Forest road network planning based on topological measures in Hyrcanian recreational forest parks using graph theory

Norouzi Sangtabi, Abbas; Parsakhoo, Aidin; Ezzati, Sattar; Mostafa, Mohsen

doi:10.22034/ijf.2025.473178.2003

Forest road network planning based on topological measures in Hyrcanian recreational forest parks using graph theory

Document Type : Research Paper

Authors

Abbas Norouzi Sangtabi ¹

Aidin Parsakhoo ²

Sattar Ezzati ³

Mohsen Mostafa ⁴

¹ M.Sc. in Forestry, Dept. of Forestry, Faculty of Forest Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

² Associate Prof., Dept. of Forestry, Faculty of Forest Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

³ Assistant Prof., Dept. of Forestry, Faculty of Forest Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

⁴ Assistant Prof., Dept. of Soil, Plant and Food Sciences (DISSPA), University of Bari, Bari, Italy.

10.22034/ijf.2025.473178.2003

Abstract

Road network connectivity significantly influences the total cost of transportation in recreation services. With recent advancements in mathematical sciences and computer technology, structural analysis using graph theory has become practical. The application of graph theory offers several benefits, including the evaluation of connectivity levels, network transportation speed, accessibility, identification of critical junctions, and detection of traffic-reducing cycles that enhance safe and convenient travel for tourists. In this paper, the existing road networks of eight recreational forest parks (Mirza Kouchak Khan, Zare, Talar, Endargeli, Javarem, Izadshahr, Haloomsar and Abbasabad) in Mazandaran Province were analyzed using graph theory. Moreover, new roads were proposed to enhance network connectivity for recreational services. Root nodes, articulation nodes, links and sub-graphs were considered as graph components, while network density, road spacing, alpha index ( ), beta index ( ), P index ( ), eta index ( ), number of cycles (u), gamma index ( ) and detour index (DI) were considered as topological and geometric measures. New road segments were proposed based on topologic standards to improve the efficiency of road networks with weak connectivity. The proposed approach was applied to eight test networks in the central highland of the Hyrcanian forests, northern Iran. The results showed that the means of the articulation nodes, total nodes, links, sub-graphs, α, β, π, η, u and ɣ in the forest land use with recreation services were 0.5, 7.25, 7.25, 1.12, 0.07, 0.95, 2.00, 1.13, 1.12 and 0.46, respectively. As a result of supplementary roads, road density increased by 15.60%, 20.30%, 37.20%, 34.3%, 8.60% in Endargeli, Javarem, Izadshahr, Haloomsar and Abbasabad, respectively. Additionally, the α index improved from 0 to 0.12, 0.2, 0.17, 0.14 and 0.33 in these study locations. Structural properties of road networks with weak connectivity were improved to the standard range by the addition of supplementary roads. The findings of the present study showed that completing the networks by introducing cycles can enhance other graph theory indicators and improve overall connectivity.

Keywords

Graph theory

Connectivity

Road network

Recreational service

Subjects

Forest Road Construction

Ascensão, F., Mestre, F., & Barbosa, M. (2019). Prioritizing road defragmentation using graph-based tools. Landscape and Urban Planning, 192, 67-79. https://doi.org/10.1016/j.landurbplan.2019.103653

Anderson, A.E., & Nelson, J. (2004). Projecting vector based road networks with shortest path algorithm. Canadian Journal of Forest Research, 34, 1444–1457. https://doi/10.1139/x04-030

Ballou, R., Rahardja, H., &Sakai, N. (2002). Selected country circuity factors for road travel distance estimation. Transportation Research Part A, 36, 843-848. https://doi.org/10.1016/S0965-8564(01)00044-1

Çalışkan, E., & Sevim, Y. (2021). Forest road detection using deep learning models. Geocarto International, 14, 87-93. https://doi.org/10.1080/10106049.2021.1926555

Contreras, M., & Chung, W. (2007). A computer approach to finding an optimal log landing location and analyzing influencing factors for ground-based timber harvesting. Canadian Journal of Forest Research, 37, 276–292. https://doi.org/10.1139/x06-219

