Relationship Between Urban Public Transportation Travel Demand And Spatial Structure Of Bay-type City Based On Multi-source Data

The misalignment between the spatial and temporal patterns of urban residents' activities and the spatial structure is one of the important reasons for many urban problems,for example,increasing traffic congestion,the continuous decline of air quality,and the relative lag of infrastructure and many other urban problems.In recent years,with the rapid development of information communications technology(ICT),large-scale,long-term sequences of human's mobile positioning big data and complex urban spatial structure data have become easier to collect.This paper starts with the preparation and preprocessing of multi-source data the spatio-temporal law of residents'mobile travel,the impact mechanism of residents'public transportation trips,and the optimization of the relationship between transport demand and urban spatial structure,etc.This paper tries to solve the mismatch of travel demand and spatial structure,relieve the jam situation.The main works as follows:Firstly,construct the interaction mechanism model of urban public transportation travel demand and spatial structure.Based on the research progress of urban transportation demand,the impact of urban spatial structure on traffic travel demand,and related issues of the interactive relationship between the them.According to the interaction between the two systems,a model of the interaction mechanism between urban public transportation demand and spatial structure is proposed.In addition,a bay-type city,Qingdao city,is selected as the research object to summarize its spatial structure and road characteristics,and found problems between transportation travel demand and urban spatial structure in the development process.Secondly,extract massive multi-source heterogeneous resident activity data and perform fusion processing on the data.It mainly includes:cleaning and storage of public transportation data.For taxi GPS trajectory data,based on the time series of passenger status,the trip chain for residents traveling by taxi is extracted.Then combined the topology and geometric information are used in matching algorithm to match track points on the map.Fusion bus smart card data and bus GPS trajectory data,and use a time stamp algorithm to improve the matching degree to obtain bus travel demand.For metro travel demand analysis,extracting residents' travel demand chains based on enter and exit data of smart card.Thirdly,according to the analysis of the spatial and temporal differentiation characteristics of public transportation travel,an accessibility model that integrates different transportation modes is constructed.First of all,in order to improve the accuracy,the time granularity divides the data into three types:weekdays,weekends,and holidays.The morning and evening peaks of different types of dates are statistically analyzed for the trip laws of the three transportation travel modes.Then,the spatial analysis uses the kernel density estimation method to identify the hotspots.In order to explore the differences in the spatio-temporal distribution of urban transport demand at last.Considering the value of opportunities,the different transportation modes are integrated to construct a public transport trip-based accessibility model to measure the level of infrastructure.The research conclusions provide decision-making basis for residents to stagger peaks and travel effectively,and make targetedsuggestions for improving the efficiency of infrastructure utilization.Fourthly,a geographically weighted regression model considering spatial heterogeneity is used to analyze the impact mechanism of residents' public transportation travel demand.Following the first law of geography,build a GWR model of travel demand with different travel modes and multi-mode integration.Extract socio-economic attributes,transportation system attributes,and land use systems as index system.Divide the urban research area into grid cells,explore the mechanism of travel demand impact on weekdays,weekends,and holidays.Then compare and analyze the sensitivity of travel time for different travel mode of variables.The conclusions obtain the main factors that influence residents' choice of public transportation travel behavior.It can help the planning and transportation operation department to evaluate the city's potential operation mechanism and improve the operation efficiency of the public transportation system.Fifthly,construct a coupling coordination model of the relationship between urban transportation travel demand and spatial structure,and propose an optimization strategy based on the conclusion.According to the study of travel spatio-temporal characteristics,travel accessibility and impact mechanism,there is a mismatch between traffic demand and urban spatial structure.The standard deviation ellipse method is used to analyze the spatio-temporal evolution characteristics of the center of gravity of residents'travel.And the indicator system of the transportation system and the land use system is constructed,the comprehensive index of indicators is also determined by the entropy weight method based on information entropy.Then propose strategies for optimizing urban spatial structure.The conclusion provides new design ideas for infrastructure construction and road network planning in the new and old urban construction planning process,and provides strategies for the optimization of urban spatial structure.The research problem in this paper are of certain universality,which can provide reference for the coordinated development of public transportation travel demand and spatial structure of the bay-type cities,improving land utilization rate and alleviating traffic congestion.Focusing on urban geographic location and spatial structure characteristics,summing up and refining the problems of bay-type city public transportation travel demand and spatial structure.The strategies and suggestions are proposed for optimizing urban spatial structure,especially for the city with zonal,radiation and clusters road network.