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Research On The Intention Of Taxi Sharing For Passengers Leaving The High-speed Railway Station Based On The Theory Of Planned Behavior

Posted on:2020-04-06Degree:MasterType:Thesis
Country:ChinaCandidate:J B LiFull Text:PDF
GTID:2392330599475090Subject:Transportation engineering
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As the meeting point of urban external traffic and urban traffic,the high-speed railway station bears the evacuation of arriving passengers.At present,the high-speed rail station faces some problems when it is evacuating passengers.For instance,the high-speed rail station in the center of a big city is prone to traffic congestion near the station area during the peak period of urban traffic,which leads to traffic chaos;the evacuation capacity of suburban high-speed railway stations in small and medium-sized cities is low,which easily leads to long waiting time for transfer when passengers arrive in peak period.Taxi carpooling can alleviate traffic congestion and improve vehicle efficiency.It is an effective way to connect high-speed rail stations.How to tap the high-speed railway outbound passengers' joint demand is particularly important.However,few scholars have studied the taxi ride from the perspective of passengers.Therefore,this paper takes high-speed railway outbound passengers as the research object,and studies the passengers' acceptance of taxi sharing on the planned behavior theory and structural equation model.This paper analyses the formation mechanism and mechanism of taxi sharing choice intention from the perspective of carpooler,with a view to providing reference for traffic management departments in the implementation of taxi sharing.Firstly,this paper reviews the theory of planned behavior and related literatures of comultiplication,reviews the research status,summarizes the research deficiencies,and puts forward the research ideas of this paper.On the premise of introducing relevant concepts and theoretical basis,through investigation and analysis,it is determined to select perceived value and perceived risk as the expanded variables of the theory of planned behavior.Secondly,the questionnaire was designed.521 valid questionnaires were distributed and collected on the spot in leshan high-speed railway station.After descriptive statistical analysis,reliability and validity test of the data,independent sample T test and one-way variance test were used to analyze the influence of passenger gender,age,education,occupation and income on the research variables.Finally,the structural equation model statistical software AMOS 24 was used to fit and modify the research model based on the survey data,and the relationship between variables was obtained.The results show that perceived risk is the most influential factor on taxi ride-sharing intention,followed by perceived behavior control,perceived value,behavioral attitude and subjective norms,among which subjective norms have the least influence on ride-sharing intention.Besides the direct negative effect of perceived risk on cooperative intention,perceived risk also indirectly affects cooperative intention by influencing behavioral attitude and perceived value.Perceived behavior control and perceived value not only play a direct positive role in cooperative intention,but also play an indirect role in cooperative intention by influencing behavioral attitude.Behavioral attitude is the most important factor that directly affects the intention of taxi sharing.Subjective norms have no direct influence on the cooperative intention.In addition,gender and occupation have significant differences in perceived risk;There are significant differences in perceived value,perceived risk and intention of cooperation.There are significant differences in the subjective norms and perceived risks of academic qualifications.Income has no significant difference in behavioral attitude,subjective norms,perceived behavior control,perceived risk,perceived value and cooperative intention.
Keywords/Search Tags:passengers leaving the high-speed rail station, Taxi carpooling, Theory of planned behavior, Structural equation modeling
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