城市交通拥堵预测中大数据与数学建模的融合研究

Research on the Integration of Big Data and Mathematical Modeling in Urban Traffic Congestion Prediction

摘要: 本文摒弃大数据与数学建模的宏观综述视角，以城市交通拥堵预测为具体应用场景，系统梳理数学建模在该场景下的核心流程，界定了大数据的三类数据类型及数据量、多样性、速度等关键特征，并详细拆解了大数据分析在数学建模中的典型流程，包括数据预处理、建模与分析、结果解释与可视化三大环节。同时，本文探讨了分布式计算、卷积神经网络、遗传算法等主流算法与大数据分析方法的适配性，列举了其在气象预报建模、癌症放疗精准治疗等典型场景中的应用实践。本文表明，大数据分析通过数据降噪、特征优化等手段显著提升了数学模型的准确性与鲁棒性，而数学建模则为海量复杂数据的分析提供了核心逻辑框架。未来，随着实时数据处理技术的发展，二者的深度融合将推动数学建模向“数据驱动的精准预测”方向迈进，在复杂系统模拟、实时决策支持等领域发挥更大价值。

Abstract: This article moves away from the macro overview perspective of big data and mathematical modeling, focusing instead on the specific application scenario of urban traffic congestion prediction. It systematically outlines the core process of mathematical modeling in this context, defines three types of big data along with key characteristics such as data volume, diversity, and velocity, and provides a detailed breakdown of the typical process of big data analysis in mathematical modeling, encompassing three major steps: data preprocessing, modeling and analysis, and result interpretation and visualization. Additionally, this article explores the adaptability of mainstream algorithms such as distributed computing, convolutional neural networks, and genetic algorithms to big data analysis methods, and lists their practical applications in typical scenarios like weather forecast modeling and precision radiotherapy for cancer treatment. This article demonstrates that big data analysis significantly enhances the accuracy and robustness of mathematical models through techniques such as data denoising and feature optimization, while mathematical modeling provides the core logical framework for analyzing massive and complex data. In the future, with the development of real-time data processing technology, the deep integration of the two will propel mathematical modeling towards the direction of “data-driven precise prediction”, exerting greater value in areas such as complex system simulation and real-time decision support.