The relationship between driver injury severity and driver, vehicle, roadway, and environment characteristics was examined. The use of two well-known neural network paradigms, the multilayer perceptron (MLP) and fuzzy adaptive resonance theory (ART) neural networks, was investigated. The use of artificial neural networks can lead to greater understanding of the relationship between the aforementioned factors and driver injury severity. Accident data for 1997 for the Central Florida area, which consists of Orange, Osceola, and Seminole Counties, were used. The analysis focuses on two-vehicle accidents that occurred at signalized intersections. The MLP neural network has a better generalization performance of 65.6 and 60.4 percent for the training and testing phases, respectively. The performance of the MLP was compared with that of an ordered logit model. The ordered logit model was able to correctly classify only 58.9 and 57.1 percent for the training and testing phases, respectively. A simulation experiment was then carried out to understand the MLP neural network model. Results show that rural intersections are more dangerous in terms of driver injury severity than urban intersections. Also, female drivers are more likely to experience a severe injury than are male drivers. Speed ratio increases the likelihood of injury severity. Drivers at fault are less likely to experience severe injury than are those not at fault. Wearing a seat belt decreases the chance of sustaining severe injuries. Vehicle type plays a role in driver injury severity. Drivers in passenger cars are more likely to experience a greater injury severity level than are drivers of vans or pickup trucks. Finally, drivers exposed to impact at their side experience greater injury severity than those exposed to impact elsewhere.

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Little research has been conducted to evaluate the traffic safety of toll plazas and the impact of electronic toll collection (ETC) systems on highway safety, but analyses indicate that toll plazas do contribute to traffic accidents. Traffic safety issues related to toll plazas and ETC systems were studied using the 1999 and 2000 toll plaza traffic accident reports of the Central Florida expressway system. The analysis focused on accident location with respect to the plaza structure (before, at, after plaza) and driver injury severity (no injury, possible, evident, severe injuries). Two well-known artificial neural network (ANN) paradigms were investigated: the Multi-Layer Perceptron and Radial Basis Functions neural networks. The performance of ANN was compared with calibrated logit models. Modeling results showed that vehicles equipped with ETC devices, especially medium/heavy-duty trucks, have higher risk of being involved in accidents at the toll plaza structure. Also, main-line toll plazas have a higher percentage of accident occurrence upstream of the toll plaza. In terms of driver injury severity, ETC users have a higher chance of being injured when involved in an accident. Older drivers tend to have higher risk of experiencing more severe injuries than younger drivers. Female drivers have a higher chance of experiencing a severe injury than do male drivers.

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