## 6 Conclusions and Future Directions
### Concluding Remarks  
This research establishes a method for traffic parameter extraction and analysis based on vehicle trajectory data through systematic theoretical analysis and extensive experimental validation. Compared to conventional loop detectors that provide only cross-sectional traffic data, this trajectory-based approach offers comprehensive path-level information andbroader spatial coverage without requiring extensive physical infrastructure installation and maintenance.  
In today's era of rapid intelligent transportation development, how to fully utilize increasingly abundant traffic big data represents an important challenge facing researchers and engineers. This study provides an application example for this challenge, demonstrating the complete path from raw trajectory data to practical traffic parameters. While this research assumes consistent GPS accuracy and sampling intervals, future research should address varying data quality conditions and validate the methodology across different vehicle types and larger-scale deployment scenarios.  
With continuous advancement in data collection technology and sustained improvement in transportation system intelligence, trajectory data-based traffic analysis will play an increasingly important role. The theoretical foundation and methodological framework of this study will provide strong support for in-depth development in this field. We look forward to collaborating with more researchers in the future to advance traffic data science, contributing to the construction of more efficient, safe, and sustainable transportation systems. The research outcomes have implications for improving urban mobility by enabling more responsive and data-driven traffic management strategies that can reduce congestion, minimize travel delays, and optimize route efficiency. Furthermore, this methodology supports smart city initiatives by providing a cost-effective alternative to expensive fixed detector infrastructure, facilitating the integration of emerging connected vehicle technologies, and enabling dynamic traffic optimization systems that can adapt to real-time urban transportation demands.  
Y.W.: Conceptualization, Methodology, Formal analysis, Writing--original draft, Investigation, Writing--review and editing. Y.L.: Conceptualization, Methodology, Formal analysis, Data curation, Visualization, Writing--review and editing. X.Y.: Conceptualization, Supervision, Writing--review and editing, Project administration. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the China Postdoctoral Science Foundation, Cooperative Optimization on Right-of-Way at Signalized Intersections in Heterogeneous traffic Environment (2022M712410). National Natural Science Foundation of China (General Program), Research on Basic Problem of Vehicle-Infrastructure Cooperative Traffic Control for Special Vehicles (52472350). Guangxi Major Science and Technology Special Subproject, Reutilization of Pinglu Canal Cross-Line Bridges and Optimization of Traffic Organization (2023 AA14006).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
All authors are grateful for the resources provided by the Intelligent Transportation System Research Center of Tongji University.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.  
The following abbreviations are used in this manuscript:  
Global Positioning System  
Identifier  
Origin-Destination