Analysis of vehicle classification and truck weight data of the New England States : is data sharing a good idea?
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Analysis of vehicle classification and truck weight data of the New England States : is data sharing a good idea?

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English

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  • OCLC Number:
    665169039
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  • NTL Classification:
    NTL-FREIGHT-Trucking Industry;NTL-REFERENCES AND DIRECTORIES-Statistics;
  • Abstract:
    This paper is about a statistical research analysis of 1995-96 classification and weigh in motion

    (WIM) data from seventeen continuous traffic-monitoring sites in New England. Data screening is

    discussed briefly, and a cusum data quality control procedure is proposed. The main purpose of the

    analysis, however, is to infer statistical methods for using data from multiple states in a common

    resource data pool. Because data sharing means cross-state extrapolation, the combined data should

    not be used without a proper statistical accounting for extrapolation error. Another major concern

    in implementing a data-sharing procedure is operational simplicity. Of particular interest are the

    possible analytical simplifications of combining vehicle classes (i.e., reducing the number of vehicle

    classes used in practice) or combining HPMS roadway functional classes. Adjusting for seasonal

    and day-of-week effects is also a concern.

    Conclusions based on the analysis are still preliminary. Analysis of the ultimate use of the data

    suggests that from the perspective of vehicle load estimation, there is little advantage to combining

    vehicle classes. Analysis of both the WIM and classification data suggests that differences among

    HPMS functional classes are sufficient to warrant against combining functional classes. But even

    without these simplifications, data sharing among states is a good idea. The analysis method used

    here, one-way analysis of variance, is reasonably simple (can be done with an ordinary spreadsheet

    program), provides an accounting for statistical error, and is thus an appropriate analysis tool for data

    sharing.

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