Research For The Analysis And Promotion Method Of Citizon’s Trip Happiness

With the continuous improvement of living standards,residents have more stringent requirements on travel environment.Travel happiness has gradually become an important indicator to measure the level of travel,and an important embodiment of travel happiness is the satisfaction of residents to travel.In recent years,the relationship between travel and happiness is getting closer and closer.The travel happiness of residents has become a new direction in the field of transportation research.The residents’ travel behavior characteristics,affecting the residents travel mode choice and travel satisfaction significantly factors,can accurately grasp the law of the residents,to predict travel demand and optimize the structure of travel.At the same time,travel happiness researches can be targeted to optimize the environment of travel,improve travel experience,promote the sustainable development of urban traffic,and improve the well-being of urban residents.It provides a reference for improving transportation infrastructure construction,making transportation policy and allocating resources reasonably.This paper takes the Futian District of Shenzhen City as the object to carry out specific research.The content is as follows:Firstly,the MNL model is constructed by using the stochastic utility maximization theory,and the parameters of the model are estimated by SPSS software.The mechanism of the influence of individual attributes,family attributes and travel characteristics on the choice of residents’ travel mode is analyzed,and the travel optimization measures are put forward based on the salient influencing factors.Secondly,the evaluation level of travelers’ travel satisfaction is obtained through questionnaire survey,which is used to reflect travelers’ travel happiness of cognitive dimension in the journey process.Combining travel factors and safety factors,a structural equation model was built to analyze the significant factors affecting residents’ travel satisfaction,and the parameters were calibrated.Then,the current sample data were divided into training set and test set,and the Support Vector Machine(SVM)model and Multi-Layer Perceptron(MLP)deep learning model were introduced to fit the travel satisfaction measurement model,and the model with the best fitting accuracy was determined.Finally,according to the model fitting results,measures to improve the satisfaction degree are proposed from the perspectives of increasing the flexibility of various transportation modes,improving the safety of travel and improving the sense of happiness of residents in travel.Thirdly,on the basis of the previous research,factors that have no significant influence on residents’ travel satisfaction in the sample data were eliminated to establish a forecasting model of residents’ travel satisfaction with the best fitting accuracy.These factors can be used to evaluate the effect of the implementation of measures to improve satisfaction and provide some reference for relevant departments to formulate transportation policies.The results showed that the proposed optimization measures have good effects,and the travel satisfaction rate has been significantly improved.Finally,the relationship between travel satisfaction and travel happiness is analyzed,and suggestions to improve travel happiness are put forward from the perspectives of transportation planning,urban policy and transportation improvement.