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Modelling mode choice of individual in linked trips with artificial neural networks and fuzzy representation

Chapter


Abstract


  • Traditional mode choice models consider travel modes of an individual in a consecutive trip to be independent. However, a persons choice of the travel mode of a trip is likely to be affected by the mode choice of the previous trips, particularly when it comes to car driving. Furthermore, traditional travel mode choice models involve discrete choice models, which are largely derived from expert knowledge, to build rules or heuristics. Their approach relies heavily on a predefined specific model structure (utility model) and constraining it to hold across an entire series of historical observations. These studies also assumed that the travel

    diaries of individuals in travel survey data is complete, which seldom occurs. Therefore, in this chapter, we propose a data-driven methodology with artificial neural networks (ANNs) and fuzzy sets (to better represent historical knowledge in an intuitive way) to model travel mode choices. The proposed methodology models and analyses travel mode choice of an individual trip and its influence on consecutive trips of individuals. The methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performance is compared with the state-of-the-art approaches such as decision trees. Experimental results indicate that the proposed methodology with ANN and fuzzy sets can effectively improve the accuracy of travel mode choice prediction.

Publication Date


  • 2016

Citation


  • Shukla, N., Ma, J., Wickramasuriya, R., Huynh, N. & Perez, P. (2016). Modelling mode choice of individual in linked trips with artificial neural networks and fuzzy representation. In S. Shanmuganathan & S. Samarasinghe (Eds.), Artificial Neural Network Modelling (pp. 405-422). Switzerland: Springer International Publishing.

Scopus Eid


  • 2-s2.0-84957570349

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5328

Book Title


  • Artificial Neural Network Modelling

Start Page


  • 405

End Page


  • 422

Abstract


  • Traditional mode choice models consider travel modes of an individual in a consecutive trip to be independent. However, a persons choice of the travel mode of a trip is likely to be affected by the mode choice of the previous trips, particularly when it comes to car driving. Furthermore, traditional travel mode choice models involve discrete choice models, which are largely derived from expert knowledge, to build rules or heuristics. Their approach relies heavily on a predefined specific model structure (utility model) and constraining it to hold across an entire series of historical observations. These studies also assumed that the travel

    diaries of individuals in travel survey data is complete, which seldom occurs. Therefore, in this chapter, we propose a data-driven methodology with artificial neural networks (ANNs) and fuzzy sets (to better represent historical knowledge in an intuitive way) to model travel mode choices. The proposed methodology models and analyses travel mode choice of an individual trip and its influence on consecutive trips of individuals. The methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performance is compared with the state-of-the-art approaches such as decision trees. Experimental results indicate that the proposed methodology with ANN and fuzzy sets can effectively improve the accuracy of travel mode choice prediction.

Publication Date


  • 2016

Citation


  • Shukla, N., Ma, J., Wickramasuriya, R., Huynh, N. & Perez, P. (2016). Modelling mode choice of individual in linked trips with artificial neural networks and fuzzy representation. In S. Shanmuganathan & S. Samarasinghe (Eds.), Artificial Neural Network Modelling (pp. 405-422). Switzerland: Springer International Publishing.

Scopus Eid


  • 2-s2.0-84957570349

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5328

Book Title


  • Artificial Neural Network Modelling

Start Page


  • 405

End Page


  • 422