行程时间预测方法研究

Translated title of the contribution: Survey of Traffic Travel-time Prediction Methods

Meng Ting Bai, Yang Xin Lin, Meng Ma, Ping Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Travel-time prediction can help implement advanced traveler information systems. In recent years, a variety of travel-time prediction methods have been developed. In this study, travel-time prediction methods are classified into two categories: model-driven and data-driven methods. Two common model-driven approaches are elaborated, namely queuing theory and cell transmission model. The data-driven methods are classified into parametric and non-parametric methods. Parametric methods include linear regression, autoregressive integrated moving average, and Kalman filtering. Non-parametric methods contain neural networks, support vector regression, nearest neighbors, and ensemble learning methods. Existing travel-time prediction methods are analyzed and concluded from source data, prediction range, accuracy, advantages, disadvantages, and application scenarios. Several solutions are proposed for some shortcomings of existing methods. A novel data preprocessing framework and a travel-time prediction model are presented, and future research challenges are highlighted.

Translated title of the contributionSurvey of Traffic Travel-time Prediction Methods
Original languageChinese (Traditional)
Pages (from-to)3753-3771
Number of pages19
JournalRuan Jian Xue Bao/Journal of Software
Volume31
Issue number12
DOIs
StatePublished - Dec 2020
Externally publishedYes

Keywords

  • Data-driven
  • Model-driven
  • Non-parametric methods
  • Parametric methods
  • Travel-time prediction

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