The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.

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.

Signage visibility along transportation corridors is critical for drivers in terms of road safety, traffic flow, and enforcement. Traffic signs that are easy to recognize by drivers and autonomous vehicles can help in avoiding accidents and improve safety. Nowadays, Mobile Mapping Systems (MMS) equipped with LiDAR units can scan road network components and its surrounding environment at a normal driving speed while collecting accurate geospatial data. Most traffic signs have well-defined geometric characteristics (e.g., linear or planar features) which can be identified in the 3D LiDAR data acquired by MMS. Therefore, MMS LiDAR data are an ideal source to recognize traffic signs. In addition to traffic sign detection, MMS can also identify vegetation along the right-of-way and evaluate signage visibility. Thus, this paper presents a framework for using MMS LiDAR data for traffic sign and vegetation detection which is a prerequisite for signage visibility analysis. For signage and vegetation detection, two alternative strategies are adopted: 1) a morphological approach and 2) a learning-based approach. For the geometric/morphological approach, Multi-Class Simultaneous Segmentation (MCSS) is utilized in this study. As for the learning-based strategy, semantic segmentation of LiDAR data are performed using Super Point Graph (SPG). Lastly, signage visibility analysis is conducted based on the occlusion rate assessed from different driver’s viewpoints.

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.