Development of Eight-Channel WIM Analysis and Measurement System Based on Analog WIM Signals

Principal Investigator:

Taek Kwon, Professor, UMD-Electrical Engineering

Project Summary:

Weigh-in-motion (WIM) data provide vital information for pavement design and maintenance. The primary purpose of this research project was to improve the present piezoelectric WIM technologies through a better system design and signal processing algorithms. Since current WIM systems are only available as proprietary systems (i.e. the internal system design and algorithms are highly guarded, making it difficult to compare and improve the underlying technology), the second objective was to develop a WIM system based on an open architecture, using a standard personal computer and off-the-shelf components, and to publish the details of the design to promote an open architecture for continuous future improvements by other developers. The main innovation introduced in this research is a hardware-in-the-loop (HIL) WIM simulator that can generate analog axle and loop signals through software control. The HIL simulator can create ideal axle signals as well as erroneous signal conditions that can be fed directly into WIM systems. The main advantage of using a WIM HIL simulator for developing a WIM system is that the developers may run an unlimited number of signal tests without actually driving a single vehicle through the WIM sensors, thereby significantly reducing the development time and cost. The erroneous signal conditions generated by the HIL simulator can also identify the error-handling capabilities of a WIM system. The proposed HIL simulator for WIM system development is new and provides an elegant solution to the unavailability of an ideal axle signal. Full details of the successful working eight-channel WIM system developed by the research team are described in the project?s final report.

Sponsor:

Project Details:

  • Start date: 09/2005
  • Project Status: Completed
  • Research Area: Transportation Safety and Traffic Flow
  • Topics: Traffic Modeling and Data