| The increasing number of motor vehicles aggravates the problem of urban traffic congestion.In order to alleviate this problem,the governments have introduced a series of policies and measures to regulate traffic supply and demand,such as "HOV lanes /3+ lanes"," Travel limited/Peak travel","Congestion charge policy" and "Bus priority ".At the same time,with the popularity of e-hailing in recent years,the combination of e-hailing and traditional carpooling,the online ridesplitting travel mode,has attracted people's interest and occupied the travel market as a relatively novel mode of travel.We believe that the online ridesplitting travel mode not only makes reasonable use of "idle seats" of vehicles,but also complies with the guidance of traffic policies,enabling travelers to enjoy HOV lanes /3+ lane fast passages.On the other hand,compared with public transportation,it is faster and more comfortable,and to reduces the travel cost of private car travelers and saves fuel to some extent.Therefore,it is of practical significance to investigate the online ridesplitting travel choice behavior of travelers and provide research suggestions for their future promotion and popularization.Firstly,based on the analysis,summary and supplement of relevant literature,the author designed the questionnaire of online ridesplitting travel intention which contains four aspects,and obtained 399 valid questionnaires.A preliminary statistical analysis was made on the data of the questionnaire survey to obtain the general travel rules of the investigated.Then,according to a dozen functional questions given in the questionnaire which may improve the passenger ridesplitting experience,people under investigation are divided into two groups according to whether they have a private car or not and were analyzed by latent class model separately.Software Mplus7.4 was used to solve the model and passengers was divided into different classes according to the different service preference.By investigating people's acceptance of the relevant service functions,we can better understand people's expected ridesplitting rights.On the basis of the above analysis,the modeling variables needed to establish the behavior model of online ridesplitting selection are sorted out,which mainly include the variables of alternative attributes,travel habit attributes,travel mode use experience attributes,individual social and economic attributes and latent class attributes.For people with or without private cars,a Mixed Logit behavior selection model with all the influencing factors taken into account was established.After R 3.6.1 language programming was used to solve the model,the non-significant factors were eliminated,and the final Mixed Logit behavior selection model under the influence of significant factors was obtained.Through the analysis of the model results,it can be concluded that the variables of alternative attributes are all factors that significantly affect people's travel mode choice,but there are differences in the positive and negative properties of individual variables among different people.In terms of the characteristics of travel habits,the average travel time and the characteristics of travel mode are significant variables to the non-private cars people.In terms of social and economic attributes,age,education and marital status have more significant influence on people with private cars than people with non-private cars.The latent class characteristic variables are significant.At last,the paper gives different "price discount" strategies for different time periods of the unique attributes of online ridesplitting "detour and waiting time",so as to attract more people to use online ridesplitting if conditions permit. |
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