使用人工智能进行交通预测：最新进展与新兴机遇综述
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
AI总结：
本文综述基于AI的交通预测方法，围绕多变量交通时间序列建模梳理数据资源、预处理、预测方法及应用，并指出研究挑战与未来方向。
摘要：
交通预测在缓解交通拥堵方面发挥着关键作用；交通拥堵是全球范围内的一个重要问题，会造成额外出行时间损失以及燃料消耗增加等负面后果。将新兴技术融入交通运输系统，为显著改善交通预测提供了机遇，同时也带来了新的研究问题。为了给理解交通预测中尚未解决的研究挑战奠定基础，本综述旨在全面概述交通预测方法。具体而言，鉴于基于人工智能（Artificial Intelligence，AI）的交通预测方法近期取得的成功及其在交通预测中的潜力，我们重点关注这些方法的最新进展和新兴研究机遇，并强调多变量交通时间序列建模。我们首先列出并解释文献中使用的各类数据类型和资源。随后，对交通预测场景中的基本数据预处理方法进行分类，并继而总结预测方法与应用。最后，我们提出交通预测中的主要研究挑战，并讨论若干未来研究方向。
英文摘要：
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
