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Road-based infrastructure is mainly impacted by traffic and climate loads. For the characterization of traffic loads it is important to know about the vehicle types (passenger cars, trucks), their individual character (gross weight, axles) and their quantity and speed. Depending on for example the real-time availability and the quantity of this information, it can be used for traffic flow management, for strategic planning (pavement deterioration analysis, collision analysis, vulnerability analysis) or both.

In combination with other data, for example weather conditions, air quality conditions, surface and subsurface characteristics and performance, geometric data, age and type of pavement, connection function, etc. the above-mentioned targets ‘traffic flow management’ and ‘pavement deterioration analysis’ can be pushed to another level of quality and trust. Additional datasets are becoming publicly available which, although not ‘big’ can give greater understanding of the role and performance of transport systems in our society. This includes, for example: population, economic, employment and health data. This level of understanding is needed to provide the business case for strategic investment in road infrastructure.

‘Big data’ terminology is worldwide in use for strategies like these, not only for transportation needs. Big data can be defined as high volume data often collected in real-time from various sources. This implies a high variety of often unstructured information assets which only evolve its potential and ‘intelligence’ with specific technology and methods. Recent level of technology (including sensing, data storage, processing power and methods) allows a complete new view on the use of existing data and access to new sources of data including data that are crowd sourced. Even exponential growth and availability of data seem to be no longer a decisive criterion.

Besides the already big challenges to store, organize and handle big data, the crucial challenge is to turn big data into Smart Data, which is useful, high-quality and secured data which can be used in asset management processes.

The following described initiative program aims to screen the whole process chain of turning big data into information to achieve improvements in asset management. This will be done as multiple pilots targeting ‘condition based pavement management’ showing the added value and feasibility of a big data approach for tackling civil engineering challenges. The developed strategies, tools and processes can then be used for further transportation based areas of application.

BD-Pave (Big Data-Pavement) is a FEHRL initiative. (download)

The FEHRL Members contributing to this initiative are outlined in 'Contributors'.