Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

A Novel Framework for Traffic Flow Prediction with Deep Learning Algorithms

Author : S.Narmadha 1 Dr.V.Vijayakumar 2

Date of Publication :28th April 2018

Abstract: Traffic flow prediction is the process of predicting future traffic conditions in short-term or long term basis, based on real time and the past traffic data. It is an important component of an Intelligent Transportation System (ITS) in smart city development. Apart from traffic data from sensors, Incidents detection, Lane closure and Weather data also some of the main reasons to cause the congestion. Combine factors and provide an efficient prediction of congestion. It provides innovative and smarter services to the transport users. Deep learning is a promising technology used to predict the traffic congestion with high accuracy. Deep belief network, Recurrent neural network, Convolutional neural network are some of the well known existing algorithms used in transportation analysis. In this paper a novel framework is proposed for intelligent transport systems which can solve so many issues such as pollution, accidents, and unwanted delays.

Reference :

    1.  Schilit B.N., Theimer M.M., “Disseminating Active Map Information to Mobile Hosts”, IEEE Network, vol. 8, issue 5, pp. 22-32, Sep. 1994.
    2. Brown P.J., Bovey J.D., Chen X., “Context-Aware Applications: From the Laboratory to the Marketplace”, IEEE Personal Communications, vol. 4, issue 5, pp. 58- 64, 1997.
    3. Ryan N., Pascoe J., Morse D., “Enhanced Reality Fieldwork: the Context-Aware Archaeological Assistant”, Computer Applications in Archaeology, Proc. of 25th anniversary conference, University of Birmingham, April 1997
    4. Dey A.K., Abowd G.D., “Towards a better understanding of context and context awareness”, Proc. of the first International Symposium on Handheld and Ubiquitous Computing, Karlsruhe, Germany, pp. 304- 307, 1999.
    5. Internet World Stats Usage and Population Statistics, available at, www.internetworldstats.com/emarketing.htm, accessed on 10th Aug. 2013.
    6. Teevan J., Dumais S.T., Horvitz E., “Personalizing search via automated analysis of interests and activities”, SIGIR‟05, Proc. of the 28th annual international ACM SIGIR conference on research and development in information retrieval, pp. 449-456, 2005.
    7. Belkin N.J., Muresan G. and Zhang X.-M., “Using user‟s context for IR personalization.” Proc. of the ACM/SIGIR Workshop on Information Retrieval in Context, ACM Press, Sheffield, UK, pp. 1-3, July 25-29, 2004.
    8. Pitkow J., Schütze H., Cass T., Cooley, R., Turnbull D., Edmonds A., Adar E., Breuel T., “Personalized Search”, Communications of ACM, vol. 45, issue 9, pp. 50-55, Sep. 2002.
    9. Bai J., Nie J.-Y., Bouchard H., Cao G., “Using Query Contexts in Information Retrieval”, SIGIR‟07, Proc. of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, Amsterdam, Netherlands, July 23–27, pp. 15- 22, 2007.
    10. Page Lawrence, Brin Sergey, Motwani Rajeev, Winograd Terry, “The PageRank Citation Ranking: Bringing Order to the Web”, Technical Report, Stanford University InfoLab, 1999.


    1.  
    2.  Arief Koesdwiady, RidhaSoua, and FakhriKarray, “Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach”, IEEE Transactions on Vehicular Technology, 2015.
    3. Fan Wang, Guozhen Tan, Yu Fang, Multiscale Wavelet support Vector Regression for Traffic Flow Prediction, IEEE, 2009. VIII.
    4. FELIX KUNDE ALEXANDER HARTENSTEIN STEPHAN PIEPER PETRA SAUER, “TRAFFIC PREDICTION USING A DEEP LEARNING PARADIGM”, CEUR-WS.ORG, 2017. IX.
    5. GUY LESHEM, AND YA'ACOV RITOV, “TRAFFIC FLOW PREDICTION USING ADABOOST ALGORITHM WITH RANDOM FORESTS AS A WEAK LEARNER”, INTERNATIONAL JOURNAL OF MATHEMATICAL AND COMPUTATIONAL SCIENCES VOL:1, NO:1, 2007.
    6. G.Y.Zhang, Q.Zhao, Z.W.Luo and H.Wei, “ShortTerm traffic flow Prediction with ACD and Particle Filter”, Ninth International Conference of Chinese Transportation professionals (ICCTP), 2009.
    7. Haiyang Yu , Zhihai Wu , Shuqin Wang , Yunpeng Wang and Xiaolei Ma, “Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks”, Sensors 2017.
    8. Hongsuk Yi, HeeJin Jung, SanghoonBae, “Deep Neural Networks for Traffic Flow Prediction”,IEEE,2017.
    9. Hongxin Shao Guy Leshem, and Ya'acov Ritov, Boon-Hee Soong, “Traffic flow prediction with Long Short-Term Memory Networks (LSTMs)”, IEEE, 2016
    10. Hua-pu Lu,Zhi-yuan Sun and Wen-congQu, “Big Data-Driven Based Real-time Traffic flow State Identification and Prediction”, Hindawi, 2015.

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