Monitoring roadway lights and pavement defects for nighttime street safety assessment by sensor data analysis and visualization

Abstract

Street maintenance and improvement are significant missions in ensuring transportation safety, especially at nighttime because the severity of injuries doubles at night. Driving visibility and road surface conditions are key factors behind nighttime traffic accidents, and they must be solved as a major priority. Having an exclusive report representing this issue becomes useful documentation for preparing an effective plan for repairing and upgrading a street at appropriate locations. However, road observations are mostly performed by humans, so reports are imprecise owing to the limitation of human cognition and documentation during observation at night. For this reason, the aim of this work is to create a visualization report for monitoring the risk on a street at nighttime. To achieve this goal, a light sensor for measuring brightness on the road, a gyro sensor and an accelerometer for detecting pavement defects, and a location sensor for marking the current latitude and longitude are placed in a car, and the data obtained are transferred to a cloud database while driving on the road. After that, all data are analyzed by machine learning techniques to identify some critical failures and report on map visualization. The result demonstrates that this approach can visualize the right defect at the correct location, and it will become an important contribution to transport safety.