Journal Home > Volume 20 , Issue 4

Cyber-Physical System (CPS) and Cyber-Physical-Social System (CPSS) computing are now challenging existing research in many realms, including Intelligent Transportation Systems (ITS). In this survey, we highlight some advances in the coevolution of CPS, CPSS, and ITS, with an emphasis on traffic data. We first explain the hierarchical architecture of CPS-ITS in terms of five layers: perception, communication, computing, control, and application. Then, we analyze the characteristics of traffic data in CPS-ITS, and enumerate some new technologies for data operation and management. Two typical cases of CPS-ITS, vehicular-communication-based traffic control systems and smart parking systems, are illustrated to describe how CPS is changing our lives and influencing the development of future ITS.


menu
Abstract
Full text
Outline
About this article

The Integration of CPS, CPSS, and ITS: A Focus on Data

Show Author's information Wei GuoYi ZhangLi Li( )
Department of Automation, Tsinghua University, Beijing 100084, China.
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China.

Abstract

Cyber-Physical System (CPS) and Cyber-Physical-Social System (CPSS) computing are now challenging existing research in many realms, including Intelligent Transportation Systems (ITS). In this survey, we highlight some advances in the coevolution of CPS, CPSS, and ITS, with an emphasis on traffic data. We first explain the hierarchical architecture of CPS-ITS in terms of five layers: perception, communication, computing, control, and application. Then, we analyze the characteristics of traffic data in CPS-ITS, and enumerate some new technologies for data operation and management. Two typical cases of CPS-ITS, vehicular-communication-based traffic control systems and smart parking systems, are illustrated to describe how CPS is changing our lives and influencing the development of future ITS.

Keywords: big data, intelligent transportation systems, Cyber-Physical System (CPS), Cyber-Physical-Social System (CPSS)

References(66)

