Black boxes data for road pavement conditions monitoring: A case study in Florence

Abstract

The Road Pavement Condition (RPC) represents one of the most important aspects of a country’s development. Maintaining an appropriate road service level and evaluating an effective road pavement maintenance programme is a major current challenge for Road Authorities (RAs). The road pavement damage represents the first risk element for most road users while travelling. In this context, road pavement conditions monitoring plays an important role in the entire process. However, the most efficient monitoring methodologies are sometimes prohibitively expensive for RAs. To detect road pavement anomalies, high-performance and low-cost methodologies are needed, in order to allow the reinvestment of the RA’s budget directly on the maintenance and conservation of the existing pavement. This research presents an innovative and proactive concept of road pavement management process, based on an efficient monitoring method that gives technicians knowledge of RPC before it poses a safety concern, especially for PTW drivers. The paper focuses on the description of operating procedures that aim to perform a screening network based on the most deteriorated sections, using the “floating car data” deriving from black boxes placed inside vehicles that routinely pass through the road network. A case study conducted in the Municipality of Florence has been described. The main purpose of the case study is to demonstrate that the vertical acceleration data obtained by black boxes allow us to identify the road sections that need urgent maintenance. At the same time, the simple processing of the recorded data makes it possible to classify the RPC in the entire network.