Study On The Jobs-housing Relationship Based On Urban Rail Transit Commuting:An Empirical Study From Beijing

The relationship between urban rail transit commuting and jobs-housing balance conditions is an issue worthy of concern.On the one hand,as an efficient way of commuting,urban rail transit can improve the previous jobs-housing spatio-temporal relations of residents.On the other hand,residents will choose different commuting modes based on their own jobs-housing characteristics and conditions.In the studies of past,due to the limitation of the data acquisition and Modifiable Area Unit Problem,researchers usually applied sampling investigation to analyse the jobs-housing conditions of individual residents.In addition,they are less involved in the study of the relationship between urban rail transit commuting and jobs-housing balance.In this paper,a series of multi-source data such as questionnaire survey data,field investigation data,official census data,Point of Interest data,Automatic Fare Collection System data,and Global Position System data is applied.Taking Beijing as an example,this study attempt to explain the relationship between urban rail transit commuting behaviour and job-housing balance based on spatial analysis methods and various mathematical modeling at various spatial scales.In addition,the effects of dynamic factor(Shared bike of catchment mode)and static factor(built environment around urban rail transit station)in the relationship between urban rail transit commuting and jobs-housing balance are analysed.The conclusions of this dissertation is expected to provide some references for optimizing the distribution and design of urban rail stations,improving the operating efficiency and ridership willingness of urban rail transit,and improving the residents' jobs-housing balance conditions.Taking 206 urban rail transit stations in the whole area of Beijing as an example and classifies them according to their jobs-housing functions based on Gaussian mixture model and smart card data.The dynamic population distribution characteristics around urban rail transit station are explored and the jobs-housing ratio was calculated by "Yichuxing" position data.After "the equality of station egrass-ingrass" and "the jobs-housing balance" are calculated by the station egrass-ingrass ratio and the jobs-housing ratio,the correlation between urban rail transit commuting behavior and jobs-housing balance was analyzed.The results of this study indicates that:(1)The closer numbers of station egrass-ingrass are,the better jobs-housing balance around the station is.The commuting characteristics of urban rail transit can reflect the local jobs-housing condition effectively;(2)There is strong positive relationship between the station of typical employment place and the jobs-housing balance around the station.Nevertheless,there is significant negative relationship between the station of typical residential place and the jobs-housing balance around the station.This result suggests that dense settlement will not generate the same quantity of workplace while well-developed employment hubs can attract a certain number of residents to live nearby;(3)There is a positive correlation between the locational conditions of urban rail transit station and the jobs-housing balance.The jobs-housing situation at the end of URT network is usually poor.There is a mismatch between the employment and living standards of many people;(4)Gaussian mixture model can effectively make cluster analysis of urban rail transit station with complex and vague attributes;(5)"Yichuxing" position data with strong real-time performance,high precision,wide coverage and feasible accessibility can effectively compensate the limitations of other big data methods in collecting and analyzing characteristics of population spatial distribution under microscopic scale.Taking 44 rail transit stations in the central area of Beijing as an example,the rational pedestrian catchment areas are studied from the perspective of potential commuters.Besides,big data method was adopted to collect point data of population from'Yichuxing',an internet application.In addition,relative values of relative riding rate was obtained by combining point data and rail transit one-card pass data during peak time within 10 working days in September 2017.In view of the abnormal distribution of data,a GARCH model is established to analyze the interactions between station relative riding rate and built environment factors within rational pedestrian catchment areas.The study results showed that(1)there is a notable positive correlation between urban rail transit relative riding rate and initial station,and negative interaction between station relative riding rate and transfer probability of station;(2)There is a strong positive relationship between relative riding rate and exit numbers of station;(3)there are no explicit relationships between conditions of station relative riding rate and walkable factors such as residential-station footpath turn times and cross numbers within rational catclment areas,whereas positive relationship was observed between station relative riding rate and bus stop density within rational pedestrian catchment areas;(4)Significant negative correlation can be found between relative riding rate and land use mixture;(5)There are positive correlations among station relative riding rate and density of road network,congested road proportion in morning peak hours in varying degree;(6)There is an ambiguous and intricate relationship between bike-sharing order quantities and urban rail transit relative riding rate;(7)Compared to cellular signaling data,"Yichuxing" point data showed higher accuracy and applicability in terms of the analysis of demographic distribution and micro-scale changes.Taking 44 rail transit stations in the central area of Beijing as an example,for examine the effects of bike-sharing on the characteristics of urban rail transit commuting behaviour and jobs-housing condition,the GARCH model was built based on the 2015 and 2017 urban rail transit smart card data and the 2017 ofo riding data.The results indicate that:(1)there is a significant positive relationship between urban rail transit commuting quantity and the amount of bike-sharing riding around the station;(2)To a certain extent,bike-sharing has an alternative effect on the commuting mode of urban rail transit.The relationship between bike-sharing catchment and urban rail transit commute support the theory of "travel time budget";(3)In addition,the characteristics of bike-sharing catchment will affected by the functional category of urban rail transit station and industrial type and residents'density around the station;(4)From the point of commuters' perspective,the negative impact of bike-sharing on the jobs-housing balance is limited.Meanwhile,bike-sharing plays a certain positive role in aspects of improving commuting efficiency and accessibility,increasing employment opportunity and optimizing residential choice.Taking the suburb area of Beijing as an example,commuting time as cost of commuting will contribute to better analyzing jobs-housing condition from commuter's perspective.Based on the statistical method of Bayesian-tobit and individual survey data of seven sub-districts in Beijing,this research examines the interaction between four kinds of commuting mode(slow traffic,automobile,urban rail transit,bus)especially urban rail transit and jobs-housing balance.Meanwhile,this paper set 'employment accessibility' and 'land use mixture' as moderator variables and exploring how the impacts of them on the relationship between varies commuting modes and jobs-housing balance.The study findings indicate that(1)there is a positive relationship between commuting mode of slow traffic and jobs-housing balance;(2)By contrast,there is a negasitive dependency among automobile,urban rail transit,bus and jobs-housing balance;(3)Employment accessibility and land use mixture would strengthen the original relationship among slow traffic,urban rail transit,bus and job-housing balance.Specifically,under relatively low employment accessibility and land use mixture conditions,the job-housing balance degree of slow traffic commuters is higher,while the job-housing balance degree of urban rail transit and bus commuters is lower;(4)Nevertheless,the relationship between commuting modes and jobs-housing balance condition will not affected by employment accessibility and land use mixture.Results above suggest that(1)Worse condition of employment accessibility and land use mixture can aggregate jobs-housing balance of commuters who use slow traffic;(2)However,lower value of employment accessibility and land use mixture will alleviate jobs-housing balance of commuters;(3)In addition,the commuting behaviour of automobile users is not easy to be influenced by external objective factors usually.