This paper surveys the field of traffic prediction, exploring various data types, preprocessing techniques, and prediction models, including traditional machine learning and deep learning methods. It discusses various applications of traffic prediction, such as ride-sharing, order dispatching, and route planning, and highlights emerging challenges and opportunities in the field, focusing on the increasing complexity of data and the need for interpretable and automated models.

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This survey provides a comprehensive overview of traffic prediction, covering various aspects from data sources and preprocessing to prediction methods and applications. It offers valuable insights into the current state of the field and identifies key challenges and opportunities, making it a valuable resource for researchers and practitioners. However, the lack of in-depth analysis and concrete recommendations limits its rating to a 4.