## 6 Discussion and Conclusions  
This paper developed a modeling framework for estimating network in the context of static wayfinding instructions based on the GNL choice model and SUE principle. SUE softens the condition that users have perfect information on network condition and always choose the correct path to minimize their cost. The GNL model is used to capture users' path choice behavior. Then, a novel data structure is proposed to modify the GNL model with consideration of the static wayfinding instructions, and revised mathematical programming is proposed to describe the GNL-SUE model in the context of the static wayfinding instructions. By solving KKT conditions, a heuristic DSD algorithm can be applied in this study, which iteratively solves subproblems generated by objective function and obtains an auxiliary solution to find decent direction. We also analyze the convergence performance of five different step size finding methods and demonstrate the effectiveness of the MSWA and the SRA method in refining MSA step size reduction approach. Finally, a cost comparison between network with static wayfinding instructions and a network without static wayfinding instructions are developed to illustrate the potential impact posed by wayfinding instructions when they are used to prevent unfamiliar users from choosing incorrect paths. The result shows that it is possible that wayfinding instructions increase travel time cost on guided links and even outer links. This effect can be considered when assigning static wayfinding instructions in urban network. The proposed method modifies the GNL model based on the HDV wayfinding process and the characteristics of static wayfinding instructions by revising the inclusion coefficients, which represent the degree to which a specific choice (i.e., a feasible path) belongs to different nests (i.e., links in the network). The modified GNL model is then integrated into the SUE framework using transportation network theory. In practice, guidance information for HDVs is not limited to static wayfinding instructions, though they remain a primary source. The proportion of HDV users relying on other wayfinding tools, such as cellphones, and the priority between static wayfinding instructions and other tools can be investigated in real urban networks to improve predictions of network flow and costs more accurately. Some topics can be further considered to extend this study. First, this study uses the network of an HDV to model and analyze. However, multiclass vehicle users may have a different route choice principle. For example, connected and automated vehicle (CAV) users can choose a route with a lower value of time due to the seamless situational awareness and connectivity of CAVs. In future studies, multiclass vehicle users can be considered to model the impact of static wayfinding instructions on network flow by applying the mixed traffic assignment model. Second, the proposed solution algorithm uses a fixed large route set because of the unknown route choice probabilities in advance, which may cause redundant computation. Updating the route set to prune in each iteration can be considered to improve this method. Third, we will employ sensitivity analysis to identify critical parameters, including link capacity and speed limits, that influence network performance. Conceptualization, Y.W. and R.Z. ; methodology, Y.W. and W.L. ; software, Y.W. and Y.C. ; validation, Y.W., R.Z. and J.Y. ; formal analysis, W.L. and Y.C. ; resources, Y.W. and R.Z. ; writing--original draft preparation, Y.W. ; writing--review and editing, W.L. ; visualization, R.Z. and J.Y. ; supervision, J.Y. and Y.C. ; project administration, Y.W; funding acquisition, Y.W., R.Z. and J.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by Anhui Provincial Department of Transportation (grant number 2023-KJQD-018) and Research Institute of Highway, Ministry of Transport (grant number QG2021-3-11-2). Not applicable.