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.

Clearly visible lane markings are 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 when agency snowplows remove retroreflective material during winter operations. Traditional surface-applied paint and glass beads typically only last one season in 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. These recessed durable markings are typically either epoxy, thermoplastic or preformed tape and are typically installed during new construction or significant pavement resurfacing projects. As a result, several dozen installations may occur in a state in any calendar year. This presents a challenge for states that need to program annual re-painting 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 classify pavement markings into 3 groupings: high-performing durable tape, non-tape, and needing maintenance. Color images collected during the LiDAR intensity data collection were used to validate the LiDAR classification. These techniques can be employed by agencies to develop accurate pavement marking inventories that ensure only painted lines (or segments with missing tape) are repainted during annual maintenance.

The Indiana Department of Transportation (INDOT) adopted the Maintenance Decision Support System (MDSS) for user-defined plowing segments in the winter of 2008–2009. Since then, many new data sources, including connected vehicle data, enhanced weather data, and fleet telematics have been integrated into INDOT winter operations activities. The objective of this study was to use these new data sources to conduct a systematic evaluation of the robustness of the MDSS forecasts. During the 2023–2024 winter season, 26 unique MDSS forecast data attributes were collected at 0-, 1-, 3-, 6-, 12-, and 23-hour intervals from the observed storm time for 6 roadway segments during 13 individual storms. In total, over 888,000 MDSS data points were archived for this evaluation. This study developed novel visualizations to compare MDSS forecasts to multiple other independent data sources, including connected vehicle data, National Oceanic and Atmospheric Administration (NOAA) weather data, road friction data and snowplow telematics. Three Indiana storms, with varying characteristics and severity, were analyzed in detailed case studies. Those storms occurred on January 6th, 2024, January 13th, 2024, and February 16th, 2024. Incorporating these visualizations into winter weather after-action reports increased the robustness of post-storm performance analysis and allowed road weather stakeholders to better understand the capabilities of MDSS. The results of this analysis will provide a framework for future MDSS evaluations and implementations and training tools for winter operation stakeholders in Indiana and beyond.

The Federal Motor Carrier Safety Administration (FMCSA) and National Highway Traffic Safety Administration (NHTSA) reported that in 2020, 7.3% of large truck driver fatalities had speed as a contributing factor. Several states have implemented truck differential speed limits (DSLs) with the objective of improving safety. This study compares truck speeds in 16 states, 8 of which have implemented DSLs (Arkansas, California, Idaho, Indiana, Michigan, Montana, Oregon, and Washington) and 8 of which have not (Illinois, Kentucky, Minnesota, Oklahoma, South Dakota, Tennessee, Wisconsin, and Wyoming). The DSLs ranged from 55 MPH in California (CA) to 70 MPH in Montana (MT). Over 240,000 speed samples from connected trucks were analyzed during a one-week period from 15–22 April 2024. The 50th percentile truck speeds ranged from 60 MPH in Oregon to 69 MPH in Wyoming. The 85th percentile truck speeds ranged from 65 MPH in Washington, Oregon, and California to 74 MPH in Wyoming. The 85th percentile speeds across all segments were greater than the posted truck speed limit in 90% of segments with DSLs, but only 12.5% of segments without DSLs. The average interquartile range (IQR) of truck speeds for the eight states with DSLs was 19% smaller than the average IQR of the eight states without DSLs. The methodologies and visuals presented by this study are easily scalable to any route and location provided the availability of a representative connected truck dataset.