###### Abstract  
Despite the availability of electronic navigators and automated vehicles, static wayfinding instructions remain widely used due to their resistance to signal disturbances, as well as their economic and environmental advantages over electronic signs. To investigate the impact of static wayfinding on the network cost and flow distribution, this paper presents an efficient method for updating the incident matrix and extends the stochastic user equilibrium (SUE) framework to incorporate static wayfinding instructions by using the generalized nested logit (GNL) choice model to represent user behavior. The SUE principle relaxes the assumption that users possess perfect knowledge of traffic conditions and always choose the optimal link to minimize their costs. The GNL model improves generalization performance of the cross-nested logit (CNL) model while solving the overlap problem of the multinomial logit (MNL) model. The disaggregate simplicial decomposition (DSD) algorithm is applied to solve proposed user equilibrium by iteratively finding decent directions through an auxiliary solution and determining step size using different methods. The results indicate that the self-regulated averaging (SRA) method can solve the proposed model efficiently. Additionally, increasing travel time cost on guided links and even outer links can be potential influences caused by static wayfinding instructions. The study results can assist decision-makers in quantitatively assessing the value of placing static wayfinding instructions at certain locations and choosing effective layout information. static wayfinding instruction; stochastic user equilibrium; generalized nested logit model; disaggregate simplicial decomposition; successive weighted averages method; self-regulated averaging method +
Footnote †: journal: Medical  
wayfinding instructions on network flow and cost is yet to be thoroughly investigated. To address this gap, this study proposed a stochastic user equilibrium (SUE) model that integrates static wayfinding instructions into travelers' decision-making processes. Existing transportation assignment models are insufficient for modeling users' wayfinding processes when static wayfinding instructions are involved. First, the stochastic user equilibrium (DUE) model cannot accurately characterize the route choice behavior of HDV users due to the adoption of perfect knowledge assumption [7; 8]. Second, some stochastic user equilibrium (SUE) models face the issue of route overlap. In SUE, it is not necessary that drivers have sufficient information to select paths minimizing their travel time costs [9] (p. 19). A multinomial logit (MNL)-based SUE model implies that all path choices are independent, which ignores overlap issue in practice [10; 11]. Nested logit (NL) models are proposed to partly overcome this problem. However, in the NL structure, a choice belongs to only one classification, which limits the flexibility of the NL model [12]. Third, an effective method for describing the impact of static wayfinding instructions on the network is still lacking in current transportation assignment models. The recently developed cross-nested logit (CNL) model addressed the overlap problem by developing a two-level nest structure to capture the relationship between choices and different nests [13]. This model was applied in transportation to characterize users' path choice behaviors, where the upper layer refers to network links and the lower layer includes all path choices [14; 15; 16]. Generalized nested logit (GNL) improving generalization performance of CNL can describe path choice behaviors more accurately and is applied in this study to analyze static wayfinding instructions' impact on network flow [17; 18]. Nonetheless, the GNL model considers only the impact of cost on route choice probabilities, while the influence of wayfinding guidance also needs to be taken into account.