Cloud-Based Information Technology Framework for Data Driven Intelligent Transportation Systems
- 1 Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
- 2 Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
Abstract
We present a novel cloud based IT framework, CloudTrack, for data driven intelligent transportation systems. We de scribe how the proposed framework can be leveraged for real-time fresh food supply tracking and monitoring. CloudTrack allows efficient storage, processing and analysis of real-time location and sensor data collected from fresh food supply vehicles. This paper describes the architecture, design, and implementation of CloudTrack, and how the pro posed cloud-based IT framework leverages the parallel computing capability of a computing cloud based on a large- scale distributed batch processing infrastructure. A dynamic vehicle routing approach is adopted where the alerts trigger the generation of new routes. CloudTrack provides the global information of the entire fleet of food supply vehicles and can be used to track and monitor a large number of vehicles in real-time. Our approach leverages the advantages of the IT capabilities of a computing cloud into the operations and supply chain.
- J. Zhang, F. Wang, K. Wang, W. Lin, X. Xu and C. Chen, “Data-Driven Intelligent Transportation Systems: A Survey,” IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 4, 2011, pp. 1624-1639. doi:10.1109/TITS.2011.2158001
- R. Claes, T. Holvoet and D. Weyns, “A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems,” IEEE Transactions on Intelligent Transportation Systems, Vol. 12 No. 2, 2011, pp. 364-373. doi:10.1109/TITS.2011.2105867
- D. A. Steil, J. R. Pate, N. A. Kraft, R. K. Smith, B. Dixon, L. Ding and A. Parrish, “Patrol Routing Expression, Execution, Evaluation, and Engagement,” IEEE Transactions on Intelligent Transportation Systems, Vol. 12 No. 1, 2011, pp. 58-72.
- E. Schmitt and H. Jula, “Vehicle Route Guidance Systems: Classification and Comparison,” Proceedings of IEEE ITSC, Toronto, 2006, p. 242247.
- M. T. Nkosi, “Cloud Computing for Enhanced Mobile Health Applications,” IEEE Second International Conference on Cloud Computing Technology and Science (Cloud-Com), Indianapolis, 30 November-3 December 2010.
- M. A. H. Masud, “Cloud Computing for Higher Education: A Roadmap,” IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Wuhan, 23-25 May 2012.
- X. Fang, S. Misra, G. L. Xue and D. J. Yang, “Managing Smart Grid Information in the Cloud: Opportunities, Model, and Applications,” IEEE Network, Vol. 26, No. 4, 2012, pp. 32-38. doi:10.1109/MNET.2012.6246750
- Z. J. Li, “Cloud Computing for Agent-Based Urban Transportation Systems,” IEEE Intelligent Systems, Vol. 26, No. 1, 2011, pp. 73-79.
- P. Jaworski, “Cloud Computing Concept for Intelligent Transportation Systems,” 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington DC, 5-7 October 2011.
- A. Bahga and V. K. Madisetti, “Analyzing Massive Machine Maintenance Data in a Computing Cloud,” IEEE Transactions on Parallel & Distributed Systems, Vol. 23, No. 10, 2012, pp. 1831-1843. doi:10.1109/TPDS.2011.306
- Department of Scientific & Industrial Research, “Fruits & Vegetables Sector: An Overview,” Department of Scientific & Industrial Research Report, India, 2011.
- Z. B. Pang, J. Chen, Z. Zhang, Q. Chen and L. R. Zheng, “Global Fresh Food Tracking Service Enabled by Wide Area Wireless Sensor Network,” IEEE Sensors Applications Symposium (SAS), Limerick, 23-25 February 2010. doi:10.1109/SAS.2010.5439425