Good visibility of lane markings is important for all road users, particularly autonomous vehicles. In general, nighttime retroreflectivity is one of the most challenging marking visibility characteristics for agencies to monitor and maintain, particularly in cold weather climates where agency snowplows remove retroreflective material during winter operations. Traditional surface-applied paint and glass beads typically only last one season in cold weather climates with routine snowplow activity. Recently, transportation agencies in cold weather climates have begun deploying improved recessed, durable pavement markings that can last several years and have very high retroreflective properties. Several dozen installations may occur in a state in any calendar year, presenting a challenge for states that need to program annual repainting of traditional waterborne paint lines, but not paint over the much more costly durable markings. This study reports on the utilization of mobile mapping LiDAR systems to classify and evaluate pavement markings along a 73-mile section of westbound I-74 in Indiana. LiDAR intensity data can be used to classify pavement markings as either tape or non-tape and then identify areas of tape markings that need maintenance. RGB images collected during LiDAR intensity data collection were used to validate the LiDAR classification. These techniques can be used by agencies to develop accurate pavement marking inventories to ensure that only painted lines (or segments with missing tape) are repainted during annual maintenance. Repeated tests can also track the marking intensity over time, allowing agencies to better understand material lifecycles.

Efficient management of transportation corridors depends on the availability and effective analysis of high-quality geospatial data. Recent advancements have focused on Mobile Mapping Systems (MMS), which integrate Global Navigation Satellite System/Inertial Navigation System (GNSS/INS), Red-Green-Blue (RGB) cameras, and LiDAR units. However, the large volume of collected data and specialized software/hardware requirements often limit data accessibility and visualization by stakeholders. This study focuses on facilitating accessibility to transportation geospatial data by developing a Potree-based web portal for better management, visualization, and quality control of such data. The developed web portal enhances end-users’ capabilities by providing primary quality control tools for assessing the georeferencing accuracy of multi-modal, multi-platform, and multi-temporal geospatial data. Users can also utilize these quality control tools to validate the calibration of Pan/Tilt/Zoom (PTZ) cameras installed along transportation corridors. Validating the calibration of PTZ cameras allows for precise pointing of these cameras to investigate the cause of traffic congestions and improve response activities by Transportation Management Centers (TMCs). Apart from the primary tools, the proposed portal also offers specialized functionalities to handle derived products from MMS data that can be used for the assessment of pavement markings condition––which is necessary for the safe operation of connected and autonomous vehicles (CAVs).

Road surface condition monitoring is critical for ensuring safe and efficient transportation networks. This study proposes and evaluates a framework that compares imagery-, Light Detection and Ranging (LiDAR), and accelerometer-based approaches for pavement anomaly detection. The analysis first focused on a 5-mile urban roadway segment, in which all three sensing modalities were evaluated under identical survey conditions using manually interpreted reference anomalies to compare detection accuracy, severity classification, and processing efficiency. The imagery-based Convolutional Transformer-based Crack Segmentation (CT-CrackSeg) model achieved a precision, recall, and F1-score of 88.5%, 88.5%, and 88.5%, respectively, but remained sensitive to environmental factors such as shadows, curbs, roadside features, and pavement texture variations. The LiDAR-based method achieved an F1-score of 93.0%, while the accelerometer-based Isolation Forest and Adaptive Threshold methods achieved F1-scores of 95.2% and 97.2%, respectively. These results indicate strong detection performance under the evaluated validation conditions; however, the reported precision values should be interpreted as dataset-specific rather than universal performance levels. Given the accelerometer-based approach’s strong detection performance, minimal processing time, and low deployment cost, it was further applied across a 36-mile roadway network to evaluate its scalability for network-level monitoring. Across the full route, the spatial agreement among accelerometer systems exceeded 0.91, while the agreement between the two detection methods exceeded 0.96, with 962–996 surface defects detected depending on the sensor and method. Integrating the anomaly detection results into a Potree-based web portal enabled interactive validation with geotagged imagery and point clouds, improving interpretability and diagnostic insight. Overall, the findings highlight that accelerometer-based monitoring, even with consumer-grade sensors, provides a practical, scalable, and low-cost solution for pavement evaluation, while LiDAR and imagery serve as complementary tools for detailed verification and characterization.

Road surface condition monitoring is critical for ensuring safe and efficient transportation networks. This study presents a multi-sensor framework that integrates imagery, LiDAR, and accelerometer-based approaches for pavement anomaly detection. The analysis first focuses on a 5-mile urban roadway segment, where all sensing modalities are evaluated under identical survey conditions to compare detection accuracy, severity classification, and processing efficiency. Imagery-based analysis using a CT-CrackSeg network successfully identifies cracks and potholes but remains sensitive to environmental conditions such as lighting and shadows. LiDAR-based methods achieve centimeter-level precision in detecting surface deviations ≥ 2 cm at highway speeds, albeit with high acquisition and processing costs. Accelerometer-based monitoring using survey-, mapping-, and consumer-grade systems demonstrates strong potential as a low-cost alternative. Two detection strategies—Isolation Forest and Adaptive Threshold—are applied to vertical acceleration data, showing high consistency with LiDAR detections and general agreement with imagery-based results. Given its strong agreement with LiDAR and imagery, minimal processing requirements, and low deployment cost, the accelerometer-based approach is extended to a 36-mile network to evaluate scalability. Across the full route, spatial and method correlations exceed 0.91 and 0.96, respectively, with 962–996 surface defects detected depending on the sensor and method. Integrating anomaly detection results into a Potree-based web portal enabled interactive validation with geotagged imagery and point clouds, improving interpretability and diagnostic insight. The results demonstrate that accelerometer-based monitoring provides a practical, scalable, and cost-effective solution for pavement evaluation, while LiDAR and imagery serve as complementary tools for detailed verification and characterization.