Context Elements Taxonomy for Intelligent Transportation Systems
DOI:
https://doi.org/10.5753/jbcs.2023.2458Keywords:
Software engineering, Intelligent Transportation Systems, Context awareness, Taxonomy, Knowledge managementAbstract
Design and development of context-aware Intelligent Transportation Systems (ITS) are not trivial due to the large number of possible context elements that may be relevant to the application and the lack of structured information to guide system designers in this task. This paper proposes that context elements with common characteristics can be grouped into categories, and these categories can be organized in a taxonomy. This taxonomy could help system designers with the task of modeling and developing new context-aware ITS. We performed a literature review of 68 articles describing 70 ITS applications with context-aware features to identify context elements used in this type of application. Furthermore, we also analyzed three commercial ITS applications. We used data collected from the analysis of these 73 projects to define the categories and identify their relationships. We propose a taxonomy with 79 categories, with 57 leaf categories (a category without children subcategories). We also performed two experiments to validate whether the exposure to this taxonomy could improve the quality of an ITS application during its design, with favorable results showing a 2.7 times increase in the average amount of relevant context elements used in the application. Finally, we compiled a knowledge base of which context element categories are used in the 73 analyzed projects. It is another companion information that can be used to help system designers. The proposed taxonomy of context element categories organizes the information of the context-aware ITS domain in a way that can ease the task of designing such systems and improve the usage of context-aware features. The overall methodology used in this work to create the taxonomy for the ITS domain could be applied to other popular domains of context-aware applications.
Downloads
References
Abowd, G. D. and Mynatt, E. D. (2000). Charting past, present, and future research in ubiquitous computing. ACM Transactions on Computer-Human Interaction, 7(1):29-58. DOI: 10.1145/344949.344988.
Arooj, A., Farooq, M. S., Akram, A., Iqbal, R., Sharma, A., and Dhiman, G. (2022). Big Data Processing and Analysis in Internet of Vehicles: Architecture, Taxonomy, and Open Research Challenges. Archives of Computational Methods in Engineering, 29(2):793-829. DOI: 10.1007/s11831-021-09590-x.
Atzori, L., Iera, A., and Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15):2787-2805. DOI: 10.1016/j.comnet.2010.05.010.
Baras, S., Saeed, I., Tabaza, H. A., and Elhadef, M. (2018). VANETs-based intelligent transportation systems: An overview. In Lecture Notes in Electrical Engineering, volume 474, pages 265-273. Springer Verlag. DOI: 10.1007/978-981-10-7605-3_44.
Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., and Riboni, D. (2010). A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing, 6(2):161-180.
Bifulco, F., Amitrano, C. C., and Tregua, M. (2014). Driving smartization through intelligent transport. Chinese Business Review, 13(4).
Brezillon, J. and Brezillon, P. (2007). Context modeling: Context as a dressing of a focus. Proceedings of the 5 th International and Interdisciplinary Conference on Modeling and Using Context.
Chagas, A. and Ferraz, C. (2012). ConnecTV: An environment to develop and run component-based customized iDTV applications. In WebMedia'12 - Proceedings of the 2012 Brazilian Symposium on Multimedia and the Web. DOI: 10.1145/2382636.2382657.
Chavhan, S., Gupta, D., Nagaraju, C., A, R., Khanna, A., and Rodrigues, J. J. (2021). An Efficient Context-Aware Vehicle Incidents Route Service Management for Intelligent Transport System. IEEE Systems Journal. DOI: 10.1109/JSYST.2021.3066776.
David, B., Xu, T., Jin, H., Zhou, Y., Chalon, R., Zhang, B., Yin, C., and Wang, C. (2013). User-oriented system for smart city approaches. In 12th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, volume 46, pages 333-340. Elsevier. DOI: 10.3182/20130811-5-US-2037.00072.
Dey, A., Abowd, G., and Salber, D. (2001). A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications. Human-Computer Interaction, 16(2):97-166. DOI: 10.1207/S15327051HCI16234_02.
Dey, A. K. (2001). Understanding and Using Context. Personal and Ubiquitous Computing, 5(1):4-7. DOI: 10.1007/s007790170019.
Dibaei, M., Zheng, X., Jiang, K., Abbas, R., Liu, S., Zhang, Y., Xiang, Y., and Yu, S. (2020). Attacks and defences on intelligent connected vehicles: a survey. Digital Communications and Networks, 6(4):399-421. DOI: 10.1016/J.DCAN.2020.04.007.
