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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>A Data-Driven and Region-Specific Optimization Framework for CCS and WIM Planning
</title>
      <link>https://rip.trb.org/View/2719328</link>
      <description><![CDATA[The primary objective is to enhance and operationalize a data-driven process for systematically managing Office of Transportation Data’s Continuous Count Stations and Weigh-in-Motion sites programs. This project will provide improved network coverage with more accurate representation of  statewide traffic and freight flow patterns, tailored approaches that account for distinct characteristics of Atlanta metropolitan area and the rest of Georgia, and a streamlined, ready to implement decision making process for Continuous Count Stations and Weigh-in-Motion sites planning and deployment.
]]></description>
      <pubDate>Thu, 25 Jun 2026 11:46:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719328</guid>
    </item>
    <item>
      <title>Integrating Weight-in-Motion (WIM) with Vehicle and Land-Use Data Sources to Characterize Freight Truck Patterns and Optimize WIM Site Placement</title>
      <link>https://rip.trb.org/View/2589064</link>
      <description><![CDATA[The objective of this research is to: support Georgia Department of Transportation (GDOT) in enhancing its freight monitoring capabilities by evaluating the effectiveness of its existing weigh-in-motion (WIM) network and assessing the potential of integrating multiple data sources to inform future WIM site placement.

]]></description>
      <pubDate>Thu, 14 Aug 2025 14:09:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2589064</guid>
    </item>
    <item>
      <title>Evaluation of Load Ratings for Alaska Legal Loads Exempted by Federal Law</title>
      <link>https://rip.trb.org/View/2512619</link>
      <description><![CDATA[In Alaska, the gross vehicle weight (GVW) is not specified. Alaska Department of Transportation and Public Facilities (DOT&PF) is working with Modjeski and Masters to evaluate how this could affect the bridge inventory. The study includes the review of weigh-in-motion (WIM) data, overload permit history, current bridge inventory capacity, plus AASHTO and National Bridge Inspection Standards (NBIS) requirements. The research study deliverables include (1) development of a notional load, rating formula, recommended maximum GVW, and live load factors to address loads on designated Alaska Interstate routes which conform to state legal limits but exceed the 80,000-pound Interstate GVW limit, (2) analysis of vehicles from legal loads up to 125% of state legal loads, and (3) recommended reduced inspection frequencies according to the new NBIS requirements for any affected bridges.]]></description>
      <pubDate>Fri, 21 Feb 2025 21:37:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2512619</guid>
    </item>
    <item>
      <title>Assessment of Infrastructure Damage Cost and Compliance of Truck Weight Limit</title>
      <link>https://rip.trb.org/View/2459071</link>
      <description><![CDATA[This proposal will synthesize a comparison between estimated damage infrastructure costs and violation fines associated with overweight trucks on the Brooklyn-Queens Expresswy (BQE) corridor post-implementation of weigh-in-motion (WIM)-based direct enforcement. Even with a reduction in the number and weight of overweight trucks, some structural damage may still occur. The team will evaluate the total damage caused by overweight trucks before and after direct enforcement to assess whether current fines are sufficient to cover the repair costs. The results will help guide legislative updates and support the creation of policies for setting appropriate violation fees through the use of WIM systems for direct overweight enforcement.]]></description>
      <pubDate>Thu, 21 Nov 2024 17:05:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2459071</guid>
    </item>
    <item>
      <title>Estimation of the Load Rating of Existing Highway Bridges Based on Bridge Weigh-in-Motion Data</title>
      <link>https://rip.trb.org/View/2417475</link>
      <description><![CDATA[To ensure safety and uninterrupted functionality, bridges are evaluated for live load capacity, including their reserve capacity for future live loads, which informs important maintenance decisions by state agencies. Previous studies have shown that conventional analytical load ratings without bridge-specific information can often result in overly conservative load capacity ratings, resulting in unnecessary load limitation and posting and remedial actions. As such, objective and data-driven knowledge of actual site-specific loads can result in more accurate load ratings and sizeable cost savings. Bridge Weigh-in-Motion (B-WIM) technology is a low-cost, practical solution to transform a bridge into a scale to characterize traffic loads. B-WIMs can use monitoring data collected from nondestructively instrumented bridges to obtain vehicle loading, speed, and type, as well as axle weights and spacings. This project aims to develop methods and processes for establishing a B-WIM program for the Illinois Department of Transportation and utilizing the data from B-WIM for load capacity ratings. Researchers will aim to come up with a system design that can deliver an accuracy within a tolerance range of ±5% with a confidence level of 95% across the majority of outcomes. The project will provide Illinois Department of Transportation (IDOT) with a comprehensive review of the best practices, a methodology to design and deploy B-WIM systems for Illinois bridges, and a load rating procedure that leverages B-WIM survey data for objective data-driven load rating of IDOT bridges.]]></description>
      <pubDate>Fri, 16 Aug 2024 10:19:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2417475</guid>
    </item>
    <item>
      <title>Weight-In-Motion (WIM) Analysis for New Jersey Bridges for Establishing Various Live Load Models for Design and Bridges Management Task</title>
      <link>https://rip.trb.org/View/2410355</link>
      <description><![CDATA[The goal of the study is to analyze NJ’s recorded weigh-in-motion (WIM) data for establishing various live load models for the design and evaluation of bridges. In addition, the objective is to calibrate the load factors based on the latest edition of the “Manual of Bridge Evaluation (MBE)” for Specialized Hauling Vehicles (SHVs) to avoid (if possible) load posting of bridges. The main tool to analyze the live load effect on bridges is utilizing reliable WIM data. Although there is a gigantic, collected WIM database for New Jersey, there is a need for a reliability-based analysis to update and improve the live load models for bridges in the state of New Jersey as follows:

