Impact of Data Quantity on the Performance of Neural Network Incident Detection ModelsInstitute of Transport Studies, Monash University, 1996 - Disabled vehicles on express highways - 18 pages |
Common terms and phrases
analysis by input ANN incident detection artificial neural network backward elimination method Civil Engineering Monash communications malfunction data needed Data Quality decrease Detection Performance Decision deterioration in performance Different detector technologies downstream speed inputs downstream stations Engineering Monash University evaluate the impact evaluate the model's false alarm rate Figure 1(a flow and occupancy full model Impact of Data impact of detector Impact of input impact of missing Impact of random implications in incident incident detection model incident detection performance incorrect data initial model input clamping technique input variables Inputs Eliminated inputs free levels on model Masters Melbourne missing speed data model performance MTTD Network Incident Detection Neural Network Incident original model Performance Decision Threshold practical implications practical perspective provide speed measurements random error random noise levels results obtained sensitivity analysis significant deterioration single loop detectors Snell Table 1(b training the full Tullamarine Freeway upstream and downstream validation-test data set