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Word count : 4687 + 2 Tables + 3 Figures Forecasting of Shared-Use Vehicle Trips using Neural Networks and Support Vector Machines

This paper compares two trip forecasting approaches, namely neural networks and support vector machines, for a multiple-station shared-use vehicle system. The neural networks trained to perform trip forecasting belong to the multilayer perceptron model. Comparative evaluation was made with the least square support vector machines with the radial basis kernel function. The forecasting models were trained or developed with six months, and validated with one month of actual trip data from the Honda Intelligent Community Vehicle System currently in commercial operations in Singapore. Each model was designed to forecast the net flow of vehicles in a three-hourly period in any day within a month, at a particular shared-use vehicle port. The models were developed for and applied to forecast net flow at each port for the entire months of January to June 2004. Results have shown that the multilayer perceptron model has a slightly better forecast accuracy in terms of monthly average absolute error and monthly maximum absolute error.