## 5 Discussion and Conclusions  
Road intersections are critical parts of traffic road networks, which are characterized by complex structures and diverse traffic rules. Due to the limited road intersection information in current road map products, numerous studies attempt to extract road intersections from crowdsource trajectory data. However, crowdsourced trajectory data may be faced with spatio-temporal heterogeneity and uneven density distribution, leading to larger improvement spaces in the robustness to extract road intersections with different shapes and structures. This paper proposes a road intersection extracting method based on adaptive density homogenization and hierarchical trajectory segmentation. Firstly, raw trajectory data are processed to identify high-density trajectory cells and a trajectory density homogenization is performed on high-density trajectory cells in order to effectively reduces the time consumption of direct processing all raw trajectory data, and simultaneously averting the misidentification of non-intersection trajectory points and the omission of true road-intersection trajectory points caused by uneven density distribution. Moreover, in light of road hierarchies, a hierarchical trajectory segmentation strategy of category-level, object-level and turning-modes is then adopted to extract multi-level road intersection elements from coarse scale to fine scale. Finally, the iterative piecewise fitting method is utilized to generate the structural models of road intersections. A practical trajectory dataset is used for comprehensive analysis and the results show that the proposed method can efficiently deal with crowdsourced trajectory data with low sampling frequency and uneven density distribution, achieving high accuracy and recall rate of road intersection extraction. Especially, the proposed indicator of turning distance ratio can effectively reduce the misidentification of irregular turning tracks at parking lots, gas stations and residential areas. Nevertheless, there are also some aspects to be studied in the future research. On the one hand, the calculation of neighborhood circle and the process of trajectory density homogenization should be improved to ensure enough trajectories of different traffic modes being reserved. On the other hand, the turning-modes segmentation method should introduce more trajectory features because eight directions spider code will be limited to extract large road intersections with more than 64 turning modes. Finally, tedious parameter settings should also be relieved, especially for decreasing the parameter number and the parameter sensitivity.  
Figure 10: Results of identifying turning modes and extracting structural models of road intersection. (**a**) the structural models of road intersection. (**b**–**e**) the enlarged views of the generated geo-metric models and extracted turning modes of the two road intersections in the green and blue circles in (**a**).  
**Author Contributions:** Conceptualization, Y.Z.; extracting method, G.T.; software, T.C.; validation, X.F.; formal analysis, F.Z.; investigation, Y.L.; writing--original draft preparation, Y.Z.; writing--review and editing, G.T.; visualization, X.F. and T.C.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Nature Science Foundation of China [grant number 41971421 and 41601495], The science and technology innovation Program of Hunan [grant number 2021 RC3099 and 2022 JJ30590].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We thank the anonymous reviewers for their constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.