• Permit trucks with various axle configurations will be identified using WIM data analysis. This will provide an opportunity for NJDOT to add additional live load models (i.e., live load models exceeding the gross weight of more than 80,000) for load rating and evaluate its process for issuing or granting annual permits.

• Validate NJ’s existing LRFD permit load model (i.e., 8-axle & 200 kips) and make necessary changes if needed. Different live load factors would be established for both new bridges and existing bridges.

• Analyze NJ’s existing steel bridge data (e.g., Rolled steel I girder with E and E’ fatigue category) to identify the risk of load-induced fatigue cracking.

• Analyze NJ’s existing steel bridge data (e.g., welded plate girder with skew angle equal to or greater than 30 degrees with staggered cross frames/diaphragms) to identify the risk of distortion-induced fatigue cracking.

• Validate NJ’s existing load factor of 1.30 for operating rating of SHVs to avoid (if possible) load posting of bridges.

]]></description>
      <pubDate>Mon, 29 Jul 2024 10:40:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2410355</guid>
    </item>
    <item>
      <title>Seamless Vehicle and Bridge Monitoring for Transportation and Infrastructure Safety
through a Wireless Internet-of-Things System – Phase I</title>
      <link>https://rip.trb.org/View/2341570</link>
      <description><![CDATA[The goal of this multi-phase project is to unite two traditionally separate vehicle and
bridge monitoring communities for a comprehensive evaluation of transportation and infrastructure safety. To achieve this goal, this project aims to (1) develop and validate a standalone, wireless Internet-of-Things (IoT) vehicle and bridge monitoring system for both collision and overstress detection, (2) deploy and calibrate the IoT system at a
highway bridge site with one type of representative trucks, (3) collect and store real-time traffic, meteorological, structural, and vehicle data, (4) cleanse and analyze heterogeneous data (numeric, image, audio, and video) through influence line analysis and machine learning for the extraction of features related to vehicle safety and infrastructure condition, and (5) develop and validate a visual mechanism to alert truck drivers as they drive underneath or across the highway bridge. The outcomes of this project are to mitigate collision-induced bridge damage, vehicle-related highway fatalities and injury rates through such an integrated vehicle and bridge monitoring in real time.

