Black Boxes data for Road Pavement Condition monitoring: a case study in Florence

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 program is one of the main current challenges 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 the 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. The research presents an innovative and proactive concept of road pavement management process, based on an efficient monitoring method which offers technicians the knowledge of the RPC before it represents a safety problem, especially for PTW drivers. The paper focuses on the description of operating procedures that aims to perform a screening network based on the most deteriorated sections, by 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 focus of the case study is to demonstrate that the vertical acceleration data obtained by black boxes allow us to identify the road sections which are in urgent need of maintenance. At the same time, the simple processing of the recorded data allows classifying the RPC in the entire network.