Transportation demand management (TDM) has received increasing attention in recent years as an important component in approaches to improving the transportation system. Another approach to transportation system improvement is the use of high occupancy vehicle (HOV) facilities. Mode choice forecasts provide the basis for planning, project evaluation, and obtaining public support for TDM measures and improvements to the HOV system.
Despite the importance of accurate mode forecasts, current mode choice methodology is insufficiently responsive to factors that influence shifts to ridesharing modes, particularly TDM policy factors that are important in encouraging commuters to shift from single occupant vehicles (SOVs). The objective of this study is to identify these mode choice factors and use them to improve the ability to analyze HOV policies for the north I-5 corridor.
Two major sets of data were analyzed in this study, both collected by Metro in cooperation with Community Transit. In one study, some 9,324 employees of 23 cooperating businesses were surveyed in north King and urban Snohomish counties. In another, a 1989 telephone survey questioned a random sample of 3,586 residents in the study area.
Several analytical approaches were used in this study, including multinomial logit modeling, factor analysis and cluster analysis. Many of the findings from this study are important to understanding the nature of mode choice. Some of the results are important for policy recommendations. Some of the results indicate a different emphasis for employer-based TDM measures in a largely suburban area than in a major CBD such as Seattle or Bellevue.
Study findings indicate the importance of (1) completing the HOV lane system, (2) providing workplace incentives for ridesharing, (3) taking the existence of two-worker households into account, (4) providing alternative ways for employees to run errands, (5) encouraging mixed use through flexible zoning laws, (6) providing non-motorized vehicle alternatives for short commutes, and (7) targeting mode shift incentives and promotions to those most likely to change modes.