1. bookVolume 22 (2021): Issue 1 (February 2021)
Journal Details
First Published
20 Mar 2000
Publication timeframe
4 times per year
access type Open Access

GIS-Based Urban Road Network Accessibility Modeling Using MLR, ANN and ANFIS Methods

Published Online: 22 Feb 2021
Page range: 15 - 28
Journal Details
First Published
20 Mar 2000
Publication timeframe
4 times per year

A sustainable transportation system is possible only through an efficient evaluation of transportation network performance. The efficiency of the transport network structure is analyzed in terms of its connectivity, accessibility, network development, and spatial pattern. This study primarily aims to propose a methodology for modeling the accessibility based on the structural parameters of the urban road network. Accessibility depends on the arrangement of the urban road network structure. The influence of the structural parameters on the accessibility is modeled using Multiple Linear Regression (MLR) analysis. The study attempts to introduce two methods of Artificial Intelligence (AI) namely Artificial Neural Networks (ANN) and Adaptive network-based neuro-fuzzy inference system (ANFIS) in modeling the urban road network accessibility. The study also focuses on comparing the results obtained from MLR, ANN and ANFIS modeling techniques in predicting the accessibility. The results of the study present that the structural parameters of the road network have a considerable impact on accessibility. ANFIS method has shown the best performance in modeling the road network accessibility with a MAPE value of 0.287%. The present study adopted Geographical Information Systems (GIS) to quantify, extract and analyze different features of the urban transportation network structure. The combination of GIS, ANN, and ANFIS help in improved decision-making. The results of the study may be used by transportation planning authorities to implement better planning practices in order to improve accessibility.


1. Abdulhai, B., Porwal, H., and Recker, W. (1999) Short-Term Freeway Traffic Flow Prediction Using Genetically Optimized Time-Delay-Based Neural Networks. Presented at 78th Annual Meeting of the Transportation Research Board, Washington, D.C. Report for MOU 360, ISSN 1055-1417.Search in Google Scholar

2. Ahmed, Geneidy M.E.I., and David, M.L. (2006) Access to destinations: Development of accessibility measures. In a research report published by Minnesota Department of Transportation, Minnesota.Search in Google Scholar

3. Arora, A., and Pandey, M.K. (2011) Transportation network model and network analysis of road networks. 12th Esri India User Conference 2011.Search in Google Scholar

4. Avika, B., and Lerman. (1977) Disaggregate travel and mobility choice models and measures of accessibility. Behavioural Travel Modelling, eds. Hensherd and Stopher, P., London: Croom Helm, pp.654-679.Search in Google Scholar

5. Bao-ping, C., and Zeng-qiang, M.A. (2009) Short-term Traffic Flow Prediction Based on ANFIS. In: International Conference on Communication Software and Networks, DOI 10.1109/ICCSN.2009.140.Search in Google Scholar

6. Bento, A.M., Cropper, M.L., Mobarak, A.M., and Vinha, K. (2003) The impact of urban spatial structure on travel demand in the United States. World Bank Policy, 2003, Research Paper No. 3007.Search in Google Scholar

7. Bhat, C., Handy, S., Kockelamn, K., Mahmassani, H., Chen, Q., and Weston, L. (2002) Urban Accessibility index: Literature Review. Research Report TX 01/7-4938-01, Texas Department of Transportation, TX.Search in Google Scholar

8. Bugday, E. (2018) Application of artificial neural network system based on ANFIS using GIS for predicting forest road network suitability mapping. Fresenius Environmental Bulletin, 27(3), 1656-1668.Search in Google Scholar

9. Burns, L.D. (1979) Transportation, Temporal and Spatial Components of Accessibility. Lexington, MA: Lexington Books.Search in Google Scholar

10. Chauhan, B. (2013) ANFIS based Trip Generation model for Meerut. International Journal of Computer Science and Mobile Computing, 2(12), 153-159.Search in Google Scholar

