Exploring determinants of feeder mode choice behavior using Artificial Neural Network: Evidences from Delhi metro

Gulnazbanu Saiyad1, Minal Srivastava2, Dipak Rathwa1
1Civil Engineering Department, The Maharaja Sayajirao University of Baroda, Vadodara, India
2Transport Planning Division, CSIR-Central Road Research Institute, New Delhi, India

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Pucher, 2005, Urban transport crisis in India, Transp. Policy, 12, 185, 10.1016/j.tranpol.2005.02.008

Eboli, 2012, Performance indicators for an objective measure of public transport service quality, Eur. Transp. Trasp. Eur.

Murray, 1998, Public transportation access, Transp. Res. D, 3, 319, 10.1016/S1361-9209(98)00010-8

Brons, 2009, Access to railway stations and its potential in increasing rail use, Transp. Res. A, 43, 136

Wu, 2018, Modeling travel mode choices in connection to metro stations by mixed logit models: a case study in Nanjing, China, Promet-Traffic Transp., 30, 549, 10.7307/ptt.v30i5.2623

Ben-Akiva, 1985

Ashalatha, 2012, Mode choice behavior of commuters in Thiruvananthapuram city, J. Transp. Eng., 139, 494, 10.1061/(ASCE)TE.1943-5436.0000533

Minal, 2016, Commuter’s sensitivity in mode choice: An empirical study of New Delhi, J. Transp. Geogr., 100, 207

DeTienne, 2003, Neural networks as statistical tools for business researchers, Organ. Res. Methods, 6, 236, 10.1177/1094428103251907

Minal, 2018, Development of neuro-fuzzy-based multimodal mode choice model for commuter in Delhi, IET Intell. Transp. Syst.

Parmar, 2020, A machine learning approach for modelling parking duration in urban land-use, Phys. A, 572

Garson, 1991, Interpreting neural network connection weights, Artif. Intell. Expert, 6, 46

Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Statist., 118, 9

Bouscasse, 2018, How does environmental concern influence mode choice habits? A mediation analysis, Transp. Res. D, 59, 205, 10.1016/j.trd.2018.01.007

Hu, 2018, Travel mode choices in small cities of China: A case study of Changting, Transp. Res. D, 59, 361, 10.1016/j.trd.2018.01.013

Goel, 2016, Access–egress and other travel characteristics of metro users in Delhi and its satellite cities, IATSS Res., 39, 164, 10.1016/j.iatssr.2015.10.001

Tangphaisankun, 2009, Influences of paratransit as a feeder of mass transit system in developing countries based on commuter satisfaction, 236

Krygsman, 2004, Multimodal public transport: an analysis of travel time elements and the interconnectivity ratio, Transp. Policy, 11, 265, 10.1016/j.tranpol.2003.12.001

Givoni, 2007, The access journey to the railway station and its role in passengers’ satisfaction with rail travel, Transp. Policy, 14, 357, 10.1016/j.tranpol.2007.04.004

Yang, 2015, Metro commuters’ satisfaction in multi-type access and egress transferring groups, Transp. Res. D, 34, 179, 10.1016/j.trd.2014.11.004

Meng, 2016, Influence of socio-demography and operating streetscape on last-mile mode choice, J. Public Transp., 19

Debrezion, 2009, Modelling the joint access mode and railway station choice, Transp. Res. E, 45, 270, 10.1016/j.tre.2008.07.001

Sohn, 2010, Factors generating boarding at metro stations in the Seoul metropolitan area, Cities, 27, 358, 10.1016/j.cities.2010.05.001

Hine, 2000, Seamless, accessible travel: users’ views of the public transport journey and interchange, Transp. Policy, 7, 217, 10.1016/S0967-070X(00)00022-6

Kuby, 2004, Factors influencing light-rail station boardings in the United States, Transp. Res. A, 38, 223

Guo, 2011, Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground, Transp. Res. A, 45, 91

Kim, 2007, Analysis of light rail rider travel behavior: Impacts of individual, built environment, and crime characteristics on transit access, Transp. Res. A, 41, 511

Fan, 2019, How have travelers changed mode choices for first/last mile trips after the introduction of bicycle-sharing systems: An empirical study in Beijing, China, J. Adv. Transp., 2019, 10.1155/2019/5426080

Martens, 2004, The bicycle as a feedering mode: experiences from three European countries, Transp. Res. D, 9, 281, 10.1016/j.trd.2004.02.005

Cervero, 2001, Walk-and-ride: Factors influencing pedestrian access to transit, J. Public Transp., 3, 1, 10.5038/2375-0901.3.4.1

Hensher, 2007, Development of commuter and non-commuter mode choice models for the assessment of new public transport infrastructure projects: a case study, Transp. Res. A, 41, 428

M. Srivastava, C. Ravi Sekhar,

Cantarella, 2005, Multilayer feedforward networks for transportation mode choice analysis: An analysis and a comparison with random utility models, Transp. Res. C, 13, 121, 10.1016/j.trc.2005.04.002

Chalumuri, 2009, Applications of neural networks in mode choice modelling for second order metropolitan cities of India, 134

Warner, 1996, Understanding neural networks as statistical tools, Amer. Statist., 50, 284, 10.1080/00031305.1996.10473554

Hensher, 2000, A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice, Transp. Res. E, 36, 155, 10.1016/S1366-5545(99)00030-7

Neelakanta, 2020

Cybenko, 1989, Approximation by superpositions of a sigmoidal function, Math. Control Signals Systems, 2, 303, 10.1007/BF02551274

Hornik, 1991, Approximation capabilities of multilayer feedforward networks, Neural Netw., 4, 251, 10.1016/0893-6080(91)90009-T

Puig-Arnavat, 2015

Goh, 1995, Back-propagation neural networks for modeling complex systems, Artif. Intell. Eng., 9, 143, 10.1016/0954-1810(94)00011-S

Delhi Statistical Handbook, 2017

Advani, 2010

Saini, 2015, Case study of cycle rickshaw on ergonomic basis, Int. J. Adv. Res. Eng. Appl. Sci., 4, 55

Mohammadian, 2002, Nested logit models and artificial neural networks for predicting household automobile choices: comparison of performance, Transp. Res. Rec., 1807, 92, 10.3141/1807-12

Sim, 2005, The kappa statistic in reliability studies: use, interpretation, and sample size requirements, Phys. Ther., 85, 257, 10.1093/ptj/85.3.257

Landis, 1977, An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers, Biometrics, 36, 3