# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Path Type**} & \multirow{2}{*}{**Total Length (km)**} & \multicolumn{2}{c}{**Congestion Length (km)**} & \multirow{2}{*}{**Total Time (hours)**} & \multirow{2}{*}{**Risk Index**} & \multicolumn{2}{c}{**Energy**} \\  & & & & **Length (km)** & & & **Consumption** & **Weighted Cost** \\ \hline \multirow{3}{*}{**Urban Commuting**} & Initial Population 5 & 75.5 & 7.657 & 1.258 & 4.674 & 77,030,846 & 0.276 \\  & Initial Population 1 & 74.517 & 7.318 & 1.242 & 4.605 & 75,980,633 & 0.21 \\  & Initial Population 2 & 70.533 & 8.113 & 1.176 & 4.93 & 72,155,424 & 0.079 \\  & Optimized Path & **69.442** & **4.077** & **1.391** & **4.623** & **70.257** & **0.075** \\ \hline \multirow{3}{*}{**Energy-Efficient**} & Initial Population 5 & 75.5 & 7.657 & 1.258 & 4.674 & 77,030,846 & 0.269 \\  & Initial Population 1 & 74.517 & 7.318 & 1.242 & 4.605 & 75,980,633 & 0.204 \\  & Initial Population 2 & 70.533 & 8.113 & 1.176 & 4.93 & 72,155,424 & 0.075 \\  & Optimized Path & **65.458** & **4.872** & **1.474** & **4.974** & **66.432** & **1.246** & **-0.192** \\ \hline \multirow{3}{*}{**Holiday Travel**} & Initial Population? & 76.499 & 9.229 & 1.275 & 4.608 & 78,344,574 & 0.410 \\  & Initial Population 5 & 75.500 & 7.657 & 1.258 & 4.674 & 77,030,846 & 0.393 \\  & Initial Population 1 & 74.517 & 7.318 & 1.242 & 4.605 & 75,980,633 & 0.304 \\  & Optimized Path & **73.622** & **12.218** & **1.560** & **4.136** & **76.066** & **1.841** & **0.196** \\ \hline \multirow{3}{*}{**Nighttime Travel**} & Initial Population? & 76.499 & 9.229 & 1.275 & 4.608 & 78,344,574 & 0.420 \\  & Initial Population 1 & 74.517 & 7.318 & 1.242 & 4.605 & 75,980,633 & 0.355 \\  & Initial Population 10 & 83.014 & 16.924 & 1.384 & 4.361 & 86,398,536 & 0.350 \\  & Optimized Path & **70.99** & **8.955** & **1.483** & **4.095** & **72.781** & **0.000** & **-0.133** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result of the multi-objective path-planning approach. To further demonstrate the multi-objective optimization advantages of this algorithm in path-planning tasks, we designed a set of comparative experiments. Using the same initial path population, we compared the performance of our algorithm with single-objective optimization algorithms. The single-objective optimization algorithms selected were simulated annealing [27] and a greedy algorithm [28]. Table 3 summarizes the optimization results of each algorithm. From the experimental results, while our algorithm shows a slight disadvantage in the total travel time of the paths, it exhibits significant advantages in multiple dimensions, including path length, congestion avoidance, energy consumption control, and safety. First, the total length and congestion length in each scenario for our algorithm are almost universally lower than those of the single-objective optimization algorithms, indicating that multi-objective optimization can more efficiently avoid congested areas and find shorter paths. Second, the energy-saving effect of the multi-objective optimization algorithm is significantly better than that of the single-objective algorithms, making it an ideal choice for environmentally conscious users and those with long-distance driving needs. Additionally, the risk index of the paths planned by our algorithm is generally lower than that of the single-objective optimization algorithms, effectively enhancing the safety of users during driving. Finally, despite balancing multiple objectives, our algorithm demonstrates a shorter execution time compared to other methods like simulated annealing and greedy algorithms, highlighting its efficiency and practicality in real-world applications. This fully demonstrates the effectiveness and superiority of our algorithm in meeting diverse path-planning requirements, providing an ideal path-planning solution for future real-world applications.