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《城市交通》杂志
2021年 第3期
基于数据融合与双重特征的居民出行调查扩样
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文章编号: 1672-5328(2021)03-0094-09

朱海明1,郑海星2,芮晓丽2,何枫鸣2
(1. 曜琅智慧科技产业(天津)有限公司,天津300201;2. 天津市城市规划设计研究总院有限公司,天津300201)

摘要: 居民出行调查数据具有个体属性清晰、交通含义明确、针对性强等特点,在城市交通规划与 交通需求模型构建方面具有不可替代的重要作用。为保障抽样调查数据准确反映总体的真实特征, 在综合考虑效率与精度基础上,提出一种多源数据融合、家庭户及人口特征双重约束、牛顿迭代法 相结合的居民出行调查数据完整扩样处理方法与流程。以天津滨海新区第一次综合交通调查数据为 例,论述了算法和流程的应用情况。结果表明,该方法能有效获取家庭户及人口的总体规模及其分 布,并考虑家庭户及其人口特征对扩样系数的双重影响,具有效率高、精度高、易于推广等特点。

关键词: 交通规划;居民出行调查;综合扩样;多源数据融合

中图分类号: U491.1+2

文献标识码:A

Expansion of Resident Travel Survey Based on Data Fusion and Dual Features

Zhu Haiming1, Zheng Haixing2, Rui Xiaoli2, He Fengming2
(1. U-LANE Integrated Transportation Solutions, Tianjin 300201, China; 2. Tianjin Urban Planning & Design Institute Co., Ltd., Tianjin 300201, China)

Abstract: Resident travel survey data can clearly identify individual features, provide well- defined travel characteristics and well-targeted pertinence information, which plays an irreplaceable important role in urban transportation planning and traffic demand model development. In order to ensure that the sample survey data can accurately reflect the true characteristics of the population with balanced survey efficiency and data accuracy, this paper proposes a method for the comprehensive expansion of resident travel survey data, which combines multi-dimensional data fusion, dual-constraint on family households and demographic characteristics, and Newton iteration method. Taking the first comprehensive transportation survey data of Tianjin Binhai New Area as an example, the paper reviews the application of algorithms and processes. The results show that the method can effectively obtain the overall size and distribution of family households and population while taking into account the dual-influence of family households and their demographic characteristics on the expansion coefficient, with the characteristics of high efficiency, high precision, and easy application.

Keywords: transportation planning; resident travel survey; comprehensive expansion; multi- dimensional data fusion