SAINT: Self-adaptive interactive navigation tool for cloud-based vehicular traffic optimization

Jaehoon Jeong, Hohyeon Jeong, Eunseok Lee, Tae Oh, David H.C. Du

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


This paper proposes a self-adaptive interactive navigation tool (SAINT), which is tailored for cloud-based vehicular traffic optimization in road networks. The legacy navigation systems make vehicles navigate toward their destination less effectively with individually optimal navigation paths rather than network-wide optimal navigation paths, particularly during rush hours. To the best of our knowledge, SAINT is the first attempt to investigate a self-adaptive interactive navigation approach through the interaction between vehicles and vehicular cloud. The vehicles report their navigation experiences and travel paths to the vehicular cloud so that the vehicular cloud can know real-time road traffic conditions and vehicle trajectories for better navigation guidance for other vehicles. With these traffic conditions and vehicle trajectories, the vehicular cloud uses a mathematical model to calculate road segment congestion estimation for global traffic optimization. This model provides each vehicle with a navigation path that has minimum traffic congestion in the target road network. Using the simulation with a realistic road network, it is shown that our SAINT outperforms the legacy navigation scheme, which is based on Dijkstra's algorithm with a real-time road traffic snapshot. On a road map of Manhattan in New York City, our SAINT can significantly reduce the travel delay during rush hours by 19%.

Original languageEnglish (US)
Article number7243355
Pages (from-to)4053-4067
Number of pages15
JournalIEEE Transactions on Vehicular Technology
Issue number6
StatePublished - Jun 2016

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning through the Basic Science Research Program (2014006438) and the Next-Generation Information Computing Development Program (2015045358). The review of this paper was coordinated by Dr. A. Chatterjee.

Publisher Copyright:
© 1967-2012 IEEE.

Copyright 2017 Elsevier B.V., All rights reserved.


  • Navigation
  • cloud
  • congestion
  • interactive
  • road network
  • self-adaptive
  • trajectory
  • vehicular network

Fingerprint Dive into the research topics of 'SAINT: Self-adaptive interactive navigation tool for cloud-based vehicular traffic optimization'. Together they form a unique fingerprint.

Cite this