An Ant Colony Optimization Framework for Rural Road Network Planning to Maximize Accessibility in Facility-Based Rural Road Infrastructure

Bharti, Himanshu; Suman, Sanjeev

doi:10.66224/NMCE.2606.1143

An Ant Colony Optimization Framework for Rural Road Network Planning to Maximize Accessibility in Facility-Based Rural Road Infrastructure

Document Type : Research

Authors

Himanshu Bharti ¹

Sanjeev Suman ²

¹ Ph.D. Scholar, Department of Civil Engineering, College of Technology, GB Pant University of Agriculture and Technology, Pantnagar, 263145, India.

² Associate Professor, Department of Civil Engineering, College of Technology, GB Pant University of Agriculture and Technology, Pantnagar, 263145, India.

10.66224/NMCE.2606.1143

Abstract

Infrastructure connectivity is essential for socio-economic development and quality of life in rural areas. However, inadequate road infrastructure often limits access to healthcare, education, markets, and other essential services. Effective rural road network planning is therefore crucial for improving accessibility and network efficiency. This study presents a novel optimization framework for rural road network design by integrating a gravity-based demand model, Ant Colony Optimization (ACO), and the Minimum Spanning Tree (MST) approach. Village importance is quantified using a composite score based on population and available facilities and incorporated into a gravity model to estimate inter-village demand. In the first stage, ACO identifies high-priority routes by maximizing the Value of Path (VOP), which balances accessibility benefits and path length. In the second stage, MST ensures complete network connectivity by linking remaining unconnected villages using the Value of Edge (VOE) metric. Overall network performance is evaluated using the Value of Network (VON), which integrates accessibility and network efficiency. The proposed framework is validated using a case study of 75 villages in the Kashipur block, India. Results demonstrate a 25.46% reduction in total network length, a 17.78% decrease in the number of links, and a 16.78% improvement in VON, along with a 21.62% increase in average score per link, indicating improved network efficiency and accessibility. The proposed model provides a practical decision-support tool for planners and policymakers to develop length-efficient and accessibility-oriented rural road networks while supporting the social and economic dimensions of sustainable rural infrastructure planning.

Keywords

Rural Road Network Planning

Ant Colony Optimization

Minimum Spanning Tree

Gravity Model

Optimization Technique

Subjects

Transportation Engineering

[1] Zhang, B., Zhong, Z., Zhou, X., Qu, Y., & Li, F.: Optimization Model and Solution Algorithm for Rural Customized Bus Route Operation under Multiple Constraints. Sustainability 15(5), 3883 (2023). https://doi.org/10.3390/su15053883

[2] Bharti, H., Dhapola, M., Suman, S., & Rawat, K. (2026). A Review of Prioritization Methods for Rural Road Network Planning: From Traditional Approaches to Modern MCDM and GIS-Based Frameworks. DMPedia Lecture Notes in Multidisciplinary Research, 326-337.

[3] Jayasree, K.: Sustainable rural road network planning with a balance of urban and rural development. In: IOP Conference Series: Earth and Environmental Science (Vol. 982, No. 1, p. 012042). IOP Publishing (2022, March). https://doi.org/10.1088/1755-1315/982/1/012042

[4] Shrestha, J. K., Benta, A., Lopes, R. B., & Lopes, N.: A methodology for definition of road networks in rural areas of Nepal. International Journal of Civil and Environmental Engineering 7(6), 422-426 (2013).

[5] Asher, S., Nagpal, K., & Novosad, P.: The cost of distance: Geography and governance in rural India. World Bank Working Paper (2018).

[6] Rao, K. L., & Jayasree, K.: Rural Infrastructure Planning with emphasis on road network connectivity by Coplanar Concurrent Theory. In: Map India Conference (2003).

[7] Khandker, S. R., & Koolwal, G. B.: Estimating the long-term impacts of rural roads: a dynamic panel approach. World Bank Policy Research Working Paper, (5867) (2011). https://doi.org/10.1596/1813-9450-5867

[8] Ministry of Rural Development [MORD]: Rural Road development plan: vision 2025, National Rural Road Development Agency, Government of India, New Delhi, India (2007).

