A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities

Muhammad Mazhar Rathore, Syed Attique Shah, Ahmed Awad, Dhirendra Shukla, Shanmuganathan Vimal and Anand Paul
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Muhammad Mazhar Rathore: Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Syed Attique Shah: Data Systems Group, Institute of Computer Science, University of Tartu, 51005 Tartu, Estonia
Ahmed Awad: Data Systems Group, Institute of Computer Science, University of Tartu, 51005 Tartu, Estonia
Dhirendra Shukla: J Herbert Smith Centre, University of New Brunswick, Fredericton, NB E3B, Canada
Shanmuganathan Vimal: Computer Science and Engineering Department, Ramco Institute of Technology, Rajapalayam 626117, India
Anand Paul: School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea

Sustainability, 2021, vol. 13, issue 14, 1-21

Abstract: In the last decade, technological advancements in the cyber-physical system have set the basis for real-time and context-aware services to ease human lives. The citizens, especially travelers, want to experience a safe, healthy, and timely journey to their destination. Smart and on-ground real-time traffic analysis helps authorities further improve decision-making to ensure safe and convenient traveling. In this paper, we proposed a transport-control model that exploits cyber-physical systems (CPS) and sensor-technology to continuously monitor and mine the big city data for smart decision-making. The system makes use of travel-time, traffic intensity, vehicle’s speed, and current road conditions to construct a weighted city graph representing the road network. Traditional graph algorithms with efficient implementation technologies are employed to respond to commuters’ and authorities’ needs in order to achieve a smart and optimum transportation system. To efficiently process the incoming big data streams, the proposed architecture uses the Apache GraphX tool with several parallel processing nodes, along with Spark and Hadoop that ultimately provide better performance against various state-of-the-art solutions. The system is thoroughly evaluated in terms of system throughput and processing time, revealing that the proposed system is efficient, robust, and scalable.

Keywords: cyber-physical system; smart transportation; big data; smart city; stream analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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