Optimizing Urban Road Networks for Resilience Using Genetic Algorithms

Authors

  • Xueyi Cheng Duke University
  • Chang Che The George Washington University

DOI:

https://doi.org/10.5281/zenodo.14032011

ARK:

https://n2t.net/ark:/40704/AJSM.v2n6a01

Disciplines:

Management

Subjects:

Management Optimization

References:

31

Keywords:

Network Optimization, Resilience, Genetic Algorithm

Abstract

Urban road networks face increasing challenges in balancing traffic efficiency, budget limitations, and environmental impacts as cities prepare for future demand. This paper presents a multi-objective optimization approach using Genetic Algorithms (GAs) to enhance the performance of an urban transportation network while integrating sustainability goals. By simultaneously optimizing travel times, reducing bottlenecks, and addressing budget constraints, this framework enables a balanced approach to infrastructure improvement. The inclusion of environmental considerations, such as greenhouse gas (GHG) emissions, aligns network development with broader sustainability objectives, promoting a healthier urban environment. Future extensions of this framework include adaptive strategies to respond to shifting traffic patterns and the potential integration of regulatory constraints, such as emission licenses. The proposed GA approach demonstrates a flexible, scalable solution for urban planners and policymakers tasked with building resilient, sustainable road networks, offering practical insights into addressing the multifaceted demands of modern urban infrastructure.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biographies

Xueyi Cheng, Duke University

Researcher at Duke University.

Chang Che, The George Washington University

The George Washington University, US.

References

Abudayyeh, D., Nicholson, A., & Ngoduy, D. (2021). Traffic signal optimisation in disrupted networks, to improve resilience and sustainability. Travel Behaviour and Society, 22, 117-128.

Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278-280.

Cheng, X., Nie, Y. M. & Lin, J. (2024), ‘An autonomous modular public transit service’, Transportation Research Part C: Emerging Technologies p. 104746.

Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281-285.

Drezner, Z. & Wesolowsky, G. O. (2003), ‘Network design: selection and design of links and facility location’, Transportation Research Part A: Policy and Practice 37(3), 241–256.

Hosseini, Y., Mohammadi, R. K., & Yang, T. Y. (2024). A comprehensive approach in post-earthquake blockage prediction of urban road network and emergency resilience optimization. Reliability Engineering & System Safety, 244, 109887.

Cheng, X. (2024). Investigations into the Evolution of Generative AI. Journal of Computer Technology and Applied Mathematics, 1(4), 117–122. https://doi.org/10.5281/zenodo.14003350

Lin, J., Cheng, Y.-L., Cheng, X., Shen, H., Pawar, A. & Koziel, K. (2024), ‘Development of commercial vehicle emission inventory and analysis’, FHWA-ICT-24-002 .

Liu, K., Ding, K., Cheng, X., Chen, J., Feng, S., Lin, H., Song, J. & Zhu, C. (2024), ‘Airport delay prediction with temporal fusion transformers’, arXiv preprint arXiv:2405.08293 .

Liu, T., & Meidani, H. (2024). Neural network surrogate models for aerodynamic analysis in truck platoons: Implications on autonomous freight delivery. International Journal of Transportation Science and Technology (2024).

Liu, T., & Meidani, H. (2023). Optimizing seismic retrofit of bridges: integrating efficient graph neural network surrogates and transportation equity. In Proceedings of Cyber-Physical Systems and Internet of Things Week 2023 (pp. 367-372).

Kaviani, A., Thompson, R. G., & Rajabifard, A. (2017). Improving regional road network resilience by optimised traffic guidance. Transportmetrica A: Transport Science, 13(9), 794-828.

Liu, T., & Meidani, H. (2024). End-to-end heterogeneous graph neural networks for traffic assignment. Transportation Research Part C: Emerging Technologies, 165, 104695.

Liu, T., & Meidani, H. (2024). Heterogeneous graph sequence neural networks for dynamic traffic assignment. arXiv preprint arXiv:2408.04131.

