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Implementation of weigh-in-motion (WIM) systems.
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  • Edition:
    Final report; 6/14/2000-12/31/2006.
  • NTL Classification:
    NTL-FREIGHT-Trucking Industry
  • Abstract:
    This research finished the development and implementation of a novel and durable, higher voltage,

    and lower temperature dependant weigh-in-motion (WIM) sensor that was begun under an earlier

    research project. These better sensors will require fewer lane closings and replacements than the

    existing sensors. They will also aid the Departments of Transportation to better identify those

    vehicles, which use the nations major highways, that do not comply with the current weight

    restrictions that are placed on larger vehicles. The primary focus of the research was to create a full

    scale WIM sensor that is less temperature dependent and more durable than traditional WIM sensors.

    Traditionally, the data collected from the sensor may be utilized in two ways. The first is by using

    static vehicle effects on the sensor, which corresponds to the weight of the vehicle, this data can be

    used for enforcement of the vehicle legal weight limits. The second is by using the dynamic loading

    of the sensor, which relates to the actual loading that the roadway is experiencing, this data will be

    useful to engineers who must design the roadway as well as plan for repair schedules. However, there

    is a growing trend to broaden the use of WIM data and use the data to its fullest extent. Instead of just

    using WIM data to screen commercial vehicles or for pavement design; there is a new recognition

    that good data can be useful for bridge structural analysis, safety analysis, traffic control and

    operations, freight management and operations, facility planning and programming, and standards

    and policy enforcement as per the recent report “Effective Use of Weigh-in-Motion Data, the

    Netherlands Case Study” FHWA October 2007. In lieu of this development, the need for better

    sensors to provide good data is more important today than ever before.

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