The Analysis Of Influencing Factors And Optimization Of Prediction Model For Public Transport Travel Choice Of Residents In Small And Medium-sized Cities In Ningxia

In recent years,China’s economy has shown a healthy and rapid development trend,which has led to the accelerating process of urbanization and motorization,leading to the increasingly serious urban traffic congestion problem.Therefore,in order to realize the long-term and stable sustainable development of the city,public transportation plays a pivotal role.However,due to various reasons,public transportation is not attractive enough to residents at present,and the share rate of public transportation is generally low.Therefore,in order to increase the attractiveness of public transport to residents,it is very important to understand the influencing factors of residents’public transport travel.Firstly investigating the typical urban public transit in NingXia,from the way to travel,travel purpose,travel time,tries to travel and travel times,analyzes the GuYuan,YinChuan,ZhongWei and ShiZuishan residents’ public transportation present situation,and contrasted the residents of the city public transportation,analyzes the commonness and difference.Then combined with the municipal social and economic statistical bulletin,on the basis of the present situation of using SPSS software and analyses the urban layout,the economic characteristics of macro and micro bus industry completeness,bus service level and the residents’ bus ride of total correlation,the results showed that the index was a positive correlation with the urban public transit total relations,namely with the increase of the indicators,The total number of public transport trips by residents also increased.Secondly,the Analytic Hierarchy Process(AHP)is used to analyze the macro-influence factors and micro-influence factors mentioned above,so as to calculate the weight of each influence factor and make a total ranking to determine the degree of influence of each factor on residents’ public transportation.The results showed that private car ownership(0.0922),bus stop coverage(0.0651),land development degree(0.0614),total urban population(0.0531),urban economic development level(0.0511),income(0.0503),motor vehicle ownership(0.0453),age(0.0439),Bus network density(0.0439),non-linear coefficient(0.0403),built-up area(0.0372),taxi industry development(0.0363),travel distance(0.0356),road congestion degree(0.0345),total number of family members(0.0307),bus congestion degree(0.0306)and other factors It has a great influence on residents’ choice of bus travel.Due to the traditional transportation demand forecasting model of travel generation forecasting model...