Designing an Urban Air Mobility Corridor Network: A Multi-Objective Optimization Approach Using U-NSGA-III
Abstract
:1. Introduction
- Theoretical contribution: The UAM corridor network design problem is mathematically formulated as a multi-objective optimization framework, balancing travel time efficiency, ground risk, and implementation costs, while considering constraints such as available airspace. This framework presents a novel theoretical approach to UAM corridor network design, fostering the application of multi-objective optimization in this domain.
- Method contribution: By combining node position vectors and edge connection vectors into a fixed-length encoding vector, the encoding scheme allows for the representation of UAM corridor networks with variable corridor counts, thereby significantly enhancing the diversity and flexibility of network designs. With the proposed encoding scheme, this study presents a UAM corridor network design approach based on the unified non-dominated sorting genetic algorithm III (U-NSGA-III).
- Application contribution: This study demonstrates the application of the proposed UAM corridor network design method in a real-world case study in Chengdu. Beyond improving efficiency, our method achieves substantial reductions in risk and cost. The results demonstrate that, compared to the traditional method, our approach reduces ground risk by 37.8%, reduces implementation costs by 69.9%, and increases time savings by 4.7%. This result validates the effectiveness of the proposed method and showcases its substantial potential in real-world applications.
2. Problem Formulation
2.1. Multi-Objective Optimization Problem of Corridor Network Design
2.2. Optimal Objective Setting
2.2.1. Travel Time-Saving Rate
2.2.2. Ground Risk
2.2.3. Implementation Cost
2.3. Constraint Setting
- No-fly zone constraint (): Each corridor in the network must avoid passing through no-fly zones, as shown in Equation (11).
- Connectivity constraint (): All nodes within the corridor network must form a connected structure. The connectivity of the corridor network is expressed as follows:
- Minimum length constraint (): The length of each corridor in the network must be no less than , as shown in Equation (12).
3. Methods
3.1. Overview of Multi-Objective Optimization Methods
3.2. Non-Dominated Sorting Genetic Algorithm
3.3. Encoding Scheme
3.4. Algorithm Flow and Parameter Setting
Algorithm 1 U-NSGA-III algorithm. |
Initialization: 1: Generate reference points with 2: Randomly initialize with Main loop: 3: 4: whiledo 5: generate offspring population with . 6: Combine and . 7: For X in : Filter out X that do not meet . 8: Perform non-dominated sorting on by . 9: Calculate the population fitness of by . 10: Select the next-generation population with . 11: Update the reference points with . 12: . 13: end while Output: 14: Return the final set of non-dominated solutions . |
4. Case Analysis
4.1. Data Collection and Processing
4.1.1. Travel Demand Data
- Travel distance filtering: Identify trips with a straight-line distance greater than 30 km.
- Travel scope filtering: Select trips where both the origin and destination lie within Chengdu’s main urban area, defined as a square region extending 50 km in all four directions (north, south, east, and west) from the city center (Tianfu Square).
- Outlier Removal: Clean and filter out abnormal data.
4.1.2. Population Distribution Data
- Travel scope filtering: This involves filtering mobile signaling data to include only locations within Chengdu’s main urban area, represented as a square area extending 50 km in all four directions (north, south, east, and west) from the city center (Tianfu Square).
- Outlier removal: This involves cleaning and filtering out abnormal data.
4.1.3. No-Fly Zone Data
4.2. Result of U-NSGA-III
4.2.1. Experiment on Selecting Crossover and Mutation Probabilities
4.2.2. Pareto Front and Corridor Design Result
4.3. Comparison of K-Means Clustering Method
5. Discussion
- Non-linear corridor design: The current model assumes linear connections between nodes. Future studies could explore non-linear pathways to enhance coverage, particularly in areas with numerous no-fly zones or geographical constraints.
- Dynamic factors: Incorporating dynamic elements such as weather conditions, time-varying demands, and real-time traffic data could improve the robustness and adaptability of the corridor network.
- Additional objectives: Expanding the optimization framework to include objectives like noise pollution, environmental impact, and social equity would provide a more comprehensive assessment of UAM network performance.
