## 6 Conclusions  
In this paper, the characteristics of hybrid traffic flows with GVs and EVs are studied in detail, and travel time and cost characteristics are analyzed. Considering the queue length, waiting time, service rate, and flow control, a travel time and cost impedance function model of mixed traffic flows is developed. Numerical analysis results show that the penetration rate, traffic volume, and travel mileage of EVs have significant effects on the travel time and cost of mixed traffic flows.  
* In the analysis of travel time characteristics, we incorporate the penetration rate of EVs and the traffic flow to derive a travel time function, which includes the vehicle queuing time during travel. The findings indicate that an increase in EV penetration leads to an increase in overall travel time, particularly under high traffic flow conditions. Furthermore, there is a notable disparity in travel times for different driving distances, with long-distance travel being the most affected.
* In the cost analysis, we comprehensively consider the additional energy costs associated with charging and refueling stations and develop a model for the cost impedance function. The findings indicate that the adoption rate and driving distance of EVs have a significant impact on travel expenses. A high adoption rate of EVs leads to low overall travel costs, particularly for short-distance and mid-range trips, highlighting the advantages of EVs.
* Through numerical analysis, we further investigated the impacts of EV penetration, traffic volume, and driving distance on road impedance. An increase in EV penetration can effectively reduce total energy consumption within the transportation system while also significantly influencing travel time and cost characteristics. Specifically, at high EV penetration rates and under heavy traffic conditions, there is a corresponding increase in traffic impedance, leading to extended travel times but reduced travel costs.  
In this paper, we explore the travel time and cost characteristics of GVs and EVs in mixed traffic flows; however, the results are mainly based on theoretical models and numerical analyses and lack verification and support with actual traffic data. These results should be combined with actual traffic flow data to conduct empirical analyses to verify the accuracy and applicability of the models. In addition, the differences among different cities and regions are not considered. In future studies, these considerations can be accounted for to provide a more comprehensive and in-depth understanding of the mixed traffic flows of GVs and EVs.  
Figure 15: Travel cost for L = 500.  
**Author Contributions:** [PERSON] and [PERSON] were responsible for the research conception and design. [PERSON], [PERSON], [PERSON], and [PERSON] were responsible for the data process and analysis. [PERSON], [PERSON], and [PERSON] were responsible for the analysis and discussion of results. [PERSON] and [PERSON] were responsible for draft manuscript writing and language. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle--Infrastructure Systems (Changsha University of Science and Technology) (Grant No. kfj220702), the Hunan Natural Science Foundation (Grant No. 2021 JJ40577), the Hunan Education Department Scientific Research Project (Grant No. 20B009), the Xinjiang Uygur Autonomous Region Key Research and Development Program (Grant No. 2023B03004-3), and the National Natural Science Foundation of China (Grant No. 52172313).  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest regarding the publication of this article.