Fare Pricing Elasticity, Subsidies, and Demand for Vanpool Services

Document Type

Journal Article

Publication Date


Subject Area

planning - travel demand management, planning - travel demand management, ridership - elasticity, ridership - demand, policy - fares, economics - pricing, organisation - management, mode - subway/metro


Vanpools, Trip reduction, Trip length, Travel distance, Travel demand management, Transportation demand management, TDM measures, Subsidies, Socioeconomic factors, Socioeconomic aspects, Seattle-Tacoma Metropolitan Area (Washington), Ridership, Puget Sound Region, Pricing, Patronage (Transit ridership), Fares, Elasticity (Economics), Discrete choice models, Demand


Practitioners of transportation demand management consider pricing a crucial determinant of vanpool market demand. Publicly sponsored programs stress the significance of fare pricing and subsidies as key tools for increasing ridership. This paper considers the use of discrete choice modeling techniques to investigate the effects of fares and fare subsidies on the demand for vanpool services. With the use of employer and employee data from the 1999 survey of the commute trip reduction program of the Puget Sound region (Washington), a conditional discrete choice model is built to analyze the choice of vanpool services, with competing means of transportation as a function of various socioeconomic characteristics. The predicted value of the direct elasticity is –0.73; a 10% increase in vanpool price is associated with a 7.3% decrease in its demand and vanpool demand is relatively inelastic with respect to fare changes. For trips shorter than 30 mi, the individual elasticities are equivalent to the aggregate estimate. As the distance from home to work increases beyond 60 mi, individuals are less responsive to price changes. Subsidies have a relevant impact in increasing ridesharing, controlling for firm size and industry sector. Whenever employees are offered a subsidy, the predicted probability of choosing vanpool more than doubles. These results show that factors other than fare pricing, such as employee profile, industry sector, firm size, parking policies, and travel patterns, must be taken into account when policies or designing fare schedules geared at stimulating ridership are implemented.