The concept of accuracy of road maps in Ukraine: economic aspect
Abstract
The article focuses on further consideration of approaches to improving the accuracy of road maps in terms of their geodetic and economic dimensions. So far, despite the definition of geometric positional accuracy parameters and the description of methods for collecting primary data by laser scanning and GNSS surveying, we have not yet paid due attention to a comprehensive assessment of the economic consequences of mapping errors.
The relevance of research on the accuracy of transport route modelling requires the latest methods of mathematical modelling of errors - from calculating the root mean square error (RMSE) for routes to comparing different sources of spatial information. Therefore, the purpose of this article is to present methodological foundations and recommendations for creating and improving the geodetic, mathematical and information base of road maps, as well as to highlight the economic benefits and results of transport logistics optimisation.
In accordance with the experience of previous developments, a thesis description of the algorithm for assessing losses and damages from inaccurate maps based on the relative deviation of route lengths and the indicators of the ICS is proposed. The influence of geometric distortions in cartographic materials and their correction by using laser scanning data is considered in detail. Examples of successful practices are given where more accurate maps have reduced losses and damages.
The importance of introducing recommendations for map modelling and using corrected methods of reproducing the road network in a single database is emphasised. This approach is in line with the postulate of the need for systematic updating of cartographic data, which allows formalising and quantifying the attribute characteristics of road transport routes in the national network.
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References
Haman, N.O. (2014). Laws and principles of socio-economic thematic mapping on the example of designing logistics maps. Human Geography Journal, 16(1), 151-158. Retrieved from https://periodicals.karazin.ua/socecongeo/article/download/347/219 [in Ukrainian].
Law of Ukraine ‘On the National Geospatial Data Infrastructure’ No. 554-IX of 13.04.2020 (2020). Bulletin of the Verkhovna Rada. No. 31. Retrieved from https://zakon.rada.gov.ua/laws/show/554-20#Text [in Ukrainian].
Cabinet of Ministers of Ukraine (1998). On approval of the list of public roads of state importance: Resolution of 30 March 1998, No. 455. Official Bulletin of Ukraine, 13, 77. Retrieved from https://zakon.rada.gov.ua/laws/show/455-98-п/ed19980406 [in Ukrainian].
Cabinet of Ministers of Ukraine. On Approval of the List of Public Roads of State Importance: Resolution of 20 April 2007, No. 865 (2007). Official Gazette of Ukraine, 29, 35. Retrieved from https://zakon.rada.gov.ua/laws/show/865-2006-%D0%BF/ed20070420 [in Ukrainian].
Cabinet of Ministers of Ukraine. On approval of the list of public roads of state importance: Resolution of 18 April 2012, No. 301 (2012). Official Gazette of Ukraine, 31, 95. Retrieved from https://zakon.rada.gov.ua/laws/show/301-2012-п/ed20120418 [in Ukrainian].
Cabinet of Ministers of Ukraine. On approval of the list of public roads of national importance: Resolution of 16 September 2015, No. 712 (2015). Official Gazette of Ukraine, 74, 32. Retrieved from https://zakon.rada.gov.ua/laws/show/712-2015-п/ed20150916 [in Ukrainian].
Cabinet of Ministers of Ukraine. On Approval of the List of Public Roads of State Importance: Resolution of 30 January 2019, No. 55 (2019). Official Bulletin of Ukraine, 11, 450. Retrieved from https://zakon.rada.gov.ua/laws/show/55-2019-п/ed20190130 [in Ukrainian].
Cabinet of Ministers of Ukraine. On approval of the list of public roads of national importance: Resolution of 17 November 2021 No. 1242 (2021). Official Gazette of Ukraine, 95, 6152. Retrieved from https://zakon.rada.gov.ua/laws/show/1242-2021-п/ed20211117 [in Ukrainian].
Cabinet of Ministers of Ukraine. On approval of the list of public roads of national importance: Resolution of 15 December 2023 No. 1318 (2023). Uryadovyi Kurier, 240, 4. Retrieved from https://zakon.rada.gov.ua/laws/show/1318-2023-п [in Ukrainian].
Overhaul of the public road of local importance O26165 bypassing the city of Storozhynets on the section km 0+000 - km 2+600 (2023). Retrieved from https://dream.gov.ua/ua/project/DREAM-UA-230424-ED60FA65/profile) [in Ukrainian].
Lozynskyi, V.V. (2010). Topographic map: a textbook. Edition 2, revised and supplemented. Lviv: Publishing Centre of Ivan Franko National University of Lviv, 106 p. Retrieved from https://geography.lnu.edu.ua/wp-content/uploads/2021/04/Lozynskyy-V-top-karta-2010-book.pdf [in Ukrainian].
