Article
MODELLING OF DAILY ACTIVITY SCHEDULE OF WORKERS USING UNSUPERVISED MACHINE LEARNING TECHNIQUE
DOI: 10.7708/ijtte.2016.6(1).07
6 / 1 / 77-91 Pages
Author(s)
Anu P. Alex - The College of Engineering Trivandrum, Kerala, India -
Abstract
Travel demand models are used to replicate the real world travel demand and to predict the future travel demand. A behavioural oriented approach in travel demand analysis is provided by activity based travel demand modelling and it provides a better understanding of the travel behaviour of an individual. A sequential modelling approach using econometric models is commonly used in activity based travel modelling. In this method, error obtained in one model will be carried forward to the second model and so on. Hence chances of accumulation of error are more at the final stage, when the models are used for prediction. Hence an attempt is made in this study to replace the sequential econometric modelling approach with simultaneous modelling approach using an unsupervised machine learning technique. Three stage Neural Network modelling used in this study replaces sixteen stage econometric models. The predictive accuracy of all the output parameters was compared in both the modelling approaches. Results shows that Artificial Neural Network (ANN) results outperform econometric models. The decrease in percentage error ranges from 2.22% to 27.17%.
Number of downloads: 1433
Keywords:
work activity;
ANN;
econometric models;
Acknowledgements:
The authors are grateful to Kerala State Council for Science Technology and Environment (KSCSTE) for funding the project.
References:
Arentze, T.A.; Timmermans, H. 2000. ALBATROSS - A learning based transportation oriented simulation system. In Proceedings of the TRB Conference 2000, USA, Issue No. 1706, 136-144.
Bhat, C.R.; Guo, J.Y.; Srinivasan, S.; Sivakumar, A. 2004. Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns, Transportation Research Record: Journal of the Transportation Research Board, No. 1894, TRB, National Research Council, Washington, D.C., 57-66.
Bhat, C.R.; Singh, S.K. 2000. A Comprehensive Daily Activity-Travel Generation Model System for Workers, Transportation Research Part A: Policy and Practice, 34(1): 1-22.
Damm, D. 1980. Interdependencies in activity behavior, Transportation Research Record, 750.
Eluru, N.; Pinjari, A.R.; Pendyala, R.M.; Bhat, C.R. 2010. An econometric Multi-Dimensional Choice Model of Activity-Travel Behaviour, Transportation Letters: The International Journal of Transportation Research, 2(4): 217-230.
Kitamura, R. 1988. An evaluation of activity-based travel analysis, Transportation, 15(1): 9-34.
Manoj, M.; Verma, A. 2013. Analysis and Modelling of Activity-Travel Behaviour of Nonworkersfrom a City of Developing Country, India. In Proceedings of the 2nd Conference of Transportation Research Group of India (2nd CTRG), Procedia - Social and Behavioral Sciences, 104(2013): 621-629.
Muralidhar, B.; Mathew, T.V.; Dhingra, S.L. 2005. Development of a Mixed Logit Model to Tour Mode Choice for an Urban Region. In Proceedings of International Conference CUPUM 2005, London U.K.
Muralidhar, B.; Mathew, T.V.; Dhingra, S.L. 2006. Prototype Time-Space Diary Design and Administration for a Developing Country, Journal of Transportation Engineering, ASCE, 132(6): 489-498.
Pendyala, R.M.; Kitamura, R.; Kikuchi, A.; Yamamoto, T.; Fujii, S. 2005. Florida Activity Mobility Simulator - Overview and Preliminary Validation Results, Transportation Research Record: Journal of the Transportation Research Board, No. 1921, Transportation Research Board of the National Academies, Washington, D.C., 123-130.
Recker, W.W.; McNally, M.G.; Root, G.S. 1986. A model of complex travel behaviour: part I - theoretical development, Transportation Research Part A, 20A(4): 307-318.
Roorda, M.J.; Miller, E.J. 2003. A Prototype Model of Household Activity/Travel Scheduling. Presented at the Transportation Research Board Annual Meeting.
Roorda, M.J.; Miller, E.J.; Habib, K.M.N. 2008. Validation of TASHA: A 24-h activity scheduling microsilulation model, Transportation Research Part A, 42(2): 360-375.
Saw, K.; Katti, B.K.; Joshi, G.K. 2015. AB temporal trip generation modelling for primary activities: a case study of fast growing metropolitan city, International Journal for Traffic and Transport Engineering, 2015, 5(2): 120-133.
Sreela, P.K.; Melayil, S.; Anjaneyulu, M.V.L.R. 2013. Modeling of Shopping Participation and Duration of Workers in Calicu. In Proceedings of the 2nd Conference of Transportation Research Group of India (2nd CTRG), Procedia - Social and Behavioral Sciences, 104(2013): 543-552.
Surekha, N. 2009. Microsimulation of Activity-Travel Pattern for Tiruchirappalli City, M. Tech Thesis at NIT Tiruchirappalli.
Vishnu, B.; Srinivasan, K.K. 2013. Tour-based departure time models for work and non-work tours ofworkers. In Proceedings of the 2nd Conference of Transportation Research Group of India (2nd CTRG), Procedia - Social and Behavioral Sciences, 104(2013): 630-639.