S&T Interventions for Intelligent Traffic Management Systems


Authors
  • John Anthony Jose
  • De La Salle University, Manila, Philippines
  • Varsolo Sunio
  • Department of Science and Technology-Philippine Council for Industry, Energy, and Emerging Technology Research and Development, Taguig City, Philippines
  • Science Engineering and Management Research Institute, University of Asia and the Pacific, Pasig City, Philippines
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Cite As
  • APA 7th Edition:
    Jose, J. A., & Sunio, V. (2022). S&T Interventions for Intelligent Traffic Management Systems. Innovatus: A Journal on Computing Technology Innovations, 5(2), 11–13. https://doi.org/10.5281/zenodo.7339938.
  • Harvard:
    Jose, J.A. and Sunio, V. (2022) “S&T Interventions for Intelligent Traffic Management Systems,” Innovatus: A Journal on Computing Technology Innovations, 5(2), pp. 11–13. Available at: https://doi.org/10.5281/zenodo.7339938.
  • IEEE:
    J. A. Jose and V. Sunio, “S&T Interventions for MMDA Intelligent Traffic Management Systems,” Innovatus: A Journal on Computing Technology Innovations, vol. 5, no. 2, pp. 11–13, Dec. 2022.


References
  • Department of Public Works and Highways. (n.d.). Road Traffic Information Application. Retrieved Aug 2022, from ArcGIS: Road Traffic Information Application - ArcGIS Hub https://www.dpwh.gov.ph/dpwh/gis/rti
  • MMDA. (2022, June 24). MMDA, JICA Hold Meeting for Metro Manila Comprehensive Traffic Management Plan. Retrieved from MMDA.gov.ph: https://mmda.gov.ph/84-news/news-2022/5583-june-4-2022-mmda-jica-meet-for-comprehensive-traffic-management-plan-ctmp-for-the-metro-manila-project.html
  • Inrix. (2018). Pennsylvania DOT Using Crowd Sourced Data to Assess and Improve Statewide Traffic Incident Management (TIM). Retrieved August 2022, from Inrix: https://inrix.com/case-studies/penn-dot-case-study/
  • Surowiecki, J. (2005). The Wisdom of Crowds. New York: Anchor.
  • U.S. Department of Transportation. (2016). Federal Highway Administration Traffic Monitoring Guide. U.S. Government
  • LeCun, Y., & Misra, I. (2021, March 4). Self-supervised learning: The dark matter of intelligence. Retrieved from Meta AI: https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
  • Nobis, F., Geisslinger, M., Weber, M., Betz, J., & Lienkamp, M. (2019). A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection. Sensor Data Fusion: Trends, Solutions, Applications (SDF). Bonn, Germany: IEEE.
  • Hwang, S., Park, J., Kim, N., Choi, Y., & Kweon, I. (2015). Multispectral pedestrian detection: Benchmark dataset and baseline. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE.
  • Bosch Connected Devices and Solutions GmbH. (2018). Parking Sensor: Wireless sensors for detecting parking space occupancy. Bosch.
  • British Security Industry Association. (2021, August). Planning, design, installation and operation of Video Surveillance Systems (VSS): Code of practice and associated guidance. The Voice of the Professional Security Industry, BSIA Form 109(5), 60.
  • UK Department for Transport. (2018). Traffic Statistics Methodology Review Alternative Data Sources. London: Crown.
  • UK Department for Transport. (2018, September 18). Position statement on artificial intelligence in transport. Retrieved August 23, 2022, from Gov.uk: https://www.gov.uk/government/publications/review-of-artificial-intelligence-in-transport-2017/position-statement-on-artificial-intelligence-in-transport
  • Dai, D., Tan, R. T., Patel, V., Matas, J., Schiele, B., & Van Gool, L. (2021, July). Guest Editorial: Special Issue on “Computer Vision for All Seasons: Adverse Weather and Lighting Conditions”. International Journal of Computer Vision, 129, 2031-2033.


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