Daniel, C.B., Saravanan, S., & Mathew, S. (2020). GIS based road connectivity evaluation using graph theory. Transportation Research, Lecture Notes in Civil Engineering, 45, 213-226. https://doi.org/10.1007/978-981-32-9042-6_17

Eker, R., Aydin, A., & Akay, A.E. (2013). Optimization of timber transportation with network approach: A Case Study from Aladag Forest Enterprise. International Caucasian Forestry Symposium, 23-26 October 2013, Artvin, Turkey, 2(4), 450-457p.

Ghaffarian, M.R., & Sobhani, H. (2007). Optimization of an existing forest road network using Network 2000. Croatian Journal of Forest Engineering, 28, 185–193. https://hrcak.srce.hr/file/28462

Gharaee, Z., Kowshik, S., Stromann, O., & Felsberg, M. (2021). Graph representation learning for road type classification. Pattern Recognition, 120, 1-11. https://doi.org/10.1016/j.patcog.2021.108174

Hallo, J., & Manning, R. (2009). Transportation and recreation: A case study of visitors driving for pleasure at Acadia National Park. Journal of Transport Geography, 17, 491–499. https://doi.org/10.1016/j.jtrangeo.2008.10.001

Heinimann, H.R. (2017). Forest road network and transportation engineering-state and perspectives. Croatian Journal of Forest Engineering, 38(2), 155-173. https://crojfe.com/site/assets/files/4077/heinimann.pdf

Kazama, V.S., Corte, A.P.D., Robert, R.C.G., Sanquetta, C.R., Eduardo Arce, J., Oliveira-Nascimento, K.A., & De Armond, D. (2021). Global review on forest road optimization planning: Support for sustainable forest management in Amazonia. Forest Ecology and Management, 492, 119-159. https://doi.org/10.1016/j.foreco.2021.119159

Kofi, G.E. (2010). Network based indicators for prioritizing the location of a new urban transport connection: case study Istanbul, Turkey. Internationnal Institute for Geo-information Science and Earth Observation Enschede, The Netherlands, 114p. https://essay.utwente.nl/90752/1/Emmanuel%20Kofi%20Gavu-22239.pdf

Levinson, D., & Yerra, B. (2006). Self-organization of surface transportation networks. Transportation Science, 40(2), 179–88. https://doi.org/10.1287/trsc.1050.0132

Liu, S., & Dong, Y. (2008). Characterizing the hierarchy of road network and its landscape effect with graph theory. The 7th International Symposium on Operations Research and Its Applications (ISORA’08), Lijiang, China, October 31–Novemver 3, 2008, pp. 152–159.

Lotfalian, M., & Parsakhoo, A. (2012). Forest road network planning. Aeezh press, 155p. (In persian).

Liu, S.F., Jiang, M., Bai, S., Zhou, T., & Liu, H. (2024). Research on automatic generation of park road network based on Skeleton Algorithm. Applied Sciences, 14(18), 21-29. https://doi.org/10.3390/app14188475

Mostafa, M., Shataee Jouibary, Sh., Lotfalian, M., & Sadoddin, A. (2015). Investigation methods evaluation of forest road network according to graph theory. Journal of Conservation and Utilization of Natural Resources, 4(2), 41-63. (In persian)

https://ejang.gau.ac.ir/article_2795_a5e1b947916f209d39e586f92edf8240.pdf

Mostafa, M., Shataee Jouibary, S., Lotfalian, M., & Sadoddin, A. (2017). Assessment of road network connectivity using graph theory in the Chehel- Chai Watershed, Golestan Province. Journal of Watershed Management Research, 8(15), 259-267. (In persian) https://doi.org/ 10.29252/jwmr.8.15.259

