International Journal of Advanced Research in Engineering and Technology (IJARET)

Source ID:00000006
Volume 11, Issue 3,March 2020, Pages 437-441, Page Count - 5

ARCHITECTURE OF TRAFFIC FLOW PREDICTION BASED ON CCF-DEEP LSTM METHOD

Nazirkar Reshma Ramchandra (1) C. Rajabhushanam (2)

(1) Research Scholar, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India.
(2) Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India.

Manuscript ID:- 00000-05106
Access Type : Open Access
Read Full Article


Cite this article:Nazirkar Reshma Ramchandra,C. Rajabhushanam,  Architecture Of Traffic Flow Prediction Based On Ccf-deep Lstm Method, International Journal of Advanced Research in Engineering and Technology(IJARET), 2020, 11(3), PP.437-441

Manuscript Level Metrics (MLM)

Views Downloads Citations Cited References Social Shares
31 13 0 0

Abstract

The foremost reason for traffic congestion is the more number of vehicles that is because of the increase in population rate and also because of the development of the economy. Due to many reason the developed cities don`t have chance to eliminate traffic, but the modern and developed technology helps to manage traffic. Over last some years, traffic data have been exploding. The traffic in the area can be predicted is done using Deep Learning concept. Deep learning is a subdivision of Machine learning algorithms. The deep leaning algorithm is applied for the detection of traffic. This method is commonly known as traffic flow model prediction. In this research article a new architecture has been proposed to predict the traffic control in concern area. Here various indicators are used to analyze the traffic data. The important indicators are CCI, ADX and DEMA.
Author Keywords
Framework Prediction Traffic System Technical Indicators.


ISSN Print: 0976-6480 ISSN Online: 0976-6499
Source Type: Journals Document Type: Journal Article
Publication Language: English DOI: 10.34218/IJARET.11.3.2020.039
Abbreviated Journal Title: IJARET Access Type: Open Access
Publisher Name: IAEME Publication Resource Licence: CC BY-NC
Major Subject:Physical Sciences Subject Area classification: Computer Science
Subject area: General Computer Science Source: SCOPEDATABASE

References (16)
  1. A. Akgunduz, B. Jaumard and G. Moeini
    Deconflicted Air-Traffic Planning With Speed-Dependent Fuel-Consumption Formulation
    (2018)IEEE Transactions on Intelligent Transportation Systems, Volume 19, Issue 6, Page No 1890-1901,
  2. Xianglong Luo, Danyang Li, Yu Yang & Shengrui Zhan
    Machine Learning in Transportation
    Research Article,
  3. Xiangyu Zhou , Zhengjiang Liu , Fengwu Wang , Yajuan Xie & Xuexi Zhang
    Using Deep Learning to Forecast Maritime Vessel Flows
    (2020), Page No 1-17,
  4. Shengdong Du1, Tianrui Li1,, Xun Gong1 and Shi-Jinn Horng
    A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning
    ,
  5. Wangyang Wei, Honghai Wu & Huadong Ma
    An AutoEncoder and LSTM-Based Traffic Flow Prediction Method
    (2019), Page No 1-16,
  6. S. Uma Devi & S. Nirmala Sugirtha Rajini
    Detection of Traffic Violation Crime Using Data Mining Algorithms
    (2019)Journal of Advanced Research in Dynamical and Control Systems, Volume 11, Issue 9, Page No 982-987,
  7. Rui Fu ; Zuo Zhang ; Li Li
    Using LSTM and GRU neural network methods for traffic flow prediction
    (2016)Publisher: IEEE, 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC),
  8. B.Karthika, N.UmaMaheswari, R.Venkatesh
    A Research of Traffic Prediction using Deep Learning Techniques
    (2019)International Journal of Innovative Technology and Exploring Engineering, Volume 8, Issue 9S2, Page No 725-728,
  9. Wang Xiangxue, Xu Lunhui & Chen Kaixun
    Data-Driven Short-Term Forecasting for Urban Road Network Traffic Based on Data Processing and LSTM-RNN
    (2019)Arabian Journal for Science and Engineering, Volume 44, Page No 3043–3060,
  10. Chan, K., Dillon, T., Singh, J
    Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm
    (2015)IEEE Transactions on Intelligent Transportation Systems, Page No 644–654,
  11. Jubair Mohammed Bilal & Don Jacob
    Intelligent Traffic Control System
    (2007)IEEE International Conference on Signal Processing and Communications, Page No 24-27,
  12. Bilal Ghazal, Khaled ElKhatib, Khaled Chahine & Mohamad Kherfan
    Smart traffic light control system
    (2016)Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA),
  13. Kasula Nagaraju, Shivarudraiah, Chandrasekhar, B,
    A New Approach to Optimize Traffic Flow Using Maximum Entropy Modeling
    (2010)International Journal of Mechanical Engineering and Technology,, Volume 1, Issue 1, Page No 134-149,
  14. Yanti Tjong, Suroto Adi, Raymond Kosala, Harjanto Prabowo,
    A SYSTEMATIC MAPPING STUDY ON ENTERPRISE ARCHITECTURE FRAMEWORK FOR HEI
    (2018)International Journal of Mechanical Engineering and Technology,, Volume 9, Issue 13, Page No 403–411,
  15. Syama K Nair, Ragimol,
    An Embedded Architecture for Feature Detection Using Modified Sift Algorithm
    (2016)International Journal of Electronics and Communication Engineering and Technology,, Volume 7, Issue 5, Page No 38–46,
  16. G.S. Sunitha, Rakesh H.M,
    Design and Implementation of Adder Architectures and Analysis of Performance Metrics
    (2017)International Journal of Electronics and Communication Engineering and Technology,, Volume 8, Issue 5, Page No 1–6,