Dovhyi, S. Lyalko, V., Babiychuk, S. Kuchma, T. Tomchenko, O. & Yurkiv, L. (2019). Fundamentals of remote sensing of the Earth: history and practical application: a textbook. K.: Institute of Gifted Child of the National Academy of Pedagogical Sciences of Ukraine, 316 p. [in Ukrainian].
SOU 71.12-37-944:2014. Topographic data base. General requirements. (2014). Standard of the Ministry of Agrarian Policy of Ukraine. K.: Ministry of Agrarian Policy of Ukraine, 35 p. Retrieved from https://nsdi.gov.ua/files/legislation/b691cae0-0a8b-11e8-a9c9-d16a7205336d.pdf [in Ukrainian].
ASPRS Positional Accuracy Standards for Digital Geospatial Data (Edition 1) (2015). Photogrammetric Engineering & Remote Sensing, 81(3), A1-A26.
BeeMaps Blog. What is Map AI and What Role Does it Play in Updating Maps? 14.10.2024: Retrieved from https://beemaps.com/blog/what-is-map-ai-and-what-role-does-it-play-in-updating-map
Cai, H., & Rasdorf, W. (2009). Accuracy Evaluation and Sensitivity Analysis of Estimating 3D Road Centreline Length using LIDAR and NED. Photogrammetric Engineering & Remote Sensing, 75(6), 681-689. https://doi.org/10.14358/PERS.75.6.657
Cai, H. et al. (2006). Geographic Information Systems/National Elevation Data Route Mileage Verification. Journal of Surveying Engineering, 132(1), 23-30. https://doi.org/10.1061/(ASCE)0733-9453(2006)132:1(40)
El-Ashmawy, K. 2016. Testing the positional accuracy of OpenStreetMap data for mapping applications. Geodesy and Cartography, 42(1), 25-30. https://doi.org/10.3846/20296991.2015.1160493
Federal Geographic Data Committee (FGDC). Geospatial Positioning Accuracy Standards, Part 3: National Standard for Spatial Data Accuracy (NSSDA). FGDC-STD-007.3-1998, 12 pp. Retrieved from https://www.asprs.org/a/society/divisions/pad/Accuracy/Draft_ASPRS_Accuracy_Standards_for_Digital_Geospatial_Data_PE&RS.pdf#:~:text=,of%20the
Girres, J.-F., & Touya, G. (2010). Quality Assessment of the French OpenStreetMap Dataset. Transactions in GIS, 14(4), 435-459. https://doi.org/10.1111/j.1467-9671.2010.01203.x
Guo, Tao & Iwamura, Kazuaki & Koga, Masashi (2007). Towards high accuracy road maps generation from massive GPS Traces data. IGARSS 2007 - IEEE International Geoscience and Remote Sensing Symposium, p. 667-670. https://doi.org/10.1109/IGARSS.2007.4422884
HD Map for Autonomous Vehicles Market by Solution (Cloud-Based & Embedded), LOA (L2, L3, L4, & L5), Usage (Passenger & Commercial), Vehicle Type, Services (Advertising, Mapping, Localisation, Update & Maintenance), & Region - Global Forecast to 2030. MarketsandMarkets, 2021. Retrieved from https://www.marketsandmarkets.com/Market-Reports/hd-map-autonomous-vehicle-market-141078517.html
ISO 19157:2013 Geographic Information - Data Quality (2013). Geneva: International Organisation for Standardisation, 80 pp.
Mapillary (2024). Retrieved from https://www.mapillary.com/?locale=uk_UA
Mosaic51. 5 Ways HD Mapping is Improving Logistics Routing & Planning (2021). Mosaic51 Blog. Retrieved from https://www.mosaic51.com/industry/improving-logistics-routing-planning-with-hd-mapping/
Plautz, J. (2020). Inaccurate maps cost logistics companies $6B: survey. Smart Cities Dive. Retrieved from https://www.supplychaindive.com/news/inaccurate-maps-cost-logistics-companies-delivery-Mapillary/572098/
PortalGIS (2024). Universal capabilities of LiDAR. PortalGIS.pro. Retrieved from https://portalgis.pro/
Sloan, S., Talkhani, R.R., Huang, T., Engert, J., & Laurance, W.F. (2024). Mapping remote roads using artificial intelligence and satellite imagery. Remote Sensing, 16(5), p. 839. https://doi.org/10.3390/rs16050839
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