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