11. Dalvi, M.Q., and Martin, K.M. (1976) The measurement of accessibility: Some preliminary results, Transportation, 5, 17-42.Search in Google Scholar

12. De Cola, L., and Lam, N. (1993) Introduction to fractals in geography. Fractals in Geography (Prentice-Hall, Englewood Cliffs, NJ), 3-22.Search in Google Scholar

13. Falconer, KJ. (1986) The geometry of fractal sets. Cambridge university press.Search in Google Scholar

14. Falconer, KJ. (2003) Fractal Geometry – Mathematical Foundations and Applications, 2nd ed. Chichester: John Wiley and Sons, 338 p, ISBN 978-0-470-84862-3.Search in Google Scholar

15. Fu, L., and Rilett, L.R. (2000) Estimation of Time-Dependent, Stochastic Route Travel Times Using Artificial Neural Networks. Transportation Planning and Technology, 24(1), 25–36.Search in Google Scholar

16. Gopal, S. (2018) Artificial neural networks in geospatial analysis. The International Encyclopaedia of Geography, DOI: 10.1002/9781118786352.wbieg0322.Search in Google Scholar

17. Hansen, W. (1959) How accessibility shapes land use. Journal of the American Institute of the Planners, 25, 73–76.Search in Google Scholar

18. Hastings, H.M., and Sugihara, G. (1993) Fractals. A user’s guide for the natural sciences. Oxford Science Publications, Oxford, New York: Oxford University Press, 1993, 1Search in Google Scholar

19. Holt, A., and Benwell, G.L. (1999) Applying case-based reasoning techniques in GIS. International Journal of Geographical Information Science, 13(1), 9-25.Search in Google Scholar

20. Hosseinpour, M., Yahaya, A.S., Ghadiri, S.M., and Prasetijo, J. (2013) Application of Adaptive Neuro-Fuzzy Inference System for Road Accident Prediction. KSCE Journal of Civil Engineering (2013) 17(7):1761-1772.Search in Google Scholar

21. Kansky, K. (1963) Structure of Transportation Networks: Relationships between Network Geometry and Regional Characteristics. Ph. D. thesis, University of Chicago, Research Paper No. 84.Search in Google Scholar

22. Khodayari, A., Ghaffari, A., Kazemi, R., and Manavizadeh, N. (2010) ANFIS based modelling and prediction car following behavior in real traffic flow based on instantaneous reaction delay. 13th International IEEE Annual Conference on Intelligent Transportation Systems Madeira Island, Portugal, September 19-22, 2010.Search in Google Scholar

23. KrólA. (2016) The application of the artificial intelligence methods for planning of the development of the transportation network. Transportation Research Procedia, 14, 4532 – 4541.Search in Google Scholar

24. Levinson, D. (2012) Network Structure and City Size, Plos One, 7(1), DOI:10.1371/journal.pone.0029721.Search in Google Scholar

25. Mackiewicz, A., and Ratajczak, W. (1996) Towards a new definition of topological accessibility. Transportation Research, Part B, 30(1), 47-79.Search in Google Scholar

26. Mandelbrot, B.B. (1982) The fractal geometry of nature. 1982. In. WH Freeman & Company.Search in Google Scholar

27. McCulloch., Warren; Walter Pitts. (1943) A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115-133. DOI: 10.1007/BF02478259.Search in Google Scholar

28. Modinpuroju, A., Prasad, C.S.R.K., and Chandra, M. (2016) Facility-based planning methodology for rural roads using spatial analysis techniques. Innovative Infrastructure Solutions, 1(1), 1-8, DOI 10.1007/s41062-016-0041-8.Search in Google Scholar

29. Mohammadi, A., Rao, K.M.L., and Baseer, M.A.K. (2013) Fractal view policy development on road infrastructure in urban areas. International Journal of Earth sciences and Engineering, 6(4), (01).Search in Google Scholar

