## 6 Conclusions  
Using the NGSIM data set in this paper, the authors analyzed the influencing factors of lane-changing behavior, summarized various factors in the lane-changing procedure by using the dimensionality reduction method, and discussed the setting of the buffer zone length by analyzing the principal component regression model in order to provide theoretical support for the autonomous driving dedicated lane-setting scheme in the future. The length of the buffer zone is shown to be influenced by vehicle speed while changing lanes. The method of improving the design speed of the buffer zone can be considered to  
Figure 6: The influencing factors of the lane-changing phenomenon.  
reduce the length of the buffer zone. Simultaneously, the acceleration of the lane-changing vehicles should be as stable as possible, and the proportion of time spent in the preparation stage must be reduced. Finally, keeping the dedicated lane relatively sparsely spaced is more conducive to improving lane-changing efficiency. The basic design of autonomous driving dedicated lanes holds an important place in the development of autonomous driving technology. The buffer zone is designed to provide a lane change alert area for AVs to drive into the dedicated lane. It also ensures safety during the lane-changing process and separates AVs from HDVs. Based on the microscopic trajectory data, we analyzed the factors influencing the longitudinal driving distance of the lane-changing procedure, which also affect the length of the buffer zone. This paper will serve as a theoretical guide in road design related to autonomous driving technology.  
The work contained in this paper also has some limitations. The trajectory data used are from NGSIM human-driven vehicles, which cannot totally reconstruct AVs' lane-changing characteristics. The consideration of buffer zone length is still under theoretical analysis due to the difficulty of creating a longitudinal driving distance model for AVs' lane-changing behavior. When the technological conditions are more mature, the quantitative analysis of autonomous vehicle lane changes will be more complete and have a refining effect on this study.  
Conceptualization, Z.S. and X.Q.; Data curation, F.H.; Formal analysis, Y.Z.; Funding acquisition, S.W.; Investigation, S.W. and P.M.; Methodology, Y.Z., Z.S. and P.M.; Project administration, S.W. and X.Q.; Resources, F.H., S.W. and P.M.; Software, S.W.; Supervision, P.M.; Validation, P.M.; Visualization, Z.S.; Writing--original draft, F.H., Y.Z. and Z.S.; Writing--review & editing, F.H., Y.Z., Z.S. and X.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shandong Provincial Key Research and Development Program (SPKR&DP) grant number 2020 CXGC010118, the Science and Technology Project of Shan-dong Transportation Department grant number 2020 BZ01-05 and 2020 BZ01-06, and the MOE (Ministry of Education in China) Project of Humanities and Social Sciences grant number 20 YJAZH083.  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. This data can be found here: [[[https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm](https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm)]([https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm](https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm))].  
The authors declare no conflict of interest.