Since the landmark paper of Spivey et al. (2005), there has been an explosion in the use of continuous trajectories for studying decision-making. In these studies, the participant makes their decision by reaching to a target. By extracting trajectory-based measures, it is possible to observe how the decision unfolds over time. However, studies have shown that these trajectories may not continuously track the decision-making process, but rather result from several discrete submovements executed intermittently. Due to this, the conclusions of papers using purely kinematic measures may be affected by these motor control properties. This study presents simulations of trajectories using human motor control models and demonstrates the relationship between non-decision-related parameters and outcome measures typically used. We also comment on the implications of these findings for using kinematic measures in decision-making and make recommendations for how the decision variable can be more appropriately sampled.

This report documents an effort to assess whetherintegrated multimodal real-time traveler information can encouragetravelers to consider transit as a more viable choice. Under the sponsorship of the California Department of Transportationand in partnership with the Los Angeles Metropolitan Transportation Authority,California PATH developed and field tested Trip2go --a suite of web-based and mobile-phone-based applications incorporating real-time transit and highway condition information. Both objective and surveys were collected to support statistical and modeling analyses to evaluate the effectiveness of real-time information inmaking choice decisions. The evaluation results show that travelers can be influenced by real-time travel information for both mode and time shifts, particularly for no-commuter trips. The mode and time shifts can potentially relieve traffic congestion, reduce fuel consumption and lower tailpipe emissions.