[1]
Rajkumar R. R., Lee I., Sha L., and Stankovic J., Cyber-physical systems: The next computing revolution, in Proceedings of the 47th Design Automation Conference, 2010, pp. 731-736.
DOI
[2]
Lee E. A., Computing foundations and practice for cyber-physical systems: A preliminary report, University of California, Berkeley, Tech. Rep. UCB/EECS-2007-72, 2007.
[3]
Shi J., Wan J., and Yan H., A survey of cyber-physical systems, in Proceedings of Wireless Communications and Signal Processing (WCSP), 2011 International Conference on, 2011, pp. 1-6.
DOI
[4]
Liu Z., Yang D. S., Wen D., Zhang W. M., and Mao W., Cyber-physical-social systems for command and control, IEEE Intelligent Systems, vol. 4, pp. 92-96, 2011.
[5]
Parolini L., Tolia N., Sinopoli B., and Krogh B. H., A cyber-physical systems approach to energy management in data centers, in Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems, 2010, pp. 168-177.
DOI
[6]
Lee E., Cyber physical systems: Design challenges, in Object Oriented Real-Time Distributed Computing (ISORC), 2008 11th IEEE International Symposium on, 2008, pp. 363-369.
DOI
[7]
Zhang J., Wang F. Y., Wang K., Lin W. H., Xu X., and Chen C., Data-driven intelligent transportation systems: A survey, IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1624-1639, 2011.
[8]
Sztipanovits J., Koutsoukos X., Karsai G., Kottenstette N., Antsaklis P., Gupta V., and Wang S., Toward a science of cyber-physical system integration, Proceedings of the IEEE, vol. 100, no. 1, pp. 29-44, 2012
[9]
Lee E. A., Cyber-physical systems are computing foundations adequate, in Proceedings of NSF Workshop on Cyber-Physical Systems: Research Motivation, Techniques and Roadmap, vol. 2, 2006.
[10]
Juan Z., Wu J., and McDonald M., Socio-economic impact assessment of intelligent transport systems, Tsinghua Science and Technology, vol. 11, no. 3, pp. 339-350, 2006.
[11]
Papadimitratos P., La Fortelle A., Evenssen K., Brignolo R., and Cosenza S., Vehicular communication systems: Enabling technologies, applications, and future outlook on intelligent transportation, Communications Magazine, IEEE, vol. 47, no. 11, pp. 84-95, 2009.
[12]
Hartenstein H. and Laberteaux K. P., A tutorial survey on vehicular ad hoc networks, Communications Magazine, IEEE, vol. 46, no. 6, pp. 164-171, 2008.
[13]
Zhao Y., Mobile phone location determination and its impact on intelligent transportation systems, IEEE Transactions on Intelligent Transportation Systems, vol. 1, no. 1, pp. 55-64, 2000.
[14]
Dia H., An agent-based approach to modelling driver route choice behaviour under the influence of real-time information, Transportation Research Part C: Emerging Technologies, vol. 10, no. 5, pp. 331-349, 2002.
[15]
Peeta S. and Jeong W. Y., A hybrid model for driver route choice incorporating en-route attributes and real-time information effects, Networks and Spatial Economics, vol. 5, no. 1, pp. 21-40, 2005.
[16]
Li L., Ding W., and Yao D. Y., A survey of traffic control with vehicular communications, IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp. 425-432, 2014.
[17]
Anda J., LeBrun J., Ghosal D., Chuah C. N., and Zhang M., VGrid: Vhicular adhoc networking and computing grid for intelligent traffic control, Vehicular Technology Conference, vol. 5, pp. 2905-2909, 2005.
[18]
Qu F. Z., Wang F.-Y., and Yang L. Q., Intelligent transportation spaces: Vehicles, traffic, communications, and beyond, Communications Magazine, IEEE, vol. 48, no. 11, pp. 136-142, 2010.
[19]
Wang F.-Y., Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications, IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, pp. 630-638, 2010.
[20]
Miller J., Vehicle-to-vehicle-to-infrastructure (V2V2I) intelligent transportation system architecture, in Intelligent Vehicles Symposium, IEEE, 2008, pp. 715-720.
DOI
[21]
Li Z. J., Cheng C., and Ka W., Cloud computing for agent-based urban transportation systems, Intelligent Systems, IEEE, vol. 26, no. 1, pp. 73-79, 2011.
[22]
Bitam S. and Abdelhamid M., Its-cloud: Cloud computing for intelligent transportation system, in Global Communications Conference (GLOBECOM), IEEE, 2012, pp. 2054-2059.
DOI
[23]
Lowrie P. R., Scats, sydney co-ordinated adaptive traffic system: A traffic responsive method of controlling urban traffic, in Proceedings of Transportation Research Board, 1990.
[24]
