Poster: Heterogeneous Data for Enhanced Traffic Services (HDETS)

The basic idea of Heterogeneous Data for Enhanced Traffic Services (HDETS) is to facilitate different traffic services that utilize heterogeneous data to develop road safety services and improve resource utilization efficiency, i.e., electric energy and road weather services (RWS). The potential for smart traffic services is substantial, as the amount of heterogeneous data generated by real-time traffic environments to exploit situational awareness is much larger than the data generated today. In this paper, we discuss the unique aspects of HDETS by combining state-of-the-art cloud and edge architectures, novel 5G and 6G communication technologies, artificial intelligence (AI) methods, and deep knowledge from this application area. The first objective of HDETS is to analyze the data sources, which together enable recognizing emerging traffic situations and physical conditions affecting traffic. The second objective is to develop AI methods that are required for the service scenarios. The third objective is to develop a combined cloud and edge computing architecture for AI methods. The fourth objective is to study wireless communication technologies for this architecture, including 5G and 6G, short-range vehicle area networking (VANET), and visible light communication (VLC). Dealing with the above-mentioned objectives, this paper paves the way to respond to our research questions on data sources and AI methodology. It enables novel services, as well as cloud and edge architectures and communication technologies that fulfill the requirements set by these AI methods.