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

Transport ◽  
2013 ◽  
Vol 30 (4) ◽  
pp. 397-405 ◽  
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.

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

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.

scholarly journals Vehicular Traffic Noise Modelling of Urban Area- A Contouring & Artificial Neural Network-Based Approach

2021 ◽  
Author(s):  
Abhijit Debnath ◽  
Prasoon Kumar Singh ◽  
Sushmita Banerjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Noise Level ◽  
Urban Areas ◽  
Traffic Noise ◽  
Traffic Volume ◽  
Noise Levels ◽  
Noise Characteristics ◽  
Artificial Neural

Abstract Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causing health risks among the urban populations. In this study we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and finds the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, Speed, traffic flow, road gradient, pavement, road side carriageway distance factors taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59% 2-wheelers and different vehicle specifications with varying speeds also effects driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicates high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 & .029 in training and 0.458 & 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ±0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

scholarly journals Short Term Load Forecasting Based Artificial Neural Network

2014 ◽  
Vol 10 (1) ◽  
pp. 42-47
Author(s):  
Adel Dakhil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Historical Series ◽  
Feed Forward Network ◽  
Artificial Neural ◽  
Input Variables ◽  
Analysis System

Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previous hour, the load of the pervious day, the load for the previous week). Short term load forecast is very important due to accurate for power system operation and analysis system security among other mandatory function. The trained artificial neural network shows good accuracy and robust in forecasting future load demands for the daily operation, mean absolute percentage error (MAPE) was calculated and it is maximum value is 0.75% in load forecasting on Monday.

scholarly journals Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing

2020 ◽  
Vol 32 (6) ◽  
pp. 747-760
Author(s):  
Changxi Ma ◽  
Limin Tan ◽  
Xuecai Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Prediction Accuracy ◽  
Feedforward Neural Network ◽  
Short Term ◽  
Combined Model ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Artificial Neural

In order to improve the accuracy of short-term traffic flow prediction, a combined model composed of artificial neural network optimized by using Genetic Algorithm (GA) and Exponential Smoothing (ES) has been proposed. By using the metaheuristic optimal search ability of GA, the connection weight and threshold of the feedforward neural network trained by a backpropagation algorithm are optimized to avoid the feedforward neural network falling into local optimum, and the prediction model of Genetic Artificial Neural Network (GANN) is established. An ES prediction model is presented then. In order to take the advantages of the two models, the combined model is composed of a weighted average, while the weight of the combined model is determined according to the prediction mean square error of the single model. The road traffic flow data of Xuancheng, Anhui Province with an observation interval of 5 min are used for experimental verification. Additionally, the feedforward neural network model, GANN model, ES model and combined model are compared and analysed, respectively. The results show that the prediction accuracy of the optimized feedforward neural network is much higher than that before the optimization. The prediction accuracy of the combined model is higher than that of the two single models, which verifies the feasibility and effectiveness of the combined model.

scholarly journals Short Term Road Traffic Flow Forecasting using Artificial Neural Network

2019 ◽  
Vol 8 (3) ◽  
pp. 7998-8000
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Management ◽  
Road Traffic ◽  
Intelligent Transport System ◽  
Short Term ◽  
Neural Network Models ◽  
Traffic Flow Forecasting ◽  
Artificial Neural

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.

scholarly journals Development of a Hybrid Artificial Neural Network-Particle Swarm Optimization Model for the Modelling of Traffic Flow of Vehicles at Signalized Road Intersections

2021 ◽  
Vol 11 (18) ◽  
pp. 8387
Author(s):  
Isaac Oyeyemi Olayode ◽  
Lagouge Kwanda Tartibu ◽  
Modestus O. Okwu ◽  
Uchechi Faithful Ukaegbu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Traffic Lights ◽  
Road Intersections ◽  
Artificial Neural

The tremendous increase in vehicular navigation often witnessed daily has elicited constant and continuous traffic congestion at signalized road intersections. This study focuses on applying an artificial neural network trained by particle swarm optimization (ANN-PSO) to unravel the problem of traffic congestion. Traffic flow variables, such as the speed of vehicles on the road, number of different categories of vehicles, traffic density, time, and traffic volumes, were considered input and output variables for modelling traffic flow of non-autonomous vehicles at a signalized road intersection. Four hundred and thirty-four (434) traffic datasets, divided into thirteen (13) inputs and one (1) output, were obtained from seven roadsites connecting to the N1 Allandale interchange identified as the busiest road in Southern Africa. The results obtained from this research have shown a training and testing performance of 0.98356 and 0.98220. These results are indications of a significant positive correlation between the inputs and output variables. Optimal performance of the ANN-PSO model was achieved by tuning the number of neurons, accelerating factors, and swarm population sizes concurrently. The evidence from this research study suggests that the ANN-PSO model is an appropriate predictive model for the swift optimization of vehicular traffic flow at signalized road intersections. This research extends our knowledge of traffic flow modelling at a signalized road intersection using metaheuristics algorithms. The ANN-PSO model developed in this research will assist traffic engineers in designing traffic lights and creation of traffic rules at signalized road intersections.

Research on Gas Load Forecasting Using Artificial Neural Network

2013 ◽  
Vol 717 ◽  
pp. 423-427 ◽  
Author(s):  
Wei Ya Shi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Weather Condition ◽  
Short Term ◽  
Implicit Relation ◽  
Accurate Forecast ◽  
Artificial Neural ◽  
Input Variables

In this paper, we give a short survey and analysis on natural gas load forecasting technology using artificial neural network. Different input variables are used to compare the result of forecasting the short term gas load. The experiment results show that the BP neural network can be used to find the implicit relation among historical gas load, weather condition and the future gas load. We also conclude that the input variables have no important influence on the accurate forecast.

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