智慧交通系统中多源异构大数据融合处理方法探析
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张明昭
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锦州市数据中心，辽宁锦州，121000
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摘要：城市交通系统越来越复杂，传统数据处理方法处理不了海量、多源、异构的交通数据，让信息孤岛现象比较突出，实时分析与决策的能力存在不足。本文提出一套由数据清洗、特征提取、分层融合组成的处理框架，来解决智慧交通系统里多源异构大数据融合处理的难题，目的是提升交通数据的一致性、可用性与价值密度。搭建涵盖传感器数据、视频流、社交媒体信息等多源数据的实验环境，同时用流式计算加批处理的方式，验证了该框架在数据对齐、冗余消除及实时融合方面的实际效果。经研究确认，这套方法能提高交通状态识别的准确性与事件检测的时效性，给动态路径规划、拥堵预警等应用提供更可靠的数据支撑。本文给出了智慧交通系统数据整合与智能分析的可行技术路径，能为提升城市交通管理效率、推动交通系统智能化转型提供实践参考。
关健词：智慧交通；数据融合；异构数据 |
Exploration of multi-source heterogeneous big data fusion processing methods in intelligent transportation systems
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Mingzhao Zhang
Jinzhou Data Center, Jinzhou Liaoning 121000, China
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Abstract： Urban transportation systems are becoming increasingly complex. Traditional data processing methods are unable to handle massive, multi-source, and heterogeneous traffic data, leading to a prominent phenomenon of information silos and insufficient real-time analysis and decision-making capabilities. This paper proposes a processing framework consisting of data cleaning, feature extraction, and hierarchical fusion to address the challenges of multi-source and heterogeneous big data fusion in smart transportation systems. The aim is to enhance the consistency, usability, and value density of traffic data. An experimental environment encompassing multi-source data such as sensor data, video streams, and social media information is established. Meanwhile, the framework's practical effectiveness in data alignment, redundancy elimination, and real-time fusion is verified through a combination of stream computing and batch processing. Research confirms that this approach improves the accuracy of traffic state recognition and the timeliness of event detection, providing more reliable data support for applications such as dynamic route planning and congestion warning. This paper presents a feasible technical path for data integration and intelligent analysis in smart transportation systems, offering practical references for enhancing urban traffic management efficiency and promoting the intelligent transformation of transportation systems.
Keywords : intelligent transportation；data fusion；heterogeneous data
参考文献
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