Knowledge of current road conditions is a necessary but insufficient step for optimal management of road assets. While a pavement KPI can quickly highlight the need for an intervention or help multiyear pavement management planning, it offers a limited view on the possible root causes behind a problem. More detailed analysis requires further investigations, often on-site, time-consuming, and expensive. The limits of databases collecting KPIs and 2D images is becoming increasingly evident, highlighting the need for more advanced tools. A Pavement Digital Twin offers the possibility to remotely and immediately analyze with geometrical information the areas of the road network that are leading to user complaints, detect root causes and improve intervention plans. XenomatiX true-solid-state LiDAR enables the generation of pavement digital twins that can be easily stored, accessed and analyzed in an on-line GIS database providing both qualitative and quantitative information to the PMS users. This webinar describes two practical case studies showing how direct access to pavement geometry and profiles helped align the interests of road authorities and road construction companies.
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