To address the first and second objectives, the scope of Phase I project includes, but is not limited to, (a) literature survey on bridge-weigh-in-motion (BWIM) and load tests, (b) development of a laboratory testbed of vehicle monitoring and BWIM system, and (c) scale-up of the laboratory testbed for field installation and validation.
]]></description>
      <pubDate>Mon, 19 Feb 2024 16:28:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2341570</guid>
    </item>
    <item>
      <title>Multiple-Sensor Weigh-In-Motion Systems to Enhance Data Accuracy and Reliability



</title>
      <link>https://rip.trb.org/View/2286618</link>
      <description><![CDATA[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.    

OBJECTIVE: 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. ]]></description>
      <pubDate>Mon, 06 Nov 2023 16:33:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2286618</guid>
    </item>
    <item>
      <title>Impact of WIM-based Direct Enforcement on the Service Life of Bridges
</title>
      <link>https://rip.trb.org/View/2283487</link>
      <description><![CDATA[The Brooklyn-Queens Expressway (BQE) in New York City is a crucial corridor connecting the two boroughs of Brooklyn and Queens with other counties. Given its substantial daily traffic for transporting goods and services, the longevity of BQE is crucial to enhance public safety and alleviate congestion. The team has collaborated with the New York City Department of Transportation (NYCDOT) to implement (1) an integrated weigh-in-motion (WIM) systems on the northern part of the BQE corridor for a direct enforcement of the high percentage of overweight (OW) trucks, and (2) a structural health monitoring (SHM) system to estimate the remaining service life of the BQE structures. This project synthesizes WIM and SHM data to study the impact of the reduction in OW percentage over time resulting from direct OW enforcement on extending the service life of the BQE. Furthermore, the team will conduct a life-cycle cost analysis (LCCA) of the network of bridges in NYC based on the established correlation. The output of this project will be a new framework to evaluate the effect of reduced OW percentage on the service life prediction. This framework will help introduce new legislation(s) for direct OW enforcement to mitigate the number of OW trucks and their OW tonnages; thus, improving bridge service life and preserving highway infrastructure. Another aspect of this proposal is to expand the potential uses of physical testbeds to investigate the feasibility of utilizing biometric sensors, including eye tracking glasses, galvanic skin response sensors, and heart rate trackers, to assess the perceived safety of micro-mobility users, encompassing both cyclists and e-scooter riders. The primary objective is to collect pilot data to develop well-structured semi-naturalistic experiment protocols, allowing for the acquisition of reliable psychological data concerning the safety perceptions of micromobility users. The acquired sensor data will be cross-referenced with qualitative survey responses to analyze the advantages and disadvantages of various methods for collecting safety perception data among micromobility users. The data collected from these experiments will play a crucial role in providing insights into the types of infrastructure designs that are well-received by micromobility users and identifying built environments considered unsafe for travel. These findings will be invaluable for informing infrastructure design improvements aimed at enhancing the travel experiences of micromobility users, supporting mode shifts, and mitigating congestion.]]></description>
      <pubDate>Mon, 30 Oct 2023 22:45:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2283487</guid>
    </item>
    <item>
      <title>Investigation of Heavier-than-Expected Vehicle Weights Observed in the Vicinity of the Savannah Port Area and their Impact on Georgia’s Pavements and Bridges and Rate of Statewide Asset Degradation </title>
      <link>https://rip.trb.org/View/2265649</link>
      <description><![CDATA[
The primary objective of Part A of this project is to investigate the impact of heavy vehicle traffic on pavement and bridge structures in Georgia. The main objective of Part B of this project is to evaluate pavement and bridge structures using weigh in motion (WIM) data, conduct field investigations, and evaluate the reliability of existing pavement and bridge structures.]]></description>
      <pubDate>Tue, 10 Oct 2023 12:24:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2265649</guid>
    </item>
    <item>
      <title>Low-Cost Sensing System for the Detection and Classification of Wide Base Tire Types and Distribution at the Network Level
</title>
      <link>https://rip.trb.org/View/1992631</link>
