道路交通流数据预测方法研究综述

Review of Research on Road Traffic Flow Data Prediciton Methods

道路交通流预测作为智能交通系统中的重要分支,在道路拥堵预测、路径规划等方面起着重要作用。道路交通流数据时空多态、关联性复杂的特性迫使大数据时代下的道路交通流预测方法转型和升级。为了深入挖掘交通流时空性的特征,学者们相继提出各类方法,包括模型融合、模型算法改进、数据定义转换等方式,以求提高模型的预测精度。为了合理综述各类交通流的预测方法,根据所用方法的种类分为三大类:基于统计学的方法、基于机器学习的方法、基于深度学习的方法。通过综述各类交通流预测方法,对近年来新出现的模型与算法进行概括与分析,旨在为相关研究学者提供研究思路。对交通流预测方法进行了总结及展望,给出未来交通流预测领域的探索方向。

As an important branch of intelligent transportation system,road traffic flow prediction plays an important role in congestion prediction,path planning.The spatio-temporal polymorphism and complex correlation of road traffic flow data force the transformation and upgrading of road traffic flow prediction methods in the era of big data.In order to mine the time-space characteristics of traffic flow,scholars have proposed various methods,including model fusion,model algorithm improvement,data definition conversion,etc,in order to improve the prediction accuracy of the model.In order to reasonably summarize all kinds of traffic flow prediction methods,they are divided into three categories according to the types of methods used:statistics based methods,machine learning based methods,and depth learning based methods.This paper summarizes and analyzes the new models and algorithms in recent years by summarizing various traffic flow prediction methods,aiming to provide research ideas for relevant researchers.Finally,the methods of traffic flow prediction are summarized and prospected,and the exploration direction of the future traffic flow prediction field is given.