多源数据融合在上海市综合交通模型中的应用

Application of Multi-Dimensional Data Fusion in Shanghai's Comprehensive Transportation Model

随着个体出行行为的多源数据日益丰富,既有的综合交通模型技术也需要持续升级。结合上海市第六次综合交通调查等数据,探讨多源数据融合在赋能综合交通模型改进中的应用。首先将多源数据划分为抽样数据和信息化大数据两大类21小类,提出基于多源数据融合的出行特征分析方法。结合居民出行调查数据及各类大数据,分析多源数据融合在上海市域人员出行需求模型、对外人员出行需求模型、道路系统运行模型、公共交通系统运行模型、货运交通模型升级中的具体应用,给出了不同交通模型参数的估计、标定及验证方法。最后,总结多源数据融合在分析精度、模型广度、研究细度和研究维度方面对上海综合交通模型的提升作用。

As multi-dimensional data for individual travel behavior become increasingly abundant,existing comprehensive transportation model technologies also need constant upgrade.Combining data from the Sixth Comprehensive Transportation Survey and other sources in Shanghai,this paper discusses the application of multi-dimensional data fusion in improving comprehensive transportation models.Firstly,the multi-dimensional data are divided into two broad categories,namely sampling data and information-based big data,with 21 subcategories.A multi-dimensional data fusion method is developed to analyze travel characteristics.Based on resident survey data and various big data,specific applications in upgrading transportation models are discussed in this paper,including Shanghai's regional travel demand model,intercity travel demand model,road system operation model,public transit system operation model,and freight transportation model.The methods of parameter estimation,calibration,and verification for transportation models are provided.Finally,the paper summarizes the role of multi-dimensional data fusion in improving analysis precision,breadth,granularity,and dimensionality of Shanghai's comprehensive transportation model.