      <description><![CDATA[This project developed and tested a low-cost novel sensing system to detect and classify wide-base tire (WBT) types and their distributions. It will also demonstrate the system's usefulness in data collection for pavement analysis and design applications. The first phase of the project involved developing and testing prototypes of a novel low-cost sensing system that detects tire widths, wheel wander, and truck/axle configurations at highway speeds using piezoelectric sensors. These sensors generate a voltage proportional to the applied force when a wheel applies pressure. Considering practical field installations, a rubber-based sensor casing was designed. A prototype sensor assembly was prepared using the ethylene propylene diene monomer (EPDM) rubber strips, incorporating piezoelectric sensors between two rubber strips for evaluating their response to varying tire widths and wander. Field tests with vehicles validated the EPDM sensor strip embedded with piezoelectric sensors. Sensor responses were collected for an SUV and a sedan from the EPDM rubber strip housing 16 piezoelectric sensors to compare model results with the experimental data in the field. The analytical model used these field tests' strain and voltage data and successfully identified the vehicle passage, classification, and tire widths. Based on the findings from field tests, a 24-foot-long EPDM rubber strip was prepared with 32 piezoelectric sensors embedded between two rubber strips. This strip was placed across Wilson Road on the Michigan State University (MSU) campus to gather traffic data using the electronic system. The developed model analyzed field traffic data to validate the time response signals and classify vehicles. Also, the sensor system collected data for axle passage time, tire width, and wheel wander over 36 hours, including 139 vehicles. The data analysis and validation results showed consistent measurements with a 1.6% error in vehicle classification. Subsequently, the team developed a 40-foot-long sensor strip that housed 48 piezoelectric sensors positioned along the expected wheel path in the outer lane. The sensor was placed adjacent to a WIM site on US127 in Mason, MI, and in St. Johns, MI, to collect vehicle passage data for vehicle classification (i.e., based on axle count, wheelbase, and wheelbase ranges), along with tire width and wheel wander. The sensor was deployed for 5 and 3 days at Mason and St. Johns locations. The sensor collected over 20,000 vehicles at Mason and identified 19% as WBTs for Class 9 trucks. Over 12,000 vehicle data was collected at the St. Johns location, with about 16% WBTs for Class 9 trucks. Compared to WIM data, the classification exhibited an error rate of less than 2%. Additionally, the team analyzed vehicle loads by matching the timing of vehicle passage over the sensor with WIM data. This provided detailed load spectra for tandem axles with WBT and dual tires.
There are several perceived benefits of the developed system to transportation stakeholders. By collecting data that directly informs pavement design, the system extends infrastructure lifespan and lowers maintenance costs. Its low-cost, scalable design makes it accessible for agencies aiming to enhance road monitoring without the high expense of traditional weigh-in-motion systems, offering a practical, budget-friendly alternative. Moreover, this system supports broader transportation safety and sustainability goals by helping enforce tire width regulations and promoting road safety through accurate tire and vehicle classifications. This product represents a forward-thinking tool for state and national agencies looking to modernize their monitoring practices, enabling cost-effective and reliable road infrastructure management.]]></description>
      <pubDate>Mon, 11 Jul 2022 17:41:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1992631</guid>
    </item>
    <item>
      <title>Evaluation of Integrated Overweight Enforcement System using High Accuracy WIM System and Non-Proprietary ALPR System</title>
      <link>https://rip.trb.org/View/1942837</link>
      <description><![CDATA[The main objective is to establish the second testbed for overweight enforcement along the BQE corridor in New York City. The team will develop the drawing for the site-specific sensor layout, install the Quartz sensors and automated-license-plate-recognition (ALPR) cameras to measure truck weight data and identify license plate and/or USDOT number, and evaluate the performance of the overweight enforcement system. The team will also estimate the impact of an extreme event using data collected for infrastructure resilience.]]></description>
      <pubDate>Fri, 22 Apr 2022 10:42:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/1942837</guid>
    </item>
    <item>
      <title>Development of a Virtual Weigh-In-Motion System for Enhanced Pavement System Management</title>
      <link>https://rip.trb.org/View/1840494</link>
