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Real-time Prediction of Accident using Big Data System

Published: 18 May 2020 Publication History

Abstract

In this paper, we propose a new prediction system in real time using Big Data to improve the VANET network. Firstly, The Traffic density and average speed are calculated in each section of road, and then the risk of vehicle accident is predicted in instantaneous manner with parallel data processing, which makes execution faster.

References

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Da Cunha, Felipe Domingos, et al. Data communication in VANETs: a survey, challenges and applications. Diss. INRIA Saclay; INRIA, 2014.
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Bedi, Punam, and Vinita Jindal. "Use of big data technology in vehicular ad-hoc networks." Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on. IEEE, 2014.
[3]
Khan, Junaid, YacineGhamri-Doudane, and Ali El Masri. "Vers une approche centrée information (ICN) pour identifier les véhicules importants dans les VANETs." Proceedings of the conference CFIP NOTERE 2015. 2015.
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Cheng, Zheyuan, et al. "A big data based deep learning approach for vehicle speed prediction." Industrial Electronics (ISIE), 2017 IEEE 26th International Symposium on. IEEE, 2017.
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Al Najada, Hamzah, and ImadMahgoub."Anticipation and alert system of congestion and accidents in VANET using Big Data analysis for Intelligent Transportation Systems." Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016.
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Marz, Nathan, and James Warren. Big Data: Principles and best practices of scalable real-time data systems. New York; Manning Publications Co., 2015.
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Mahajan, Anshul, and ArvinderKaur. "Predictive Urban Traffic Flow Model using Vehicular Big Data." Indian Journal of Science and Technology 9.42 (2016).
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Bo-Rang Lin, Tsan-Pin Wang "Dynamic Reporting Mechanisms for Trust Management in Vehicular Adhoc Networks", 2016
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Daniel, Alfred, Anand Paul, and Awais Ahmad. "Near real-time big data analysis on vehicular networks." Soft-Computing and Networks Security (ICSNS), 2015 International Conference on. IEEE, 2015.
[10]
ALINANI, Annadil et ALINANI, Karim. Real-Time Push-Based Data Forwarding for Target Tracking in Vehicular Named Data Networking. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/ SCALCOM/ UIC/ ATC/ CBDCom/IOP/SCI). IEEE, 2018. p. 1587--1592.
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GAWAS, Mahadev A., MULAY, Manas, et BHATIA, Vinamra. Cross Layer Approach for Neighbor Node Selection in VANET Routing. In: 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP). IEEE, 2018. p. 1--6.

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  • (2024)Priority based inter-twin communication in vehicular digital twin networksInternational Journal of Parallel, Emergent and Distributed Systems10.1080/17445760.2024.2398082(1-16)Online publication date: 2-Sep-2024

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NISS '20: Proceedings of the 3rd International Conference on Networking, Information Systems & Security
March 2020
528 pages
ISBN:9781450376341
DOI:10.1145/3386723
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2020

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Author Tags

  1. Big Data
  2. Lambda architecture
  3. Traffic density
  4. VANET

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Cited By

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  • (2024)Priority based inter-twin communication in vehicular digital twin networksInternational Journal of Parallel, Emergent and Distributed Systems10.1080/17445760.2024.2398082(1-16)Online publication date: 2-Sep-2024

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