Developing a Real-Time Traffic Reporting and Forecasting Back-End System

T. Toliopoulos, N. Nikolaidis, A.-V. Michailidou, A. Seitaridis, A. Gounaris, N. Bassiliades, A. Georgiadis, F. Liotopoulos, "Developing a Real-Time Traffic Reporting and Forecasting Back-End System", In: Dalpiaz F., Zdravkovic J., Loucopoulos P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham, pp. 58-75, 2020.

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Author(s): T. Toliopoulos, N. Nikolaidis, A.-V. Michailidou, A. Seitaridis, A. Gounaris, N. Bassiliades, A. Georgiadis, F. Liotopoulos

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Appeared In: In: Dalpiaz F., Zdravkovic J., Loucopoulos P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham, pp. 58-75, 2020.

Keywords: Data Analytics; Traffic forecasting; Back-end system; Satellite navigation; Real-time data processing

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2020, Bassiliades, Georgiadis, Gounaris, Liotopolous, Michailidou, Nikolaidis, Seitaridis, Toliopoulos

Abstract: This work describes the architecture of the back-end engine of a real-time traffic data processing and satellite navigation system. The role of the engine is to process real-time feedback, such as speed and travel time, provided by in-vehicle devices and derive real-time reports and traffic predictions through leveraging historical data as well. We present the main building blocks and the versatile set of data sources and processing platforms that need to be combined together to form a working and scalable solution. We also present performance results focusing on meeting system requirements keeping the need for computing resources low. The lessons and results presented are of value to additional real-time applications that rely on both recent and historical data.