ASCE India Conference 2017
A Fuzzy Approach for Quantifying Accessibility to Public Transit System
Publication: Urbanization Challenges in Emerging Economies: Energy and Water Infrastructure; Transportation Infrastructure; and Planning and Financing
ABSTRACT
The measures used to define accessibility of a public transit system (PTS) fail to address the user perspective of accessibility to the transit system with a feeder service. In this study, a fuzzy accessibility measure employing temporal impedances experienced by the PTS users and the value they associate with each of the impedance was developed for quantifying access to the PTS. A predictive model was developed using neural networks to ascertain the impact of quantified accessibility on choice behavior of the users. Data for this study were collected in Indore City in India onboard a BRT through a questionnaire survey which comprised of both revealed and stated preference nature. A sensitivity analysis performed for the accessibility measure revealed that time spent waiting for, and travelling in the feeder service impact the overall choice of mode.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Altay, E., Satman, M. H., & Hakan. (2005). STOCK MARKET FORECASTING: ARTIFICIAL NEURAL NETWORK AND LINEAR REGRESSION COMPARISON IN AN EMERGING MARKET.Journal of Financial Management & Analysis, 18(18), 18-33. Retrieved from https://search.proquest.com/docview/215227414/fulltextPDF/5B4EBF7FBC4F4AFBPQ/1?accountid=81487
Bertolini, L., Le Clercq, F., & Kapoen, L. (2005). Sustainable accessibility: a conceptual framework to integrate transport and land use plan-making. Two test-applications in the Netherlands and a reflection on the way forward. Transport Policy, 12(3), 207-220.
Cerdá, A. (2009). Accessibility : a performance measure for land-use and transportation planning in the Montréal Metropolitan Region.
Chandra, S., Bari, M. E., Devarasetty, P. C., & Vadali, S. (2013). Accessibility evaluations of feeder transit services. TRANSPORTATION RESEARCH PART A, 52, 47-63. https://doi.org/10.1016/j.tra.2013.05.001
Chang, P. T., & Hung, K. C. (2005). Applying the fuzzy-weighted-average approach to evaluate network security systems. Computers and Mathematics with Applications, 49(11-12), 1797-1814. https://doi.org/10.1016/j.camwa.2004.10.042
Chen, Y., Fung, R. Y. K., & Tang, J. (2006). Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174(3), 1553-1566. https://doi.org/10.1016/j.ejor.2004.12.026
Cheng, T., Huang, -Kui, & Huang, C.-H. (2010). An integrated decision model for evaluating educational web sites from the fuzzy subjective and objective perspectives. Computers & Education, 55, 616-629. https://doi.org/10.1016/j.compedu.2010.02.022
Comrie, A. C. (1997). Comparing Neural Networks and Regression Models for Ozone Forecasting. Journal of the Air & Waste Management Association, 47(6), 653-663. https://doi.org/10.1080/10473289.1997.10463925
Department of Economic and Social Affairs. (2014). World Urbanization Prospects: The 2014 Revision. New York. https://esa.un.org/unpd/wup/publications/files/wup2014-highlights.Pdf
Dissanayake, D., & Morikawa, T. (2010). Investigating household vehicle ownership, mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: case study in Bangkok Metropolitan Region. Journal of Transport Geography, 18(3), 402-410. https://doi.org/10.1016/j.jtrangeo.2009.07.003
Dong, W. M., & Wong, F. S. (1987). Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets and Systems, 21(2), 183-199. https://doi.org/10.1016/0165-0114(87)90163-1
Dreiseitl, S., & Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics, 35(5-6), 352-359. https://doi.org/10.1016/S1532-0464(03)00034-0
Dubey, S. K., Mishra, D., Arkatkar, S. S., Singh, A. P., & Sarkar, A. K. (2013). Route Choice Modelling Using Fuzzy logic and Adaptive Neuro-fuzzy. Modern Traffic and Transportation Engineering Research (MTTER) MTTER, 2(4), 11-19. Retrieved from http://s3.amazonaws.com/academia.edu.documents/35510502/Route_Choice_Modelling_Using_Fuzzy_logic_and_Adaptive_Neuro-fuzzy.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3AExpires=1500130376Signature=%2Bv1qRzQgPmz%2FAmrjrinAD0iaKIE%3D&response-content-disposition=
Geurs, K. T. K. T., & Van Wee, B. (2013). Accessibility measures: a literature review. Journal of Transport Geography, 12(3), 127-140. https://doi.org/10.1111/1467-9671.00112
Geurs, K. T., & van Eck, jan R. (2001). Accessibility measures: Review and applications. Utrecht. Retrieved from http://www.rivm.nl/Documenten_en_publicaties/Wetenschappelijk/Rapporten/2001/juni/Accessibility_measures_review_and_applications_Evaluation_of_accessibility_impacts_of_land_use_transportation_scenarios_and_related_social_and_economic_impact/Download/Acces
Guh, Y.-Y., Hon, C.-C., & Lee, E. S. (2001). Fuzzy weighted average: The linear programming approach via Charnes and Cooper’s rule. Fuzzy Sets and Systems, 117(1), 157-160. https://doi.org/10.1016/S0165-0114(98)00333-9
Hagoort, M. J. (1999). De bereikbaarheid bestaat niet. RIVM, University of Utrecht, Bilthoven/Utrecht.
