## 2 Related Work  
The generalized nested logit (GNL) model captures stochastic choice behavior and allows for more flexible substitution patterns between alternatives. In the field of transportation, modifying the GNL model to account for user behavior is an effective approach to align the model with specific practical conditions. [PERSON] et al. [23] introduced a joint choice cross-nested logit model to understand the demand for alternative fuel vehicles (AFVs) and to study substitution patterns between fuel types and vehicle segments using a revealed preference approach. [PERSON] et al. [24] investigated tourists' urban travel mode choices in prominent Chinese tourist cities using latent class-nested logit modeling, while [PERSON] et al. [25] explored the differences between express and ride-pooling services in urban transportation networks. [PERSON] et al. [26] used an integrated choice and latent variable-nested logit model (ICLV-NL) to incorporate social benefit and satisfaction with bike-sharing into a discrete choice model, capturing relationships between attitudinal factors and observed variables. [PERSON] et al. [27] presented a novel approach utilizing a combination of emerging data sources for non-private modes, such as public transit smart card data, taxi GPS trajectory data, and taxi trip transaction data, to calibrate an integrated taxi and transit mode and route choice model.  
It is natural to view the path choice process for human-driven vehicles (HDVs) as a stochastic behavior, making it essential to refine the SUE model by considering elements that impact path choice behavior. [PERSON] et al. [28] proposed a path-based unified equilibrium condition to address the combined mode split and traffic assignment (CMSTA) problem in a multimodal transportation network, finding that the removal of key park-and-ride (P&R) interchanges carrying substantial flow can significantly increase total travel costs. [PERSON] et al. [29] applied a logit-based choice model to capture travelers' preferences among three available modes, incorporating elastic demand. [PERSON] et al. [30] proposed a refined multimodal network equilibrium model to account for the effect of captive travelers on network equilibrium performance, suggesting that policy efforts should focus on adjusting the activity structure to influence perceptions and preferences for different green transport modes. [PERSON] et al. [31] developed a flow equilibrium model to balance passenger flow across high-speed railway trains on common lines and increase revenue through behavior-based dynamic pricing. [PERSON] et al. [32] formulated and proved the solution existence for new internally consistent SUE models using GEV structures and correction term logit route choice models, where the functional forms in the correlation components are based on generalized, flow-dependent congested costs, rather than traditional metrics such as length or free-flow travel time.  
Static wayfinding instructions, as traffic control devices, help users reduce uncertainty in path choice decisions and should be considered within the discrete choice model. Incorporating a refined GNL into the SUE framework can provide a better understanding of network equilibrium and total network costs for HDVs. These results can inform strategies for placing static wayfinding instructions and help develop more precise flow equilibrium models that consider CAVs and other user types.