Dzemydienė, D. and Burinskienė, A. (2021). Integration of Context Awareness in Smart Service Provision System Based on Wireless Sensor Networks for Sustainable Cargo Transportation. Sensors 2021, Vol. 21, Page 5140, 21(15):5140. DOI: 10.3390/S21155140.
Environmental Protection Agency (2021). Code of Federal Regulations. 40 CFR 156.62 - Toxicity Category.
Essien, A., Petrounias, I., Sampaio, P., and Sampaio, S. (2020). A deep-learning model for urban traffic flow prediction with traffic events mined from twitter. World Wide Web 2020 24:4, 24(4):1345-1368. DOI: 10.1007/S11280-020-00800-3.
Figueiredo, L., Jesus, I., Machado, J. A. T., Ferreira, J. R., and de Carvalho, J. L. M. (2001). Towards the development of intelligent transportation systems. In ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), pages 1206-1211, Oakland, CA, USA. IEEE. DOI: 10.1109/itsc.2001.948835.
Gomes, J. Z., Victória Barbosa, J. L., Geyer, C. F. R., Anjos, J. C. S. D., Canto, J. V., and Pessin, G. (2020). Ubiquitous intelligent services for vehicular users: A systematic mapping. Interacting with Computers, 31(5):465-479. DOI: 10.1093/iwcomp/iwz030.
Guerrero-Ibanez, J. A., Zeadally, S., and Contreras-Castillo, J. (2015). Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies. IEEE Wireless Communications, 22(6):122-128. DOI: 10.1109/mwc.2015.7368833.
Hu, X., Li, X., Ngai, E. C., Leung, V. C., and Kruchten, P. (2014). Multidimensional context-aware social network architecture for mobile crowdsensing. IEEE Communications Magazine, 52(6):78-87. DOI: 10.1109/MCOM.2014.6829948.
Johnson, D. A. and Trivedi, M. M. (2011). Driving style recognition using a smartphone as a sensor platform. In 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 1609-1615. IEEE. DOI: 10.1109/ITSC.2011.6083078.
Kaltz, W. J., Ziegler, J., and Lohmann, S. (2005). Context-aware Web engineering: Modeling and applications. Revue d'intelligence artificielle, 19(3):439-458.
Kannan, S., Thangavelu, A., and Kalivaradhan, R. (2010). An intelligent Driver Assistance System (I-DAS) for vehicle safety modelling using ontology approach. International Journal of UbiComp, 1(3):15-29.
Khekare, G. S. and Sakhare, A. V. (2012). Intelligent traffic system for VANET: a survey. International Journal of Advanced Computer Research, 2(6):99-102.
Klotz, B., Troncy, R., Wilms, D., and Bonnet, C. (2018). VSSo-A Vehicle Signal and Attribute Ontology. In SSN Workshop at ISWC. CEUR Workshop Proceedings.
Laña, I., Sanchez-Medina, J. J., Vlahogianni, E. I., and Ser, J. D. (2021). From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability. Sensors 2021, Vol. 21, Page 1121, 21(4):1121. DOI: 10.3390/S21041121.
Leyvand, T., Meekhof, C., Yi-Chen Wei, Jian Sun, and Baining Guo (2011). Kinect Identity: Technology and Experience. Computer, 44(4):94-96. DOI: 10.1109/MC.2011.114.
Li, Y., Tao, J., and Wotawa, F. (2020). Ontology-based test generation for automated and autonomous driving functions. Information and Software Technology, 117:106200. DOI: 10.1016/J.INFSOF.2019.106200.
Lohmann, S., Negru, S., Haag, F., and Ertl, T. (2016). Visualizing ontologies with VOWL. Semantic Web, 7(4):399-419. DOI: 10.3233/SW-150200.
Mitchell, M., Meyers, C., Wang, A.-I. A., and Tyson, G. (2011). Contextprovider: Context awareness for medical monitoring applications. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, pages 5244-5247.
Papatheocharous, E., Wnuk, K., Petersen, K., Sentilles, S., Cicchetti, A., Gorschek, T., and Shah, S. M. A. (2018). The GRADE taxonomy for supporting decision-making of asset selection in software-intensive system development. Information and Software Technology, 100:1-17. DOI: 10.1016/J.INFSOF.2018.02.007.
Rajaram, H. K., Loane, J., MacMahon, S. T., and Caffery, F. M. (2019). Taxonomy-based testing and validation of a new defect classification for health software. Journal of Software: Evolution and Process, 31(1):e1985. DOI: 10.1002/SMR.1985.