Hansen, W. G. (1959). How Accessibility Shapes Land Use. Journal of the American Institute of Planners, 25(2), 73-76. https://doi.org/10.1080/01944365908978307
Hendra Wijaya, D., Enquist, B., & Sebhatu, S. P. (2009). SERVICE FAILURE IN JAKARTA PUBLIC BUS TRANSPORT Service Science Project Report. Karlstad. Retrieved from https://www.diva-portal.org/smash/get/diva2:232163/FULLTEXT01.pdf
Hensher, D. A., & Ton, T. T. (2000). A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice. Transportation Research Part E: Logistics and Transportation Review, 36(3), 155-172. https://doi.org/10.1016/S1366-5545(99)00030-7
Hilbers, H. D., & Verroen, E. J. (1993). Measuring accessibility, a key factor for successful transport and land-use planning strategies. In Environmental issues(Vol. P 363, pp. 25-36). Manchester: PTRC Education and Research Services, on behalf of the Planning and Transport Research and Computation International Association. Retrieved from https://trid.trb.org/view.aspx?id=1169711
Hosmer, D. W., Hosmer, T., Cessie, S. LE, & Lemeshow, S. (1997). A COMPARISON OF GOODNESS-OF-FIT TESTS FOR THE LOGISTIC REGRESSION MODEL. STATISTICS IN MEDICINE Ltd. Stat. Med, 16, 965-980. Retrieved from http://www2.stat.duke.edu/~zo2/dropbox/goflogistic.pdf
Hsieh, F. Y., Bloch, D. A., & Larsen, M. D. (1998). A SIMPLE METHOD OF SAMPLE SIZE CALCULATION FOR LINEAR AND LOGISTIC REGRESSION. STATISTICS IN MEDICINE Statist. Med, 17, 1623-1634. Retrieved from http://personal.health.usf.edu/ywu/logistic.pdf
Hsu, K., Gupta, H. V., Gao, X., & Sorooshian, S. (1999). Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation. Water Resources Research, 35(5), 1605-1618. https://doi.org/10.1029/1999WR900032
Ichikawa, Y., & Sawa, T. (1992). Neural network application for direct feedback controllers. IEEE Transactions on Neural Networks, 3(2), 224-231. https://doi.org/10.1109/72.125863
Jiang, H., & Eastman, J. R. (2000). Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science, 14(2), 173-184. https://doi.org/10.1080/136588100240903
Jin, X. (2005). Impacts of Accessibility, Connectivity and Mode Captivity on Transit Choice. Transit, 200, 145.
Jones, S. R. (1981). Accessibility measures: a literature review. Crawthorne, Berkshire.
Kedia, A. S., Dhulipala, S., Salini, P. S., Jabeena, M., & Katti, B. K. (2017). Transit Shift Response Analysis through Fuzzy Rule Based - Choice Model : A Case Study of Indian Metropolitan City. Transportation in Developing Economies, 3(1), 1-13. https://doi.org/10.1007/s40890-017-0038-9
Kitamura, R., Akiyama, T., Yamamoto, T., & Golob, T. (2001). Accessibility in a Metropolis: Toward a Better Understanding of Land Use and Travel. Transportation Research Record, 1780(1), 64-75. https://doi.org/10.3141/1780-08
Kitamura, R., Mokhtarian, P. L., & Laidet, L. (1997). A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area. Transportation, 24(2), 125-128.
