SHORT TERM TRAFFIC FLOW FORECASTING USING ARTIFICIAL NEURAL NETWORKS (original) (raw)
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Short Term Road Traffic Flow Forecasting using Artificial Neural Network
International Journal of Recent Technology and Engineering (IJRTE), 2019
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.
Short Term Traffic Flow Prediction in Heterogeneous Condition Using Artificial Neural Network
TRANSPORT, 2013
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 in...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014
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A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL
Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies in Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper.
Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network
Procedia - Social and Behavioral Sciences, 2013
This study applies Artificial Neural Network (ANN) for short term prediction of traffic flow using past traffic data. The model incorporates traffic volume, speed, density, time and day of week as input variables. Speed of each category of vehicles was considered separately as input variables in contrast to previous studies reported in literature which consider average speed of combined traffic flow. Results show that Artificial Neural Network has consistent performance even if time interval for traffic flow prediction was increased from 5 minutes to 15 minutes and produced good results even though speeds of each category of vehicles were considered separately as input variables.
Short-term traffic volume prediction using neural networks
2018
There are many modeling techniques that can predict the behavior of complex systems, such as traffic volumes in regional transportation systems, with high accuracy. However, predictive power suffers significantly when non-recurring events, such as adverse weather, occur in these systems. Therefore, introducing novel ways to identify and quantify disruptions can improve projection accuracy and performance. Proactive traffic management requires the ability to predict traffic conditions. A relatively new mathematical model, the neural network, offers an attractive approach to modeling undefined, complex, and nonlinear situations. This algorithm is trained by using both historical data and non-recurring phenomena such as weather. In this study, we test our algorithm on traffic data collected on four highways and high-resolution weather data within the Dallas area. The test indicates the model’s high accuracy and efficiency in predicting short-term
Journal of Information and Communication Technology, 2018
Monitoring and understanding traffic congestion seems difficult due to its complex nature. This is because the occurrence of traffic congestion is dynamic and interrelated and it depends on many factors. Traffic congestion can also propagate from one road to neighbouring roads. Recent research shows that there is a spatial correlation between neighbouring roads with different traffic flow pattern on weekdays and on weekends. Previously, prediction of traffic flow propagation was based on day and time during weekdays and on weekends. Results obtained from past studies show that further investigation is needed to reduce errors using a more efficient method. We observed from previous research that similarity of traffic condition on weekdays and weekends was not taken into account in predicting traffic flow propagation. Hence, our study is to create and evaluate a new prediction model for traffic flow propagation at neighbouring roads using similarity of traffic flow pattern on weekdays...
Short-term traffic flow forecasting by mutual information and artificial neural networks
2012
Short-term traffic flow forecasting is an important problem in the research area of intelligent transportation system (ITS). Recently, artificial neural networks modeling, such as MLP, have been used in various applications over nonlinear time series forecasting such as traffic control. In modeling, irrelevant inputs cause the deterioration of performance and increment of calculation cost. Therefore, to have an accurate model, some strategies are needed to choose a set of most relevant inputs. Mutual information (MI) is very effective in evaluating the nonlinear relevance of each input from the view of information theory. Feature selection (FS) method is an improved version of the MI technique. This paper presents a novel short-term traffic flow prediction model using MLP predictor and MIFS algorithm. Performance of the proposed MIFS algorithm and MLP predictor is evaluated via simulations using MATLAB subroutine. To validate the algorithm, two different types of data, namely regular and irregular (with high uncertainty) data, are used.
IEEE Transactions on Intelligent Transportation Systems, 2012
This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs.
The purpose of this study is to develop a model for traffic volume forecasting of the road network in Anamorava Region. The description of the current traffic volumes is enabled using PTV Visum software, which is used as an input data gained through manual and automatic counting of vehicles and interviewing traffic participants. In order to develop the forecasting model, there has been the necessity to establish a data set relying on time series which enables interface between demographic, socioeconomic variables and traffic volumes. At the beginning models have been developed by MLR and ANN methods using original data on variables. In order to eliminate high correlation between variables appeared by individual models, PCA method, which transforms variables to principal components (PCs), has been employed. These PCs are used as input in order to develop combined models PCA-MLR and PCA-RBF in which the minimization of errors in traffic volumes forecasting is significantly confirmed. The obtained results are compared to performance indicators such R 2 , MAE, MSE and MAPE and the outcome of this undertaking is that the model PCA-RBF provides minor errors in forecasting.