Liu, T., & Meidani, H. (2024). Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems. Journal of Infrastructure Systems, 30(4), 05024004.

Che, C., & Tian, J. (2024). A Comprehensive Framework for Multimodal Sensor Fusion in Intelligent Manufacturing: Innovations, Interpretability, and Real-world Applications. Journal of Computer Technology and Applied Mathematics, 1(4), 36–46. https://doi.org/10.5281/zenodo.13924235

Huang, Z., Zheng, H., Li, C., & Che, C. (2024). Application of machine learning-based k-means clustering for financial fraud detection. Academic Journal of Science and Technology, 10(1), 33-39.

Che, C., Lin, Q., Zhao, X., Huang, J., & Yu, L. (2023, September). Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (pp. 414-418).

Al-Madi, N. A., & Hnaif, A. A. (2022). Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm. Computer Systems Science & Engineering, 40(1).

Che, C., & Tian, J. (2024). Game Theory: Concepts, Applications, and Insights from Operations Research. Journal of Computer Technology and Applied Mathematics, 1(4), 53–59. https://doi.org/10.5281/zenodo.13924241

Yan, Y., Chow, A. H., Ho, C. P., Kuo, Y.-H., Wu, Q. & Ying, C. (2022), ‘Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities’, Transportation Research Part E: Logistics and Transportation Review 162, 102712.

Yan, Y., Cui, S., Liu, J., Zhao, Y., Zhou, B. & Kuo, Y.-H. (2024), ‘Multimodal fusion for large-scale traffic prediction with heterogeneous retentive networks’, Information Fusion p. 102695

Rifai, A. I., Hadiwardoyo, S. P., Correia, A. G., & Pereira, P. A. U. L. O. (2016). Genetic Algorithm Applied for Optimization of Pavement Maintenance under Overload Traffic: Case Study Indonesia National Highway. Applied Mechanics and Materials, 845, 369-378.

Yan, Y., Wen, H., Deng, Y., Chow, A. H., Wu, Q. & Kuo, Y.-H. (2024), ‘A mixed-integer programming-based q-learning approach for electric bus scheduling with multiple termini and service routes’, Transportation Research Part C: Emerging Technologies 162, 104570

Che, C., Li, C., & Huang, Z. (2024). The Integration of Generative Artificial Intelligence and Computer Vision in Industrial Robotic Arms. International Journal of Computer Science and Information Technology, 2(3), 1-9.

Ying, C., Chow, A. H., Yan, Y., Kuo, Y.-H. & Wang, S. (2024), ‘Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach’, Transportation Research Part B: Methodological 188, 103067

Yu, B., Kong, L., Sun, Y., Yao, B. & Gao, Z. (2015), ‘A bi-level programming for bus lane network design’, Transportation Research Part C: Emerging Technologies 55, 310–327.

Cheng, X., Liu, T., Su, G., Che, C., Zhu, C., Liu, K., ... & Hu, X. (2024). Smart Navigation System for Parking Assignment at Large Events: Incorporating Heterogeneous Driver Characteristics. arXiv preprint arXiv:2410.18983.

Che, C., & Tian, J. (2024). Maximum flow and minimum cost flow theory to solve the evacuation planning. Advances in Engineering Innovation, 12, 60-64.

Che, C., & Tian, J. (2024). Analyzing patterns in Airbnb listing prices and their classification in London through geospatial distribution analysis. Advances in Engineering Innovation, 12, 53-59.

Liu, H., Wang, C., Zhan, X., Zheng, H., & Che, C. (2024). Enhancing 3D Object Detection by Using Neural Network with Self-adaptive Thresholding. arXiv preprint arXiv:2405.07479.

Downloads

Published

2024-11-16

How to Cite

Cheng, X., & Che, C. (2024). Optimizing Urban Road Networks for Resilience Using Genetic Algorithms. Academic Journal of Sociology and Management, 2(6), 1–7. https://doi.org/10.5281/zenodo.14032011

Issue

Section

Articles

ARK