Abstract
Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challenging due to the lack of available human resources. This study was conducted to develop an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S. It was found that the detection model of the framework was very precise, which means most of the identified pavement markings were correctly classified. In addition, in the proposed framework, visibility was confirmed as an important factor of driver safety and maintenance, and visibility standards for pavement markings were defined.
Highlights
Pavement markings play a critical role in reducing crashes and improving safety on public roads
The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module
The framework was validated through a case study of pavement marking training data sets in the U.S From the quantitative results
Summary
Pavement markings play a critical role in reducing crashes and improving safety on public roads. They do convey traffic regulations, road guidance, and warnings for drivers, and supplement other traffic control devices such as signs and signals. Without good visibility conditions of pavement markings, the safety of drivers is not assured. Conditions of pavement markings vary even if they were installed at the same time. This paper discusses a study that developed an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S
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