Research On The Commuting Behavior Characteristics And Influencing Mechanism Of Shared Motorcycles

Commuting is an indispensable part of urban transportation,as a green way of travel,shared motorcycle has the advantages of good flexibility,high accessibility,time and labor saving,and has gradually become an important choice for commuting.This paper is based on the data of shared motorcycle trip,traffic district division,built environment,social economy and weather in Ningbo.Identify shared motorcycle commuting activities.Then analyze the temporal and spatial heterogeneity of commuting and explore the impact mechanism of commuting demand.Finally put forward relevant policy recommendations.Firstly,screen and eliminate invalid data,redundant data,and system error data in the original data;use the GIS platform to match geographic information data,land use data and other datas to realize the fusion of multi-source data.Next explore the travel characteristics of shared motorcycle users from both time and space,and Analyze the temporal and spatial distribution imbalance and tidal characteristics of the demand for borrowing and returning shared motorcycle.So the results show that: the amount of travel changes over time,showing a clear pattern of morning and evening peaks.Regardless of the working day or weekend,the travel time is mainly concentrated within 5min-30 min.Meanwhile the travel space is generally distributed within the range close to the city center,and the distribution of space during weekdays and evening peaks is more concentrated.Secondly,use the DBSCAN spatial clustering algorithm to delineate the hot spots of the morning and evening peak shared motorcycles.Generated OD matrix for shared motorcycle trips,and calibrated the commuter recognition threshold parameters.And just propose a commuter recognition method for shared motorcycle travel,and analyze the commuting characteristics of shared motorcycles.The results show that there are significant differences in the amounts of commuting trips in different working days,morning peaks or evening peaks at different times.The travel time and distance of the evening peak is greater than that of the morning peak.Compared with non-commuting travel,the distribution of commuting travel space is more concentrated,and most of them are clustered around enterprises,institutions and schools in the city center.Thirdly,based on the identified commuting travel records,construct the traditional linear regression model(OLS model),geographically weighted regression model(GWR model)and spatio-temporal geographically weighted regression model(GTWR model)to analyze the relationship between commuting demand and potential variables such as the built environment,transportation facilities,social economy,and weather conditions in time and space.The results show that the coefficient calibration and model accuracy,error and stability of the GTWR model are significantly better than the other two.In the time dimension,schools,working population,parking spots,subway stations and points of interest in the morning and evening peak hours are positively correlated with commuting travel needs and the positive impact of the morning peak is higher than that of the evening peak.On the other hand,in the spatial dimension,the variables of schools,companies,parking lots,and living services are positively correlated with commuting demand along subway stations,downtown or around the CBD.Finally,based on subjective shared motorcycle user reviews and shared bicycle commuter travel questionnaire data,analyze the influencing factors of shared motorcycle commuting using and current use problems.Whatever next,this paper combineds with the significant variables that objectively affect the commuting demand of shared motorcycles in the previous article,it deeply explores the problems existing in shared motorcycles commuting,and puts forward relevant policy recommendations for the government,operators and users.