Research On Value Measurement Method Of Personal Travel Data

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.