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Developing a disaggregate travel demand system of models using data mining techniques

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  • Ghasri, Milad
  • Hossein Rashidi, Taha
  • Waller, S. Travis

Abstract

The travel demand modelling has experienced a paradigm shift from aggregate to disaggregate models, leading to an increase in computational time and simulation cost. Meanwhile, transferability models have emerged to reduce the associated cost and computational burden, but haven’t discounted the disaggregation level. This research proposes the proof of the concept of an innovative transferability modelling framework to estimate total number of trips and trip attributes in a tour of trips at a disaggregate level. In contrast to tour-based or activity-based models, the focus of transferability models is on replicating trip patterns rather than reflecting travellers’ behaviour. Similar to previous transferability models, classifying decision tree is utilized as one of the modelling techniques in this study. Moreover, the merits of a modified version of decision tree and the random forest methods are examined. Victorian Integrated Survey of Travel and Activity (VISTA) in 2007 and 2009 are utilized to calibrate and validate the proposed framework, respectively. According to the results, the random forest method shows highest individual-level accuracy while matching the system-level observed distributions.

Suggested Citation

  • Ghasri, Milad & Hossein Rashidi, Taha & Waller, S. Travis, 2017. "Developing a disaggregate travel demand system of models using data mining techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 138-153.
    • Handle: RePEc:eee:transa:v:105:y:2017:i:c:p:138-153
    DOI: 10.1016/j.tra.2017.08.020
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    References listed on IDEAS

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    1. Kevin Krizek, 2003. "Neighborhood services, trip purpose, and tour-based travel," Transportation, Springer, vol. 30(4), pages 387-410, November.
    2. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    3. Taha Rashidi & Abolfazl Mohammadian, 2011. "Household travel attributes transferability analysis: application of a hierarchical rule based approach," Transportation, Springer, vol. 38(4), pages 697-714, July.
    4. Barrett Jennifer H & Cairns David A, 2008. "Application of the Random Forest Classification Method to Peaks Detected from Mass Spectrometric Proteomic Profiles of Cancer Patients and Controls," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-22, February.
    5. Eric Miller & Matthew Roorda & Juan Carrasco, 2005. "A tour-based model of travel mode choice," Transportation, Springer, vol. 32(4), pages 399-422, July.
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    Cited by:

    1. Ma, Xiaobo & Karimpour, Abolfazl & Wu, Yao-Jan, 2020. "Statistical evaluation of data requirement for ramp metering performance assessment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 248-261.

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