Intelligent transportation has emerged as a critical paradigm in the
transportation sector, underscoring the growing significance of digital
information. The extent to which travelers comprehend transportation network
information fundamentally influences the dynamics of traffic flow evolution.
Traditional random user equilibrium models assume that travelers possess
knowledge of segment flow information; however, they fail to account for route
flow information. To date, research has yet to investigate how travelers’
decision-making behaviors are altered following the acquisition of route flow
information. When endowed with such information, travelers frequently
demonstrate behaviors influenced by the bandwagon effect, adjusting their routes
to conform to the choices of the majority. This behavioral modification disrupts
the existing equilibrium, resulting in a continued evolution of traffic flow
until a new stable state is achieved. To examine the implications of
transportation network information on traffic flow evolution, this study
proposes a generalized travel impedance function that incorporates the bandwagon
effect, extending conventional route impedance frameworks. Subsequently, an
entropy-based Logit-SUE flow evolution model is constructed using
Kullback-Leibler divergence, with a comprehensive analysis of its properties and
a demonstration of its consistency with the proposed generalized impedance
function. Numerical analyses reveal that when travelers fully grasp route flow
information, several salient characteristics emerge as traffic flow stabilizes:
(1) The distribution of flow becomes concentrated on a limited number of paths,
in contrast to the random user equilibrium; (2) The inertia exhibited by
travelers has a negligible impact on the final evolution outcome; (3) The
system’s total travel time is significantly lower than that of the random user
equilibrium model. Furthermore, travelers equipped with transportation network
information can optimize their route choices, enhancing flow distribution and
minimizing overall travel costs. The findings of this research provide essential
theoretical support and practical guidance for the advancement of traffic
guidance software.