- Implementation feasibility: Further research is necessary to evaluate the practical feasibility of deploying the optimized network, including aspects of regulatory compliance, public acceptance, and economic viability.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Name | Symbol | Value |
---|---|---|
Velocity of fling in corridors | 240 km/h | |
Minimum length of a corridor | 8 km | |
The number of nodes | [4–8] | |
Population size | ||
Population generation number | 1000 | |
Reference direction division number | 20 | |
Crossover probability | [0.2–0.8] | |
Mutation probability | [0.2–0.8] |
Name | Height | Latitude | Longitude |
---|---|---|---|
Chengdu Greenland Tower | 468 | 30.61055 | 104.1408 |
Tiantou International Business Center | 284 | 30.42122 | 104.0730 |
International Commerce Center | 280 | 30.63604 | 104.1115 |
Chengdu IFS Tower | 247 | 30.65705 | 104.0780 |
Global Times Center | 243 | 30.55739 | 104.0628 |
Western IFC Conrad Hotel | 241 | 30.65101 | 104.0808 |
Tianxi Twin Towers | 222 | 30.63745 | 104.0933 |
Tianfu IFC | 220 | 30.58707 | 104.0643 |
Waldorf Astoria Chengdu | 220 | 30.58685 | 104.0665 |
Oriental Hope Intertek Plaza | 219 | 30.55477 | 104.0656 |
Minyoun Financial Plaza | 206 | 30.64957 | 104.0886 |
OPPO Headquarters | 206 | 30.58233 | 104.0673 |
Chengdu Fantasia Meinian Plaza | 204 | 30.53615 | 104.0660 |
Sichuan Airlines Plaza | 204 | 30.66147 | 104.0678 |
Huarun Tower | 201 | 30.65247 | 104.1135 |
Pinnacle One | 200 | 30.65258 | 104.0816 |
Chengdu World Financial Center | 200 | 30.55341 | 104.0608 |
Twin Rivers International Office Tower | 200 | 30.56217 | 104.0550 |
Palm Springs International Center | 200 | 30.55763 | 104.0672 |
0.2 | 0.4 | 0.6 | 0.8 | ||
---|---|---|---|---|---|
0.2 | 0.886 | 0.914 | 0.926 | 0.921 | |
0.4 | 0.868 | 0.879 | 0.895 | 0.884 | |
0.6 | 0.845 | 0.868 | 0.883 | 0.875 | |
0.8 | 0.825 | 0.842 | 0.837 | 0.855 |
Number of Clusters (K) | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|
Travel time-saving rate () | 30.9% | 33.7% | 35.7% | 38.5% | 42.4% |
e population density () | 2488.4 | 2968.1 | 3609.5 | 3059.3 | 2164.9 |
Total corridor length () | 112.3 | 225.4 | 412.7 | 445.2 | 636.2 |
Demand coverage rate | 77.9% | 82.6% | 87.8% | 89.7% | 90.6% |
Travel time-saving rate of covered demands | 37.3% | 39.0% | 39.2% | 41.7% | 45.5% |
Evaluation Metrics | U-NSGA-III | K-Means |
---|---|---|
Travel time-saving rate () | 47.1% | 42.4% |
Average population density () | 1346.1 | 2164.9 |
Total corridor length () | 191.6 | 636.2 |
Demand coverage rate | 88.1 | 90.6% |
Travel time-saving rate of covered demands | 51.9 | 45.5% |
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Zhang, Z.; Zheng, Y.; Li, C.; Jiang, B.; Li, Y. Designing an Urban Air Mobility Corridor Network: A Multi-Objective Optimization Approach Using U-NSGA-III. Aerospace 2025, 12, 229. https://doi.org/10.3390/aerospace12030229
Zhang Z, Zheng Y, Li C, Jiang B, Li Y. Designing an Urban Air Mobility Corridor Network: A Multi-Objective Optimization Approach Using U-NSGA-III. Aerospace. 2025; 12(3):229. https://doi.org/10.3390/aerospace12030229
Chicago/Turabian StyleZhang, Zhiyuan, Yuan Zheng, Chenglong Li, Bo Jiang, and Yichao Li. 2025. "Designing an Urban Air Mobility Corridor Network: A Multi-Objective Optimization Approach Using U-NSGA-III" Aerospace 12, no. 3: 229. https://doi.org/10.3390/aerospace12030229
APA StyleZhang, Z., Zheng, Y., Li, C., Jiang, B., & Li, Y. (2025). Designing an Urban Air Mobility Corridor Network: A Multi-Objective Optimization Approach Using U-NSGA-III. Aerospace, 12(3), 229. https://doi.org/10.3390/aerospace12030229