Determining the Access routes to Kirkuk University and Kirkuk Technical College using GIS-Based Network Analysis
Keywords:
Network Analysis, Travel Time Cost, Accessibility Area, GISAbstract
The large number of staff and students at Kirkuk University and Kirkuk Technical Engineering College turn the University to the most crowded place in the City, causing traffic jams on nearby roadways. Remedies to reduce travel time to and from these focal points to the rest of the city must be found. The purpose of this study is to conduct network analysis of the roads in the study region (Kirkuk city), to determine the optimum routes to reach Kirkuk University and Kirkuk Technical College.The findings of the study of 6 designated routes from 6 sectors of the city of Kirkuk to reach Kirkuk University and Kirkuk Technical Engineering College indicate that travel time vary depending on the distance between each sector and the destination point. Scenario 6 has the shortest travel time of 4.97 minutes, cutting a distance of 5.168 km, Scenario 2, instead, has the longest travel time with 13.97 minutes for a distance of 12.892 km. Scenarios 1 and 3 have the same travel time of 13.61 minutes despite having different distances of 13.053 km and 10.559 km, respectively. The analysis of the remaining two scenarios show that scenario 4, has a travel time of 8.73 minutes with a distance of 8.179 km, while scenario 5 has a travel time of 9.02 minutes with a distance of 8.134 km.
References
S. Aggarwal and N. Kumar, "Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges," Computer Communications, vol. 149, pp. 270–299, 2020. [Online]. Available: https://doi.org/10.1016/j.comcom.2019.10.014
S. Ahmed, R. F. Ibrahim, and H. A. Hefny, "GIS-based network analysis for the roads network of the Greater Cairo area," CEUR Workshop Proceedings, vol. 2144, 2017.
C. H. V. Cooper and A. J. F. Chiaradia, "sDNA: 3-d spatial network analysis for GIS, CAD, Command Line & Python," SoftwareX, vol. 12, p. 100525, 2020. [Online]. Available: https://doi.org/10.1016/j.softx.2020.100525
K. M. Curtin, "Network analysis in geographic information science: Review, assessment, and projections," Cartography and Geographic Information Science, vol. 34, no. 2, pp. 103–111, 2007. [Online]. Available: https://doi.org/10.1559/152304007781002163
D. Das, A. K. Ojha, H. Kramsapi, P. P. Baruah, and M. K. Dutta, "Road network analysis of Guwahati city using GIS," SN Applied Sciences, vol. 1, no. 8, pp. 1–11, 2019. [Online]. Available: https://doi.org/10.1007/s42452-019-0907-4
S. De Winter, T. Decuypere, S. Mitrovic, B. Baesens, and J. De Weerdt, "Combining temporal aspects of dynamic networks with Node2Vec for a more efficient dynamic link prediction," in Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018), 2018, pp. 1234–1241. [Online]. Available: https://doi.org/10.1109/ASONAM.2018.8508272
B. George, S. Kim, and S. Service, Spatio-temporal Networks: Modeling and Algorithms, Springer, 2013. [Online]. Available: http://dx.doi.org/10.1007/978-1-4614-4918-8
M. Hasnat, "Optimum Route Modeling for Emergency Response During Disastrous Situation in Densely Populated Urban Area," Master of Science in Department of Civil Engineering, December 2016.
P. Hetamsaria, A. Nasir, and B. Matori, "Applications of Network Analysis Features in GIS," A Journal of Engineering, vol. 46, no. 2, pp. 26369877, 2019.
K. F. Hasan, I. A. Shawkat, and Q. M. Ajaj, "Population Density Extraction of Kirkuk City using Landsat Thermal band and GIS," Eurasian Journal of Engineering and Technology, vol. 12, 2022. [Online]. Available: https://geniusjournals.org/index.php/ejet/article/view/2545
M. Kattwinkel, E. Szöcs, E. Peterson, and R. B. Schäfer, "Preparing GIS data for analysis of stream monitoring data: The R package openSTARS," PLOS ONE, vol. 15, no. 9, 2020. [Online]. Available: https://doi.org/10.1371/journal.pone.0239237
S. Kharel, P. Shivananda, K. Ramesh, K. N. Jothi, and K. G. Raj, "Use of transportation network analysis for bus stop relocation, depiction of service area and bus route details," Journal of Geomatics, vol. 13, no. 2, pp. 224–229, 2019.
