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Decision framework for corridor planning within the roadside right-of-way.

File Language:
English


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  • NTL Classification:
    NTL-HIGHWAY/ROAD TRANSPORTATION-HIGHWAY/ROAD TRANSPORTATION ; NTL-PLANNING AND POLICY-PLANNING AND POLICY
  • Description:
    A decision framework was developed for context-sensitive planning within the roadside ROW in

    Michigan. This framework provides a roadside suitability assessment model that may be used to

    support integrated decision-making and policy level considerations for ROW use and

    development. The model accommodates a broad range of potential types of ROW developments

    and corridor conditions, while considering a diverse range of roadside contextual features,

    including land use (current and future), land cover, environmental features, natural resources,

    and plant and animal habitats, among other features. The primary function of the model is to

    determine the area(s) along a highway corridor that are most (or least) suitable for development

    within the roadside ROW. Calculation of a suitability index for each area along a corridor

    provides a relative indication of the overall suitability for development within the roadside

    ROW. The roadside suitability assessment was demonstrated using a 20-mile pilot section of I-

    94 in Kalamazoo and Calhoun Counties within Michigan. Five types of non-traditional ROW

    development were considered for the corridor, including solar and wind power production,

    vegetation management/landscaping, agriculture/farming, and green infrastructure. The resulting

    suitability index scores showed good relative agreement with standard land-use planning

    considerations. Robust and accurate statewide geospatial data is crucial for effective utilization

    of this model. To that end, recommendations for reconciliation of gaps in the existing geospatial

    datasets were provided.
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    PDF
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    Filetype[PDF - 1.47 MB]
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  • Main Document Checksum:
    urn:sha256:3b6fc7fe3a0f4f49ee6a1b6325616db204de85b328cdc198ad7d4eb487402272
File Language:
English
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