【作者】 李博；

【导师】 徐洪峰；

【作者基本信息】 大连理工大学 ， 建筑与土木工程（专业学位）， 2022， 硕士

【摘要】 数据正在成为改变全球竞争格局的关键力量,个人数据作为其中最有价值的部分迸发蓬勃生机。在智慧交通系统建设和完善过程中,居民出行调查、视频、地磁、公交车载POS机、移动通信等主动或被动采集了海量的个人出行数据,且人工智能等先进技术的发展使得数据蕴含的经济价值得以更大程度的展现,交通服务运营商作为上述海量数据的实际控制者事实上垄断了数据中所蕴含的巨大经济价值,而现阶段数据价值测度方法的不足使得个体出行者即便有意主张自身权益也缺少定量依据。鉴于此,本研究结合城市客运交通的典型场景,以质性分析的方法从数据产生者的角度研究影响其提供自身出行数据的因素,梳理出“个体知识”“平台特征”“风险意识”“收益期望”和“社会规范”五个影响因素。接下来以意愿价值评估法为指导,设计调查问卷。基于问卷数据,构建并求解个人出行数据受偿意愿双尾Tobit模型以测度数据价值。最后,将质性分析得到的影响因素作为心理潜变量,通过因子分析求解潜变量适配值并纳入模型,建立并求解考虑心理潜变量的个人出行数据受偿意愿双尾Tobit模型,并与未加入心理潜变量的模型加以对比,系统分析各解释变量对个人出行数据价值的影响及程度。研究结果表明,考虑心理潜变量后的模型估计结果拟合程度更高,性别、年龄、使用网约车APP的频次、使用共享单车APP的频次等解释变量对相应类别的个人出行数据受偿意愿有显著的抑制作用。以自然人信息为例,女性被调查者的自然人信息受偿意愿比男性少149.80元;55岁以上的被调查者的自然人信息受偿意愿比18～35岁的人群少487.80元;每月使用共享单车APP11～20次的人群比使用频次为10次以下人群的自然人信息受偿意愿低170.78元。月收入等解释变量与相应类别的个人出行数据受偿意愿正相关,以出发地和到达地信息为例,与月收入5000元以下学历的被调查者相比,月收入10000元以上的群体出发地和到达地信息受偿意愿要高816.08元。本研究通过估计考虑心理潜变量的个人出行数据受偿意愿双尾Tobit模型中显著自变量的参数,更深入地理解个体出行者的人口统计学特征和心理潜变量对受偿意愿的影响和作用,最终形成基于个体视角的个人出行数据价值测度方法,为出行者主张自身合理权益、确立公平合理的数据共享机制提供定量依据,促进交通数据资源开放共享。更多还原

【Abstract】 Data is becoming a key force to change the global competitive landscape,and personal data,as the most valuable part,is bursting with vitality.In the process of construction and improvement of intelligent transportation system,residents’ travel survey,video,geomagnetism,on-board bus POS,mobile communication,etc.have actively or passively collected a large amount of personal travel data,and the development of artificial intelligence and other advanced technologies has enabled the economic value contained in the data to be displayed to a greater extent.As the actual controller of the above massive data,transportation service operators actually monopolize the huge economic value contained in the data,At present,the lack of data value measurement methods makes individual travelers lack quantitative basis even if they intend to claim their own rights and interests.In view of this,this study investigates the factors influencing the provision of travel data from the perspective of data generators by combining the typical scenarios of urban passenger transportation with qualitative analysis,and identifies "individual knowledge","platform characteristics","risk awareness","revenue expectation" and "social norms" were identified as the five influencing factors.Next,the questionnaire is designed under the guidance of the willing value evaluation method.Based on the questionnaire data,a two tailed Tobit model of personal travel data willingness to pay is constructed and solved to measure the data value.Finally,the factors obtained from the qualitative analysis were used as psychological latent variables,and the latent variables were included in the model through factor analysis to solve the two-tailed Tobit model of personal travel data willingness to pay considering the psychological latent variables,and compared with the model without the psychological latent variables to systematically analyze the influence and degree of each explanatory variable on the value of personal travel data.The results show that the fitting degree of the model estimation results after considering the psychological potential variables is higher,and the explanatory variables such as gender,age,frequency of using the online car Hailing app and frequency of using the shared bicycle app have a significant inhibitory effect on the willingness to be compensated for the corresponding categories of personal travel data.Taking natural person information as an example,female respondents’ willingness to pay for natural person information is 149.80 yuan less than that of men;The willingness of the respondents over 55 years old to receive information from natural persons is 487.80 yuan less than that of the 18-35 years old;The people who use the shared bicycle app11-20 times a month have a lower willingness of170.78 yuan than the people who use the shared bicycle app11-20 times a month.Explanatory variables such as monthly income are positively correlated with the willingness to be compensated for the corresponding categories of personal travel data.Taking the departure and arrival information as an example,the willingness to be compensated for the departure and arrival information of the group with a monthly income of more than 10000 yuan is816.08 yuan higher than that of the respondents with a monthly income of less than 5000 yuan.By estimating the parameters of significant independent variables in the two tailed Tobit model of willingness to pay for personal travel data considering psychological latent variables,this study has a deeper understanding of the demographic characteristics of individual travelers and the impact and role of psychological latent variables on willingness to pay,and finally forms a value measurement method of personal travel data based on an individual perspective,so as to claim their own reasonable rights and interests for travelers Establish a fair and reasonable data sharing mechanism to provide quantitative basis and promote the open sharing of traffic data resources.更多还原