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.

Shiju George

Ajit Kumar Santra

Wireless Personal Communications

移动通信与计算杂志 ...发表关于移动通信和计算的教程、调查和原始研究论文；研究无线电通信、语音、数据、图像和多媒体的理论、工程和实验方面；探索传播、系统模型、语音和图像编码、多址技术、协议、性能评估、无线电局域网以及网络和架构等；98% 的回答调查的作者表示他们肯定会或可能再次在期刊上发表.WIRELESS PERSONAL COMMUNICATIONS 是一份归档、同行评审的科技期刊，涉及移动通信和计算。它研究无线电通信、语音、数据、图像和多媒体的理论、工程和实验方面。该期刊包含的部分主题列表是：传播、系统模型、语音和图像编码、多址技术、协议性能评估、无线电局域网以及网络和架构。除了上述领域，该期刊还接受涉及无线通信跨学科方面的论文，以及：大数据和分析、商业和经济、社会和环境。该期刊有五种主要类型的论文：完整的技术论文、短论文、政策和标准化的技术方面，提供新的研究思想和实验想法的信件，以及由知名专家撰写的关于重要和新兴主题的特邀论文。

Wireless Personal Communications is a peer-reviewed scientific journal focusing on the fields of mobile communications and computing. The journal publishes theoretical, engineering, and experimental research on radio communications, voice, data, images, and multimedia. Key research topics include propagation, system modeling, voice and image coding, multiple access techniques, protocol performance evaluation, wireless local area networks, and network architectures. Additionally, the journal welcomes interdisciplinary papers related to wireless communications, covering areas such as big data and analytics, business and economics, as well as social and environmental issues. The journal accepts five main types of papers: full technical papers, short papers, policy and standardization papers, letters providing new research ideas and experimental concepts, and invited papers written by renowned experts on important and emerging topics. 98% of surveyed authors indicated that they would definitely or probably publish again in this journal.

《Wireless Personal Communications》是一份同行评审的科技期刊，专注于移动通信和计算领域。期刊发表关于无线电通信、语音、数据、图像和多媒体的理论、工程和实验研究。主要研究主题包括传播、系统模型、语音和图像编码、多址技术、协议性能评估、无线局域网以及网络和架构。此外，期刊还欢迎涉及无线通信跨学科的论文，涵盖大数据与分析、商业与经济、社会与环境等领域。期刊接受五种主要类型的论文：完整技术论文、短论文、政策与标准化技术方面的论文、提供新研究思想和实验想法的信件，以及由知名专家撰写的关于重要和新兴主题的特邀论文。98%的调查作者表示他们肯定会或可能再次在本期刊上发表。

Wireless Pers Commun

Traffic Prediction Using Multifaceted Techniques: A Survey

A detailed survey of the latest forecasting technologies and contributes to understand the key concept behind the prediction approaches 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.

Category	Quartile
计算机科学	4
计算机科学, 电信学	4

Traffic Prediction Using Multifaceted Techniques: A Survey

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