Automatic road network selection method considering functional semantic features of roads with graph convolutional networks

References

• Ai, T., 2022. Some thoughts on deep learning empowering cartography. Journal of Geography and Cartography, 5 (2), 25–40.
   Google Scholar
• Benz, S.A., and Weibel, R., 2014. Road network selection for medium scales using an extended stroke-mesh combination algorithm. Cartography and Geographic Information Science, 41 (4), 323–339.
   Web of Science ®Google Scholar
• Brandes, U., 2008. On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30 (2), 136–145.
   Web of Science ®Google Scholar
• Brintrup, A., Ledwoch, A., and Barros, J., 2016. Topological robustness of the global automotive industry. Logistics Research, 9 (1), 1–17.
   Google Scholar
• Chen, J., et al., 2009. Selective omission of road features based on mesh density for automatic map generalization. International Journal of Geographical Information Science, 23 (8), 1013–1032.
   Web of Science ®Google Scholar
• Çolak, S., Lima, A., and González, M.C., 2016. Understanding congested travel in urban areas. Nature Communications, 7 (1), 10793.
   PubMedGoogle Scholar
• Courtial, A., Touya, G., and Zhang, X., 2023. Deriving map images of generalised mountain roads with generative adversarial networks. International Journal of Geographical Information Science, 37 (3), 499–528.
   Web of Science ®Google Scholar
• Foerster, T., Stoter, J., and Kraak, M.J., 2010. Challenges for automated generalisation at European mapping agencies: a qualitative and quantitative analysis. The Cartographic Journal, 47 (1), 41–54.
   Web of Science ®Google Scholar
• Gülgen, F., and Gökgöz, T., 2011. A block-based selection method for road network generalization. International Journal of Digital Earth, 4 (2), 133–153.
   Web of Science ®Google Scholar
• Guo, X., et al., 2023. A method for intelligent road network selection based on graph neural network. ISPRS International Journal of Geo-Information, 12 (8), 336.
   Web of Science ®Google Scholar
• Han, Y., et al., 2020. Application of AHP to road selection. ISPRS International Journal of Geo-Information, 9 (2), 86.
   Web of Science ®Google Scholar
• Hu, Y.G., et al., 2007. Selective omission of road features based on mesh density for digital map generalization. Acta Geodaetica et Cartographica Sinica, 36 (3), 351–357.
   Google Scholar
• Jeatrakul, P., and Wong, K.W., 2009. Comparing the performance of different neural networks for binary classification problems. In: 2009 Eighth International Symposium on Natural Language Processing, 111–115.
   Google Scholar
• Jiang, B., 2015. The fractal nature of maps and mapping. International Journal of Geographical Information Science, 29 (1), 159–174.
   Web of Science ®Google Scholar
• Jiang, B., and Claramunt, C., 2004. A structural approach to the model generalization of an urban street network. GeoInformatica, 8 (2), 157–171.
   Web of Science ®Google Scholar
• Jiang, B., and Harrie, L., 2004. Selection of streets from a network using self‐organizing maps. Transactions in GIS, 8 (3), 335–350.
   Google Scholar
• Jiang, B., Zhao, S., and Yin, J., 2008. Self-organized natural roads for predicting traffic flow: a sensitivity study. Journal of Statistical Mechanics: Theory and Experiment, 2008 (07), P07008.
   Google Scholar
• Karsznia, I., Wereszczyńska, K., and Weibel, R., 2022. Make it simple: effective road selection for small-scale map design using decision-tree-based models. ISPRS International Journal of Geo-Information, 11 (8), 457.
   Web of Science ®Google Scholar
• Kingma, D.P., and Ba, J., 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv: 1412.6980.
   Google Scholar
• Kipf, T.N., and Welling, M., 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
   Google Scholar
• Li, Z., and Openshaw, S., 1992. Algorithms for automated line generalization1 based on a natural principle of objective generalization. International Journal of Geographical Information Systems, 6 (5), 373–389.
   Web of Science ®Google Scholar
• Li, Z., and Zhou, Q., 2012. Integration of linear and areal hierarchies for continuous multi-scale representation of road networks. International Journal of Geographical Information Science, 26 (5), 855–880.
   Web of Science ®Google Scholar
• Liu, X., Ai, T., and Liu, Y., 2009. Road density analysis based on skeleton partitioning for road generalization. Geo-Spatial Information Science, 12 (2), 110–116.
   Google Scholar
• Liu, X., Zhan, F.B., and Ai, T., 2010. Road selection based on Voronoi diagrams and “strokes” in map generalization. International Journal of Applied Earth Observation and Geoinformation, 12, (2010), S194–S202.
   Web of Science ®Google Scholar
• Liu, Y., and Li, W., 2019. A new algorithms of stroke generation considering geometric and structural properties of road network. ISPRS International Journal of Geo-Information, 8 (7), 304.
   Web of Science ®Google Scholar
• Lou, Y., et al., 2009. Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, 352–361.
   Google Scholar
• Mackaness, W.A., and Beard, K.M., 1993. Use of graph theory to support map generalization. Cartography and Geographic Information Systems, 20 (4), 210–221.
   Google Scholar
• Mathew, B.S., and Isaac, K.P., 2014. Optimisation of maintenance strategy for rural road network using genetic algorithm. International Journal of Pavement Engineering, 15 (4), 352–360.
   Web of Science ®Google Scholar
• Peng, J., et al., 2023. Exploring crowd travel demands based on the characteristics of spatiotemporal interaction between urban functional zones. ISPRS International Journal of Geo-Information, 12 (6), 225.
