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- W574335019 abstract "Neural networks simulate the abilities of the brain. Artificial neural networks (ANNs), synonymous with neural networks, represent a form of computer intelligence and work similarly to how the human brain works, but on a very reduced scale. The transportation industry has been a popular application domain for ANNs. Examples of existing transportation applications of ANNs include roadway classification from visual images, vehicle classification using signals from inductive loop detectors in the pavement, and prioritizing pavement markings replacement from deterioration rates taken from visual surveys. Following a general introduction of ANNs, this paper summarizes the experiences and results to date regarding the application of ANNs in the evaluation and selection of employee trip reduction programs. Approximately 18,000 data records of employee site characteristics from the Los Angeles area (input), each with 250 fields per record, are being utilized to build, train, and evaluate various ANNs. Performance and selection of the best ANN model will be based on neural network predicted output compared to actual average vehicle ridership (AVR) observations. Subsequently, this model will be tested in Florida. After nine months of ANN model building effort, a significant improvement over existing AVR prediction models has already been realized. This improvement can be attributed to the fact that a neural network can more efficiently determine which inputs best contribute to improved predictability." @default.
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- W574335019 date "1996-01-01" @default.
- W574335019 modified "2023-10-18" @default.
- W574335019 title "SOLVING TRANSPORTATION PROBLEMS WITH ARTIFICIAL NEURAL NETWORKS" @default.
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