Effective Coordination with Towing Companies for Incident Response and Clearance

Tow truck operators are directly exposed to traffic when responding to incidents. In addition, stalled vehicles on the highway can hinder safety and operations on the highway and can create a risk of secondary incidents. Fast and effective removal of stalled vehicles on the highway can improve traffic operations and safety for tow truck operators and all users of the transportation system. The objectives of this research study are to synthesize existing department of transportation (DOT) practices regarding coordination with towing companies and to identify potential collaboration and training opportunities that will improve towing worker safety as well as traffic safety by improving incident response and clearance. The research methodology to meet this objective includes a literature review, survey, and coordination with the tow trucking industry, specifically the Missouri Tow Trucking Association (MTTA). Results indicate that training programs (26 responding DOTs) and coordination with the state towing association (23 responding DOTs) are the strategies most used by responding DOTs for partnering with the towing industry. Eight responding DOTs have implemented a Towing Recovery Incentive Program (TRIP) that provides incentive payments for towing companies to clear incidents within a specified timeframe. DOTs have found TRIPs to be beneficial in clearing incidents sooner. During an informal focus group as part of their monthly meeting, MTTA provided feedback on various topics, such as communication needs, interest in training, and the need for regulations that improve the quality of tow operators. The research identified potential opportunities for MoDOT and the State of Missouri to enhance its coordination practices with MTTA, such as including the towing industry in the planning process for major projects, implementation of regulations for requirements of tow operators, and partnering to strategize on how to best manage incidents involving electric vehicle fires.

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Report number: cmr 25-014
Published: September 2025

Project number: TR202514

Authors: Henry Brown, Carlos Sun, and Zhu Qing  

Performing organization: University of Missouri-Columbia

Assessment and Repair of Prestressed Bridge Girders Subjected to Over-height Truck Impacts

This research investigates the impact of over-height vehicle collisions on prestressed concrete bridge girders and explores effective repair strategies to restore their structural integrity. The study addresses a critical concern in bridge resilience, as vehicle impacts can cause varying degrees of damage and severe prestressing strands which compromise girder flexural strength and overall safety. A comprehensive approach was employed, integrating numerical modeling and experimental testing to assess damage mechanisms and evaluate the effectiveness of different repair techniques. Key findings include the determination of equivalent static force for semi-tractor trailers and rigid objects. Shear failure was identified as the dominant failure mode of prestressed concrete girders under impact loading. Additionally, accidental lateral eccentricity was found to reduce flexural resistance, necessitating a 15% reduction factor in AASHTO LRFD guidelines. A practical technique for measuring residual prestress forces was developed and validated with an experimental test and found to be conservative by 9.4%. Repair methods using mechanical strand splicing successfully restored up to 95% of the original strength for strand losses ranging from 17% to 33% using innovative confinement techniques. Moreover, externally bonded carbon fiber-reinforced polymer (CFRP) repairs fully restored flexural strength for up to 33% strand loss, with an additional strength reserve of 15%–23%.

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Report number: cmr 25-013
Published: September 2025

Project number: TR202011

Authors: Haitham AbdelMalek, Francis Ashun, Mohamed Elshazli, Mohanad Abdulazeez, Ahmed Gheni, Ahmed Ibrahim, and Mohamed ElGawady

Performing organization: Missouri University of Science and Technology

AI-Enabled Vision System for Intersection Analytics

This project developed and demonstrated an AI-enabled vision system for intersection analytics capable of automatically generating Turning Movement Counts (TMCs) and integrating them with Signal Phase and Timing (SPaT) data to evaluate traffic signal performance. Using existing Missouri Department of Transportation (MoDOT) CCTV (Closed Circuit Television) infrastructure, the system eliminated the need for manually placed detection zones and significantly reduced the labor traditionally required for data collection.

