Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Vascular leakage stimulates phenotype alteration in ocular cells, contributing to the pathology of proliferative vitreoretinopathy

Plasma leaking from damaged retinal blood vessels can have a significant impact on the pathologies of the posterior segment of the eye. Inflammation in the eye and metabolic change resulting from diabetes mellitus causes vascular leakage with alteration of the phenotype of retinal pigment epithelial (RPE) cells and fibrocytes, resulting in changes in cell function. Phenotypically altered cells then significantly contribute to the pathogenesis of retinopathies by being incorporated into tractional membranes in the vitreous, where they secrete matrix molecules, such as fibronectin, and express altered cell surface antigens. We hypothesize that there is a direct relationship between the leaking of plasma and the proliferation and phenotypic change of RPE cells and fibroblasts, thus exacerbating the pathology of retinal disease. If the hypothesis is correct, control of vascular leakage becomes an important target of therapy in proliferative vitreoretinopathy.