The Effect of Candidate Routes Alignment over Transit Coverage in a Grid Network

Document Type : Original Article

Authors

1 Department of Civil Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran

2 Department of Business and Management, Aston Business School, Birmingham, United Kingdom

Abstract

The design of public transportation (transit) route networks involves identifying the most efficient configuration of routes in an urban setting so as to maximize an objective function, such as network coverage, within the available budget. This problem is generally addressed through two key stages: the generation of potential candidate routes to be selected and the subsequent selection of final routes. According to the literature, the “pool” of candidate routes in the first stage plays a critical role in determining the quality of the selected routes in the second stage. However, in certain network topologies, such as grid-structured networks, urban planners often prefer introducing candidate routes oriented horizontally (east-west) or vertically (south-north). The impact of restricting the candidate routes to exclusively horizontal and vertical routes has not been studied much in existing research. To address this gap, this study examines two scenarios: (1) unrestricted candidate routes and (2) candidate routes restricted to horizontal and vertical orientations. The results averaged for a 6×10 grid network suggest that adopting horizontally and vertically restricted candidate routes results in only a 2% reduction in network coverage compared to using unrestricted candidate routes.

Keywords

Main Subjects


  1. Shah, K. J., Pan, S.-Y., Lee, I., Kim, H., You, Z., Zheng, J.-M., Chiang, P.-C. Green transportation for sustainability: Review of current barriers, strategies, and innovative technologies. Journal of Cleaner Production, 2021; 326: 129392. doi:10.1016/j.jclepro.2021.129392.
  2. Ceder, A. Urban mobility and public transport: future perspectives and review. International Journal of Urban Sciences, 2021; 25: 455-479. doi:10.1080/12265934.2020.1799846.
  3. Guihaire, V., Hao, J.-K. Transit network design and scheduling: A global review. Transportation Research Part A: Policy and Practice, 2008; 42: 1251-1273. doi:10.1016/j.tra.2008.03.011.
  4. Zarrinmehr, A., Saffarzadeh, M., Seyedabrishami, S., Nie, Y. M. A path-based greedy algorithm for multi-objective transit routes design with elastic demand. Public Transport, 2016; 8: 261-293. doi:10.1007/s12469-016-0131-1.
  5. Gattermann, P., Harbering, J., Schöbel, A. Line pool generation. Public Transport, 2017; 9: 7-32. doi:10.1007/s12469-016-0127-x.
  6. Mejri, I., Layeb, S. B., Zeghal, F. A survey on network design problems: main variants and resolution approaches. European Journal of Industrial Engineering, 2023; 17: 253-309. doi:10.1504/EJIE.2023.129443.
  7. Salhi, S., Thompson, J. An Overview of Heuristics and Metaheuristics. In: S. Salhi, J. Boylan editors. The Palgrave Handbook of Operations Research. Berlin (DE): Springer International Publishing; 2022. p. 353-403. doi:10.1007/978-3-030-96935-6_11.
  8. Almufti, S., Ahmad Shaban, A., Arif Ali, Z., Ismael Ali, R., A. Dela Fuente, J. Overview of Metaheuristic Algorithms. Polaris Global Journal of Scholarly Research and Trends, 2023; 2: 10-32. doi:10.58429/pgjsrt.v2n2a144.
  9. Durán-Micco, J., Vansteenwegen, P. A survey on the transit network design and frequency setting problem. Public Transport, 2022; 14: 155-190. doi:10.1007/s12469-021-00284-y.
  10. Nnene, O. A., Zuidgeest, M. H. P., Joubert, J. W. BRT network design for transit cost reduction in Cape Town, South Africa. Journal of Public Transportation, 2023; 25: 100042. doi:10.1016/j.jpubtr.2023.100042.
  11. Wei, Y., Jiang, N., Li, Z., Zheng, D., Chen, M., Zhang, M. An Improved Ant Colony Algorithm for Urban Bus Network Optimization Based on Existing Bus Routes. ISPRS International Journal of Geo-Information, 2022; 11: doi:10.3390/ijgi11050317.
  12. Oudani, M. A Simulated Annealing Algorithm for Intermodal Transportation on Incomplete Networks. Applied Sciences, 2021; 11: doi:10.3390/app11104467.
  13. Zarrinmehr, A., Saffarzadeh, M., Seyedabrishami, S. A local search algorithm for finding optimal transit routes configuration with elastic demand. International Transactions in Operational Research, 2018; 25: 1491-1514. doi:10.1111/itor.12359.
  14. Zarrinmehr, A., Moloukzade, H. Application of a Hill-Climbing Algorithm to Public Transportation Routes Design in Grid Networks. International Journal of Transportation Engineering, 2021; 9: 597-612. doi:10.22119/ijte.2021.285454.1569.
  15. Wan, T., Lu, W., Sun, P. Equity impacts of the built environment in urban rail transit station areas from a transit-oriented development perspective: a systematic review. Environmental Research Communications, 2023; 5: 092001. doi:10.1088/2515-7620/acf8b2.
  16. Piracha, A., Chaudhary, M. T. Urban Air Pollution, Urban Heat Island and Human Health: A Review of the Literature. Sustainability, 2022; 14: doi:10.3390/su14159234.
  17. Wang, L., Jin, J. G., Sibul, G., Wei, Y. Designing Metro Network Expansion: Deterministic and Robust Optimization Models. Networks and Spatial Economics, 2023; 23: 317-347. doi:10.1007/s11067-022-09584-7.
  18. Zhang, L., Wen, H., Lu, J., Lei, D., Li, S., Ukkusuri, S. V. Exploring cascading reliability of multi-modal public transit network based on complex networks. Reliability Engineering & System Safety, 2022; 221: 108367. doi:10.1016/j.ress.2022.108367.
  19. Mauttone, A., Cancela, H., Urquhart, M. E. Network Design with Applications to Transportation and Logistics. 1st ed. Berlin (DE): Springer International Publishing; 2021. doi:10.1007/978-3-030-64018-7.
  20. Abdallah, T. Sustainable Mass Transit: Challenges and Opportunities in Urban Public Transportation. 2nd ed. Amsterdam (NL): Elsevier; 2023. doi:10.1016/B978-0-443-15271-9.00006-6.
  21. Xie, F., Levinson, D. Topological evolution of surface transportation networks. Computers, Environment and Urban Systems, 2009; 33: 211-223. doi:10.1016/j.compenvurbsys.2008.09.009.
  22. Miyagawa, M. Spacing of intersections in hierarchical road networks. Journal of the Operations Research Society of Japan, 2018; 61: 272-280. doi:10.15807/jorsj.61.272.
  23. Daganzo, C. F. Structure of competitive transit networks. Transportation Research Part B: Methodological, 2010; 44: 434-446. doi:10.1016/j.trb.2009.11.001.
  24. Stern, R. Passenger Transfer System Review. 1st ed. Washington D.C. (DC): National Academy Press; 1996.
Volume 1, Issue 3
August 2025
Pages 62-69
  • Receive Date: 05 May 2025
  • Revise Date: 13 May 2025
  • Accept Date: 17 May 2025
  • First Publish Date: 17 June 2025