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.

翻译：交通预测在缓解交通拥堵方面扮演着关键角色，而交通拥堵是全球性的严峻问题，导致额外旅行时间损失和燃油消耗增加等负面后果。将新兴技术融入交通系统为显著提升交通预测能力提供了机遇，同时也带来了新的研究课题。为奠定理解交通预测中开放性研究挑战的基础，本文旨在全面综述交通预测方法论。具体而言，我们聚焦于基于人工智能的交通预测方法的最新进展与新兴研究机遇，因其在交通预测领域近期取得的成功和展现的潜力，尤其侧重多变量交通时间序列建模。我们首先梳理并解释文献中使用的各类数据类型与资源。随后，对交通预测背景下关键的数据预处理方法进行分类，并依次总结预测方法及其应用。最后，我们提出交通预测中的主要研究挑战，并探讨未来研究方向。