The Application of Adaptive Neuro-Fuzzy Inference System and Fuzzy Delphi Technique to Assess Socio-Economic Impacts of Construction of Rural Roads

1. Aderamo, A. J., and Magaji, S. A. (2010) Rural transportation and the distribution of public facilities in Nigeria: a case of Edu local government area of Kwara State. Journal of Human Ecology, 29, 171-179. DOI:10.1080/09709274.2010.11906260.10.1080/09709274.2010.11906260Otwórz DOISearch in Google Scholar

2. Akbulut, S., Hasiloglu, A. S., and Pamukcu, S. (2004) Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system. Soil Dynamics and Earthquake Engineering, 24(11), 805–814. DOI:10.1016/j.soildyn.2004.04.006.10.1016/j.soildyn.2004.04.006Otwórz DOISearch in Google Scholar

3. Andrade, K., Uchida, K., and Kagaya, S. (2006) Development of transport mode choice model by using adaptive neuro-fuzzy inference system. Transportation Research Record: Journal of the Transportation Research Board, 1977, 8-16. DOI:10.3141/1977-04.10.3141/1977-04Otwórz DOISearch in Google Scholar

4. Asomani-Boateng, R., Fricano, R. J., and Adarkwa, F. (2015) Assessing the socio-economic impacts of rural road improvements in Ghana: A case study of Transport Sector Program Support (II). Journal of Case Studies on Transport Policy, 3, 355-366. DOI:10.1016/j.cstp.2015.04.006.10.1016/j.cstp.2015.04.006Otwórz DOISearch in Google Scholar

5. Baker, J. L. (2000) Evaluating the impact of development projects on poverty: A handbook for practitioners, Washington, D. C.: World Bank Publications.10.1596/0-8213-4697-0Search in Google Scholar

6. Banister, D., and Berechman, Y. (2000) The economic development effects of transport investments, London: University College London Press.Search in Google Scholar

7. Braathen, S., and Hervik, A. (1997) Strait Crossings and Economic Development – Developing Economic Impact Assessment by Means of Ex-post Analyses. Transport Policy, 4, 193-200. DOI:10.1016/S0967-070X(97)00017-6.10.1016/S0967-070X(97)00017-6Otwórz DOISearch in Google Scholar

8. Chang, N. B., and Wei, Y. L. (2000) Siting recycling drop-off stations in urban area by genetic algorithm-based fuzzy multi-objective nonlinear integer programming modeling. Fuzzy Sets and Systems, 114, 133-149. DOI:10.1016/S0165-0114(98)00192-4.10.1016/S0165-0114(98)00192-4Search in Google Scholar

9. Dalkey, N., and Helmer, O. (1963) An experimental application of the Delphi method to the use of experts. Management Science, 9, 458-467.10.1287/mnsc.9.3.458Search in Google Scholar

10. Edelman, M. J. (1985) The symbolic uses of politics, USA: University of Illinois Press Urbana-Champaign.Search in Google Scholar

11. Grootaert, C., and Calvo, C. M. (2002) Socio-economic impact assessment of rural roads: methodology and questionnaires, Washington, D. C.: INFTD, World Bank.Search in Google Scholar

12. Hosseinlou, M. H., and Sohrabi, M. (2009) Predicting and identifying traffic hot spots applying neuro-fuzzy systems in intercity roads. International Journal of Environmental Science & Technology, 6, 309-314. DOI:10.1007/BF03327634.10.1007/BF03327634Otwórz DOISearch in Google Scholar

13. Hsu, Y. L., Lee, C. H., and Kreng, V. B. (2010) The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Application, 37, 419-425. DOI:10.1016/j.eswa.2009.05.068.10.1016/j.eswa.2009.05.068Otwórz DOISearch in Google Scholar

14. Islam, R., Musabbir, S. R., Ahmed, I. U., Hadiuzzaman, M., Hasnat, M., and Hossain, S. (2016) Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system. Canadian Journal of Civil Engineering, 43, 822-829. DOI:10.1139/cjce-2016-0119.10.1139/cjce-2016-0119Otwórz DOISearch in Google Scholar