Kumar, M., Sarkar, P., & Madhu, E. (2013). Development of Fuzzy Logic based Mode Choice Model Considering Various Public Transport Policy Options. International Journal for Traffic and Transport Engineering, 3(4), 408-425.
Lättman, K., Olsson, L. E., & Friman, M. (2016). Development and test of the Perceived Accessibility Scale (PAC) in public transport. Journal of Transport Geography, 54, 257-263. https://doi.org/10.1016/j.jtrangeo.2016.06.015
LeChun, Y., Bottou, L., Orr, B., G., & Muller, K.-R. (2002). Efficient Backprop. In Neural Networks: Tricks of the trade(pp. 9-50). Salem, Orlando: Springer. https://doi.org/10.1007/3-540-49430-8_2
Lin, C., Tan, B., & Hsieh, P.-J. (2005). Application of the Fuzzy Weighted Average in Strategic Portfolio Management*. Decision Sciences, 36(3), 489-511. https://doi.org/10.1111/j.1540-5414.2005.00081.x
Liou, T.-S., & Wang, M.-J. J. (1992). Fuzzy weighted average: An improved algorithm. Fuzzy Sets and Systems, 49(3), 307-315. https://doi.org/10.1016/0165-0114(92)90282-9
Manel, S., Dias, J.-M., & Ormerod, S. J. (1999). Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling, 120(2-3), 337-347. https://doi.org/10.1016/S0304-3800(99)00113-1
Maunganidze, L., & Mistro, R. D. E. L. (2012). The Role of Bus Rapid Transit in Improving Public Transport Levels of Service, Particularly for the Urban Poor Users of Public Transport: A Case of Cape Town, South Africa. Southern African Transport Conference, (July).
Mavoa, S., Witten, K., Mccreanor, T., O.D. ’sullivan, & Oapos;Sullivan, D. (2012). GIS based destination accessibility via public transit and walking in Auckland, New Zealand. Journal of Transport Geography, 20(1), 15-22. https://doi.org/10.1016/j.jtrangeo.2011.10.001
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133. https://doi.org/10.1007/BF02478259
Ministry of Urban Development. (2005). Jawaharlal Nehru National Urban Renewal Mission: Overview. New Delhi. Retrieved from http://jnnurm.nic.in/wp-content/uploads/2011/01/PMSpeechOverviewE.pdf
Ministry of Urban Development. (2015). Mission Statement & Guidelines, Atal Mission for Rejuvenation and Urban Transformation (AMRUT). Retrieved from http://amrut.gov.in/writereaddata/AMRUTGuidelines.pdf
Niger, M., Zuidgeest, I. P., & Van Den Bosch, F. H. M. (2011). RATIONALIZING PUBLIC TRANSPORT: A EURO-ASIAN PERSPECTIVE. University of Twente. Retrieved from http://s3.amazonaws.com/academia.edu.documents/36319051/niger.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3AExpires=1500046989Signature=lrGIq4j1wgWNNX5jIe%2BvjLHDuZo%3D&response-content-disposition=inline%3Bfilename%3DRationalizing_public_transport_A_Euro_A
Nurdden, A., Atiq Rahmat, R. O., & Ismail, A. (2007). Effect of Transportation Policies on Modal Shift from Private Car to Public Transport in Malaysia. Journal of Applied Sciences, 7(7), 1013-1018.
Office of the Registrar General & Census Commissioner. (n.d.). Census 2011. Retrieved July 14, 2017, from http://www.censusindia.gov.in/2011census/population_enumeration.html
Okuyucu, H., Kurt, A., & Arcaklioglu, E. (2007). Artificial neural network application to the friction stir welding of aluminum plates. Materials & Design, 28(1), 78-84. https://doi.org/10.1016/j.matdes.2005.06.003
Pavlaĉka, O., & Talaŝová, J. (2007). Application of the fuzzy weighted average of fuzzy numbers in decision making models. In New Dimensions in Fuzzy Logic and Related Technologies - Proceedings of the 5th EUSFLAT 2005 Conference (Vol. 2, pp. 455-462). Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-77749287358&partnerID=tZOtx3y1
Pojani, D., & Stead, D. (2015). Sustainable urban transport in the developing world: beyond megacities. Sustainability, 7(6), 7784-7805.