Mirchandani P. and Larry H., A real-time traffic signal control system: Architecture, algorithms, and analysis, Transportation Research Part C: Emerging Technologies, vol. 9, no. 6, pp. 415-432, 2001.
[25]
Gartner N. H., Chronis S., and Philip J. T., Development of advanced traffic signal control strategies for intelligent transportation systems: Multilevel design, Transportation Research Record, vol. 1494, pp. 98-105, 1995.
[26]
Choy M. C., Dipti S., and Ruey L. C., Cooperative, hybrid agent architecture for real-time traffic signal control, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 33, pp. 597-607, 2003.
[27]
Abdulhai B., Rob P., and Grigoris J. K., Reinforcement learning for true adaptive traffic signal control, Journal of Transportation Engineering, vol. 129, no. 3, pp. 278-285, 2003.
[28]
Gettman D., Shelby S., Head L., Bullock D., and Soyke N., Data-driven algorithms for real-time adaptive tuning of offsets in coordinated traffic signal systems, Transportation Research Record: Journal of the Transportation Research Board, vol. 2035, pp. 1-9, 2007.
[29]
Wu X. K., Liu H. X., and Gettman D., Identification of oversaturated intersections using high-resolution traffic signal data, Transportation Research Part C: Emerging Technologies, vol. 18, no. 4, pp. 626-638, 2010.
[30]
Mazzarello M. and Ennio O., A traffic management system for real-time traffic optimization in railways, Transportation Research Part B: Methodological, vol. 41, no. 2, pp. 246-274, 2007.
[31]
McCall J. C. and Mohan M. T., Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation, IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, pp. 20-37, 2006.
[32]
Chen C.-D., Fan Y.-W., and Farn C.-K., Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior, Transportation Research Part C: Emerging Technologies, vol. 15, no. 5, pp. 300-311, 2007.
[33]
Srikanth S. V., Pramod P. J., Dileep K. P., Tapas S., Patil M. U., and Sarat C. B. N., Design and implementation of a prototype smart parking (SPARK) system using wireless sensor networks, in Advanced Information Networking and Applications Workshops, 2009, pp. 401-406.
DOI
[34]
Lee C., Bruce H., and Frank S., Real-time crash prediction model for application to crash prevention in freeway traffic, Transportation Research Record: Journal of the Transportation Research Board, vol. 1840, pp. 67-77, 2003.
[35]
Tang L., Han J., and Jiang G., Mining sensor data in cyber-physical systems, Tsinghua Science and Technology, vol. 19, no. 3, pp. 225-234, 2014.
[36]
Soulier P., Li D., and Williams J. R., A survey of language-based approaches to cyber-physical and embedded system development, Tsinghua Science and Technology, vol. 20, no. 2, pp. 130-141, 2015.
[37]
Wang Y., Yan H., Wan J., and Zhou K., Mobile agents for CPS in intelligent transportation systems, in Advanced Technologies, Embedded and Multimedia for Human-centric Computing. Springer, 2014, pp. 721-729.
DOI
[38]
Conti M., Das S. K., Bisdikian C., Kumar M., Ni L. M., and Passarella A., Looking ahead in pervasive computing: Challenges and opportunities in the era of cyber-physical convergence, Pervasive and Mobile Computing, vol. 8, no. 1, pp. 2-21, 2012.
[39]
Chang G., Zhang Y., and Yao D., Missing data imputation for traffic flow based on improved local least squares, Tsinghua Science and Technology, vol. 17, no. 3, pp. 304-309, 2012.
[40]
Wan Y., Cao J., Zhong S., Tu G., Lu C., Xu X., and Li K., An integrated cyber-physical simulation environment for smart grid applications, Tsinghua Science and Technology, vol. 19, no. 2, pp. 133-143, 2014.
[41]
Saber A. Y. and Ganesh K. V., Efficient utilization of renewable energy sources by gridable vehicles in cyber-physical energy systems, Systems Journal, vol. 4, no. 3, pp. 285-294, 2010.
[42]
Gokhale A., McDonald M. P., Drager S., and McKeever W., A cyber physical systems perspective on the real-time and reliable dissemination of information in intelligent transportation systems, Air Force Research Lab, 2010.
DOI
[43]
Wan J., Yan H., Suo H., and Li F., Advances in cyber-physical systems research, KSII Transactions on Internet and Information Systems (TIIS), vol. 5, no. 11, pp. 1891-1908, 2011.
[44]
El F., Nour E., Henry L., and Ajeesh K., Data fusion in intelligent transportation systems: Progress and challenges—A survey, Information Fusion, vol. 12, no. 1, pp. 4-10, 2011.
[45]
