Volume 23 Issue 5
Oct.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(5): 253-263
YOU Lin-lin, HE Jun-shu, CHEN Kun-xu, HE Jia-qi, YUAN Shao-xin, ZHAO Juan-juan, CAI Ming. Federated heterogeneous model and algorithm for personal travel recommendation[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 253-263. doi: 10.19818/j.cnki.1671-1637.2023.05.018
Citation: YOU Lin-lin, HE Jun-shu, CHEN Kun-xu, HE Jia-qi, YUAN Shao-xin, ZHAO Juan-juan, CAI Ming. Federated heterogeneous model and algorithm for personal travel recommendation[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 253-263. doi: 10.19818/j.cnki.1671-1637.2023.05.018
shu

Federated heterogeneous model and algorithm for personal travel recommendation

doi: 10.19818/j.cnki.1671-1637.2023.05.018
  • 1.

    School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China

  • 2.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

Funds:

National Natural Science Foundation of China 62002398

National Natural Science Foundation of China 41901188

National Key Research and Development Program of China 2020YFB1600400

Guangdong Basic and Applied Basic Research Foundation 2023A1515012895

Science and Technology Planning Project of Guangzhou 202206010056

More Information
  • Author Bio:

    YOU Lin-lin(1987-), male, associate professor, PhD, youllin@mail.sysu.edu.cn

    ZHAO Juan-juan(1988-), female, assistant professor, PhD, zhao@cnu.edu.cn

  • Received Date: 2023-03-27
    Available Online: 2023-11-17
  • Publish Date: 2023-10-25
    Abstract
  • To achieve personal travel recommendations by considering both the preference heterogeneity and data privacy, based on the model parametric aggregation and distributed training supported by the federated learning (FL) computing paradigm, a federated mixed Logit (FMXL) model was proposed by decoupling the standard mixed Logit model to separate the parameter estimation of local individual preferences and global population differences. In order to eliminate the dependence of the model on the original data, two federated Gibbs sampling algorithms, to be standardized or aggregated, were designed, to achieve the hierarchical estimation of the model through the interaction of local and global parameters. The proposed model and algorithm were validated for offline and online travel recommendation scenarios based on Swiss Metro data. Analysis results show that for offline scenario, the FMXL model based on two federated Gibbs sampling algorithms increase the log-likelihood value by 157.8 and 153.2, and the prediction rate by 12.3% and 12.1%, respectively, compared with the standard multinomial Logit model. In addition, the computation time reduce by 64.2% and 76.9%, respectively, and the communication times both reduce by 86.2% compared with the mixed Logit model based on the centralized Gibbs sampling algorithm. For the online scenario, both the log-likelihood value and the prediction rate of the FMXL model show an increasing trend, and the computation and communication times of the whole estimation process are lower than those of the standard mixed Logit model. Overall, with the data privacy as the precondition, the federated training of the MXL model ensures the accuracy of travel recommendations and effectively enhances the timeliness of travel recommendations by fully utilizing the idle computing power on the user side, reflecting the high adaptability and scalability of the proposed model and algorithms. In addition, the intelligent progress of the transportation system can be promoted effectively by personal travel recommendations based on the FMXL model. 3 tabs, 4 figs, 30 refs.

     

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