Santos, G. and Nikolaev, N. (2021). Mobility as a Service and Public Transport: A Rapid Literature Review and the Case of Moovit. Sustainability 2021, Vol. 13, Page 3666, 13(7):3666. DOI: 10.3390/SU13073666.
Sobral, T., Galvão, T., and Borges, J. (2020). An Ontology-based approach to Knowledge-assisted Integration and Visualization of Urban Mobility Data. Expert Systems with Applications, 150:113260. DOI: 10.1016/J.ESWA.2020.113260.
Soyturk, M., Muhammad, K. N., Avcil, M. N., Kantarci, B., and Matthews, J. (2016). Chapter 8 - From vehicular networks to vehicular clouds in smart cities. In Obaidat, M. S. and Nicopolitidis, P., editors, Smart Cities and Homes, pages 149 - 171. Morgan Kaufmann, Boston. DOI: https://doi.org/10.1016/B978-0-12-803454-5.00008-0.
Swarnamugi, M. and Chinnaiyan, R. (2020). Context—Aware Smart Reliable Service Model for Intelligent Transportation System Based on Ontology. Lecture Notes in Electrical Engineering, 597:23-30. DOI: 10.1007/978-3-030-29407-6_3.
Temdee, P. and Prasad, R. (2018). Context and Its Awareness. In Context-Aware Communication and Computing: Applications for Smart Environment, pages 15-31. Springer, Cham, Switzerland. DOI: 10.1007/978-3-319-59035-6_2.
Vahdat-Nejad, H., Ramazani, A., Mohammadi, T., and Mansoor, W. (2016). A survey on context-aware vehicular network applications. Vehicular Communications, 3(C):43-57. DOI: 10.1016/j.vehcom.2016.01.002.
Vieira, V., Caldas, L. R., and Salgado, A. C. (2011). Towards an Ubiquitous and Context Sensitive Public Transportation System. In 2011 Fourth International Conference on Ubi-Media Computing, pages 174-179, São Paulo, Brazil. IEEE. DOI: 10.1109/U-MEDIA.2011.19.
Vieira, V., Tedesco, P., and Salgado, A. C. (2005). Towards an ontology for context representation in groupware. In 11th International Workshop, CRIWG, volume 3706, pages 367-375, Porto de Galinhas, Brazil.
Vieira, V., Tedesco, P., and Salgado, A. C. (2009). A process for the design of Context-Sensitive Systems. In 2009 13th International Conference on Computer Supported Cooperative Work in Design, pages 143-148, Santiago, Chile. IEEE. DOI: 10.1109/cscwd.2009.4968049.
Villegas, N. M., Müller, H. A., Muñoz, J. C., Lau, A., Ng, J., and Brealey, C. (2011). A dynamic context management infrastructure for supporting user-driven web integration in the personal web. In Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research, pages 200-214, Toronto, Canada. IBM Corp. Riverton, NJ, USA.
Winder, C., Azzi, R., and Wagner, D. (2005). The development of the globally harmonized system (GHS) of classification and labelling of hazardous chemicals. Journal of Hazardous Materials, 125(1-3):29-44. DOI: 10.1016/J.JHAZMAT.2005.05.035.
Wongcharoen, S. and Senivongse, T. (2016). Twitter analysis of road traffic congestion severity estimation. 2016 13th International Joint Conference on Computer Science and Software Engineering, JCSSE 2016. DOI: 10.1109/JCSSE.2016.7748850.
Woodard, M., Wisely, M., and Sarvestani, S. S. (2016). A Survey of Data Cleansing Techniques for Cyber-Physical Critical Infrastructure Systems. Advances in Computers, 102:63-110. DOI: 10.1016/BS.ADCOM.2016.05.002.
Xiong, Z., Dixit, V. V., and Waller, S. T. (2016). The development of an Ontology for driving Context Modelling and reasoning. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 13-18. DOI: 10.1109/ITSC.2016.7795524.
Zheng, X., Chen, W., Wang, P., Shen, D., Chen, S., Wang, X., Zhang, Q., and Yang, L. (2016). Big Data for Social Transportation. IEEE Transactions on Intelligent Transportation Systems, 17(3):620-630. DOI: 10.1109/tits.2015.2480157.
Zimmermann, A., Lorenz, A., and Oppermann, R. (2007). An Operational Definition of Context. In Modeling and Using Context, pages 558-571. Springer Berlin Heidelberg. DOI: 10.1007/978-3-540-74255-5_42.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 The authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
