# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 6 Conclusions  
This study proposes a multi-objective path-planning method for vehicles in urban transportation tailored for various scenarios. The method introduces a dynamic weight-adjustment mechanism to accommodate the requirements of different traffic scenarios. Experiments conducted on the road network data from OpenStreetMap validate the effectiveness of this approach in meeting the demands of various scenarios, including urban commuting, energy-efficient driving, holiday travel, and nighttime travel. Compared with traditional single-objective optimization algorithms, the proposed method balances multiple conflicting objectives in path planning, resulting in more reasonable routes. This study provides a practical solution to the problem of multi-scenario, multi-objective path planning and demonstrates strong potential for application in urban transportation system optimization. As traffic networks expand and demands become more diverse, future research could further enhance the computational efficiency of the algorithm and introduce additional constraints to improve its applicability in real-time traffic environments. This study highlights the flexibility of the proposed method in addressing diverse traffic conditions, paving the way for its integration and application in intelligent transportation systems. Future research should explore the application of this method in more complex and dynamic traffic networks, such as multi-modal transportation systems or scenarios with highly variable traffic flow. Additionally, efforts could be made to further enhance the algorithm's real-time computational performance and incorporate more optimization objectives or constraints to expand its practical applicability.  
Conceptualization, Z.W. and M.Z.; methodology, Z.W.; software, M.Z.; validation, Z.W., M.Z., and C.Z.; formal analysis, S.Y.; investigation, S.L.; resources, C.Z.; data curation, S.D.; writing--original draft preparation, Z.W. and M.Z.; writing--review and editing, Z.W. and S.D.; visualization, S.L.; supervision, S.D.; project administration, Z.W.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Industry-University-Research Cooperation Project, grant number 2024-P-01-00001536-07-1453.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Path Type** & **Total Length** & **Congestion Length** & **Total Time** & **Risk Index** & **Energy Consumption** & **Execution Time (s)** \\ \hline Genetic Algorithm Multi-Objective & 69.442 & **4.077** & 1.391 & 4.623 & 70,257.355 & 144.33 \\ Optimization (Urban Commuting) & **65.458** & 4.872 & 1.474 & 4.974 & **66,432.146** & 152.21 \\ Genetic Algorithm Multi-Objective & 69.532 & **4.077** & 1.409 & 4.621 & 70,347.685 & 148.17 \\ Optimization (Holiday Travel) & 73.63 & 12.218 & 1.56 & **4.174** & 76,073.595 & **140.94** \\ Simulated Annealing Single-Objective & 75.493 & 8.199 & 1.258 & 4.691 & 77,133.551 & 173.14 \\ Operation & 75.117 & 8.199 & **1.251** & 4.701 & 76,756.651 & 165.49 \\ Operation & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results with other optimization methods.  
Conflicts of Interest: Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company China Satellite Network Digital Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.