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 -
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.
Number of downloads: 1266
Acknowledgements:
We are very grateful to the Director, CSIR-Central Road Research Institute for allowing us to publish this paper.
References:
Adams, W.T. 1959. Factors Influencing Mass Transit and Automobile Travel in Urban Areas, Public Roads 30(11): 256-60.
Ben-Akiva, M.E.; Lerman, S.R. 1985. Discrete choice analysis: theory and application to travel demand (Vol. 9). MIT press.
Bhat, C.R. 1998. Analysis of travel mode and departure time choice for urban shopping trips, Transportation Research Part B: Methodological 32(6): 361-371.
Black, W.R. 1995. Spatial interaction modeling using artificial neural networks, Journal of Transport Geography 3(3): 159-166.
Cantarella, G.E.; de Luca, S. 2003. Modeling transportation mode choice through artificial neural networks. In Proceedings of the Fourth Uncertainty Modeling and Analysis, 2003. ISUMA 2003, 84-90.
Chaei, S.R. 1976. Disaggregated transportation demand model for home based trip generations, Doctoral dissertation, Department of Civil engineering, University of Roorkee.
Chin, S.M.; Hwang, H.L.; Miaou, S.P. 1992. Transportation demand forecasting with a computer-simulated neural network model. In Proceedings of the International Conference on Artificial Intelligence Applications in Transportation Engineering, 349-390.
Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function, Mathematics of control, signals and systems 2(4): 303-314.
Dayhoff, J.E. 1990. Neural Network Architectures: An Introduction. New York. Van Nostrand Reinhold.
Dell’Orco, M.; Circella, G.; Sassanelli, D. 2008. A hybrid approach to combine fuzziness and randomness in travel choice prediction, European Journal of Operational Research 185(2): 648-658.
Dougherty, M. 1995. A review of neural networks applied to transport, Transportation Research Part C: Emerging Technologies 3(4): 247-260.
Edara, P.K. 2003. Mode choice modeling using artificial neural networks, Doctoral dissertation, Virginia Polytechnic Institute and State University.
Garson, G.D. 1991. Interpreting neural-network connection weights, Artificial Intelligence Expert 6(4): 46-51.
Golias, I.; Karlaftis, M.G. 2001. An international comparative study of self-reported driver behavior, Transportation Research Part F: Traffic Psychology and Behaviour 4(4): 243-256.
Hensher, D.A.; Ton, T. 2002. TRESIS: A transportation, land use and environmental strategy impact simulator for urban areas, Transportation 29(4): 439-457.
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.
Himanen, V.; Nijkamp, P.; Reggiani, A. 1998. Neural networks in transport applications. Ashgate Publishing Company.
Hornik, K.; Stinchcombe, M.; White, H. 1989. Multilayer feedforward networks are universal approximators, Neural networks 2(5): 359-366.
Kanafani, A. 1983. Transportation Demand Analysis. Mcgraw Hill. 320 p.
Koppelman, F.S.; Bhat, C. 2006. A self instructing course in mode choice modeling: multinomial and nested logit models. Available from internet: http://www.caee.utexas.edu
Quoted IJTTE Works
There is no quoted studies.
Related Keywords