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Journal of Software:2020.31(12):3753-3771

行程时间预测方法研究
柏梦婷,林杨欣,马萌,王平
(北京大学 软件与微电子学院, 北京 102600;软件工程国家工程研究中心(北京大学), 北京 100871;北京大学 软件与微电子学院, 北京 102600;软件工程国家工程研究中心(北京大学), 北京 100871;高可信软件技术教育部重点实验室(北京大学), 北京 100871)
Survey of Traffic Travel-time Prediction Methods
BAI Meng-Ting,LIN Yang-Xin,MA Meng,WANG Ping
(School of Software and Microelectronics, Peking University, Beijing 102600, China;National Engineering Research Center for Software Engineering(Peking University), Beijing 100871, China;School of Software and Microelectronics, Peking University, Beijing 102600, China;National Engineering Research Center for Software Engineering(Peking University), Beijing 100871, China;Key Laboratory of High Confidence Software Technologies of Ministry of Education(Peking University), Beijing 100871, China)
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Received:November 03, 2018    Revised:May 08, 2019
> 中文摘要: 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.
文章编号:     中图分类号:TP391    文献标志码:
基金项目:国家重点研发计划(2017YFB1200700);国家自然科学基金(61701007) 国家重点研发计划(2017YFB1200700);国家自然科学基金(61701007)
Foundation items:National Key Research and Development Program of China (2017YFB1200700); National Natural Science Foundation of China (61701007)
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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

BAI Meng-Ting,LIN Yang-Xin,MA Meng,WANG Ping.Survey of Traffic Travel-time Prediction Methods.Journal of Software,2020,31(12):3753-3771