基于机器学习的交通流量预测模型研究

摘    要

随着城市化进程的加快和汽车保有量的持续增长，交通流量预测成为城市交通规划和管理中的关键问题。准确的交通流量预测有助于优化交通信号控制、缓解交通拥堵、提高道路通行能力，对提升城市交通效率和出行体验具有重要意义。近年来，机器学习技术的快速发展为交通流量预测提供了新的解决方案。本研究旨在探讨基于机器学习的交通流量预测模型，以提高预测精度和实时性。传统的交通流量预测方法，如基于统计学的方法和基于时间序列的方法，往往受限于数据的不确定性和模型的简单性，难以应对复杂的交通环境和突发事件。而机器学习技术以其强大的数据驱动能力和模型学习能力，在交通流量预测中展现出巨大的潜力。本研究通过构建基于机器学习的交通流量预测模型，旨在提高预测的准确性和实时性，为城市交通管理提供有力支持。本研究首先对交通流量数据进行了预处理和特征提取，包括数据清洗、缺失值填充、异常值处理等步骤，以确保数据的质量和完整性。接着，通过深入分析交通流量的影响因素和变化规律，提取了包括时间特征、空间特征、天气特征等在内的多个特征变量。


关键词：机器学习  交通流量预测  神经网络  


Abstract 
With the acceleration of urbanization and the continuous growth of car ownership, traffic flow forecasting has become a key issue in urban traffic planning and management. Accurate traffic flow prediction helps to optimize traffic signal control, alleviate traffic congestion, improve road capacity, and is of great significance to improve urban traffic efficiency and travel experience. In recent years, the rapid development of machine learning technology has provided new solutions for traffic flow prediction. The purpose of this study is to explore the traffic flow prediction model based on machine learning in order to improve the prediction accuracy and real-time performance. Traditional traffic flow forecasting methods, such as those based on statistics and time series, are often limited by the uncertainty of data and the simplicity of the model, which is difficult to deal with the complex traffic environment and emergencies. Machine learning technology, with its strong data-driven ability and model learning ability, shows great potential in traffic flow prediction. By constructing a traffic flow prediction model based on machine learning, this study aims to improve the accuracy and real-time performance of the prediction and provide strong support for urban traffic management. In this study, the traffic flow data were preprocessed and feature extracted, including data cleaning, missing value filling, outlier processing, etc., to ensure the quality and integrity of the data. Then, through in-depth analysis of the influencing factors and changing rules of traffic flow, we extract several characteristic variables, including time characteristics, space characteristics and weather characteristics.


Keyword:Machine learning  Traffic flow forecast  Neural network 




目    录
1引言 1
2机器学习与交通流量预测理论基础 1
2.1交通流量预测的概念 1
2.2交通流量数据特性 2
2.3相关技术与工具 2
3交通流量数据预处理与特征工程 3
3.1数据收集与清洗 3
3.2数据融合与集成 3
3.3特征提取与选择 4
3.4数据可视化与分析 4
4机器学习模型构建与训练 5
4.1模型选择与评估 5
4.2模型训练与超参数调优 5
4.3模型泛化与正则化 6
4.4模型集成与融合 7
5结论 7
参考文献 9
致谢 10