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A shared-use vehicle system has a small number of vehicles reserved exclusively for use by a relatively larger group of members. Challenged by accessible and economical public transportation systems, multiple-station shared-use vehicle companies are driven to gain a competitive edge by using an operator-based relocation system to ensure privacy, simplicity, and convenience to their users. To help operators identify measures to maximize resources and enhance service levels, a simulation model is developed, with an emphasis on operator-based relocation techniques. A qualitative analysis conducted on operator-based relocation systems provides insights on the key issues involved and their influences over each other. On the basis of this analysis, a time-stepping simulation model is developed, and three performance indicators are proposed to evaluate the effectiveness of the different relocation techniques. The model has been validated by using commercially operational data from a local shared-use vehicle company. With the existing operational data as the base scenario, two proposed relocation techniques, namely, shortest time and inventory balancing techniques, and various operating parameters are studied. The simulation results have shown that if the inventory balancing relocation technique is used, the system can afford a 10% reduction in car park lots and 25% reduction in staff strength, generating cost savings of approximately 12.8% without lowering the level of service for users.

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Two trip-forecasting approaches—neural networks and support vector machines—are compared 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 least-squares support vector machines with the radial basis kernel function. The forecasting models were trained or developed for 6 months, then validated with 1 month of actual trip data from the Honda Intelligent Community Vehicle System, currently in commercial operation in Singapore. Each model was designed to forecast the net flow of vehicles for a 3-h period on any day in a month at a particular shared-use vehicle port. The models were developed for and applied to forecasting the net flow at each port for each entire month from January 2004 to June 2004. Results indicate that the multilayer perceptron model has a slightly better forecast accuracy in terms of monthly average absolute error and monthly maximum absolute error.