Warianty tytułu
Języki publikacji
Abstrakty
This paper presents a method to capture the computational intensity and computing resource requirements of data analysis in intelligent transportation systems (ITS). These requirements can be transformed into a generic methodological framework for Cyber-ITS, mainly consisting of region-based ITS data divisions and tasks scheduling for processing, to support the efficient use of cyber-infrastructure (CI). To characterize the computational intensity of a particular ITS data analysis, the computational transformation is performed by data-centric and operation-centric transformation functions. The application of this framework is illustrated by two ITS data analysis cases: multi-sensor data fusion for traffic state estimation by integrating rough set theory with Dempster- Shafer (D-S) evidence theory, and geospatial computation on Global Positioning System (GPS) data for advertising value evaluation. To make the design of generic parallel computing solutions feasible for ITS data analysis in these cases, an approach is developed to decouple the region-based division from specific high performance computer (HPC) architecture and implement a prototype of the methodological framework. Experimental results show that the prototype implementation of the framework can be applied to divide the ITS data analysis into a load-balanced set of computing tasks, therefore facilitating the parallelized data fusion and geospatial computation to achieve remarkable speedup in computation time and throughput, without loss in accuracy.
Czasopismo
Rocznik
Tom
Strony
35--53
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
autor
- Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou, China, xiayingjie@zju.edu.cn
autor
- Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou, China
autor
- Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou, China
Bibliografia
- [1] Bazan, J. G.: Behavioral pattern identification through rough set modeling, Fundamenta Informaticae, 72(1), 2006, 37–50.
- [2] Bazan, J. G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J. J.: Rough set approach to behavioral pattern identification, Fundamenta Informaticae, 75(1), 2007, 27–47.
- [3] Candamo, J., Shreve, M., Goldgof, D. B., Sapper, D. B., Kasturi, R.: Understanding transit scenes: A survey on human behavior-recognition algorithms, IEEE Transactions on Intelligent Transportation Systems, 11(1), 2010, 206–224.
- [4] Cao, Y.: Transportation routing with real-time events supported by grid computing, Ph.D. Thesis, Thesis (PhD). George Mason University, 2007.
- [5] Cho, Y., Rice, J.: Estimating velocity fields on a freeway from low-resolution videos, IEEE Transactions on Intelligent Transportation Systems, 7(4), 2006, 463–469.
- [6] Devlin, K.: Sets, Functions and Logic (Third Edition), Chapman and Hall, NY, 2003.
- [7] El Faouzi, N.-E., Lefevre, E.: Classifiers and distance-based evidential fusion for road travel time estimation, Defense and Security Symposium, International Society for Optics and Photonics, 2006.
- [8] Flynn, M.: Very high-speed computing systems, Proceedings of the IEEE, 54(12), 1966, 1901–1909.
- [9] Gartner, N. H., M. C., Rathi, A. K.: Monograph on Traffic Flow Theory, Fed. Highway Admin.,Washington, DC, 1996.
- [10] Kari, J., Le Gloannec, B.: Modified traffic cellular automaton for the density classification task, Fundamenta Informaticae, 116(1), 2012, 141–156.
- [11] Koźlak, J., Créput, J.-C., Hilaire, V., Koukam, A.: Multi-agent approach to dynamic pick-up and delivery problem with uncertain knowledge about future transport demands, Fundamenta Informaticae, 71(1), 2006, 27–36.
- [12] Ma, Y., Chowdhury, M., Sadek, A., Jeihani, M.: Real-time highway traffic condition assessment framework using vehicle–infrastructure integration (VII) with artificial intelligence (AI), IEEE Transactions on Intelligent Transportation Systems, 10(4), 2009, 615–627.
- [13] Panwai, S., Dia, H.: Comparative evaluation of microscopic car-following behavior, IEEE Transactions on Intelligent Transportation Systems, 6(3), 2005, 314–325.
- [14] Pawlak, Z.: Rough sets, International Journal of Computer and Information Sciences, 11, 1982, 341–356.
- [15] Plaza, A., Valencia, D., Plaza, J.: An experimental comparison of parallel algorithms for hyperspectral analysis using heterogeneous and homogeneous networks of workstations, Parallel Computing, 34(2), 2008, 92–114.
- [16] Rzeszótko, J., Nguyen, S. H.: Machine Learning for Traffic Prediction, Fundamenta Informaticae, 119(3), 2012, 407–420.
- [17] Sánchez-Medina, J. J., Galán-Moreno, M. J., Rubio-Royo, E.: Traffic signal optimization in La Almozara district in saragossa under congestion conditions, using genetic algorithms, traffic microsimulation, and cluster computing, IEEE Transactions on Intelligent Transportation Systems, 11(1), 2010, 132–141.
- [18] Shan, Z., Xia, Y., Li, K., Shi, X.: A Meta-Level K-Means Method for Evaluating the Advertising Value of Urban Roads, 1st ASCE of Transportation Information and Safety, Wuhan, China, ASCE Publications, 2011.
- [19] Smets, P., Kennes, R.: The transferable belief model, Artificial intelligence, 66(2), 1994, 191–234.
- [20] Wang, F.-Y.: Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications, IEEE Transactions on Intelligent Transportation Systems, 11(3), 2010, 630–638.
- [21] Wang, S., Armstrong, M. P.: A theoretical approach to the use of cyberinfrastructure in geographical analysis, International Journal of Geographical Information Science, 23(2), 2009, 169–193.
- [22] Xia, Y., Li, X., Shan, Z.: Parallelized Fusion on Multisensor Transportation Data: A Case Study in Cyber-ITS, International Journal of Intelligent Systems, 28(6), 2013, 540–564.
- [23] Xia, Y., Liu, Y., Ye, Z., Wu, W., Zhu, M.: Quadtree-based domain decomposition for parallel map-matching on GPS data, 15th International Conference on Intelligent Transportation Systems, IEEE, 2012.
- [24] Xia, Y., Ye, Z., Fang, Y., Zhang, T.: Parallelized extraction of traffic state estimation rules based on bootstrapping rough set, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, IEEE, 2012.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-52388616-a737-432b-a2e3-6864d92958f0