| On the one hand,in order to make up for the lack of traffic data in small and medium-sized cities,reduce the traffic survey time and economic cost in the early stage of urban traffic demand forecasting,alleviate the traffic congestion in small and medium-sized cities,and improve the scientific nature of traffic planning in small and medium-sized cities;The study of big data in the field of urban planning and urban transportation planning strengthens the application of the "human-centered" thinking in solving urban traffic problems.This paper uses Baidu heat map data,POI data,and land use data,combined with Getis-Ord General G,sample standard deviation,chi-square test and other spatiotemporal behavior analysis methods and mathematical statistics analysis to explore the flood The spatial and temporal agglomeration characteristics of the county population,as well as the relationship between the crowds' time and space agglomeration and urban land use and POI.The Baidu heat map data and taxi trajectory data are introduced into the traffic four-stage method,and the traffic generation and attraction of the city are calculated by the human clustering feature.Secondly,the curve fitting method is used to calibrate the parameters of the traffic gravity model impedance function,and the traffic flow of the Shehong County is calculated.The situation;finally,through the county traffic analysis results to summarize its traffic problems,and propose corresponding improvement strategies.According to the research analysis,the main conclusions are as follows:In terms of the time-space agglomeration characteristics of the crowd,the clustering degree of the working day in Shehong County is mostly higher than the rest day,and the volatility of the people on the working day is greater than the rest day,and the speed at which the workers gather and disperse is greater than the rest day;secondly,and work The clustering of people on days and rest days has a high degree of overlap.Regarding the correlation between human clustering and land use and POI,the correlation between cluster concentration and land use type of Shehong County was not significant,and it was positively correlated with POI density,floor area ratio and building area,and negatively correlated with land area.In terms of traffic demand,it is feasible to use the time and space agglomeration characteristics of residents to construct a mathematical model to estimate the traffic volume and attraction of the urban traffic community.It is feasible to use Graph to combine the taxi trajectory data to calibrate the gravity model parameters.According to the results of traffic analysis,the road structure is optimized,the road function is defined;the core area of the old city is demarcated,one-way traffic is organized;the road isolation facilities are arranged to optimize the road connection;the parking area is delineated,the parking resources are optimized;the walking quality is improved,and the walking is guaranteed.Five aspects of safety,such as the traffic improvement strategy of Shehong County.This paper uses time-space behavior analysis and mathematical statistics to analyze Baidu heat map data,enriching Baidu heat map data research methods.Through the induction of the clustering characteristics of the city people in Shehong County,the awareness of clustering in small and medium-sized cities has been expanded.Using Baidu heat map data to construct a mathematical model to calculate traffic generation and attraction,and expand the calculation method of traffic generation and attraction.The curve fitting method is used to calibrate the impedance function parameters of the gravity model,which enriches its calibration method.In turn,the four-stage method is easier to apply in practice engineering and the results are more accurate.It provides a lower cost transportation demand forecasting method and a practical traffic improvement strategy for cities similar to the traffic problems in Shehong County. |
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