Prediction of Recreational Travel Using Genetically Designed Regression and Time-Delay Neural Network Models

Selection of appropriate input variables is a crucial step in developing the statistical or neural network model for short-term traffic prediction. Recently, genetic algorithms have provided some success in input variable selection. Extensive experimentation with recreational traffic volume projections from Banff National Park in Alberta, Canada, is reported. Genetic algorithms (GAs) were used to select a set of historical traffic volumes that had higher correlation to the next hourly traffic volume. Universal models developed using GAs were accurate within 10%, on average. Separation of time series for individual hours revealed a linear trend in traffic volumes. Genetically designed regression submodels for individual hours had average prediction errors of less than 1% for the training sets. Even the 95th-percentile errors for the test sets were between 2% and 8%. Many highway agencies expect to deploy an advanced traveler information system (ATIS) for all highway categories. On the basis of such accurate predictions of traffic conditions from an ATIS, recreational drivers will be able to reschedule their travel time as well as routes. Such rescheduling will alleviate stress caused by traffic congestion during recreational travel.