      <description><![CDATA[This research project proposes to leverage the data collected by P-WIM developed by the PI Wang and streams captured by the traffic surveillance video from the state DOTs to develop a low-cost, powerful Virtual Weigh-In-Motion (V-WIM) system based on analytical, computer vision, and machine learning techniques. This proposed system is capable of capturing a diversity of actionable information of moving vehicles on roadways that will enhance the pavement management system. In addition to the P-WIM system that captures weights of passing vehicles, the proposed V-WIM system provides functions including vehicle detection, speed measurement, identification of vehicle’s axle configurations, recognition of vehicle types, and measurement of volumes of different types of vehicles that have passed the installation location of the system within certain period of time. This proposed system will eliminate the need of the induction loop installation in the pavement for vehicle presence detection. A wireless transmission module will be included in the package of the proposed system to enable wireless data transmission to transportation agencies. The V-WIM will be powered by the energy harvested from pavement deformations and vibrations, developed as a function in the P-WIM system. If the system is successfully developed, it will allow better pavement maintenance practices by determining a diversity of useful parameters of the passing vehicles over the pavement where the system is installed. The developed low-cost system will enrich the functionalities of the current WIM system and generate actionable information for transportation agencies in support of achieving enhanced pavement system management.]]></description>
      <pubDate>Fri, 12 Mar 2021 12:08:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/1840494</guid>
    </item>
    <item>
      <title>Study on hybrid model combining super learner and physic-based models for SHM in bridges using low-cost BWIM</title>
      <link>https://rip.trb.org/View/1751158</link>
      <description><![CDATA[Many structural health monitoring (SHM)
techniques have been devised over the past
decades. However, there is no one-size-fits-all
solution that can be applied to all bridges for
structural assessments. Bridge-based weight-in-motion systems (BWIM) use the structure’s
response to estimate vehicles’ load distribution.
This technology is primarily used to obtain vehicle
axle weights efficiently in public. BWIM can be a
candidate that overcomes the shortfall of SHM.
The use of BWIM systems for SHM has rarely been
investigated. The objectives are (1) to study and
deploy low-cost BWIM sensors for accurate SHM,
(2) to evaluate the S-BWIM system, and (3)
assessment of the hybrid model capacity
combining physics-based mathematical models
(PSM) and practical machine-learning (ML)
models. A new low-cost BWIM system verified
with numerical results will be installed in the local
area, Dallas, and Fort Worth (DFW). This study will
help Region 6 communities, where low-cost
measurements are already used, and prediction
models are publicly available for monitoring both
traffic loads and bridge conditions. The
development of a hybrid model generally
adaptable for various conditions of bridges will be
a major contribution to the research community.
A comparative study of the proposed machine
learning algorithm (super learner) with low-cost
BWIM sensors will also be implemented in Region
6. 
]]></description>
      <pubDate>Tue, 10 Nov 2020 20:35:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1751158</guid>
    </item>
    <item>
      <title>Implementation and Effectiveness of Autonomous Enforcement of Overweight Trucks in an Urban Infrastructure Environment</title>
      <link>https://rip.trb.org/View/1704056</link>
      <description><![CDATA[The objective of this proposal is to implement an Advanced Weigh-In-Motion (A-WIM) system for autonomous enforcement of overweight trucks and study its effectiveness in reducing the number of illegal overweight trucks in an urban infrastructure environment. The work includes the development of various algorithms to help reduce the error in weighing vehicle weight due to environmental conditions and inherent factors, to accurately quantify the effects of illegal overweight trucks on infrastructure. In addition, the team will integrate and implement different technologies, such as camera, radio frequency identification (RFID), automatic license plate recognition (ALPR), etc. with A-WIM system at two potential sites located on the Brooklyn-Queens Expressway, Brooklyn, NY. The team will also perform life cycle cost analysis for various types of WIM sensors and systems to promote the most efficient and appropriate WIM system for use in autonomous enforcement.]]></description>
      <pubDate>Tue, 05 May 2020 09:22:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/1704056</guid>
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