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Report number: cmr 25-012
Published: September 2025

Project number: TR202506

Authors: Yaw Adu-Gyamfi and Muturi Turner

Performing organization: University of Missouri-Columbia

Traffic Disruption-free Bridge Inspection Initiative with Robotic Systems

The current practice of bridge inspection requires the use of snooper trucks and, when parked on bridges, the control of passing traffic, thus causing an operation safety concern for both passengers and inspectors. It also leads to inherently subjective and qualitative results. This pooled-fund study aims to develop case studies on the deployment and performance of Bridge Inspection Robot Deployment Systems (BIRDS) developed in the INSPIRE University Transportation Center for faster, safer, cheaper, and more quantitative bridge inspection with minimum impact on traffic flow. To this endeavor, an automated bridge preservation framework was envisioned to integrate advanced robotics, remote sensing, and nondestructive testing into the practice of visual inspection and associated maintenance. By evaluating the advanced technologies at 59 bridges in diverse types, age groups, and geographical locations, the best practices of the technologies were summarized in inspection protocols and guidelines using commercial drones, structural crawlers, and custom-built hybrid uncrewed vehicles. Vision-based instance segmentation via machine learning efficiently and effectively detected, located, and quantified weld defects in steel bridges, including cracks, debonding, and cavity, in real time. Topside and underside deck inspections were compared to ensure the reliability of traffic disruption-free bridge inspection from the underside of the bridge deck. By combining flying, traversing, and crawling capabilities, the award-winning invention - BIRDS offered a versatile robotic solution that addressed the limitations of commercial drone technologies. Its ability to seamlessly transition between aerial and ground-based inspection modes ensured a comprehensive coverage of bridge structures. This innovation enabled both global visual monitoring and local detailed inspection using remote sensing (e.g., microscope imager and laser scanner) and nondestructive testing (e.g., ultrasonic metal thickness gauge) for the detection and quantification of bridge surface and substrate defects. Since a limited type and number of defects were observed from the selected bridges, more bridges should be inspected to collect big data required in machine learning to develop decision-making support tools toward data-driven bridge asset management.

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Report number: cmr 25-011
Published: September 2025

Project number: TR202004

Authors: Genda Chen, Zhenhua Shi, Son Nguyen, Mohammad Hossein Afsharmovahed, Peter Damilola Ogunjinmi, Ying Zhuo

Performing organization: Missouri University of Science & Technology

GFRP Reinforced Bridge Barriers: Experiment Testing

The reported work includes the experiment testing of glass fiber reinforced polymer (GFRP) and steel reinforced concrete bridge barriers. A total of four reinforced concrete barriers were prepared, including three GFRP reinforced barriers and one mild steel reinforced barrier, and cast on two concrete slabs. Impact testing was conducted using a cart with weight and a long straight sled system. The force, displacement, and strain data were measured. The results demonstrated that GFRP is fully capable of withstanding impact loads that could occur in real-world scenarios.

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Report number: cmr 25-010
Published: July 2025

Project number: TR202319

Authors: Congjie Wie, Manish Kumar Gadhe, John J. Myers, and Chenglin Wu

Performing organization: Missouri University of Science & Technology

Shear Wave Velocity Measurements

The Missouri Department of Transportation (MoDOT) commissioned SCI Engineering, Inc. to investigate cost-effective, non-intrusive geophysical methods for determining time-averaged shear wave velocity (Vs) profiles to a depth of 100 feet, based on anticipated updates to AASHTO seismic site classification specifications. Following a comprehensive literature review, eight candidate methods were identified. Of these, four methods—Active Multichannel Analysis of Surface Waves (Active MASW), Passive Multichannel Analysis of Surface Waves (Passive MASW), Active Refraction Microtremor (Active ReMi), and Passive Refraction Microtremor (Passive ReMi)—were selected for field testing based on their practicality and effectiveness.

Field evaluations were conducted at three sites: the SCI Office in O’Fallon, Illinois; the I-270 Chain of Rocks Bridge; and the MLK Connector in Illinois. The sites were selected based on access, availability of existing groundtruth subsurface soil information, as well as representing a variety of subsurface profiles.  Various geophone spacings and array configurations were tested. Performance metrics included depth of investigation, ease of deployment, data quality, and interpretability. Results demonstrated that combining Active and Passive MASW methods offered the most reliable and practical solution, providing consistent results, minimal operational complexity, and shared equipment and software requirements.

