多模态交通大数据融合与可视化

Multimodal Traffic Big Data Fusion and Visualization Technology

摘要: 为缓解当前城市交通数据的复杂性及利用率低下的问题，本文设计一套适用于中国现代化城市的交通大数据可视化系统。本文遵循前后端分层设计的原则，前端设计了直观易用的可视化界面，包括交通大数据中心、交通事件管理等模块的交互展示；后端则运用Python技术，实现对路网结构、交通设施、交通事件等多元数据的精细化管理和高效处理，从而实现了对时空相关性理论研究成果的直观呈现。此系统的开发，将原本繁杂的交通数据以清晰直观的数据统计图表形式展示给决策者，为城市交通拥堵治理和短时流量预测提供了有力的数据支撑，极大地提高了城市管理者的决策效率和准确性。

Abstract: To alleviate the complexity of the current urban traffic data and low utilization rate of the problem, this paper designs a set of applicable in China’s modern city traffic data visualization system. In this paper, follow the principle of front and back side hierarchical design, front-end design the visual interface intuitive and easy to use, including traffic large data centers, traffic incident management modules, such as interactive display; At the back end, Python technology is used to realize the fine management and efficient processing of multivariate data such as road network structure, traffic facilities, and traffic events, so as to realize the intuitive presentation of the theoretical research results of spatio-temporal correlation. The development of this system presents the originally complicated traffic data to the decision-makers in the form of clear and intuitive data statistics charts, which provides powerful data support for urban traffic congestion management and short-term flow prediction, and greatly improves the decision-making efficiency and accuracy of urban managers.