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    Multimodal Assessment of Recurrent and Non-recurrent Conditions on Urban Streets

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    Kastenhofer_IO_D_2014.pdf (1.344Mb)
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    Date
    2014-09-15
    Author
    Kastenhofer, Ilona Ottilia
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    Abstract
    The methodology to measure the performance of urban streets was significantly revised in the latest edition of the Highway Capacity Manual (HCM 2010). Urban Streets, which include urban and suburban signalized arterial highways, typically serve the four modes of transportation (auto, transit, pedestrian and bicycle) and are frequently congested. Analyzing both recurrent and non-recurrent conditions is essential. In this dissertation, the author addressed several urban streets related issues by developing an alternative method to measure recurrent multimodal conditions on urban streets; gathering feedback relating to the key elements of the developed method; and developing a probabilistic method to analyze and measure non-recurrent conditions. Real life sample applications were performed for both developed methods. The developed multimodal method addresses the following: (1) the use of level of service (LOS) step functions; (2) the comparability of LOS results across modes; (3) the impacts of modes on other modes; (4) the establishment of thresholds; (5) accuracy; and (6) user perceptions in measuring multimodal conditions on urban streets. Feedback gathered from transportation professionals through focus group meetings and surveys supported most of the features of the developed multimodal method and provided default values for method application. They were divided on the naming of condition levels and on the number of condition levels to use. Non-recurrent conditions were addressed through the development of a Markovian probabilistic method to analyze and measure the resilience of congested, signalized, arterial highways, for which availability of existing analytical tools is limited. The method results provide a plexiform of information about the rate and speed of recovery of the arterial traffic flow.
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    http://hdl.handle.net/10919/64908
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    • Doctoral Dissertations [14973]

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