Development of predictive and evaluation models for the retro reflectivity performance of pavement lane markings

The functions of pavement-markings are to safely and smoothly guide drivers and vehicles along a road-lane, alert drivers not to leave the lane, and provide traffic information. These functions are directly related to the safety and economy of passengers and vehicles on roads. The visibility of pavement-markings is linked to surface damage during the daytime and is significantly affected by luminance at night. Therefore, damage and luminance should be considered key-factors for maintenance and should be measured in detail. In Seoul, mobile equipment such as image-scanners and damage-detectors has been used to measure damage conditions, and a luminance measurement vehicle has been introduced to observe reflected light intensity from road-markings. The mobile measurement methods are respectively utilized due to their objective difference of the measurement. However, measurement discrepancies in temporal, locational, and environmental conditions can lead to considerable errors. Moreover, a standardized maintenance index is currently lacking for planning repair work in Seoul. In that regards, one objective of this research was to develop a retroreflectivity prediction model using a vehicle that detects pavement and marking damage.

The model enables the prediction of retroreflectivity without additional measurement equipment. Another objective was to develop degradation models based on the age of pavement-markings and to evaluate their performance for maintenance and budget planning.

As a result, a regression model was determined to be the optimal predictive model for retroreflectivity. A logarithmic model was selected to predict retroreflectivity degradation. Additionally, the Seoul-Road-Marking-Index was developed.

The developed models are expected to assist in establishing maintenance plans, formulating budgets, and determining repair work, as well as developing relevant regulations and standards in Seoul.