数据驱动的交通拥堵要因分析迁移学习方法研究

合理分析不同时空条件下交通拥堵要因是解决交通拥堵问题的前提和基础，然而，时空数据非独立同分布，使得传统的特征选择方法不能直接用于分析拥堵的要因；同时，经处理的交通拥堵的地理信息、社交网络等数据均为高维海量数据，增加了解决问题的难度。因此，本项目拟运用迁移学习、特征选择、大数据并行计算等理论和方法，研究数据驱动的交通拥堵要因分析迁移学习方法。首先，构建面向高维海量数据的时空特征迁移学习方法相关理论；然后，通过构建面向高维海量数据的特征选择方法和分类器，构建时空特征迁移学习方法；接着，将提出的方法应用于城市交通拥堵要因分析问题，提出融合GIS和社交网络媒体的交通拥堵要因分析迁移学习方法；最后，开发交通拥堵要因分析决策支持原型系统并进行实证研究。研究成果丰富了迁移学习和特征选择的文献，可为地理、农业等领域构建时空迁移学习方法提供借鉴。方法可以为交通管理、城市规划、交警等部门提供决策支持。

Reasonable analysis of traffic congestion under different conditions of time and space is the premise and basis for solving the traffic congestion problem. However, the spatio-temporal data are not independent and identically distributed, therefore, traditional methods of feature selection cannot be directly used to analyze the cause of congestion. At the same time, relevant geographic information, social networks and other data are high-dimensional mass data, increasing the difficulty of solving the problem. Therefore, this project intends to apply the theories and methods of transfer learning, feature selection and big data parallel computing to study a data-driven transfer learning method for analyzing the key factors of traffic congestions. First, we construct the theory of spatio-temporal feature transfer learning for high-dimensional massive data; then we construct the spatio-temporal feature transfer learning method by constructing the feature selection method and classifier for high-dimensional massive data; we apply the proposed method to the analysis of traffic congestion of urban area. The project proposes a method to analyze the problem of traffic congestion by integrating GIS with social network media. Finally, we develop a prototype system to analyze the causes of traffic congestion and conduct empirical research. The research results have enriched the literature of transfer learning and feature selection, which can provide reference for the construction of spatial and temporal transfer learning methods in the fields of geography and agriculture. The proposed method can provide decision support for traffic management, urban planning, traffic police and other departments.