Novikov, A.V., Sumarukova, O.V., Timoshkin, A.D., Lagutina, N.V., & Barsukova, M.V. (2022). Assessment of the development of the road and path network on the territory of the reserve park in order to reduce the anthropogenic impact on nature. IOP Conf. Series: Earth and Environmental Science, 981, 1-7. https://doi.org/10.1088/1755-1315/981/4/042079

Pung, J., DSouza, R.M., Ghosal, D., Zhang, M. (2022). A road network simplification algorithm that preserves topological properties. Applied Network Science, 79, 112-120. https://doi.org/10.1007/s41109-022-00521-8

Parsakhoo, A., & Lotfalian, M. (2017). Locating log depots and forest roads using a weighted-graph optimization algorithm. Nova meh Šumar, 38(1), 67-77. https://hrcak.srce.hr/file/283599

Rodrigue, J.P., Comtois, C., & Slack, B. (2006). The geography of transport systems. Taylor & Francis e-Library, 297p. https://doi.org/10.4324/9781003343196

Sarkar, D. (2013). Structural analysis of existing road networks of Cooch Behar district, West Bengal, India: a transport geographical appraisal. Ethiopian Journal of Environmental Studiesand Management, 6(1), 74–81. https://doi.org/10.4314/ejesm.v6i1.9

Sohouenou, P.Y.R., Christidis, P., Christodoulou, A., Neves, L.A.C., & Presti, D.L. (2020). Using a random road graph model to understand road networks robustness to link failures. International Journal of Critical Infrastructure Protection, 29, 1-17. https://doi.org/10.1016/j.ijcip.2020.100353

Sreelekha, M.G., Krishnamurthy, K., & Anjaneyulu, M.V.L. (2014). Assessment of topological pattern of urban road transport system of Calicut city. Transportation Research Procedia, 17, 253-262. https://doi.org/10.1016/j.trpro.2016.11.089

Sreelekha, M.G., Krishnamurthy, K., & Anjaneyulu, M.V.L. (2016). Interaction between road network connectivity and spatial pattern. Procedia Technology, 24, 131–139.

https://doi.org/10.1016/j.protcy.2016.05.019

Tacnet, J.M., Mermet, E., & Maneerat, S. (2012). Analysis of importance of road networks exposed to natural hazards. Proceedings of the AGILE'2012 International Conference on Geographic Information Science, Avignon, April, 24-27pp.

Thompson, M., Boston, K., Arthur, J., & Sessions, J. (2007). Intelligent deployment of forest road graders. International Journal of Forest Engineering, 18, 15-23. https://doi.org/10.1080/14942119.2007.10702546

Watanabe, D. (2010). A study on analyzing the grid road network patterns using relative neighborhood graph. The Ninth International Symposium on Operations Research and Its Applications (ISORA’10), Chengdu-Jiuzhaigou, China, August 19–23, 2010, 112–119pp.

Xie, F., & Levinson, D. (2007). Measuring the structure of road networks. Geographical Analysis, 39, 336-356. https://doi.org/10.1111/j.1538-4632.2007.00707.x

Yang, B., Luan, X., & Li, Q. (2009). An adaptive method for identifying the spatial patterns in road networks. Computers, Environment and Urban Systems, 34, 40-48. https://doi.org/ 10.1016/j.compenvurbsys.2009.10.002

Yildirim, F., & Kadi, F. (2020). Production of optimum forest roads and comparison of these routes with current forest roads: a case study in Maçka, Turkey. Geocarto International, 37(7), 2175-2195. https://doi.org/10.1080/10106049.2020.1818852

Yu, B., Lee, Y., & Sohn, K. (2020). Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN). Transportation Research Part C: Emerging Technologies, 114, 189-204. https://doi.org/10.1016/j.trc.2020.02.013

Zhao, Y., Chen, Z., Zhao, Z., Li, C., Bai, Y., Wu, Z., Wang, D., & Chen, P. (2024). A deeply supervised vertex network for road network graph extraction in high-resolution images. International Journal of Applied Earth Observation and Geoinformation, 133, 45-62. https://doi.org/10.1016/j.jag.2024.104082