One of the duties of asset managers at municipalities is to keep road markings in a standard condition (visible and understandable) to avoid fatal accidents. Therefore, municipalities need up-to-date information about the condition of road markings to efficiently and effectively plan road maintenance to enhance safety.
According to SWOV report, in 2020, 52% of road deaths occurred on municipal roads. In addition to the fatal accidents, 60% of serious road injuries occurred in urban areas. Bear in mind that, around 85% of fatal crashes occurred on road sections and intersections. The societal costs of road crashes has been estimated at 17 billion euro just in 2018, that increased by 5% with respect to 2010.
Road markings are one of the most effective safety elements of road infrastructures. Some studies reported that roads without edge lines have to 15% higher risk of accidents than roads with edge lines. There are similar results for other road markings. However, these markings tend to be vanished and damaged due to environmental conditions and wear. Due to traffic density and line changes in the sections and intersections, the most defective markings located in those areas. Therefore, monitoring the quality of road markings and scheduling the maintenance plan of these markings can significantly increase road safety.
INSPECTIMARK provides an accurate and inexpensive condition monitoring system for the road markings that can be installed on every vehicles including municipality-owned vehicles (lease vehicles of municipality employees, service vehicles like garbage trucks, public transport vehicles). This system monitors the road surface to inspect the condition of road markings.
The data is collected by a commercial in-vehicle camera (mobile or dashcam) that is then transferred to a web server. Afterward, a data analyzing platform developed based on computer vision and artificial intelligence technologies analyzes the raw data to provide insight about the condition of the road markings and to report the degraded markings as an alert.
According to the location of the defected markings, the risk factors of the markings can be determined and the maintenance can be optimized based on these factors. It can also recognize different symbolic road markings and evaluate markings qualitatively to establish predictive maintenance to increase reliability.
Automated driving depends on the quality of road markings. The Netherlands is becoming prepared for autonomous cars as the future of Smart Mobility. One of the important infrastructure is marking that should be kept in a standard condition. The markings can be considered as the rail of the Self-driving cars.
Netherlands has reached the 1st rank in the EU and the 2nd rank in the world (Score: 6.4 of 7.0) according to roads quality. From 2012 to 2019, the Netherlands has improved its global rank from the 11th to the 2nd rank. One of the key elements to assess the quality of roads is the road marking quality. Road markings effectively control traffic, by employing lines and symbols to present visual guidance for road users.
“Self-explaining roads” is the terminologies which was first implemented in the Netherlands. self-explaining roads refer to the concept of the roads that guide drivers in a manner that they behave consistent with the road design subconsciously. This goal is achieved by providing information about the upcoming situation to the drivers in an easy and cost-effective way, i.e., road markings.
Elon Musk, CEO Tesla
Lex Kerssemakers, CEO Volvo North America
James Sayer, University of Michigan Transportation Research