Short Term Road Traffic Flow Forecasting Using Multi Layer Perceptron Neural Networks

被引:0
作者
Sumalatha, V [1 ]
Dingari, Manohar [2 ]
Jayalakshmi, C. [3 ]
机构
[1] OSMANIA Univ, Dept Math, Hyderabad 500007, India
[2] GITAM Univ, Dept Math, Hyderabad 502329, India
[3] OSMANIA Univ, Dept Stat, Hyderabad 500007, India
来源
INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND APPLICATIONS (ICMSA-2019) | 2020年 / 2246卷
关键词
Traffic volume; Multilayer Perceptron; Artificial Neural Network; Feed forwarded; Intelligent Transport System; Forecasting; PREDICTION;
D O I
10.1063/5.0014561
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In recent days, road traffic management and congestion control has become major problems in any busy junction in Hyderabad city. Hence short term traffic flow forecasting has gained greater importance in Intelligent Transport System(ITS). Artificial Neural Network(ANN) models have been fruitfully applied for classification and prediction of time series. In this paper, an attempt has been made to model and forecast short-term traffic flow at 6.no. junction in Amberpet, Hyderabad, Telangana state, India applying Neural Network models. The traffic data has been considered for peak hours in the morning for 8A.M to 12 Noon, for 5 days. Multilayer Perceptron (MLP) network model is used in this study. These results can be considered to monitor traffic signals and explore methods to avoid congestion at that junction.
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页数:5
相关论文
共 6 条
  • [1] Klevecka Irina, 2011, TRANSPORT TELECOMMUN, V12
  • [2] Kumar K, 2015, TRANSPORT-VILNIUS, V30, P397
  • [3] Sharma Bharti, J BIG DATA-GER
  • [4] Sun SL, 2004, 2004 IEEE INTELLIGENT VEHICLES SYMPOSIUM, P437
  • [5] Short Term Traffic Flow Prediction Using Hybrid ARIMA and ANN Models
    Zeng, Dehuai
    Xu, Jianmin
    Gu, Jianwei
    Liu, Liyan
    Xu, Gang
    [J]. 2008 WORKSHOP ON POWER ELECTRONICS AND INTELLIGENT TRANSPORTATION SYSTEM, PROCEEDINGS, 2008, : 621 - +
  • [6] Short-term freeway traffic flow prediction: Bayesian combined neural network approach
    Zheng, WZ
    Lee, DH
    Shi, QX
    [J]. JOURNAL OF TRANSPORTATION ENGINEERING, 2006, 132 (02) : 114 - 121