Road transportation is the largest and complex nonlinear entity of the traffic management system. Accurate prediction of traffic-related information is necessary for an effective functioning of Intelligent Transportation System (ITS). It is still a challenge for the departments of transportation to choose an appropriate prediction technique for the ITS applications. That is, a user must be able to utilize the disseminated information effectively by the forecasting models. This paper provides a detailed survey of the latest forecasting technologies and contributes to understand the key concept behind the prediction approaches. To provide guidelines to the decision-maker, this paper reviews multifaceted techniques developed by various authors for traffic prediction. We start classifying each technique into four categories namely, Machine Learning (ML), Computational Intelligence (CI), Deep Learning (DL), and hybrid algorithms. Many have conducted survey using model-driven or data-driven methods. We are the first to explore the area of traffic prediction based on the advances in multifaceted techniques proposing algorithmic approaches for key traffic characteristics in the forecasting process. The role of dependent factors in the prediction are analyzed thoroughly. We have analyzed each algorithm chronologically based on various traffic traits. The approaches are summarized based on the rational usage and performance of each technique. The analysis led to several research queries, and the appropriate responses are provided based on our detail survey. Finally, it is confirmed that currently, CI-MLs and DL hybrid techniques outperforms the rest in the field of traffic prediction. Ultimately suggested open challenges and future direction to explore the capability of DL and hybrid techniques further in the field of traffic prediction. 

公路运输是交通管理系统中最大，最复杂的非线性实体。为了使智能交通系统（ITS）有效运行，必须准确预测与交通有关的信息。对于运输部门来说，为ITS应用选择合适的预测技术仍然是一个挑战。也就是说，用户必须能够通过预测模型有效地利用传播的信息。本文提供了对最新预测技术的详细调查，并有助于理解预测方法背后的关键概念。为了向决策者提供指导，本文回顾了由多位作者开发的用于流量预测的多方面技术。我们开始将每种技术分为四类：机器学习（ML），计算智能（CI），深度学习（DL）和混合算法。许多人使用模型驱动或数据驱动的方法进行了调查。我们是第一个根据多方面技术的进展来探索交通预测领域的方法，并提出了预测过程中关键交通特征的算法方法。彻底分析了相关因素在预测中的作用。我们已根据各种流量特征按时间顺序分析了每种算法。根据每种技术的合理用法和性能总结了这些方法。分析导致了一些研究疑问，并根据我们的详细调查提供了适当的答复。最后，确认目前 CI-ML和DL混合技术在流量预测领域的表现优于其他技术。最终提出了开放的挑战和未来的方向，以进一步探索DL和混合技术在交通预测领域的能力。