R. Laurini and D. Thompson, Fundamentals of Spatial Information Systems, 673–680, 1992. [Online]. Available: https://doi.org/10.1016/c2009-0-03224-3
J. Lerner, D. Wagner, and K. A. Zweig, Algorithmics of Large and Complex Networks, vol. 5515, Springer, 2009. [Online]. Available: http://www.springerlink.com/index/10.1007/978-3-642-02094-0
Q. Liang and E. Modiano, "Survivability in Time-Varying Networks," IEEE Transactions on Mobile Computing, vol. 16, no. 9, pp. 2668–2681, 2017. [Online]. Available: https://doi.org/10.1109/TMC.2016.2636152
C. C. Licence, "Shortest path distance vs. least directional change: empirical testing of space syntax and geographic theories concerning pedestrian route choice behaviour," Journal of Transport Geography, vol. 74, pp. 37–52, 2019.
K. Lucas, B. van Wee, and K. Maat, "A method to evaluate equitable accessibility: combining ethical theories and accessibility-based approaches," Transportation, vol. 43, no. 3, pp. 473–490, 2016. [Online]. Available: https://doi.org/10.1007/s11116-015-9585-2
D. Mears and H. B. Pollard, "Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease," Journal of Neuroscience Research, vol. 94, no. 6, pp. 590–605, 2016. [Online]. Available: https://doi.org/10.1002/jnr.23705
Q. Najat, M. Sazan, J. Ibrahim, Q. Shuokr, and A. Imad, "Integrating remote sensing, GIS and AHP analysis for suitable Arbil landfill site selection," International Conference on Geotechnical Engineering Iraq, 2024. [Online]. Available: http://doi.org/10.1007/978-981-97-9364-8_33
M. Oliveira and J. Gama, "An overview of social network analysis," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 99–115, 2012. [Online]. Available: https://doi.org/10.1002/widm.1048
T. Samsonov, "Review of Cartography: Visualization of Geospatial Data, Fourth Edition," Cartographic Perspectives, no. 97, 2021. [Online]. Available: https://doi.org/10.14714/cp97.1715
S. A. Shaheen, C. Bell, A. Cohen, and B. Yelchuru, "Travel Behavior: Shared Mobility and Transportation Equity," Federal Highway Administration- Department of Transportation, p. 66, 2017. [Online]. Available: https://www.fhwa.dot.gov/policy/otps/shared_use_mobility_equity_final.pdf
L. Shen, H. Shao, T. Wu, W. H. K. Lam, and E. C. Zhu, "An energy-efficient reliable path finding algorithm for stochastic road networks with electric vehicles," Transportation Research Part C: Emerging Technologies, vol. 102, pp. 450–473, 2019. [Online]. Available: https://doi.org/10.1016/j.trc.2019.03.020
D. Turner, A. Lucieer, and C. Watson, "An automated technique for generating georectified mosaics from ultra-high resolution Unmanned Aerial Vehicle (UAV) imagery, based on Structure from Motion (SFM) point clouds," Remote Sensing, vol. 4, no. 5, pp. 1392–1410, 2012. [Online]. Available: https://doi.org/10.3390/rs4051392
P. Vanegas, J. R. Peeters, D. Cattrysse, P. Tecchio, F. Ardente, F. Mathieux, W. Dewulf, and J. R. Duflou, "Ease of disassembly of products to support circular economy strategies," Resources, Conservation and Recycling, vol. 135, pp. 323–334, 2018. [Online]. Available: https://doi.org/10.1016/j.resconrec.2017.06.022
J. Wang, "Advances in Cartography and Geographic Information Engineering," in Advances in Cartography and Geographic Information Engineering, 2021. [Online]. Available: https://doi.org/10.1007/978-981-16-0614-4
Y. Wang, W. Y. Szeto, K. Han, and T. L. Friesz, "Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications," Transportation Research Part B: Methodological, vol. 111, pp. 370–394, 2018. [Online]. Available: https://doi.org/10.1016/j.trb.2018.03.011
E. Y. C. Wong, A. H. Tai, and S. So, "Container drayage modelling with graph theory-based road connectivity assessment for sustainable freight transportation in new development area," Computers and Industrial Engineering, vol. 149, 2019. [Online]. Available: https://doi.org/10.1016/j.cie.2020.106810
C. Zhang, S. Zhang, J. J. Q. Yu, and S. Yu, "FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting," IEEE Transactions on Industrial Informatics, vol. 17, no. 12, pp. 8464–8474, 2021. [Online]. Available: https://doi.org/10.1109/TII.2021.3055283
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