Sboing4Real: A real-time crowdsensing-based traffic management system

• Published in: Journal of parallel and distributed computing 2022-04, Vol.162, p.59-75
• Main Authors: Toliopoulos, Theodoros, Nikolaidis, Nikodimos, Michailidou, Anna-Valentini, Seitaridis, Andreas, Nestoridis, Theodoros, Oikonomou, Chrysa, Temperekidis, Anastasios, Gioulekas, Fotios, Gounaris, Anastasios, Bassiliades, Nick, Katsaros, Panagiotis, Georgiadis, Apostolos, Liotopoulos, Fotis K.
• Format: Article
• Language: English
• Subjects: IoT; Massive parallelism; OLAP; Stream processing; Vehicle traffic monitoring
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2022.01.017

•We present an end-to-end crowdsensing-based solution for supporting real-time traffic reporting and forecasting.•We introduce a modular architecture and discuss several alternatives regarding design choices.•Lessons learnt can be transferred to other similar settings.•The solution is thoroughly evaluated.•We present a pilot case study, where the system has been successfully deployed on 800 taxis. 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 while 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 was deployed and processed data from 800 taxis for a period of 3 months.