Research On Behavioral Intention To Travel By HSR On Web Crawlers

With the increase of official holidays and the improvement of people's living standards,people's consumption concepts have gradually changed,and short-term travel has gradually increased.The increase in holiday travel also has a series of traffic problems.The highways are congested and the parking lot is crowded,which contray to the high-quality travel that people pursuing.The emergence of high-speed rail reduces the time cost between regions and shortens the distance between cities.Its price stability,speed,safety and comfort make the car travel and aircraft travelers gradually shift the mode of travel.With the opening high-speed rail lines,the high-speed rail travel market is hot,and even high-speed rail tourism projects have been derived from travel websites.The influencing factors of tourists taking high-speed rail have gradually become a hot issue in this research field.In this thesis,considering the characteristics of personal characteristics and the characteristics of high-speed railway stations and high-speed railway lines,the characteristics of tourists traveling on high-speed rail are studied,aiming to present better high-speed rail services for tourists.Traditional data relies on questionnaires and official statistical documents,but official statistical documents are updated at a slower pace.Questionnaires require a lot of manpower and time.The kinds of travel websites has emerged with various tourist information.A large amount of travel information is accumulated with tourist sharing travel experiences,which provide a huge data for sentific research.As tourism information is self-uploading,the information is authentic,so this thesis uses online travel to obtain personal characteristics and travel characteristics.Based on the characteristics of HSR trains,this paper establishes a regression model and randomforest model of high-speed rail for tourists.Whether the tourists take high-speed rail as the dependent variable,personal characteristics and high-speed rail stations and high-speed rail lines are the independent variables.Starting from the overall accuracy,recall rate,and f1 score,the predictive power of the random forest model is more accurate than the logistic regression,which can improve the overall accuracy of 10% and the f1-score of 10%.The model results show that the tourist attractions are negatively affected by the driving time of between the high-speed rail station and the local site,while the service time of the site has a positive impact.During the 15:00-16:00,17:00-18:00 time period,the high-speed trains have a positive impact on the high-speed rail and the train,while the time period during the 19:00-20:00 and 21:00-22:00 have a negative impact.High-speed rail travel distances range from the price between 0-500 yuan.In the high-speed rail,people who willing to spend 3000-4000,5000-6000 have the high possibility to take HSR,and the staff is more likely to take the high-speed rail than the students,indicating that the high-speed rail service group is biased towards middle-and high-level income groups.