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An IoT approach for context-aware smart traffic management using ontology

Published:23 August 2017Publication History

ABSTRACT

This paper exhibits a novel context-aware service framework for IoT based Smart Traffic Management using ontology to regulate smooth traffic flow in smart cities by analyzing real-time traffic environment. The proposed approach makes smarter use of transport networks to achieve objectives related to performance of transport system. This requires efficient traffic planning measures which relate to the actions designed to adjust the demand and capacity of the network in time and space by use of IoT technologies. The adoption of sensors and IoT devices in Smart Traffic System helps to capture the user's preferences and context information which can be in the form of travel time, weather conditions or real-life driving patterns. We have employed multimedia ontology to derive higher level descriptions of traffic conditions and vehicles from perceptual observation of traffic information which provides important grounds for our proposed IoT framework. The multimedia ontology encoded in Multimedia Web Ontology Language(MOWL) helps to define classes, properties, and structure of a possible traffic environment to provide insights across the transportation network. MOWL supports Dynamic Bayesian networks (DBN) to deal with time-series data and uncertainties linked with context observations which fits the definition of an intelligent IoT system. Thus, our proposed smart traffic framework aggregates information corresponding to traffic domain such as traffic videos captured using CCTV cameras and allows automatic prediction of dynamically changing situations which helps to make traffic authorities more responsive. We have illustrated use of our approach by utilizing contextual information, to assess real-time congestion situation on roads thus allowing to visualize planning services. Once the congestion situation is predicted, alternate congestion free routes which are in accordance with the coveted criteria are suggested that can be propagated through text-messages or e-mails to the users.

References

  1. Cristina Barbero, Paola Dal Zovo, and Barbara Gobbi. 2011. A flexible context aware reasoning approach for iot applications. In 2011 IEEE 12th International Conference on Mobile Data Management, Vol. 1. IEEE, 266--275. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Norbert Baumgartner, Wolfgang Gottesheim, Stefan Mitsch, Werner Retschitzegger, and Wieland Schwinger. 2010. BeAware!fi!?situation awareness, the ontology-driven way. Data & Knowledge Engineering 69, 11 (2010), 1181--1193. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Steven Davy, Keara Barrett, Martín Serrano, John Strassner, Brendan Jennings, and Sven van der Meer. 2007. Policy interactions and management of traffic engineering services based on ontologies. In 2007 Latin American Network Operations and Management Symposium. IEEE, 95--105.Google ScholarGoogle Scholar
  4. Aditya Gaur, Bryan Scotney, Gerard Parr, and Sally McClean. 2015. Smart City Architecture and its Applications Based on IoT. Procedia Computer Science 52 (2015), 1089--1094.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2014. Spatial pyramid pooling in deep convolutional networks for visual recognition. In European Conference on Computer Vision. Springer, 346--361.Google ScholarGoogle ScholarCross RefCross Ref
  6. Freddy Lécué, Anika Schumann, and Marco Luca Sbodio. 2012. Applying semantic web technologies for diagnosing road traffic congestions. In International Semantic Web Conference. Springer, 114--130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Freddy Lécué, Robert Tucker, Veli Bicer, Pierpaolo Tommasi, Simone Tallevi-Diotallevi, and Marco Sbodio. 2014. Predicting severity of road traffic congestion using semantic web technologies. In European Semantic Web Conference. Springer, 611--627.Google ScholarGoogle Scholar
  8. Anupama Mallik, Hiranmay Ghosh, Santanu Chaudhury, and Gaurav Harit. 2013. MOWL: An ontology representation language for web-based multimedia applications. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 10, 1 (2013), 8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Anupama Mallik, Anurag Tripathi, Ravi Kumar, Santanu Chaudhury, and Komal Sinha. 2015. Ontology based context aware situation tracking. In Internet of Things (WF-IoT), 2015 IEEE 2nd World Forum on. IEEE, 687--692. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Daniel Meyer-Delius, Christian Plagemann, and Wolfram Burgard. 2009. Probabilistic situation recognition for vehicular traffic scenarios. In Robotics and Automation, 2009. ICRA'09. IEEE International Conference on. IEEE, 459--464. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. V Mihajlovic and Milan Petkovic. 2001. Dynamic bayesian networks: A state of the art. (2001).Google ScholarGoogle Scholar
  12. Syed Misbahuddin, Junaid Ahmed Zubairi, Abdulrahman Saggaf, Jihad Basuni, A Sulaiman, Ahmed Al-Sofi, et al. IoT based dynamic road traffic management for smart cities. In 2015 12th International Conference on High-capacity Optical Networks and Enabling/Emerging Technologies.Google ScholarGoogle Scholar
  13. Ralf Regele. 2008. Using ontology-based traffic models for more efficient decision making of autonomous vehicles. In Fourth International Conference on Autonomic and Autonomous Systems (ICAS'08). IEEE, 94--99. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Andrea Zanella, Nicola Bui, Angelo Castellani, Lorenzo Vangelista, and Michele Zorzi. 2014. Internet of things for smart cities. IEEE Internet of Things Journal 1, 1 (2014), 22--32.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Conferences
    WI '17: Proceedings of the International Conference on Web Intelligence
    August 2017
    1284 pages
    ISBN:9781450349512
    DOI:10.1145/3106426

    Copyright © 2017 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 23 August 2017

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    WI '17 Paper Acceptance Rate118of178submissions,66%Overall Acceptance Rate118of178submissions,66%

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