| 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. |
| 关键词: 行程时间预测 模型驱动 数据驱动 参数方法 非参数方法 |
| DOI：10.13328/j.cnki.jos.005875 |
| 分类号: |
| 基金项目:国家重点研发计划（2017YFB1200700）；国家自然科学基金（61701007） |
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| Survey of Traffic Travel-time Prediction Methods |
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BAI Meng-Ting1, LIN Yang-Xin1, MA Meng2, WANG Ping1,2,3
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1.School of Software and Microelectronics, Peking University, Beijing 102600, China;2.National Engineering Research Center for Software Engineering(Peking University), Beijing 100871, China;3.Key Laboratory of High Confidence Software Technologies of Ministry of Education(Peking University), Beijing 100871, China
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| 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. |
| Key words: travel-time prediction model-driven data-driven parametric methods non-parametric methods |
