## 2 Literature Review
[PERSON] [18] proposed a new road impedance model based on queue theory, but it had inconsistencies in terms of parameter definitions. [PERSON] [19] corrected the Davidson function and used coordinate transformation to derive a time-dependent Davidson function, thus overcoming parameter definition inconsistencies. [PERSON] et al. [20] proposed an asymptotic impedance function based on queue theory, where the travel time approaches infinity as the traffic volume reaches the road capacity, and conducted fitting analysis research. [PERSON] [21] used a discrete-time method to associate cost with the flow, improved the impedance function, and established a method suitable for dynamic network loading, proposing a new path-based solution algorithm. [PERSON] et al. [22] re-established and calibrated urban road segment vehicle travel time models and downstream signal-controlled intersection traffic delay models by analyzing the traffic flow characteristics of road segments and downstream signal-controlled intersections. [PERSON] et al. [23] derived the relationship between segment flow and travel time under congested and noncongested conditions based on the Edie traffic flow model and proposed an impedance function expression. [PERSON] et al. [24] divided urban road traffic impedance into segment impedance and node impedance, optimized the BPR function using the relationship between traffic volume and density to obtain segment impedance and selected different models based on intersection road saturation to obtain node impedance. [PERSON] et al. [25] derived the relationship between segment flow and travel time, compared the differences between the BPR function and the derived relationship, and performed exponential parabolic fitting for the derived relationship. Some scholars have also improved the theoretical model of traffic flow for mixed traffic. For example, [PERSON] et al. [26] discussed urban road segment travel time function models under mixed traffic conditions in China using traffic flow theory to establish travel time function models for different road widths. [PERSON] et al. [27] analyzed the operating characteristics of major traffic modes (such as cars, buses, and bicycles) in urban mixed-traffic networks, developed road segment impedance functions for these networks, and introduced methods for calibrating relevant parameters. [PERSON] et al. [28] proposed an improved mixed-traffic road impedance function that explicitly considers the interactions between motorized and nonmotorized traffic. [PERSON] et al. [29] proposed a new fuzzy neural network self-organizing measurement model to simulate impedance changes for mixed-traffic roads. [PERSON] et al. [30] considered the impact of trucks on the travel time of other vehicles on highways and found that an increase in the number of trucks significantly affects the travel time of passenger cars, thereby greatly increasing road impedance. [PERSON] et al. [31] considered the effects of the traffic volume and the proportion of various vehicles (trucks, passenger cars, etc.) on highways on road impedance, proposed an improved model, and performed Vissim microscopic simulations for parameter estimation. The authors concluded that the traffic composition has a significant impact on the traffic volume and travel time. [PERSON] et al. [32] incorporated transportation costs and tolls into impedance functions and considered delays in time. The parameters were calibrated using the maximum likelihood estimation method, and the model was suitable for fully enclosed highways in China and able to fit actual traffic volumes well. Through the review of existing studies, it is evident that significant progress has been made in optimizing road impedance and travel time models through various methods. However, with the gradual popularization of EVs and the increasing complexity of traffic systems, current models still have deficiencies in several key areas that urgently require further improvement. Below, potential improvements are described in the context of the key points of this study.