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Model Information Exchange System (MIXS).
  • Published Date:
    2013-08-01
  • Language:
    English
Filetype[PDF-4.13 MB]


Details:
  • Publication/ Report Number:
    BDK75 977-55
  • Resource Type:
  • Geographical Coverage:
  • NTL Classification:
    NTL-PLANNING AND POLICY-PLANNING AND POLICY ; NTL-PLANNING AND POLICY-Travel Demand ;
  • Format:
  • Abstract:
    Many travel demand forecast models operate at state, regional, and local levels. While they share the same physical network in overlapping geographic areas, they use different and uncoordinated modeling networks. This creates difficulties for models to exchange common information and can result in data collection redundancies and difficulties in finding data errors or comparing future projections. This research investigated the issues of network information exchange among models and proposed a framework to accomplish the information exchange using a unified statewide Geographic Information Systems network approach. Named 'Model Information eXchange System' (MIXS), the proposed solution includes a geospatial relational data model that can support all participating models with their input variables and forecast scenarios; protocols to guide the exchange process; Web-based tools to visualize, compare, extract and upload models; and a process to handle network updates. Two tests, one with a small database and another with a full statewide model, confirmed the feasibility of the MIXS database and processes. Although participation in MIXS is not without challenges, most technical problems considered can be solved. Successful implementation of MIXS will require Department of Transportation leadership and support, participation and commitment of various regional and local modeling agencies, and a one-time conversion to the unified network for any model to participate in MIXS. MIXS will create an environment that promotes convergence, standardization and unification of data and potentially model assumptions, reduction of duplicate data collection efforts, reduction of errors, and ultimately will result in better and more consistent models throughout the state. A potential linkage of MIXS with a cloud-based modeling engine is recommended as one of the future items to explore during its practical implementation.

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