Study On The Travel Demand And Information Guidance In Urban Hotspots Considering The Influence Of Social Network

In recent years,urban hotspots such as business districts and scenic spots have become areas with high incidence of traffic congestion due to the high concentration of time and space in travel demand and relatively limited traffic supply.At the same time,the advancement of communication network technology and the popularity of smart mobile terminals have made social network platforms become a medium for the interaction between urban hotspots,travel demand and guidance information.On the one hand,urban hotspots continue to increase their attractiveness as destinations through the dissemination of information through social network platforms,which further exacerbates the problem of traffic congestion.On the other hand,social network platforms have also become one of the important channels for transportation authorities to release guidance information for travel guidance.Therefore,the analysis of travel demand in urban hotspots under the influence of social network and the formation of information guidance methods are new exploration of the improvement of existing traffic behavior theories and methods,and new attempt of transportation demand management under the influence of social network,as well as the key to improve the operating efficiency of the traffic system.Following the research system of "the identification of urban hotspot – the analysis of travel demand influencing factors – the analysis of information adoption intention – the selection of information guidance strategy",the main research achievements obtained are as follows:(1)From three dimensions of historical travel demand,traveler source,and social network information dissemination characteristics,the paper carries out detailed identification of urban hotspots.Firstly,the urban hot spots are qualitatively divided into four categories: residential cluster,industrial cluster,transportation cluster,and entertainment cluster,and the decision-making process of urban hotspots in the social network environment is analyzed.Secondly,the number of on-site micro-blogs,the proportion of on-site non-local users,the number of related topics,the total number of topic reads,and the total number of topic discussions are selected as classification indicators.Based on second-order clustering,a hot spot identification model is constructed to further subdivide the entertainment cluster into balanced hotspots with high travel demand,internal hotspots with low travel demand,and external hotspots with medium travel demand.Thirdly,according to the clustering results,the characteristics of the three entertainment hotspots are analyzed respectively,which lays a foundation for predicting the travel demand of different types of hotspots.(2)Considering the influence of social network information,the local and foreign travel demand of the three entertainment hotspots are predicted respectively.Firstly,based on the S-O-R paradigm,the influence of social network information is quantified as an indicator of social network affective tendency.Secondly,five variables,including local/foreign sunrise travel demand,social network affective tendency,weather,legal holidays and epidemic situation in the same period of last year,were taken as model inputs,and local and foreign sunrise demand in hot areas are taken as model outputs.The analysis models of local and foreign travel demand of hotspots are constructed based on gradient boosting regression tree(GBRT).Thirdly,the importance of each model variable to local and foreign travel demand is quantitatively analyzed for the three entertainment hotspots respectively,providing a basis for the release of guidance information for travel demand management.(3)Based on the traveler’s perspective,an intention model of in-group effect and inter-group effect was established to analyze the release strategy of guidance information from the perspective of traveler’s psychological perception.Firstly,on the basis of the theory of planned behavior(TPB)and the technology acceptance model(TAM),a multilevel structural equation model(MSEM)of guidance information adoption intention in social network is constructed,which includes within-group model and between-group model.Secondly,the intraclass correlation coefficient is used to verify the intraclass correlation of the adoption intention of guidance information in social network and its influencing factors.Thirdly,based on the within-group model and between-group model that describe the relationship between various factors at the individual level(i.e.,users of social network platforms)and the group level(i.e.,social network platforms),the influence mechanism of the adoption intention of guidance information in social network is analyzed,and targeted policy recommendations are put forward.(4)Based on the traffic system perspective,a behavioral experiment is established to evaluate the impact of guidance information in social network,and the release strategy of guidance information is analyzed from the perspective of travelers’ actual revenue.Firstly,the information release strategy set is formed by combining the coverage rate of guidance information and the integrity of guidance information.Secondly,based on the behavior experiment platform,a trip scene containing three hotspots is built,and the revenue function of hotspots is respectively demarcated with reference to sigmoid function.Thirdly,25 subjects are recruited to carry out 135 rounds of hotspot selection behavior experiments to evaluate the effect of different release strategies of guidance information based on the revenue obtained by travelers in the traffic system,and targeted policy recommendations are put forward.