Private connected vehicles data to support the implementation of urban PMS: the case study of the City of Palermo

This work aims to explore the potential of leveraging big data—particularly that generated by private connected vehicles—for extensive monitoring of road infrastructure in urban areas. Central to this exploration is the following research question: are data collected from connected vehicles (CVs) reliable and cost-effective source for road condition monitoring within urban Pavement Management Systems? In order to give an answer, at first characteristics of the few encountered urban PMS in literature were identified. On this basis, a prototype of urban PMS, leveraging connected vehicles as source of pavement condition monitoring was designed and applied to a case study within the city of Palermo (Italy). The application was validated by comparing these results with an additional visual monitoring operated by using the PASER method. Overall, the comparison supports the potential of using CVs data as a viable and scalable alternative for pavement monitoring in urban contexts; in fact, measurement accuracy is considered adequate to the level of precision realistically possible to obtain a cost-effective solution in such a dynamic and complex environment such as urban road pavements.