Estimation of road profiles and classes using neural networks on measured data. Discrete obstacles are reconstructed with higher correlation than Belgian pave. Ride comfort mode has better quality in reconstructed profiles than handling mode. Consistently good approximations of DSDs road and track road test data panel pdf between 0.
This paper reports the performance of an Artificial Neural Network based road condition monitoring methodology on measured data obtained from a Land Rover Defender 110 which was driven over discrete obstacles and Belgian paving. In a previous study it was demonstrated, using data calculated from a numerical model, that the neural network was able to reconstruct road profiles and their associated defects within good levels of fitting accuracy and correlation. In this study, the true profiles are not available and the test data are obtained from field measurements. Training data are numerically generated by making minor adjustments to the real measured profiles and applying them to a full vehicle model of the Land Rover. This is done to avoid using the same road profile and acceleration data for training and testing or validating the neural network.
A static feed-forward neural network is trained and consequently tested on the real measured data. The results show very good correlations over both the discrete obstacles and the Belgian paving. The random nature of the Belgian paving necessitated correlations to be made using their displacement spectral densities as well as evaluations of RMS error percent values of the raw road profiles. The use of displacement spectral densities is considered to be of much more practical value than the road profiles since they can easily be interpreted into road roughness measures by plotting them over an internationally recognized standard roughness scale.
Check if you have access through your login credentials or your institution. What is the potential of trauma registry data to be used for road traffic injury surveillance and informing road safety policy? Information from hospital trauma registries is increasingly being used to support injury surveillance efforts. This research examines the potential of using trauma registry data for road traffic injury surveillance for different types of road users in terms of both the information collected and how representative trauma data are compared to two population-based road traffic injury data collections.
The three data collections were assessed against recommended variables to be collected for injury surveillance purposes and the representativeness of the distribution of road traffic-related injury data from the trauma registry was compared to hospital admission and road traffic authority data collections. Data from the trauma registry was largely not representative of the distribution of age groups or activities compared to the two population-based collections, but was representative for gender for some road user groups to at least one population-based data collection. Trauma data could be used to supplement information from population-based data collections to inform road safety efforts. Road safety policy makers should be aware of the potential and the limitations of using trauma registry data for road traffic injury surveillance.