The investigation of traffic flow prediction and optimization based on Spark

Spark and flink have made great strides in big data processing in recent years. Although the current spark can process a large amount of data at the same time, it still has a lot of shortcomings in the transparency, credibility, and practicality of the model. This paper provides a comprehensive overview of how to tackle the performance bottlenecks, insufficient model interpretability, and lack of regional adaptability faced by Spark and Flink in big data processing. It discusses the introduction of interpretable algorithms such as SHAP and LIME to enhance the transparency and user trust of neural network models. Then it discusses how to combine time-aware transfer learning and Geographic Information Systems (GIS) technology to enhance the technology generalization and adaptability of Spark machine learning models. Time-aware transfer learning uses historical datas temporal evolution to ensure models perform well in new time periods or scenarios, while GIS technology enables more precise predictions and analyses based on geographical data, enhancing spatial adaptability. Lastly, the study explores hybrid processing strategies by integrating Apache Flink, Kafka Streams, and Spark batch processing frameworks. This approach not only facilitates efficient real-time data processing and detailed analysis but also enhances the models flexibility and processing capabilities in complex data scenarios. By integrating these techniques, it is possible to improve the efficiency and effectiveness of big data processing frameworks in addressing complex real-world challenges, thereby advancing technology and application development in related fields.