| 基于居民出行活动特征的个体经济水平推断方法 |
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| 引用本文： | 李秋萍,刘逸诗,巩诗瑶,周素红,卓莉,陶海燕,栾学晨.基于居民出行活动特征的个体经济水平推断方法[J].武汉大学学报(信息科学版),2019,44(10):1575-1580. |
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| 作者姓名： | 李秋萍 刘逸诗 巩诗瑶 周素红 卓莉 陶海燕 栾学晨 |
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| 作者单位： | 1.中山大学地理科学与规划学院综合地理信息研究中心, 广东 广州, 510275 |
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| 基金项目： | 国家自然科学基金41971345国家自然科学基金41522104国家自然科学基金41531178国家自然科学基金51678577广东省自然科学基金2016A030310168测绘遥感信息工程国家重点实验室开放基金16S02 |
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| 摘 要： | 提出了一种基于居民出行活动特征的个体经济水平推断方法。从出行轨迹的移动性指标、基于居住地的出行特征和出行活动链模式3个方面提取13维出行活动特征，以广州市居民出行日志调查数据为训练和测试数据，利用随机森林方法进行个体经济收入水平的推断与检验。结果表明，该方法能够获得最高80%的个体收入水平推断精度。基于家的出行特征（如工作时间（9:00-18:00）离家距离众数等、出行链模式）以及与出行范围有关的移动性指标（如最大距离、回旋半径）在推断个体经济水平上的重要性较高，而衡量出行地点空间异质性的指标（如空间多样性等）重要性相对较低。
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| 关 键 词： | 居民出行 活动特征 移动性指标 个体经济收入水平 随机森林 |
| 收稿时间： | 2018-06-29 |
Individual Income Level Inference Method Based on Travel Behavior of Urban Residents |
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| Institution: | 1.Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China |
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| Abstract: | In this paper, we propose an individual income level inference method based on travel behavior of urban residents. Firstly, we define 13 residents' travel behavior features from 3 different aspects. These features are the mobility indicators based on trajectories, home-based travel characteristics, and activity chain patterns. Secondly, 80% of the daily travel diary data of residents in Guangzhou in 2013 is taken as the training sample and the remaining as the test data. Thirdly, the random forest method is trained and used to infer individual's income level. The experiment results show that our proposed method can obtain an overall accuracy of 80%. Among 13 travel behavior features, the home-based features (i.e. the mode of the travel distance from home during working time (9:00 AM to 18:00 PM)), the activity chain pattern, and travel distance and scope related features (i.e., the maximum distance between two successive activity points, and radius of gyration) have high feature importance. However, the features representing the spatial heterogeneity of the activity points have relatively low importance, such as the spatial diversity. |
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