Driver En Route Guidance Compliance and Driver Learning with Advanced Traveler Information Systems: Analysis with Travel Simulation Experiment

Abstract

If real-time driver en route guidance advice does not meet driver preferences (e.g., preference for taking the freeway) or the advice is not correct, drivers are very likely to ignore the information, and the guidance system becomes ineffective in their route choice no matter how advanced the system. There is a need to investigate the factors affecting driver compliance with en route guidance advice. A travel simulation experiment was used to investigate significant factors affecting driver route choice behavior. A linear mixed model was developed for describing the factors affecting driver compliance with guidance advice using the compliance rate over several simulated trips as a dependent variable. The issue of repeated observations is addressed. The system accuracy and subjects’ learning experience in their spatial experience at the same intersection and temporal experience in the same day are also taken into account. The model results show that significant factors are involved: freeway advice, turning advice, congestion occurrence, incident occurrence, subjects’ spatial experience, subjects’ temporal experience, and subjects’ education level; there are several important interactions as well.