30. Mohammady, S. (2016) Optimization of adaptive neuro-fuzzy inference system based urban growth model. City, Territory and Architecture, 3, Article 10, pp. 1-15, DOI 10.1186/s40410-016-0039-8.Search in Google Scholar

31. Morris, D., Antoniades, A., and Took, C.C. (2017) On making sense of neural networks in road analysis. International Joint Conference on Neural Networks (IJCNN), 4416-4421, DOI: 10.1109/IJCNN.2017.7966415.Search in Google Scholar

32. Murat, Y.S. (2006) Comparison of fuzzy logic and artificial neural networks approaches in vehicle delay modelling. Transportation Research Part C 14, 316–334. DOI:10.1016/j.trc.2006.08.003Search in Google Scholar

33. Nijagunappa, R., Shekhar, S., Gurugnanam, B., Raju, P.L.N., and De, P. (2007) Road Network Analysis of Dehradun City Using High-Resolution Satellite Data and GIS. Journal of the Indian Society Of Remote Sensing, 35(3), 267-274.Search in Google Scholar

34. Obafemi, A.A., Eludoyin, O.S., Opara, D.R. (2011) Road network assessment in Trans-Amadi, Port Harcourt in Nigeria using GIS. International Journal of Traffic and Transportation Engineering, 1(4), 257-264.Search in Google Scholar

35. Sahitya, K.S., and Prasad, C.S.R.K. (2019) Modelling structural interdependent parameters of an urban road network using GIS. Spatial Information Research, DOI: 10.1007/s41324-019-00295-9.Search in Google Scholar

36. Sahitya, K.S., and Prasad, C.S.R.K. (2020) Evaluation of opportunity based urban road network accessibility using GIS. Spatial Information Research, DOI: 10.1007/s41324-019-00309-6.Search in Google Scholar

37. Sreeleka, M.G., Krishnamurthy, K., and Anjaneyulu, M.V.L.R. (2017) Fractal assessment of road transport system. European transport\ Transport Europei, 65, paper number 5, 1-13.Search in Google Scholar

38. Stojčić, M. (2018) Application of the ANFIS model in road traffic and transportation: a literature review from 1993 to 2018. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 40-61, DOI: https://doi.org/10.31181/oresta19012010140s.Search in Google Scholar

39. Sun, Z., Jia, P., Kato, H., and Hayashi, Y. (2007) Distributive Continuous Fractal analysis for urban transportation network. Journal of the Eastern Asia Society for transportation studies, 7.Search in Google Scholar

40. Takagi, T., and Sugeno, M. (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. on Systems, Man, and Cybernetics, 15:116–132.Search in Google Scholar

41. Thipparat, T., and Thaseepetch, T. (2012) Application of Neuro-Fuzzy System to Evaluate Sustainability in Highway Design. International Journal of Modern Engineering Research (IJMER), 2(5), 4153-4158.Search in Google Scholar

42. Transportation Research Board of the National Academies Circular Number E-C113, January (2007). Artificial Intelligence in Transportation, Information for application, ISSN 0097-8515.Search in Google Scholar

43. Transportation Research Board of the National Academies Circular Number E-C168, November (2012). Artificial Intelligence applications to critical transportation issues, ISSN 097-8515.Search in Google Scholar

44. Voort, M.D., Dougherty, M., and Watson, S. (1996) Combining Kohonen Maps with ARIMA Time Series Models to Forecast Traffic Flow. In: Transportation Research Part C, 4(5), 1996, 307–318.Search in Google Scholar

45. Voženílek, V. (2009) Artificial intelligence and GIS: mutual meeting and passing. International Conference on Intelligent Networking and Collaborative Systems (INCOS 2009), 279-284. ISBN 978-1-4244-5165-4.Search in Google Scholar

46. Wu, Y.H., and Miller, H.J. (2002) Computational tools for measuring space-time accessibility within transportation networks with dynamic flow. Journal of Transportation Statistics, 4(2/3), 1-14.Search in Google Scholar

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