Survey of Traffic Travel-time Prediction Methods

被引：0
作者：
Bai M.-T. [1 ]
Lin Y.-X. [1 ]
Ma M. [2 ]
Wang P. [1 ,2 ,3 ]
机构：
[1] School of Software and Microelectronics, Peking University, Beijing
[2] National Engineering Research Center for Software Engineering, Peking University, Beijing
[3] Key Laboratory of High Confidence Software Technologies of Ministry of Education, Peking University, Beijing
来源：
Ruan Jian Xue Bao/Journal of Software | 2020年 / 31卷 / 12期
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
Data-driven; Model-driven; Non-parametric methods; Parametric methods; Travel-time prediction;
D O I：
10.13328/j.cnki.jos.005875
中图分类号：
学科分类号：
摘要：
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. © Copyright 2020, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：3753 / 3771
页数：18
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