effect the increased probability of occurrence of human errors on RCI. Finally, strategies for human error identification and mitigation of RCI are proposed. The identification of ability of maintainer in the case study as the most important factor influencing the probability of RCI implies the priority need to strengthen the maintenance management of the MTR system and that improving the inspection ability of the maintainer is likely to be an effective strategy for RCI risk mitigation.

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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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Transportation Systems Center, Cambridge, Mass.

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Urban Mass Transportation Administration, Washington, D.C.

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Urban Mass Transportation Administration, Washington, D.C.

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Urban Mass Transportation Administration, Washington, D.C.

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"UMTA Technical Assistance Program"--Cover.

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Urban Mass Transportation Administration, Washington, D.C.

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Urban Mass Transportation Administration, Washington, D.C.

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Transportation Systems Center, Cambridge, Mass.

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Transportation Systems Center, Cambridge, Mass.

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Urban Mass Transportation Administration, Washington, D.C.

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Transportation Systems Center, Cambridge, Mass.