Lee W.-H., Tseng S.-S., and Shieh W.-Y., Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system, Information Sciences, vol. 180, no. 1, pp. 62-70, 2010.
[46]
Tan Y., Steve G., and Lance C. P., A prototype architecture for cyber-physical systems, ACM Sigbed Review, vol. 5, no. 1, p. 26, 2008.
[47]
Sridhar S., Adam H., and Manimaran G., Cyber-physical system security for the electric power grid, Proceedings of the IEEE, vol. 100, no. 1, pp. 210-224, 2012.
[48]
Sun C. and Venkat C., Secondary accident data fusion for assessing long-term performance of transportation systems, in Proceedings of Midwest Trasportation Consortium Seminar, 2007.
[49]
Platzer A., Verification of cyberphysical transportation systems, IEEE Intelligent Systems, vol. 24, no. 4, pp. 10-13, 2009.
[50]
MacGregor J. and Ali C., Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods, Computers & Chemical Engineering, vol. 47, pp. 111-120, 2012.
[51]
Zhao T., Zhang Y., and Huang B., RMT-based urban traffic cross-correlation analysis and its application on traffic incident impact analyses, Tsinghua Science and Technology, vol. 17, no. 1, pp. 104-112, 2012.
[52]
Hou L., Zhang Z., Lu B., Xu R., and Zhang Y., Estimation of incident-induced congestion on signalized arteries using traffic sensor data, Tsinghua Science and Technology, vol. 17, no. 3, pp. 296-303, 2012.
[53]
Yang X., Song W., and De D., LiveWeb: A sensorweb portal for sensing the world in real-time, Tsinghua Science and Technology, vol. 16, no. 5, pp. 491-504, 2011.
[54]
Li Z., Huang H., Lam W. H. K., and Wong S. C., Optimization of time-varying parking charges and parking supply in networks with multiple user classes and multiple parking facilities, Tsinghua Science and Technology, vol. 12, no. 2, pp. 167-177, 2007.
[55]
Jin X., Su Y., Zhang Y., Wei Z., and Li L., Modeling of spacing distribution of queuing vehicles at signalized junctions using random-matrix theory, Tsinghua Science and Technology, vol. 14, no. 2, pp. 252-254, 2009.
[56]
Hou L., Zhang Z., Lu B., Xu R., and Zhang Y., Estimation of incident-induced congestion on signalized arteries using traffic sensor data, Tsinghua Science and Technology, vol. 17, no. 3, pp. 296-303, 2012.
[57]
Geng Y. and Cassandras C. G., New ‘smart parking’ system based on resource allocation and reservations, IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1129-1139, 2013.
[58]
Xu M., Zhang Z., Wan Y., and Li L., A simulation study on real-time parking guidance, in Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on, 2014, pp. 2269-2270.
[59]
Guo W., Zhang Y., Xu M., Zhang Z., and Li L., Parking spaces repurchase strategy design via simulation optimization, Journal of Intelligent Transportation System, 2015. (in Press)
[60]
Wang F.-Y., The emergence of intelligent enterprises: From CPS to CPSS, IEEE Intelligent Systems, vol. 25, no. 4, pp. 85-88, 2010.
[61]
Sheth A., Anantharam P., and Henson C., Physical-cyber-social computing: An early 21st century approach, IEEE Intelligent Systems, vol. 28, no. 1, pp. 78-82, 2013.
[62]
Asakura Y. and Hato E., Tracking survey for individual travel behaviour using mobile communication instruments, Transportation Research Part C: Emerging Technologies, vol. 12, nos. 3-4, pp. 273-291, 2004.
[63]
Wang F.-Y., Carley K. M., Zeng D., and Mao W., Social computing: From social informatics to social intelligence, IEEE Intelligent Systems, vol. 22, no. 2, pp. 79-83, 2007.
[64]
Morstatter F., Liu H., and Zeng D., Opening doors to sharing social media data, IEEE Intelligent Systems, vol. 27, no. 1, pp. 47-51, 2012.
[65]
Wang F.-Y., Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications, IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, pp. 630-638, 2010.
[66]
Horanont T., Witayangkurn A., Sekimoto Y., and Shibasaki R., Large-scale auto-GPS analysis for discerning behavior change during crisis, IEEE Intelligent Systems, vol. 28, no. 4, pp. 26-34, 2013.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 09 June 2015
Revised: 14 June 2015
Accepted: 26 June 2015
Published: 03 August 2015
Issue date: August 2015

Copyright

© The authors 2015

Acknowledgements

This work was supported in part by the National Key Technology Research and Development Program (No. 2013BAG18B00), the National Natural Science Foundation of China (No. 61273238), and the Project Supported by Tsinghua University (No. 20131089307).

Rights and permissions

Return