SCI Engineering developed a comprehensive User Manual and conducted field demonstrations to train MoDOT personnel in the acquisition, processing, and interpretation of MASW data. Adoption of these methods will streamline MoDOT’s seismic site classification processes, align practices with forthcoming AASHTO requirements, and enhance MoDOT’s internal technical capacity.

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Report number: cmr 25-009
Published: June 2025

Project number: TR202403

Authors: Evgeniy "Eugene" Torgashov, Neil Anderson, and Thomas J. Casey

Performing organization: SCI Engineering, Inc.

Testing Survey Methods for Detecting Bats Roosting in Bridges

Bats are a critical component of our natural world, and many species are at risk. Protecting roosting habitat is one way we can help conserve a variety of species. Although many bats use natural roosts, a growing number are adapting to anthropogenic structures due to habitat encroachment. In this study, we tested methods for detecting bats using bridges as roosts. We visited 20 bridges four times each to test six daytime methods (human visual and hearing, use of an acoustic detector, use of an agitator to induce bat vocalization, visual search with a spotlight, use of a thermal camera, and a borescope) and three evening emergence methods (human visual, thermal camera, and acoustic detector). Occupancy modeling revealed that the most effective way to document bat use at bridges is with an acoustic detector during evening emergence. This was followed by the use of thermal cameras during evening emergence, and the third best model was use of thermal cameras during the day. Surveying longer did not increase detectability in any of the top models. Based on our findings and suggestions in guidance documents for detecting bats in bridges, the first step is to survey a bridge with a spotlight, listening for bat vocalizations, and noting smell. If bats are not detected during the day, using acoustic detectors and thermal cameras during emergence will determine if bats are using bridges and can provide additional data if they are documented using them during the day.

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Report number: cmr 25-008
Published: June 2025

Project number: TR202420

Authors: Piper L. Roby, Crystal Birdsall, and Timothy Divoll

Performing organization: Copperhead Environmental Consulting, Inc.

Audible Alert and TMA Lighting

Truck Mounted Attenuators (TMAs) are designed to mitigate crash severity. Currently, TMA drivers rely on visual checks via driving mirrors to manually trigger warnings thus placing the duty on drivers. To address this limitation, the Automated TMA Warning System (AutoTMA) replaces or augments manual driver interventions with an AI-enabled, sensor-fused platform. By integrating high-definition cameras, LiDAR, and radar with GPU-accelerated multi-task learning, AutoTMA continuously detects and classifies oncoming vehicles, segments lane and drivable areas, and calculates dynamic distance thresholds—safe, warning, and danger—in real time. Validation of the AutoTMA included comprehensive trials within a Unity 3D simulation environment and test-track deployments on Missouri Department of Transportation (MoDOT) TMAs. Through iterative refinements, the system’s response latency has been reduced from three seconds to 0.25 seconds, substantially improving both visual and audible alert accuracy. AutoTMA’s modular architecture and robust sensor calibration mechanisms ensure rapid component replacement and resilience in variable operational conditions. Drawing on insights from prior research, including National Cooperative Highway Research Program (NCHRP) 05 24, the system optimizes lighting and audio cues while integrating adaptive safety zone parameters to overcome the limitations of fixed configurations. Preliminary findings confirm AutoTMA’s ability to detect imminent collisions and deliver timely, context-sensitive warnings—significantly enhancing driver awareness and reducing the probability of TMA-involved crashes. AutoTMA marks a transformative shift in work zone safety protocols, offering a viable pathway for nationwide adoption. Future work will focus on expanding sensor modalities, further refining AI models to boost accuracy, and broadening field trials across diverse environments. By bridging the gap between manual vigilance and automated safety, the AutoTMA system not only improves operational workflows but also holds the promise of shaping policy and accelerating the integration of proactive safety technologies in transportation.

