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首页 > 过刊浏览>2020年第31卷第12期 >3753-3771. DOI:10.13328/j.cnki.jos.005875
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行程时间预测方法研究
作者:
作者简介:

柏梦婷(1999-),女,学士,主要研究领域为机器学习,深度学习在智能交通领域的应用.
林杨欣(1992-),男,学士,主要研究领域为智能交通系统.
马萌(1986-),男,博士,副研究员,CCF专业会员,主要研究领域为物联网,智能感知计算,复杂事件处理.
王平(1961-),男,博士,教授,博士生导师,CCF专业会员,主要研究领域为网络安全,智能计算与感知,操作系统与中间件.

通讯作者:

马萌,E-mail:mameng@pku.edu.cn;王平,E-mail:pwang@pku.edu.cn

基金项目:

国家重点研发计划（2017YFB1200700）；国家自然科学基金（61701007）


Survey of Traffic Travel-time Prediction Methods
Author:
Fund Project:

National Key Research and Development Program of China (2017YFB1200700); National Natural Science Foundation of China (61701007)

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    摘要:

    行程时间预测，有助于实施高级旅行者信息系统.自20世纪90年代起，已经有多种行程时间预测方法被研发出来.将行程时间预测方法分为模型驱动方法和数据驱动方法两大类.介绍了两种常见的模型驱动方法，即排队论模型和细胞传输模型.数据驱动方法被分类为参数方法和非参数方法：参数方法包括线性回归、自回归集成移动平均和卡尔曼滤波，非参数方法包括神经网络、支持向量回归、最近邻和集成学习方法.对现有行程时间预测方法从源数据、预测范围、准确率、优缺点和适用范围等方面进行了分析总结.针对现有方法的一些缺点，提出了可能的解决方案.给出了一种新颖的数据预处理框架和一个行程时间预测模型，最后指出了未来的研究方向.

    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.

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柏梦婷,林杨欣,马萌,王平.行程时间预测方法研究.软件学报,2020,31(12):3753-3771

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  • 收稿日期:2018-11-03
  • 最后修改日期:2019-05-08
  • 在线发布日期: 2020-12-03
  • 出版日期: 2020-12-06
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