Customized recursive model for drivers’ navigation compliance behaviors under abnormal events

In recent years, the resilience of road traffic during abnormal events has drawn considerable attention. Intelligent navigation systems, which proactively guide drivers along optimal routes in such situations, are viewed as a promising solution to facilitate recovery of road network performance. A key question arises: How do drivers choose routes when guided by navigation systems? This study addresses that question by modeling drivers’ decision-making behavior at each decision point using a nested framework. At the upper level, drivers decide whether to strictly follow the route recommended by the navigation system, while at the lower levels, they make route choices in the absence of guidance. A Customized Nested Dynamic Recursive Logit (C-NDRL) model was developed to capture these behaviors. Parameters for both decision levels were jointly estimated using a Broyden-Fletcher-Goldfarb-Shanno (BFGS) ​Method-based algorithm, and the model was verified on the Sioux-Falls network. The model was then applied to real navigation route and driving trajectory data from Canton, China, for parameter estimation and the analysis of the additional utility provided by navigation. The results indicate that the C-NDRL model significantly outperformed other models. Furthermore, the study quantifies the substantial impact of external environmental factors and navigation-related internal factors on drivers’ compliance on navigation systems, highlighting that during rainstorm days, the additional utility from navigation increases by 17%.