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Report number: cmr 25-007
Published: June 2025

Project number: TR202309

Authors: Yaw Adu-Gyamfi, Carlos Sun, Mark Amo-Boateng, Gahan Gandi, and Neema Jakisa Owor 

Performing organization: University of Missouri-Columbia/Missouri Center for Transportation Innovation

Evaluation of Stripping Tests for Asphalt Mixtures to Replace AASHTO T283 Method in Missouri

Currently, many paving agencies in the U.S. including the Missouri Department of Transportation (MoDOT) use the AASHTO T¬-283 method (Tensile Strength Ratio (TSR) test) to determine the moisture damage susceptibility of asphalt mixtures. However, the TSR test has been shown to have a poor correlation with field results based on a review of literature and based on observations reported by MoDOT. In addition, the TSR test is time consuming and may be redundant in light of current requirements to conduct the Hamburg Wheel Tracking Test (HWTT) as part of balanced mix design. As a result, further research on these test methods was conducted. For this research five asphalt mixtures were investigated. The mixtures were subjected to the TSR test and HWTT. The Stripping Inflection Point (SIP) parameter was computed from HWTT using the Iowa method. The SIP parameter was found to be superior to the TSR test in correlating to field performance. Comparison of the RT-Index results with the SIP parameter suggested that the RT index is likely a weak indicator of moisture damage in asphalt mixtures. Based on the results obtained in this limited study, a framework was proposed to replace the TSR method. The framework is as follows; first, the mixtures are screened for rut depths lower than 4.0 mm at 20,000 passes in the Hamburg test. If the mixture exhibits low rut depths in the Hamburg test (less than or equal to 4 mm), it is highly likely that it is resistant to moisture damage and therefore judged as non-stripping. Second, if the rut depth is greater than 4.0 mm then the slope ratio is computed. If the slope ratio is found to be less than 2.0, then the mixture can be categorized as non-stripping. Finally, if the slope ratio is greater than or equal to 2.0, then the SIP is determined. A minimum threshold of 15,000 passes was chosen as the SIP threshold for initial implementation of the framework. Mixtures possessing SIP values less than 15,000 are scored as failing the stripping requirement.

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Report number: cmr 25-006
Published: May 2025

Project number: TR202306

Authors: William Buttlar, Punyaslok Rath, Jim Meister, and Katie Distelrath 

Performing organization: University of Missouri-Columbia/Missouri Center for Transportation Innovation

Asset Characterization Using Automated Methods

Increasing and intensifying flood events pose serious challenges to highway agencies in maintaining water crossing assets and assessing potential flood hazards. This project aimed to develop an automated flood risk assessment program for small water crossing assets, focusing on culverts with less than a 20-foot span not listed in the 2023 National Bridge Inventory (NBI). Information on such small assets is often limited and not readily available. Using a Geographic Information System (GIS), the project identified potential non-NBI water crossing sites and conducted field surveys to collect the asset information on Missouri's highways. Based on the field survey data, the project developed a GIS-based flood risk assessment tool for small water crossing assets, called the Missouri Automated Culvert Analysis Tool (MoACAT) QGIS Plugin, using publicly available high-resolution Light Detection and Ranging (LiDAR) data. The tool was built with Python scripts that process four sequential steps on the Quantum Geographic Information System (QGIS), the most popular non-proprietary open-source GIS software program. The project conducted a pilot study of culverts in Cass County, Missouri. The plugin hydraulic results were compared against HEC-RAS 2D modeling results. The results indicate that the tool has the potential to identify small water crossing assets and predict flood overtopping efficiently.  

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Report number: cmr 25-005
Published: April 2025

Project number: TR202311

Authors: Sungyop Kim, Donald Baker, Jejung Lee, Aaron Sprague, and Charles Mwaipopo

Performing organization: University of Missouri-Kansas City and Water Resources Solutions, Inc.