Most municipalities cannot afford accurate documentation of their infrastructure. The maintenance of roads, for example, can therefore not be planned in advance. However, reliable condition monitoring would even enable data-based forecasts using artificial intelligence.
The great potential for long-term cost reduction can therefore not be exploited. What is necessary, are particularly cost-effective methods that record and evaluate the condition of the infrastructure and derive optimal measures from it.
The research project “Data-based decisions for cost-effective road maintenance”, or DatEnKoSt in short, aims to achieve precisely this: unnecessary follow-up costs can be avoided by using inexpensive recording methods, predictive forecasts and simple derivation of optimal measures. This makes the municipal maintenance of transport routes more efficient and sustainable.
DatEnKoSt pursues three subgoals:
The project consortium of DatEnKoSt consists of the AWS-Institute and the Cyface GmbH. Since 2016, Cyface GmbH, based in Dresden, has been developing software to investigate the quality and use of traffic routes, with the current focus on the analysis of Smart-Traffic and Big-Data in the traffic sector. In addition, the start-up company has already developed a mobile application that makes it possible to collect data on traffic flow and road condition using conventional smartphones.
The software has been in use since mid 2017. The company contributes its expertise to the research project in data acquisition with smartphone hardware and its know-how in the development of application software for modern platforms.
The AWSi is involved in DatEnKoSt mainly in the research of correlation and forecasting methods based on machine learning and is also responsible for project management.
The project DatEnKoSt is funded by the Federal Ministry of Transport and Digital Infrastructure.
The project DatEnKoSt is funded by the research initative mFund.