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Article

WEB SURVEY DATA AND COMMUTER MODE CHOICE ANALYSIS USING ARTIFICIAL NEURAL NETWORK

DOI: 10.7708/ijtte.2018.8(3).08


8 / 3 / 359-371 Pages

Author(s)

Minal Srivastava - Transportation Planning Division, CSIR- Central Road Research Institute, New Delhi 10025, India -

Chalumuri Ravi Sekhar - Transportation Planning Division, CSIR- Central Road Research Institute, New Delhi 10025, India -


Abstract

Dealing with the present bottlenecks as well as creating long lasting and sustainable transport systems has been the greatest challenge of urban transport planning. Calibrating the present need and forecasting the future demand is the underlying agenda of travel demand forecasting. Mode choice forms an integral part of this process as it gives a complete insight to the mode choice preferences of the commuters validating the introduction of new transport systems to existing ones. This study aims at modelling the mode choice of commuters in National Capital Region of Delhi in India. The data collected for the study was not through the conventional household survey but through a technique of Web-based survey. This survey was hosted at the site of Central Road Research Institute (CRRI) and reached out to people from different walks of life and with varying socio-economic characters. The survey, released in the month of February, 2013 collected about 100 responses and after sifting 94 responses were considered for the analysis. The present study uses the most prominent discrete choice model such as Multinomial Logit (MNL) and a non-conventional machine learning method Artificial Neural Network (ANN) for mode choice analysis. This sample was utilised for developing MNL models using NLOGIT econometric software. The ANN models were configured separately in MATLAB neural network tool box. The results shows that the model developed by ANN is the superior of the two due to higher accuracy and better exploratory power.


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Acknowledgements:

We are very grateful to the Director, CSIR-Central Road Research Institute for allowing us to publish this paper.


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