摘 要
随着城市化进程的加速和机动车保有量的持续增长，交通拥堵、环境污染等问题日益严重，传统交通管理模式已难以满足现代城市发展需求。为此，本研究旨在构建基于大数据技术的智能交通系统，以实现交通资源优化配置与高效管理。通过融合多源异构数据，包括车辆GPS轨迹、电子警察记录、气象信息等，采用深度学习算法对交通流量进行精准预测，并提出一种改进型卷积神经网络模型用于实时路况识别。该系统创新性地引入了边缘计算架构，在降低云端压力的同时提高了响应速度。实验结果表明，所设计的智能交通系统能够有效缓解高峰时段道路拥堵状况，平均车速提升了15%，交通事故发生率降低了10%。此外，通过对历史数据的深度挖掘，为城市规划部门提供了科学决策依据，实现了从被动式管理向主动式服务转变。本研究不仅为解决当前城市交通问题提供了新思路，也为未来智慧城市建设奠定了坚实基础，具有重要的理论意义和应用价值。
关键词：智能交通系统 大数据技术 深度学习
Abstract
With the acceleration of urbanization and the continuous increase in motor vehicle ownership, traffic congestion and environmental pollution have become increasingly severe, making traditional traffic management models inadequate for modern urban development needs. This study aims to construct an intelligent transportation system based on big data technology to achieve optimal allocation and efficient management of traffic resources. By integrating multi-source heterogeneous data, including vehicle GPS trajectories, electronic police records, and meteorological information, deep learning algorithms are employed to accurately predict traffic flow, while an improved convolutional neural network model is proposed for real-time road condition recognition. The system innovatively incorporates an edge computing architecture, reducing cloud pressure and enhancing response speed. Experimental results demonstrate that the designed intelligent transportation system effectively alleviates peak-hour road congestion, with average vehicle speeds increasing by 15% and traffic accident rates decreasing by 10%. Furthermore, through in-depth mining of historical data, this system provides scientific decision-making support for urban planning departments, facilitating a shift from reactive management to proactive service. This research not only offers new insights into addressing current urban traffic issues but also lays a solid foundation for future smart city construction, possessing significant theoretical implications and practical value.
Keyword:Intelligent Transportation System Big Data Technology Deep Learning
目 录
引言 1
1大数据与智能交通系统概述 1
1.1智能交通系统发展现状 1
1.2大数据技术在交通中的应用 2
1.3系统设计的关键挑战 2
2数据采集与预处理方法 3
2.1多源交通数据获取途径 3
2.2数据清洗与质量控制策略 3
2.3数据融合与特征提取技术 4
3智能交通系统架构设计 4
3.1整体架构规划与模块划分 4
3.2数据存储与管理方案 5
3.3实时数据分析与处理机制 5
4系统功能实现与优化 6
4.1交通流量预测模型构建 6
4.2路径规划与导航服务 6
4.3系统性能评估与改进措施 7
结论 7
参考文献 9
致谢 9
