Mody University International Journal of Computing and Engineering Research

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Short Term Traffic Flow Prediction Methodologies:A Review


Affiliations
1 Department of Computer Science and Engineering, Mody University of Science and Technology, Lakshmangarh, Rajasthan, India
2 College of Engineering and Technology, Mody University of Science and Technology, Lakshmangarh, Sikar, Rajasthan-332311, India
3 Department of Computer Science and Engineering,Engineering, Mody University of Science and Technology, Lakshmangarh, Sikar, Rajasthan-332311, India
 

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Successful traffic speed forecasting is an intact component for traffic management agencies. It has great grandness for benefit of road users. Intelligent traffic management system provides time based traffic flow information, so that travellers can reach their destination at an estimated time. In previous few years a series of traffic speed prediction applications have been formulated, in which most of the approaches are relied on short-term speed prediction which includes some traditional models and machine learning techniques. The traffic flow has greatly increased due to the current system and existing methods are still unsatisfying. This composition examined few of the current short-term traffic speed methods.

Keywords

Machine Learning, Speed Prediction Methods, Deep Learning.
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  • Yuhan Jia, Jianping Wu, and Yiman Du, “Traffic speed prediction using deep learning method,” IEEE 19th ITSC, Brazil, November 1-4, 2016.

  • Han Jiang, Yajie Zou, Shen Zhang, Jinjun Tang, and Yinhai Wang, “Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model,” Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2016, Article ID 9236156.

  • Daniel Kubek, Pawel Wiecek, Konrad Chwastek, “The impact of short term traffic forecasting on the effectiveness of vehicles routes planning in urban areas,” ScienceDirect, Transportation Research Procedia 18, 2016, pp. 172-179.

  • S. Vasantha Kumar, Lelitha Vanajakshi, “Short-term traffic flow prediction using seasonal ARIMA model with limited input data,” Springer, Eur. Transp. Res. Rev. 2015, 7:21.

  • Zhiheng Yu, Tieli Sun, Hongguang Sun, and Fengqin Yang, “Research on Combinational Forecast Models for the Traffic Flow,” Hindawi Publishing Corporation, Mathematical Problems in Engineering Volume 2015, Article ID 201686.

  • Sivabalaji Manoharan, “Short Term Traffic Prediction Using Deep Learning Approach,” Msc Research Report in Data Analytics, 21st December 2016.

  • S.Piramu Kailasam, K.Aruna Anushiya, Dr. M.Mohammed Sathik, “Traffic flow Prediction with Big Data Using SAE’S Algorithm,” IJCSMC, vol. 5, no. 7 July 2016, pp.186-193.

  • Jia Zheng Zhu, Jin Xin Cao, Yuan Zhu, “Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent Intersections,” Article in Transportation Research Part C Emerging Technologies • July 2014.

  • Kartikeya Jha, Nishita Sinha, Shriniwas S. Arkatkar, and Ashoke K. Sarkar, “A comparative study on application of time series analysis for traffic forecasting in India: prospects and limitations,” Research Articles.

  • Yiannis I. Kamarianakis and Poulicos P. Prastacos, “Space-Time Modelling Of Traffic Flow,” 42nd Congress of the European Regional Science Association: "From Industry to Advanced Services - Perspectives of European Metropolitan Regions", August 27th - 31st, 2002, Dortmund, Germany.


Abstract Views: 392

PDF Views: 266




  • Short Term Traffic Flow Prediction Methodologies:A Review

Abstract Views: 392  |  PDF Views: 266

Authors

Pregya Poonia
Department of Computer Science and Engineering, Mody University of Science and Technology, Lakshmangarh, Rajasthan, India
V. K. Jain
College of Engineering and Technology, Mody University of Science and Technology, Lakshmangarh, Sikar, Rajasthan-332311, India
Anil Kumar
Department of Computer Science and Engineering,Engineering, Mody University of Science and Technology, Lakshmangarh, Sikar, Rajasthan-332311, India

Abstract


Successful traffic speed forecasting is an intact component for traffic management agencies. It has great grandness for benefit of road users. Intelligent traffic management system provides time based traffic flow information, so that travellers can reach their destination at an estimated time. In previous few years a series of traffic speed prediction applications have been formulated, in which most of the approaches are relied on short-term speed prediction which includes some traditional models and machine learning techniques. The traffic flow has greatly increased due to the current system and existing methods are still unsatisfying. This composition examined few of the current short-term traffic speed methods.

Keywords


Machine Learning, Speed Prediction Methods, Deep Learning.

References