Sboing4real: a real-time crowdsensing-based traffic management system

Th. Toliopoulos, N. Nikolaidis, A.-V. Michailidou, A. Seitaridis, Th. Nestoridis, Ch. Oikonomou, A. Temperekidis, F. Gioulekas, A. Gounaris, N. Bassiliades, P. Katsaros, A. Georgiadis, F. Liotopoulos, "Sboing4real: a real-time crowdsensing-based traffic management system", Journal of Parallel and Distributed Computing, Vol. 162, 2022, pp. 59-75.

Th. Toliopoulos, N. Nikolaidis, A.-V. Michailidou, A. Seitaridis, Th. Nestoridis, Ch. Oikonomou, A. Temperekidis, F. Gioulekas, A. Gounaris, N. Bassiliades, P. Katsaros, A. Georgiadis, F. Liotopoulos

vehicle traffic monitoring, IoT, stream processing, massive parallelism, OLAP, empirical evaluation

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 fully-functional 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. Finally, we discuss the application of the aforementioned solution to a successful pilot study, where the full system has deployed and processed data from 800 taxis for a period of 3 months.