[9] World Bank, RT-4: Rural Road master planning process, World Bank Infrastructure Notes, Transport Note No. RT-4 (2000).

[10] Van der Tak, H. G., & Ray, A.: The economic benefits of road transport projects (1971).

[11] van Oudheusden, D. L., & Khan, M. L. R.: Planning and development of rural road networks in developing countries. European Journal of Operational Research 32(3), 353-362 (1987). https://doi.org/10.1016/S0377-2217(87)80004-8

[12] Makarachi, G., Tillotson, H. T., & Kumar, A.: Road planning in rural areas. In: Int. Seminar on Rural Transportation, pp. 26-28 (1989, April).

[13] Shrestha, J. K.: Rural Road network decision model for hilly regions of Nepal. Journal of Advanced College of Engineering and Management 4, 51-64 (2018). https://doi.org/10.3126/jacem.v4i0.23178

[14] Shrestha, J. K., Benta, A., Lopes, R. B., & Lopes, N.: A multi-objective analysis of a rural road network problem in the hilly regions of Nepal. Transportation Research Part A: Policy and Practice 64, 43-53 (2014). https://doi.org/10.1016/j.tra.2014.03.005

[15] Kumar, A., & Tillotson, H. T.: Planning model for rural roads. Transp. Res. Rec.: J. Transp. Res. Board 1291, 171-181 (1991).

[16] Modinpuroju, A., & Prasad, C. S. R. K.: Design optimal rural road network using GIS. Archives of Transport 41(1), 63-71 (2017). https://doi.org/10.5604/01.3001.0009.7387

[17] Kanuganti, S., Dutta, B., Sarkar, A. K., & Singh, A. P.: Development of a need-based approach for rural road network planning. Transportation in Developing Economies 3, 1-11 (2017). https://doi.org/10.1007/s40890-017-0044-y

[18] Kumar, A., & Kumar, P.: User-friendly model for planning rural roads. Transportation Research Record 1652(1), 31-39 (1999). https://doi.org/10.3141/1652-05

[19] Hasan, M. M. U., Quium, A. A., Rahman, M., Khatun, F., Akther, M. S., Haque, A., ... & Shubho, T. H.: A methodology for planning and prioritisation of rural roads in Bangladesh. Sustainability 14(4), 2337 (2022). https://doi.org/10.3390/su14042337

[20] Ma, C., Ma, C., Ye, Q., He, R., & Song, J.: An Improved Genetic Algorithm for the Large‐Scale Rural Highway Network Layout. Mathematical Problems in Engineering 2014(1), 267851 (2014). https://doi.org/10.1155/2014/267851

[21] Gallo, M., D'Acierno, L., & Montella, B.: The Road Network Design Problem in Regional Contexts: Meta-Heuristic Algorithms for Solving Real-Scale Problems (No. 12-1415) (2012).

[22] Joshi, Y., Suman, S., & Bharti, H.: Planning of rural road network using sustainable practices to maximize the accessibility to health and education facilities using ant colony optimization. Materials Today: Proceedings (2023). https://doi.org/10.1016/j.matpr.2023.07.096

[23] Chen, Y.: Optimizing Locations of Primary Schools in Rural Areas of China. Complexity 2021, 1-13 (2021). https://doi.org/10.1155/2021/7573700

[24] Li, J., Guo, X., Lu, R., & Liu, P.: Bus Route Design Problem for Rural Tourism Objectives: A Multiobjective Approach. Journal of Advanced Transportation 2022 (2022). https://doi.org/10.1155/2022/1339598

[25] Wang, J., & Wu, Y.: Optimal Design of Urban and Rural Public Transportation Network Based on Spatio Temporal Constraints. Journal of Applied Science and Engineering 21(1), 51-58 (2018).

[26] Suresh Kumar, J., & Shobana, D. (2025). Impact of PMGSY on rural connectivity and economic development in India: A state-wise analysis. EPRA International Journal of Agriculture and Rural Economic Research.

[27] Ministry of Rural Development, Government of India. (2023). GIS-based GeoSadak and PMGSY National GIS data for rural road planning.