15. Jang, J. S. (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665-685.10.1109/21.256541Search in Google Scholar

16. Kanuganti, S., Sarkar, A. K., and Singh, A. P. (2016) Evaluation of access to health care in rural areas using enhanced two-step floating catchment area (E2SFCA) method. Journal of Transport Geography, 56, 45-52. DOI:10.1016/j.jtrangeo.2016.08.011.10.1016/j.jtrangeo.2016.08.011Otwórz DOISearch in Google Scholar

17. Kanuganti, S., Sarkar, A. K., Singh, A. P. and Arkatkar, S. S. (2015) Quantification of Accessibility to Health Facilities in Rural Areas, Journal of Case Studies on Transport Policy, 3(3), 311–320.10.1016/j.cstp.2014.08.004Search in Google Scholar

18. Keshavarzi, A., Sarmadian, F., Shiri, J., Iqbal, M., Tirado-Corbalá, R., and Omran, E.S.E. (2017) Application of ANFIS-based subtractive clustering algorithm in soil cation exchange capacity estimation using soil and remotely sensed data. Measurement, 95, 173-180. DOI:10.1016/j.measurement.2016.10.010.10.1016/j.measurement.2016.10.010Otwórz DOISearch in Google Scholar

19. Khandker, S. R., Bakht, Z., and Koolwal, G. B. (2009) The poverty impact of rural roads: evidence from Bangladesh. Economic Development and Cultural Change, 57, 685-722. DOI:10.1086/598765.10.1086/598765Otwórz DOISearch in Google Scholar

20. Khandker S. R., and Koolwal, G. B. (2011) Estimating the long-term impacts of rural roads: a dynamic panel approach, Washington, D. C.: World Bank Publication.10.1596/1813-9450-5867Search in Google Scholar

21. Kaufmann, A., and Gupta, M. M. (1988) Fuzzy mathematical models in engineering and management science, North-Holland: Elsevier Science Publishers.Search in Google Scholar

22. Lee, S., Wong, S. C., and Li, Y. C. (2015) Real-time estimation of lane-based queue lengths at isolated signalized junctions. Transportation Research Part C: Emerging Technologies, 56, 1-17. DOI:10.1016/j.trc.2015.03.019.10.1016/j.trc.2015.03.019Otwórz DOISearch in Google Scholar

23. Liu, W. K. (2013) Application of the Fuzzy Delphi Method and the Fuzzy Analytic Hierarchy Process for the Managerial Competence of Multinational Corporation Executives. International Journal of e-Education, e-Business, e-Management, and e-Learning, 3, 313. DOI:10.7763/IJEEEE.2013.V3.248.10.7763/IJEEEE.2013.V3.248Otwórz DOISearch in Google Scholar

24. Louwa, E., Leijtenb, M., and Meijersa, E. (2013) Changes subsequent to infrastructure investments: Forecasts, expectations and ex-post situation. Transport Policy, 29, 107–117. DOI:10.1016/j.tranpol.2013.04.012.10.1016/j.tranpol.2013.04.012Otwórz DOISearch in Google Scholar

25. Mamdani, E. H., and Assilian, S. (1975) An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine. Studies, 7, 1-13. DOI:10.1016/S0020-7373(75)80002-2.10.1016/S0020-7373(75)80002-2Otwórz DOISearch in Google Scholar

26. MATLAB and Statistics Toolbox Release. (2016) The MathWorks, Inc., Natick, Massachusetts, United States.Search in Google Scholar

27. Mucsi, K., Khan, A. M., and Ahmadi, M. (2011) An adaptive neuro fuzzy inference system for estimating the number of vehicles for queue management at signalized intersections. Transportation Research Part C: Emerging Technologies, 19, 1033-1047. DOI:10.1016/j.trc.2011.05.016.10.1016/j.trc.2011.05.016Otwórz DOISearch in Google Scholar

28. Nirban, V. S., Metri, B. A., Singh, A. P., and Sarkar, A. K. (2003) Socioeconomic Benefits of PMGSY Projects: Perceptions of Rural Community. In: Proceedings of a Seminar on Integrated Development of Rural and Arterial Road Network for Socio-Economic Growth, New Delhi, December 2003, 166-173.Search in Google Scholar

