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- W648481145 abstract "Two methods based on a neural network model were developed to predict slipperiness of a road surface in winter. One method directly predicts the slipperiness based on the assumption that friction coefficients are periodically measured by road patrols. The dynamic behavior of the slipperiness was described using a multi-layer neural network model. In order to cover a wide range of measurement data, the neural network model was deformed so as to have two input sources: one for area-wide data measured at a few restricted points and the other for the local data measured at each prediction point. This method was applied to a section of road in a mountainous area for predicting a friction coefficient two hours ahead. The other method indirectly predicts slipperiness through the road surface temperature, which is much easier to measure than friction coefficients. To realize this indirect method, a neural network model was integrated into the Kalman filter. Both the state observation equations were described using a multi-layer neural network model. This method was applied to an intersection in a suburban area for predicting a friction coefficient 30 minutes ahead. Although many problems remain to be resolved, these neural methods showed good potential as future prediction methods." @default.
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- W648481145 date "1998-01-01" @default.
- W648481145 modified "2023-09-23" @default.
- W648481145 title "DIRECT AND INDIRECT METHODS FOR PREDICTING SLIPPERINESS OF ROAD SURFACES IN WINTER USING A NEURAL NETWORK MODEL" @default.
- W648481145 hasPublicationYear "1998" @default.
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