The National Academies

NCHRP 10-128 [Pending]

Multiple-Sensor Weigh-In-Motion Systems to Enhance Data Accuracy and Reliability

  Project Data
Funds: $450,000
Contract Time: 30 months
Staff Responsibility: Camille Crichton-Sumners
Comments: In development


Weigh-in-motion (WIM) systems measure the axle weight of moving vehicles as they traverse WIM measurement sites. WIM data are essential for the design, assessment, and maintenance activities related to pavement and bridge infrastructure and may be used for monitoring and enforcing motor carrier truck weights and dimensions and collecting tolls. 

WIM sensors vary from instrumented metal plates to piezoelectric, quartz, and strain gauge strip sensors. Their accuracy is evaluated with reference to static loads referenced in the American Society for Testing and Materials, Standard Specification for Highway Weigh-In-Motion (WIM) Systems with User Requirements and Test Methods, ASTM E1318-09 (2017) and is affected by the interaction between roadway roughness, vehicle dynamics, and speed. The narrow strip-type WIM sensors that sample a smaller part of the dynamic axle loads applied to the road may be strategically spaced to capture more data points of the dynamic axle load waveforms by using multiple strip sensors (two or more). The use of multiple strip sensors will potentially result in increased data reliability and more accurate estimates of the corresponding static axle loads, reduce measurement error, improve data quality, and reduce maintenance costs.  

State departments of transportation (DOTs) require accurate and cost-effective WIM technology. Research is needed to assess and optimize multiple-sensor spacing and determine its benefits and feasibility.    


The objective of this project is to develop a model to determine the optimal number of WIM strip sensors and array layout, given specified levels of accuracy and reliability considering pavement, environmental, and traffic conditions. 

Accomplishment of the project objective will require at least the following tasks. 


When developing the research approach, consideration should be given, but not limited to, the following factors or concepts:

  • Strip sensor specifications;
  • The need for WIM sensor redundancy;
  • Maintenance of WIM sites (i.e., pavement, controller, sensors);
  • Durability of installed sensor array;
  • A range of what state DOTs consider accurate and reliable; and
  • Direct or automatic enforcement is defined as specified in the ISWIM Guide for Users of WIM. 


Task 1. Prepare a literature review related to multiple-sensor WIM technology. 

Task 2. Prepare a summary report that describes the state of practice for WIM strip sensors and identifies a broad range of strip sensors that are accessible to state DOTs. 

Task 3. Identify the data inputs for the development and validation of a multiple-sensor WIM optimization model(s) that will be used for the development of a decision support spreadsheet tool, readily available to state DOTs and easy to use. 

Task 4. Prepare an interim report that summarizes the findings from Tasks 1 through 3 and a detailed Phase II work plan that describes how the findings of Tasks 1 through 3 will inform the development of a multiple-sensor WIM optimization model(s) and practitioner’s spreadsheet tool. 


Task 5. Develop the multiple-sensor WIM optimization model(s) and validate it using field data. Provide the test criteria and procedures used to validate the model. 

Task 6. Using the model developed in Task 5, develop a WIM optimization decision-support spreadsheet tool for practitioners that incorporates inputs related to pavement, environmental, and traffic conditions, and sensors. Within the spreadsheet tool, provide a graphical representation that shows the implications of alternative sensor layouts (i.e., sensor spacing and the number of sensors) and their impact on accuracy. End-user instructions should be included. 

Task 7. Identify a core group of subject matter experts to beta test the decision-support spreadsheet tool developed in Task 6 and conduct beta testing. 

Task 8. Convene a virtual workshop to provide a tool demonstration and solicit additional feedback from the community of practice. 

Task 9. Modify the multiple-sensor WIM optimization model and the spreadsheet tool as needed based on feedback solicited during beta testing and the virtual workshop. 

Task 10. Prepare the final deliverables.

STATUS: Pending.

To create a link to this page, use this URL: http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=5509