Cloud-Based Information Technology Framework for Data Driven Intelligent Transportation Systems

Abstract

We present a novel cloud based IT framework, CloudTrack, for data driven intelligent transportation systems. We describe 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 proposed 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.

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A. Bahga and V. Madisetti, "Cloud-Based Information Technology Framework for Data Driven Intelligent Transportation Systems," Journal of Transportation Technologies, Vol. 3 No. 2, 2013, pp. 131-141. doi: 10.4236/jtts.2013.32013.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] 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
[2] 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
[3] 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.
[4] E. Schmitt and H. Jula, “Vehicle Route Guidance Systems: Classification and Comparison,” Proceedings of IEEE ITSC, Toronto, 2006, p. 242247.
[5] 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.
[6] 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.
[7] 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
[8] Z. J. Li, “Cloud Computing for Agent-Based Urban Transportation Systems,” IEEE Intelligent Systems, Vol. 26, No. 1, 2011, pp. 73-79.
[9] P. Jaworski, “Cloud Computing Concept for Intelligent Transportation Systems,” 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington DC, 5-7 October 2011.
[10] 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
[11] Department of Scientific & Industrial Research, “Fruits & Vegetables Sector: An Overview,” Department of Scientific & Industrial Research Report, India, 2011.
[12] 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
[13] Y. Xi, W. Yang, N. Yamauchi, Y. Miyazaki, N. Baba and H. Ikeda, “Real-Time Data Acquisition and Processing in a Miniature Wireless Monitoring System for Strawberry during Transportation,” TENCON, Hong Kong, 2006, pp. 1-4.
[14] Y. L. Bu and L. Wang, “Leveraging Cloud Computing to Enhance Supply Chain Management in Automobile Industry,” International Conference on Business Computing and Global Informatization, Shanghai, 29-31 July 2011. doi:10.1109/BCGIn.2011.45
[15] Apache Hadoop. http://hadoop.apache.org
[16] F. Glover, “Tabu Search Part I,” ORSA Journal on Computing, 1989.
[17] M. M. Solomon, “Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints,” Operations Research, Vol. 35, No. 2, 1987, pp. 254-265.
[18] S. R. Thangiah, I. H. Osman, R. Vinayagamoorthy and T. Sun, “Algorithms for the Vehicle Routing Problems with Time Deadlines,” American Journal of Mathematical and Management Sciences, Vol. 13, No. 3-4, 1993, pp. 323-355.
[19] http://aws.amazon.com/ec2/instance-types

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