交通运输系统工程与信息

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (5): 29-35.

• 综合交通运输体系论坛 • 上一篇    下一篇

基于出行特征的用地类型推断方法研究

张政1，陈艳艳*1，梁天闻2   

  1. 1.北京工业大学 城市交通学院，北京 100124；2.交通运输部公路科学研究院，北京 100088
  • 收稿日期:2020-05-10 修回日期:2020-06-25 出版日期:2020-10-25 发布日期:2020-10-26
  • 作者简介:张政(1992-)，男，山东人，博士生.
  • 基金资助:

    国家重点研发计划/National Key Research and Development Program of China(2017YFC0803903).

Inferring Land Use Characteristics Using Travel Patterns

ZHANG Zheng1, CHEN Yan-yan1, LIANG Tian-wen2   

  1. 1. College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China; 2. Research Institute of Highway Ministry of Transport, Beijing 100088, China
  • Received:2020-05-10 Revised:2020-06-25 Online:2020-10-25 Published:2020-10-26

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摘要：

提出一种基于卷积神经网络推测城市交通小区内用地特征的算法，同时对交通小区内多种用地类型进行预测.选用公共交通出行数据集和网约车出行数据集，融合多种出行方式的出行特征对交通小区内用地特征刻画.提取交通小区内发生强度，吸引强度和产吸差强度3个指标作为模型输入，训练得到基于区域内出行特征双通道的卷积神经网络模型，采用网格寻优方法确定最优网络结构.选取北京市六环内交通小区作为研究对象，结果表明，本文算法能够同时推断交通小区内居住、工作和休闲用地特征，并获得各用地类型在小区内占比分布.

关键词: 城市交通, 用地类型推测, 卷积神经网络, 交通小区, 出行特征

Abstract:

This paper proposes a land use inferring method based on the convolutional neural network (CNN), which can infer multiple lane use types at the traffic analysis zones (TAZs) simultaneously. The study combines public transport mobility dataset and online car- hailing mobility dataset for inferring land use type. Generation intensity, attraction intensity, and difference between generation and attraction intensity are extracted from the travel dataset, which are then used to train the CNN. The optimal network structure is determined by grid search. The TAZs within the 6th Ring Road of Beijing are taken as examples for the analysis. The results indicate that the proposed method is able to estimate the proportion distribution of several land use types at the same time within the TAZs, such as resident, workplace and leisure land uses.

Key words: urban traffic, inferring land use type, convolutional neural network (CNN), traffic analysis zone (TAZ), travel patterns

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