| 基于智能手机调查数据的居民出行活动特征分析 |
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| 引用本文： | 杨超, 朱荣荣, 涂然. 基于智能手机调查数据的居民出行活动特征分析[J]. 交通信息与安全, 2015, (6): 25-32. doi: 10.3963/j.issn 1674-4861.2015.06.004 |
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| 作者姓名： | 杨超 朱荣荣 涂然 |
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| 作者单位： | 同济大学道路与交通工程教育部重点实验室 上海 201804;同济大学道路与交通工程教育部重点实验室 上海 201804;弗吉尼亚理工学院暨州立大学土木与环境工程学院 弗吉尼亚 黑堡 美国 24061 |
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| 摘 要： | 城市居民出行活动信息是城市规划、交通管理和居民行为研究的重要参考依据。采用传统的基于入户访问和纸质问卷的居民出行调查方式存在受访者负担重、调查精度低、调查成本高等问题,设计并实现了一种基于嵌入 GPS(Global Positioning System)模块的智能手机的居民出行调查系统。通过高频的手机 GPS 定位获取居民出行轨迹,设计基于规则的轨迹数据处理算法,自动提取出行信息。以上海市杨浦区同济新村为例,对比传统问卷调查和基于手机的调查所得的出行数据。利用调查结果对基于智能手机调查的出行生成模型进行系数修正,并对传统调查方式的误差进行分析。发现传统调查的总体误差在33%左右,其中非基家出行的误差更是达到近159%。最后,基于手机调查的数据,对区域内居民活动特征进行分析。
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| 关 键 词： | 交通大数据 居民出行调查 出行特征 智能手机 GPS |
Analysis of the Travel Characteristics of Residents in Shanghai Using the Itinerary Data Collected from Smartphones |
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| YANG Chao, ZHU Rongrong, TU Ran. Analysis of the Travel Characteristics of Residents in Shanghai Using the Itinerary Data Collected from Smartphones[J]. Journal of Transport Information and Safety, 2015, (6): 25-32. doi: 10.3963/j.issn 1674-4861.2015.06.004 |
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| Authors: | YANG Chao ZHU Rongrong TU Ran |
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| Abstract: | Travel data of urban residents is an important base for urban planning,traffic management and behavior research.Traditional methods such as face-to-face interview and paper-based questionnaire are of high cost and low accura-cy.This study develops a travel survey system based on GPS-enabled smartphones.A rule-based algorithm is proposed to extract travel information automatically from high-frequency GPS data collected by smartphones.A case study is carried out at New Tongji Village,a residential area in Shanghai.A comparison of travel data obtained from traditional question-naire-based surveys and smartphone-based surveys is also made.The results from the questionnaire survey are used to im-prove the parameters of the trip generation model based on the data from the smartphone-based survey,and the inaccura-cy in traditional questionnaire-based survey is also studied.It is shown that the overall error of the traditional survey is a-bout 33%,and the error for non-home-based travel is as high as 1 59%.Finally,travel behavior of urban residents is also analyzed based on the data from smartphone-based survey. |
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| Keywords: | big data household travel survey trip characteristics smartphone GPS |
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