901 resultados para Public Transportation Attitudes.


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Transportation Department, Secretary of Transportation, Washington, D.C.

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The influence of information about trip time variability, personal benefits, or environmental harm from cars or public transportation on commuting mode choice (car or subway) is examined in an experimental study. In addition to these experimentally manipulated variables, the influence of prior attitudes towards the subway was verified. The sample is made up of habitual users of the car to travel to work (N = 220, age M = 37.4, SD = 8.1, 63.2% women). The results show that providing information about the advantages of public transportation, as well as prior attitudes towards the subway, decrease the preference, choice, and perceived control of car use. Of the experimentally manipulated variables, information about the variability of trip time had the greatest influence. These results highlight the importance of taking into account these variables to implement institutional campaigns to reduce car use as transportation mode.

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Public transportation is an environment with great potential for applying location-based services through mobile devices. The BusTracker study is looking at how real-time passenger information systems can provide a core platform to improve commuters’ experiences. These systems rely on mobile computing and GPS technology to provide accurate information on transport vehicle locations. BusTracker builds on this mobile computing platform and geospatial information. The pilot study is running on the open source BugLabs computing platform, using a GPS module for accurate location information.

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Thesis (Master's)--University of Washington, 2012

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Thesis to obtain the Master of Science Degree in Computer Science and Engineering

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In Metropolitan Area of Mexico City, most of urban displacements happen through semi formal public transportation: small and medium capacity vehicles operated by small private enterprises, through a concession scheme. This kind of public transportation has been playing a major role in the Mexican capital. On one hand, it has been one of the conditions for urbanization to be possible. On the other hand, despite its uncountable deficiencies, public transportation has allowed for a long time the whole population to be able to move within this huge metropolis. However, that important function with regards to integration has now reached its limits in the most recent suburbs of the city, where a new mode of urbanization is taking place, based on massive production of very big social housing gated settlements. Public transportation tends to constitute here a factor of exclusion and households meet with important difficulties for their daily mobility. 

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http://digitalcommons.colby.edu/atlasofmaine2008/1024/thumbnail.jpg

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Currently, there is a public bus transportation route in Waterville, Maine. However, this system could be improved. Our goal was to use GIS to find optimal public transportation routes throughout the city based on given points of interest and high population density areas. Three different groups of points of interest were created in the North, West, and South sections of Waterville. Using the Network Analyst tool, which calculates optimal routes, using existing street data, based on the input of stops, barriers, and impedance, we ran an analysis of what we thought would be the routes that best served the greatest number of people. Two different sets of routes were found: one with length as the impedance (the shortest length between the selected stops was favored), and one with population density as the impedance (the roads with the highest population density were favored). Finally, the times of the resulting routes (given a constant speed limit of 25 mph) were calculated and evaluated.

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Academicians and practitioners generally agree that there is a positive correlation between more and better infrastructure and economic growth. From the broader perspective of development, attempts have been made in the literature to identify the different theoretical connections and the empirical patterns that link infrastructure to productivity, on the one hand, and those that link it to social inclusion and equity, on the other hand. Infrastructure contributes to development in different ways. The capital involved is not homogeneous, nor is its effect on the distributive aspects. Water and sanitation have a particularly strong association with the health of the general population and with infant mortality, early childhood health, learning abilities and the acquisition of labour skills. With respect to transportation, the reduction of costs and travel times has a direct economic impact on economic activities of production and domestic and international distribution. That infrastructure also has a social and distributive role to play by reducing the number of fatal accidents and serious injuries in the sectors that are naturally most susceptible to them, namely, the poor. Under the broad umbrella of infrastructure, we can include a number of facilities that make possible the provision of certain services. Some of these facilities require very significant fixed capital investments; some of them are residential, while others are not necessarily. What they all have in common is the existence of networks (transportation, wiring, pipelines) and a strong convergence of physical capital and/or technology, as well as the need for major investments in periodic maintenance.

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To reach the goals established by the Institute of Medicine (IOM) and the Centers for Disease Control's (CDC) STOP TB USA, measures must be taken to curtail a future peak in Tuberculosis (TB) incidence and speed the currently stagnant rate of TB elimination. Both efforts will require, at minimum, the consideration and understanding of the third dimension of TB transmission: the location-based spread of an airborne pathogen among persons known and unknown to each other. This consideration will require an elucidation of the areas within the U.S. that have endemic TB. The Houston Tuberculosis Initiative (HTI) was a population-based active surveillance of confirmed Houston/Harris County TB cases from 1995–2004. Strengths in this dataset include the molecular characterization of laboratory confirmed cases, the collection of geographic locations (including home addresses) frequented by cases, and the HTI time period that parallels a decline in TB incidence in the United States (U.S.). The HTI dataset was used in this secondary data analysis to implement a GIS analysis of TB cases, the locations frequented by cases, and their association with risk factors associated with TB transmission. ^ This study reports, for the first time, the incidence of TB among the homeless in Houston, Texas. The homeless are an at-risk population for TB disease, yet they are also a population whose TB incidence has been unknown and unreported due to their non-enumeration. The first section of this dissertation identifies local areas in Houston with endemic TB disease. Many Houston TB cases who reported living in these endemic areas also share the TB risk factor of current or recent homelessness. Merging the 2004–2005 Houston enumeration of the homeless with historical HTI surveillance data of TB cases in Houston enabled this first-time report of TB risk among the homeless in Houston. The homeless were more likely to be US-born, belong to a genotypic cluster, and belong to a cluster of a larger size. The calculated average incidence among homeless persons was 411/100,000, compared to 9.5/100,000 among housed. These alarming rates are not driven by a co-infection but by social determinants. The unsheltered persons were hospitalized more days and required more follow-up time by staff than those who reported a steady housing situation. The homeless are a specific example of the increased targeting of prevention dollars that could occur if TB rates were reported for specific areas with known health disparities rather than as a generalized rate normalized over a diverse population. ^ It has been estimated that 27% of Houstonians use public transportation. The city layout allows bus routes to run like veins connecting even the most diverse of populations within the metropolitan area. Secondary data analysis of frequent bus use (defined as riding a route weekly) among TB cases was assessed for its relationship with known TB risk factors. The spatial distribution of genotypic clusters associated with bus use was assessed, along with the reported routes and epidemiologic-links among cases belonging to the identified clusters. ^ TB cases who reported frequent bus use were more likely to have demographic and social risk factors associated with poverty, immune suppression and health disparities. An equal proportion of bus riders and non-bus riders were cultured for Mycobacterium tuberculosis, yet 75% of bus riders were genotypically clustered, indicating recent transmission, compared to 56% of non-bus riders (OR=2.4, 95%CI(2.0, 2.8), p<0.001). Bus riders had a mean cluster size of 50.14 vs. 28.9 (p<0.001). Second order spatial analysis of clustered fingerprint 2 (n=122), a Beijing family cluster, revealed geographic clustering among cases based on their report of bus use. Univariate and multivariate analysis of routes reported by cases belonging to these clusters found that 10 of the 14 clusters were associated with use. Individual Metro routes, including one route servicing the local hospitals, were found to be risk factors for belonging to a cluster shown to be endemic in Houston. The routes themselves geographically connect the census tracts previously identified as having endemic TB. 78% (15/23) of Houston Metro routes investigated had one or more print groups reporting frequent use for every HTI study year. We present data on three specific but clonally related print groups and show that bus-use is clustered in time by route and is the only known link between cases in one of the three prints: print 22. (Abstract shortened by UMI.)^

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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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Federal Highway Administration, Office of Highway Planning, Washington, D.C.