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Integrating Machine Learning in a Semantic Web Platform for Traffic Forecasting and Routing

Semantic Web technologies proved to be successful in integrating and interlinking data. However, traditional processing operations offered by Semantic Web technologies are limited to conceptual query answering. Growing the size of datasets, typical operations from Machine Learning or other data-intensive techniques would become important to be jointly used with Semantic Web processing. In this paper, we explain how we exploited the capabilities of a pluggable Semantic Web application framework – the LarKC platform – to incorporate techniques from Machine Learning (recurring neural networks) and from Operational Research (routing algorithms) in a scenario of Traffic Forecasting and Routing in the Milano area. The result is a Web-based application that let a user calculate the most suitable path in the urban environment by taking into consideration the city streets characteristics and the traffic predictions.