   Web of Science ®Google Scholar
• Peng, J., et al., 2024. A movement-aware measure for trajectory similarity and its application for ride-sharing path extraction in a road network. International Journal of Geographical Information Science, 1–25.
   Web of Science ®Google Scholar
• Porta, S., Crucitti, P., and Latora, V., 2006. The network analysis of urban streets: a dual approach. Physica A: Statistical Mechanics and Its Applications, 369 (2), 853–866.
   Web of Science ®Google Scholar
• Richardson, D., and Thomson, R., 1996. Integrating thematic, geometric, and topological information in the generalization of road networks. Cartographica: The International Journal for Geographic Information and Geovisualization, 33 (1), 75–83.
   Google Scholar
• Schipperijn, J., et al., 2010. Factors influencing the use of green space: Results from a Danish national representative survey. Landscape and Urban Planning, 95 (3), 130–137.
   Web of Science ®Google Scholar
• Shore, J., and Johnson, R., 1980. Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Transactions on Information Theory, 26 (1), 26–37.
   Web of Science ®Google Scholar
• Siła-Nowicka, K., et al., 2016. Analysis of human mobility patterns from GPS trajectories and contextual information. International Journal of Geographical Information Science, 30 (5), 881–906.
   Web of Science ®Google Scholar
• Thomson, R.C., and Richardson, D.E., 1995. A graph theory approach to road network generalisation. In: Proceeding of the 17th International Cartographic Conference, 1871–1880.
   Google Scholar
• Thomson, R.C., and Richardson, D.E., 1999. The ‘good continuation’ principle of perceptual organization applied to the generalization of road networks. In: Proceedings of the 19th International Cartographic Conference, 1215–1223.
   Google Scholar
• Tian, J., et al., 2016. Road density partition and its application in evaluation of road selection. Geomatics and Information Science of Wuhan University, 41 (9), 1225–1231.
   Google Scholar
• Tian, J., et al., 2019. A comparative study of two strategies of road network selection. Geomatics and Information Science of Wuhan University, 44 (2), 310–316.
   Google Scholar
• Töpfer, F., and Pillewizer, W., 1966. The principles of selection. The Cartographic Journal, 3 (1), 10–16.
   Google Scholar
• Touya, G., Zhang, X., and Lokhat, I., 2019. Is deep learning the new agent for map generalization? International Journal of Cartography, 5 (2-3), 142–157.
   Web of Science ®Google Scholar
• Van Nimwegen, E., Crutchfield, J.P., and Mitchell, M., 1999. Statistical dynamics of the royal road genetic algorithm. Theoretical Computer Science, 229 (1-2), 41–102.
   Web of Science ®Google Scholar
• Weiss, R., and Weibel, R., 2014. Road network selection for small-scale maps using an improved centrality-based algorithm. Journal of Spatial Information Science, 9 (9), 71–99.
   Google Scholar
• Xu, Z., et al., 2012. Road selection based on evaluation of stroke network functionality. Acta Geodaetica et Cartographica Sinica, 41(5), 769–776.
   Google Scholar
• Xu, Z., et al., 2018. A method for automatic road selection combined with POI data. Journal of Geo-Information Science, 20 (2), 159–166.
   Google Scholar
• Yan, X., et al., 2022. Revealing spatiotemporal matching patterns between traffic flux and road resources using big geodata-a case study of Beijing. Cities, 127, 103754.
   Web of Science ®Google Scholar
• Yang, B., Zhang, Y., and Lu, F., 2014. Geometric-based approach for integrating VGI POIs and road networks. International Journal of Geographical Information Science, 28 (1), 126–147.
   Web of Science ®Google Scholar
• Yang, M., Ai, T., and Zhou, Q., 2013. A method of road network generalization considering stroke properties of road object. Acta Geodaetica et Cartographica Sinica, 42 (4), 581–587.
   Google Scholar
• Yi, W., Dong, P., and Wang, J., 2018. Node risk propagation capability modeling of supply chain network based on structural attributes. In: Proceedings of the 2018 9th International Conference on E-business, Management and Economics, 50–54.
   Google Scholar
• Yu, W., and Chen, Y., 2022. Data-driven polyline simplification using a stacked autoencoder‐based deep neural network. Transactions in GIS, 26 (5), 2302–2325.
   Web of Science ®Google Scholar
• Yu, W., et al., 2020. Road network generalization considering traffic flow patterns. International Journal of Geographical Information Science, 34 (1), 119–149.
   Web of Science ®Google Scholar
• Zheng, J., et al., 2021. Deep graph convolutional networks for accurate automatic road network selection. ISPRS International Journal of Geo-Information, 10 (11), 768.
   Web of Science ®Google Scholar
• Zhou, Q., and Li, Z., 2011. Evaluation of properties to determine the importance of individual roads for map generalization. In: M.P. Peterson, ed. Advances in cartography and GIScience. Heidelberg: Springer, 459–475.
   Google Scholar
• Zhou, Q., and Li, Z., 2012. A comparative study of various strategies to concatenate road segments into strokes for map generalization. International Journal of Geographical Information Science, 26 (4), 691–715.
   Web of Science ®Google Scholar
• Zhou, Q., and Li, Z., 2014. Use of artificial neural networks for selective omission in updating road networks. The Cartographic Journal, 51 (1), 38–51.
   Web of Science ®Google Scholar
• Zhou, Q., and Li, Z., 2017. A comparative study of various supervised learning approaches to selective omission in a road network. The Cartographic Journal, 54 (3), 254–264.
   Web of Science ®Google Scholar