Training neural networks with heterogeneous data

Drakopoulos, John A.; Abdulkader, Ahmad

doi:10.1016/j.neunet.2005.06.011

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Studies reporting the use of ANN for heterogeneous and less‐lane‐disciplined traffic are limited. Drakopoulos and Abdulkader studied the neural network training for heterogeneous data and proposed data pruning (removal of noisy data) and ordered training (partitioning of data) as effective methods to deal with heterogeneous data. Padiath et al compared the performance of a historic technique, an ANN‐based technique and a model‐based technique in predicting traffic density under Indian traffic conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Badhrudeen

Raj

Vanajakshi

2016

J of Advced Transportation

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Summary The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Literature Reviewmentioning

confidence: 99%

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Badhrudeen

Raj

Vanajakshi

2016

J of Advced Transportation

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“…Neural networks offer significant support in terms of organizing, classifying, and summarizing data. It also helps to discern patterns among input data, requires few assumptions, and achieves a high degree of prediction accuracy [17]. These characteristics make neural network technology a potentially promising alternative tool for recognition, classification, and prediction in the area of construction safety, in terms of accuracy, adaptability, robustness, effectiveness, and efficiency.…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Safety Assessment on Construction Sites

Zhong

2011

2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics

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This paper innovatively proposes a hybrid intelligent system combining rough set approach and artificial neural network (ANN) that predicts the safety performance of construction site for breaking through the limitations of conventional method. Redundant attribute is removed with no information loss through rough set approach, by which the reduced information table is obtained. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. The rules developed by rough set analysis show the best prediction accuracy if an empirical does match any of the rules. The effectiveness of our methodology was verified with an empirical study that compared neural network approach with the hybrid approach. And the results show that this method can be an effective tool to predict the safety performance of construction project sites, which is useful to provide a scientific basis for the management and decisions of accident prevention.

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Application of Data Mining Techniques for Traffic Density Estimation and Prediction

Raj

Bahuleyan

Vanajakshi

2016

Transportation Research Procedia

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Training neural networks with heterogeneous data

Cited by 7 publications

References 5 publications

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Safety Assessment on Construction Sites

Application of Data Mining Techniques for Traffic Density Estimation and Prediction

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