29. Pamučar, D., Lukovac, V., and Pejčić-Tarle, S. (2013) Application of adaptive neuro fuzzy inference system in the process of transportation support. Asia-Pacific Journal of Operational Research, 30. DOI:10.1142/S0217595912500534.10.1142/S0217595912500534Otwórz DOISearch in Google Scholar

30. Phogat V. S. M., and Singh, A. P. (2013) Selection of Equipment for Construction of a Hilly Road Using Multi Criteria Approach. Procedia - Social and Behavioral Sciences, 104, 282-291.10.1016/j.sbspro.2013.11.121Search in Google Scholar

31. Pribyl, O. and Goulias, K. G. (2003) Application of adaptive neuro fuzzy inference system to analysis of travel behavior. Transportation Research Record: Journal of the Transportation Research Board, 1854, 180-188. DOI:10.3141/1854-20.10.3141/1854-20Otwórz DOISearch in Google Scholar

32. Relich, M., and Pawlewski, P. (2016) A Multi-agent Framework for Cost Estimation of Product Design, In: International Conference on Practical Applications of Agents and Multi-Agent Systems, Cham: Springer, 102–114.10.1007/978-3-319-39387-2_7Search in Google Scholar

33. Singh, A. P., and Vidyarthi, A. K. (2008) Optimal allocation of landfill disposal site: A fuzzy multi-criteria approach. Journal of Environmental and Health Science and Engineering, 5, 25-34.Search in Google Scholar

34. Tahriri, F., Mousavi, M., Haghighi, S.H., and Dawal, S. Z. M. (2014) The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection. Journal of Industrial Engineering International, 10(3). DOI:10.1007/s40092-014-0066-6.10.1007/s40092-014-0066-6Otwórz DOISearch in Google Scholar

35. Takagi, T., and Sugeno, M. (1985) Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15, 116-132.10.1109/TSMC.1985.6313399Search in Google Scholar

36. Tortum, A., Yayla, N., and Gökda, M. (2009) The modelling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system. Expert Systems with Applications, 36, 6199-6217. DOI: 10.1016/j.eswa.2008.07.032.10.1016/j.eswa.2008.07.032Otwórz DOISearch in Google Scholar

37. Tunde, A. M., and Adeniyi, E. E. (2012) Impact of road transport on agricultural development: A Nigerian example. Ethiopian of Journal of Environmental Studies and Management, 5, 232-238. DOI:10.4314/ejesm.v5i3.3.10.4314/ejesm.v5i3.3Otwórz DOISearch in Google Scholar

38. Wagale, M., Singh, A. P., and Singh, A. (2016) Neural networks approach for evaluating quality of service in public transportation in rural areas. In: Information Processing (IICIP), 1st India International Conference, IEEE, New Delhi, India, August 2016, 1-5. DOI:10.1109/IICIP.2016.7975391.10.1109/IICIP.2016.7975391Otwórz DOISearch in Google Scholar

39. Wagale M., Singh, A. P., and Sarkar, A. K. (2019) Impact of rural road construction on the local livelihood diversification: Evidence from Pradhan Mantri Gram Sadak Yojana in Jhunjhunu district, India. GeoJournal, Springer. DOI:10.1007/s10708-019-10007-3.10.1007/s10708-019-10007-3Otwórz DOISearch in Google Scholar

• Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane
• Lidar and AI Based Surveillance of Industrial Process Environments
• Effect of Spatio-Temporal Granularity on Demand Prediction for Deep Learning Models
• Utilizing Voip Packet Header’s Fields to Save the Bandwidth
• Assessment of the Interaction of the Logistics Company’s Information Technologies with the Technological Infrastructure
• Autonomous Cargo Bike Fleets – Approaches for AI-Based Trajectory Forecasts of Road Users
• Fast Timeline Based Multi Object Online Tracking
• Laboratory Experiments on Soil Stabilization to Enhance Strength Parameters for Road Pavement