Pucher, J., Korattyswaroopam, N., & Ittyerah, N. (2004). The crisis of public transport in India : overwhelming needs but limited resources. Journal of Public Transportation, 7(3), 1-20.
Rowley, H. A., Baluja, S., & Kanade, T. (1998). Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 23-38. https://doi.org/10.1109/34.655647
Shaft, I., Ahmad, J., Shah, S. I., & Kashif, F. M. (2006). Impact of varying neurons and hidden layers in neural network architecture for a time frequency application. In 10th IEEE International Multitopic Conference 2006, INMIC (pp. 188-193). https://doi.org/10.1109/INMIC.2006.358160
Smith, B. L., & Demetsky, M. J. (n.d.). Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics (Vol. 2, pp. 1706-1709). IEEE. https://doi.org/10.1109/ICSMC.1994.400094
Tahir Masood, M., Khan, A., & Naqvi, H.A. (2011). Transportation Problems in Developing Countries Pakistan: A Case-in-Point. International Journal of Business and Management, 6(11). https://doi.org/10.5539/ijbm.v6n11p256
Teodorovic, D., & Vukadinovic, K. (2012). Traffic Control and Transport Planning: A Fuzzy Sets and Neural Networks Approach (Vol. 13). Springer Science & Business Media.
Trans-Africa Consortium. (2008). Overview of public transport in Sub-Saharan Africa. TransAfrica project, UITP, Brussels. Belgium. Retrieved from http://www.uitp.org/sites/default/files/cck-focus-papers-files/Transafrica_UITP_UATP_PublicTransport_in_SubSaharan_Africa_2008.pdf
Tso, G. K. F., & Yau, K. K. W. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761-1768. https://doi.org/10.1016/j.energy.2006.11.010
Van Broek, P. Den, & Noppen, J. (2006). Fuzzy weighted average: Alternative approach. In Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS(pp. 126-130). https://doi.org/10.1109/NAFIPS.2006.365872
Vanegas, L. V., & Labib, A. W. (2001). Application of new fuzzy-weighted average (NFWA) method to engineering design evaluation. International Journal of Production Research, 39(6), 1147-1162. https://doi.org/10.1080/00207540010023592
Wang, T.-C., & Lee, H.-D. (n.d.). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. https://doi.org/10.1016/j.eswa.2008.11.035
Werbos, P. J. (1988). Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1(4), 339-356. https://doi.org/10.1016/0893-6080(88)90007-X
Werbos, P. J. (1990). Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10), 1550-1560. https://doi.org/10.1109/5.58337
Yesilnacar, E., & Topal, T. (2005). Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology, 79(3-4), 251-266. https://doi.org/10.1016/j.enggeo.2005.02.002
Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3), 77-85. Retrieved from http://go.galegroup.com/ps/anonymous?id=GALE%7CA15061349sid=googleScholarv=2.1it=rlinkaccess=fulltextissn=00010782p=AONEsw=wauthCount=1&isAnonymousEntry=true
Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90, 111-127. Retrieved from http://ac.els-cdn.com/S0165011497000778/1-s2.0-S0165011497000778-main.pdf?_tid=1fb8bfee-6927-11e7-9caf-00000aab0f27&acdnat=1500100421_cc62116803b2ac1938fbfbac7e316639
Information & Authors
Information
Published In
Urbanization Challenges in Emerging Economies: Energy and Water Infrastructure; Transportation Infrastructure; and Planning and Financing
Pages: 569 - 579
Editors: Udai P. Singh, B. R. Chahar, Indian Institute of Technology, H. R. P. Yadav, Institution of Engineers (India), and Satish K. Vij
ISBN (Online): 978-0-7844-8202-5
Copyright
© 2018 American Society of Civil Engineers.
History
Published online: Dec 13, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.