Long-term Traffic Flow Forecasting Based on an Artificial Neural Network

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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Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches

Structural Control and Health Monitoring ◽

10.1002/stc.492 ◽

2011 ◽

Vol 20 (3) ◽

pp. 282-303 ◽

Cited By ~ 56

Author(s):

Ching-Yun Kao ◽

Chin-Hsiung Loh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Arch Dam ◽

Static Deformation ◽

Artificial Neural

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Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

Communications - Scientific letters of the University of Zilina ◽

10.26552/com.c.2022.2.e74-e86 ◽

2022 ◽

Vol 24 (2) ◽

pp. E74-E86

Author(s):

Isaac Oyeyemi Olayode ◽

Alessandro Severino ◽

Tiziana Campisi ◽

Lagouge Kwanda Tartibu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Flow ◽

Traffic Control ◽

Traffic Congestion ◽

Autonomous Vehicles ◽

Time Of Day ◽

Vehicle Speed ◽

Road Transportation ◽

Artificial Neural

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

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Long‐term streamflow forecasting using artificial neural network based on preprocessing technique

Journal of Forecasting ◽

10.1002/for.2564 ◽

2019 ◽

Vol 38 (3) ◽

pp. 192-206 ◽

Cited By ~ 7

Author(s):

Fang‐Fang Li ◽

Zhi‐Yu Wang ◽

Jun Qiu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Streamflow Forecasting ◽

Artificial Neural ◽

Preprocessing Technique

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Transfer learning with long term artificial neural network memory (LTANN-MEM) and neural symbolization algorithm (NSA) for solving high dimensional multi-objective symbolic regression problems

2017 34th National Radio Science Conference (NRSC) ◽

10.1109/nrsc.2017.7893495 ◽

2017 ◽

Author(s):

Amr K. Deklel ◽

Mohamed A. Saleh ◽

Alaa M. Hamdy ◽

Elsayed M. Saad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Transfer Learning ◽

Symbolic Regression ◽

High Dimensional ◽

Multi Objective ◽

Regression Problems ◽

Artificial Neural

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Hybrid artificial neural network and locally weighted regression models for lane-based short-term urban traffic flow forecasting

Transportation Planning and Technology ◽

10.1080/03081060.2018.1526988 ◽

2018 ◽

Vol 41 (8) ◽

pp. 901-917 ◽

Cited By ~ 7

Author(s):

Asif Raza ◽

Ming Zhong

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Flow ◽

Regression Models ◽

Urban Traffic ◽

Weighted Regression ◽

Locally Weighted Regression ◽

Short Term ◽

Artificial Neural ◽

Hybrid Artificial Neural Network

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SHORT TERM TRAFFIC FLOW PREDICTION IN HETEROGENEOUS CONDITION USING ARTIFICIAL NEURAL NETWORK

Transport ◽

10.3846/16484142.2013.818057 ◽

2013 ◽

Vol 30 (4) ◽

pp. 397-405 ◽

Cited By ~ 37

Author(s):

Kranti Kumar ◽

Manoranjan Parida ◽

Vinod Kumar Katiyar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Flow ◽

Traffic Congestion ◽

Traffic Volume ◽

Flow Data ◽

Highway Traffic ◽

Short Term ◽

Artificial Neural ◽

Input Variables

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables.

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