Computerized Road network modeling using open street map sources in GIS: Basrah province as a case study
Keywords:Road, Traffic, GIS, OSM, Automated, Procedure, Al Basrah.
Information sources have developed considerably in recent years; many electronic platforms are able to provide valuable information regards engineering topics. One of the most important data sources is the open street map (OSM) platform, providing editable geographic information for most of the world, with different levels of accuracy and at different points in time. Road network mapping requires a high level of effort and accuracy, due to the complexity of the modelling and the amount of information that needs to be included in the feature class. OSM can support road network modelling by providing a different kind of data. In this paper, a systematic procedure was investigated for the production of an automated road network for Basrah city, as a case study for the use of OSM in Geographic Information System (GIS) 10.8 software. Specific spatial analysis tools such as road density and network analysis were also implemented. This study validated a computerised procedure to extract OSM data via two methods of validation and demonstrated the immediate applicability of this data for density and network analysis.
The research results show a significant reduction in time and effort required to produce an accurate Basrah city road network using OSM data sources. Density analysis and network analysis show the importance and validity of the produced road network.
Obe, R. O., & Hsu, L. S. (2017). PostgreSQL: Up and Running: a Practical Guide to the Advanced Open Source Database. O'Reilly Media, Inc.
Zhang, Y., Mesaros, A., Fujita, K., Edkins, S. D., Hamidian, M. H., Ch’ng, K., … & Kim, E. A. (2019). Machine learning in electronic-quantum-matter imaging experiments. Nature, 570(7762), 484-490.
Gunadi, G. (2019). Qualitative system dynamics modelling of the impacts of maintenance, effort, competence and collaboration on e-government website availability. Electronic Government, an International Journal, 15(2): 189-212.
Weber, D., Nasim, M., Mitchell, L., & Falzon, L. (2020, December). A method to evaluate the reliability of social media data for social network analysis. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM): 317-321, IEEE.
Mooney, P., & Minghini, M. (2017). A review of OpenStreetMap data.
Herfort, B., Lautenbach, S., de Albuquerque, J. P., Anderson, J., & Zipf, A. (2021). The evolution of humanitarian mapping within the OpenStreetMap community. Scientific reports, 11(1): 1-15.
WIKI, (2019) OSM Routing Data Layer. Available at: https://wiki.openstreetmap.org/wiki/OSM_Routing_Data_Layer (Accessed: November 2021).
Sehra, S. S., Singh, J., & Rai, H. S. (2017). Assessing OpenStreetMap data using intrinsic quality indicators: an extension to the QGIS processing toolbox. Future Internet, 9(2), 15.
Yagoub, M. M. (2017). Assessment of OpenStreetMap (OSM) Data: The Case of Abu Dhabi City, United Arab Emirates. Journal of Map & Geography Libraries, 13(3):300-319.
Zhao, P., Jia, T., Qin, K., Shan, J., & Jiao, C. (2015). Statistical analysis on the evolution of OpenStreetMap road networks in Beijing. Physica A: Statistical Mechanics and its Applications, 420:59-72.
Funke, S., Schirrmeister, R., & Storandt, S. (2015, July). Automatic extrapolation of missing road network data in OpenStreetMap. In Proceedings of the 2nd International Conference on Mining Urban,Data-Volume 1392 :27-35.
Brovelli, M. A., Minghini, M., Molinari, M., & Mooney, P. (2017). Towards an automated comparison of OpenStreetMap with authoritative road datasets. Transactions in GIS, 21(2), 191-206.
Keller, S., Gabriel, R., & Guth, J. (2020). Machine learning framework for the estimation of average speed in rural road networks with OpenStreetMap data. ISPRS International Journal of Geo-Information, 9(11), 638.
Ahmadi, M., Valinejadi, A., Goodarzi, A., Safari, A., Hemmat, M., Majdabadi, H. A., & Mohammadi, A. (2017). Geographic information system (GIS) capabilities in traffic accident information management: a qualitative approach. Electronic physician, 9(6), 4533.
Causevic, S., Deljanin, A., Begovic, M., & Deljanin, E. (2018, May). Potentials and advantages of applying geographic information systems in various fields of traffic engineering. In CETRA'18, 5th International Conference on Road and Rail Infrastructure (1285).
Albayati, A. H., & Ramadan, Z. A. (2021). The effects of speed and flow characteristics on crash rates for Wasit multi-lane highways in Iraq. Wasit journal of engineering sciences, 9(1):22-36.
Abdulwahab, A. M., Ismael, N. T., & Al-Nuaimi, S. F. (2018, October). Institutional Framework Sustainable Transportation for Iraq. In 2018 International Conference on Advanced Science and Engineering (ICOASE) (485-490). IEEE.
Shafabakhsh, G. A., Famili, A., & Bahadori, M. S. (2017). GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran. Journal of traffic and transportation engineering (English edition), 4(3): 290-299.
Ma, Q., Huang, G., & Tang, X. (2021). GIS-based analysis of spatial–temporal correlations of urban traffic accidents. European Transport Research Review, 13(1):1-11.
Ahmed, S., Ibrahim, R. F., & Hefny, H. A. (2017). GIS-based network analysis for the roads network of the Greater Cairo area. In Proc. of 2nd International Conference on Applied Research in Computer Science and Engineering.
Sushma, M. B., & Reddy, V. (2021). Finding an optimal path with hospital information system using GIS-based Network analysis. WSEAS Transactions on Information Science and Applications, 18, 1-6.
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