A Neural Network Based Algorithms for Project Duration Prediction

2014 ◽  
Author(s):  
Wanjiang Han ◽  
Heyang Jiang ◽  
Xiaoyan Zhang ◽  
Weijian Li
Keyword(s):  
Neural Network ◽  
Project Duration ◽  
Duration Prediction

scholarly journals Prediction Methods for the Actual Duration of Greek Highway Projects

2021 ◽  
Vol 18 ◽  
pp. 1389-1396
Author(s):  
Magdalini Titirla ◽  
Walid Larbi ◽  
Georgios Aretoulis
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Models ◽  
Estimation Methods ◽  
Linear Regression Models ◽  
Neural Network Models ◽  
Highway Projects ◽  
Project Duration ◽  
The Neural Network ◽  
Actual Duration

This study presents an overview of estimation methods to predict the actual project duration of Greek highway projects. Emphasis is given to the selection of the appropriate parameters that correlate with the actual project duration and to compare the performances of the main two methods, the linear regression (LR) with the neural network models (NN) based on data available at the bidding stage. In the context of the current research, thirty-seven highway projects were examined, constructed in Greece with similar available data like the extent, the type of work packages and the significance. Selection and ranking variables through correlation analyses using SPSS 25 has been carried on, in order to identify the most significant project variables. These include archeological findings, type of terrain, land expropriation, the existence of bridge, tunnel and embankment. Next step was the use of WEKA application, that highlighted the most efficient subset of variables. After the definition and grouping of the variables for actual duration prediction, these were used as input data for linear regression models (LR) and neural network models (NN). Various models have been created from each investigated method. While their performance and the comparison of linear regression and neural network models to estimate the actual duration of Greek highway projects are presented in this paper. Results’ discussion and conclusions along with limitations and further research are appropriately analyzed.

scholarly journals Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways

2017 ◽  
Vol 43 (3) ◽  
pp. 91-104 ◽  
Author(s):  
Ying Lee ◽  
Chien-Hung Wei ◽  
Kai-Chon Chao
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Drunk Driving ◽  
Nearest Neighbour ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Comparison Results ◽  
Artificial Neural ◽  
Duration Prediction

Traffic accidents usually cause congestion and increase travel-times. The cost of extra travel time and fuel consumption due to congestion is huge. Traffic operators and drivers expect an accurately forecasted accident duration to reduce uncertainty and to enable the implementation of appropriate strategies. This study demonstrates two non-parametric machine learning methods, namely the k-nearest neighbour method and artificial neural network method, to construct accident duration prediction models. The factors influencing the occurrence of accidents are numerous and complex. To capture this phenomenon and improve the performance of accident duration prediction, the models incorporated various data including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data are collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. Based on the performance comparison results, an artificial neural network model can provide good and reasonable prediction for accident duration with mean absolute percentage error values less than 30%, which are better than the prediction results of a k-nearest neighbour model. Based on comparison results for circumstances, the Model which incorporated significant variables and employed the ANN method can provide a more accurate prediction of accident duration when the circumstances involved the day time or drunk driving than those that involved night time and did not involve drunk driving. Empirical evaluation results reveal that significant variables possess a major influence on accident duration prediction.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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