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Improved imputation of rule sets in class association rule modeling: application to transportation mode choice

Author

Listed:
  • Jiajia Zhang

    (Dalian Maritime University
    Eindhoven University of Technology)

  • Tao Feng

    (Eindhoven University of Technology
    Hiroshima University)

  • Harry Timmermans

    (Eindhoven University of Technology
    Nanjing University of Aeronautics and Astronautics)

  • Zhengkui Lin

    (Dalian Maritime University)

Abstract

Predicting transportation mode choice is a critical component of forecasting travel demand. Recently, machine learning methods have become increasingly more popular in predicting transportation mode choice. Class association rules (CARs) have been applied to transportation mode choice, but the application of the imputed rules for prediction remains a long-standing challenge. Based on CARs, this paper proposes a new rule merging approach, called CARM, to improve predictive accuracy. In the suggested approach, first, CARs are imputed from the frequent pattern tree (FP-tree) based on the frequent pattern growth (FP-growth) algorithm. Next, the rules are pruned based on the concept of pessimistic error rate. Finally, the rules are merged to form new rules without increasing predictive error. Using the 2015 Dutch National Travel Survey, the performance of suggested model is compared with the performance of CARIG that uses the information gain statistic to generate new rules, class-based association rules (CBA), decision trees (DT) and the multinomial logit (MNL) model. In addition, the proposed model is assessed using a ten-fold cross validation test. The results show that the accuracy of the proposed model is 91.1%, which outperforms CARIG, CBA, DT and the MNL model.

Suggested Citation

  • Jiajia Zhang & Tao Feng & Harry Timmermans & Zhengkui Lin, 2023. "Improved imputation of rule sets in class association rule modeling: application to transportation mode choice," Transportation, Springer, vol. 50(1), pages 63-106, February.
    • Handle: RePEc:kap:transp:v:50:y:2023:i:1:d:10.1007_s11116-021-10238-9
    DOI: 10.1007/s11116-021-10238-9
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    References listed on IDEAS

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    1. Lee, Joon-Kyu & Yoo, Kwang-Eui & Song, Ki-Han, 2016. "A study on travelers' transport mode choice behavior using the mixed logit model: A case study of the Seoul-Jeju route," Journal of Air Transport Management, Elsevier, vol. 56(PB), pages 131-137.
    2. Kim, Seheon & Rasouli, Soora & Timmermans, Harry & Yang, Dujuan, 2018. "Estimating panel effects in probabilistic representations of dynamic decision trees using bayesian generalized linear mixture models," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 168-184.
    3. Jarad Beckman & Konstadinos Goulias, 2008. "Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California," Transportation, Springer, vol. 35(5), pages 655-671, August.
    4. Marcel Paulssen & Dirk Temme & Akshay Vij & Joan Walker, 2014. "Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice," Transportation, Springer, vol. 41(4), pages 873-888, July.
    5. 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.
    6. Zhan, Guangjun & Yan, Xuedong & Zhu, Shanjiang & Wang, Yun, 2016. "Using hierarchical tree-based regression model to examine university student travel frequency and mode choice patterns in China," Transport Policy, Elsevier, vol. 45(C), pages 55-65.
    7. Li, Linchao & Zhu, Jiasong & Zhang, Hailong & Tan, Huachun & Du, Bowen & Ran, Bin, 2020. "Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 282-292.
    8. Mohammad Hesam Hafezi & Lei Liu & Hugh Millward, 2019. "A time-use activity-pattern recognition model for activity-based travel demand modeling," Transportation, Springer, vol. 46(4), pages 1369-1394, August.
    9. Zheng Zhu & Xiqun Chen & Chenfeng Xiong & Lei Zhang, 2018. "A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice," Transportation, Springer, vol. 45(5), pages 1499-1522, September.
    10. Liang Tang & Chenfeng Xiong & Lei Zhang, 2015. "Decision tree method for modeling travel mode switching in a dynamic behavioral process," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(8), pages 833-850, December.
    11. Koppelman, Frank S. & Sethi, Vaneet, 2005. "Incorporating variance and covariance heterogeneity in the Generalized Nested Logit model: an application to modeling long distance travel choice behavior," Transportation Research Part B: Methodological, Elsevier, vol. 39(9), pages 825-853, November.
    12. Hensher, David A. & Ton, Tu 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, Elsevier, vol. 36(3), pages 155-172, September.
    13. Diana Kusumastuti & Els Hannes & Davy Janssens & Geert Wets & Benedict Dellaert, 2010. "Scrutinizing individuals’ leisure-shopping travel decisions to appraise activity-based models of travel demand," Transportation, Springer, vol. 37(4), pages 647-661, July.
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