[28] World Bank. (2021). The Road to Opportunities in Rural India: Impact Evaluation of PMGSY. World Bank Report. https://doi.org/10.1596/978-1-4648-1778-6

[29] Ministry of Rural Development, Government of India. (2022). Database and master planning for rural roads using GIS platform under PMGSY.

[30] Noirot, C. H., & Pasteels, J. M.: Ontogenetic development and evolution of the worker caste in termites. Experientia 43, 851-860 (1987). https://doi.org/10.1007/BF01951642

[31] Goss, S., Aron, S., Deneubourg, J. L., & Pasteels, J. M.: Self-organized shortcuts in the Argentine ant. Naturwissenschaften 76(12), 579-581 (1989). https://doi.org/10.1007/BF00462870

[32] Deneubourg, J. L., Aron, S., Goss, S., & Pasteels, J. M.: The self-organizing exploratory pattern of the Argentine ant. Journal of Insect Behavior 3, 159-168 (1990). https://doi.org/10.1007/BF01417909

[33] Dorigo, M., Maniezzo, V., & Colorni, A.: The ant system: An autocatalytic optimizing process (1991).

[34] Dorigo, M., & Gambardella, L. M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53-66 (1997). https://doi.org/10.1109/4235.585892

[35] Bullnheimer, B.: A new rank-based version of the Ant System. A Computational Study Working Paper (1997).

[36] Stützle, T., & Hoos, H. H.: MAX-MIN ant system. Future Gener. Comput. Syst. 16(8), 889-914 (2000). https://doi.org/10.1016/S0167-739X(00)00043-1

[37] Bonabeau, E.: Swarm Intelligence: From Natural to Artificial Systems. Oxford: Oxford University Press, pp. 25-34 (1999). https://doi.org/10.1093/oso/9780195131581.001.0001

[38] Eyckelhof, C. J., & Snoek, M.: Ant systems for a dynamic TSP: Ants caught in a traffic jam. In: Proc. Int. Workshop Ant Algorithms, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 88-99 (2002). https://doi.org/10.1007/3-540-45724-0_8

[39] Guoying, G. L., Subing, Z., & Zemin, L.: Distributed dynamic routing using ant algorithm for telecommunication networks. In: Proc. WCC 2000-ICCT 2000 Int. Conf. Commun. Technol., vol. 2, pp. 1607-1612 (2000). https://doi.org/10.1109/ICCT.2000.890968

[40] Angus, D., & Hendtlass, T.: Dynamic ant colony optimisation. Appl. Intell. 23, 33-38 (2005). https://doi.org/10.1007/s10489-005-2370-8

[41] Angus, D.: The current state of ant colony optimisation applied to dynamic problems. Technical Report TR009, Centre for Intelligent Systems & Complex Processes, Faculty of Information & Communication Technologies, Swinburne University of Technology, Melbourne, Australia (2006).

[42] PMGSY Documents, https://pmgsy.nic.in/pmgsy-documents (last accessed 2026/03/12).

[43] Kaminski, B. (1999). World Bank Technical Paper No. 441.

[44] Liang, T., Liu, H., & Tan, Y.: Research on the gravity planning model of prefecture city rail transit network. In: E3S Web of Conferences (Vol. 145, p. 02005). EDP Sciences (2020). https://doi.org/10.1051/e3sconf/202014502005

[45] Suman, S., & Kumar, P.: Ant colony optimization algorithms for vehicle routing, route planning and scheduling. Highway Research 55 (2014).

[46] Bharti, H., & Suman, S. (2025). A review on the implementation of ant colony optimization in transportation engineering. Journal of Emerging Trends in Engineering, Sciences and Technology, 8(2), 121-132.

[47] Kruskal, J. B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society 7(1), 48-50 (1956). https://doi.org/10.1090/S0002-9939-1956-0078686-7

[48] OMMS Portal, https://pmgsy.dord.gov.in/ (last accessed 2026/03/12).

[49] Geosadak PMGSY, https://pmgsygeosadak.dord.gov.in/OpenData (last accessed 2026/03/12).

[50] Calvo, C. M.: Options for managing and financing rural transport infrastructure (Vol. 411). World Bank Publications (1998).