2021
Zhang, Jiajia; Feng, Tao; Timmermans, Harry; Lin, Zhengkui
Improved imputation of rule sets in class association rule modeling: application to transportation mode choice Journal Article
In: Transportation, vol. XX, no. X, 2021, ISSN: 0049-4488, (Funding Information: This work was supported by China Scholarship Council.).
Abstract | Links | BibTeX | Tags: Class association rules, FP-tree, Rule merging, Transportation mode choice
@article{c8074a50b1504dc9bbb68a3a8a592d14,
title = {Improved imputation of rule sets in class association rule modeling: application to transportation mode choice},
author = {Jiajia Zhang and Tao Feng and Harry Timmermans and Zhengkui Lin},
doi = {10.1007/s11116-021-10238-9},
issn = {0049-4488},
year = {2021},
date = {2021-11-26},
journal = {Transportation},
volume = {XX},
number = {X},
publisher = {Springer},
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.},
note = {Funding Information: This work was supported by China Scholarship Council.},
keywords = {Class association rules, FP-tree, Rule merging, Transportation mode choice},
pubstate = {published},
tppubtype = {article}
}
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.