CN114331058B - Assessment method for influence of built environment on traffic running condition - Google Patents
Assessment method for influence of built environment on traffic running condition Download PDFInfo
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Abstract
The invention discloses an evaluation method for influence of an urban hot spot area built environment on traffic running conditions, which comprises the following specific steps: collecting multi-source data such as POIs, road networks, public transportation sites and the like, and cleaning the data; extracting urban hot spot areas by using a kernel density estimation algorithm based on POI data; establishing a hot spot area buffer influence area, and calculating four types of built environment element indexes including POI data density, land utilization entropy index, road network density and public transportation accessibility index in the area; obtaining the road congestion proportion of the hot spot area, and obtaining a traffic running index through conversion so as to describe the traffic running condition of the hot spot area; and establishing an evaluation model for the influence of the built environment on the traffic running condition, and quantitatively evaluating the influence of the built environment of the hot spot area on the traffic running condition. The invention can guide reconstruction and remodelling of the built environment of the hot spot area, optimize the traffic running condition of the hot spot area and provide theoretical basis for urban traffic resource allocation reconstruction and newcastle planning construction.
Description
Technical Field
The invention relates to an evaluation method for influence of an urban hot spot area built environment on traffic running conditions, and belongs to the field of urban land utilization and traffic influence evaluation.
Background
The gathering effect of the large city gradually enhances the urban land development density, especially the hot spot area with large traffic flow and high business density in the radiation influence range of the rail transit station gradually appears, and huge impact is caused to urban traffic operation, and the generation and attraction of passenger flow are closely related to the land development and utilization, the neighborhood design, the accessibility of public transportation facilities and other built-up environment factors, so that the optimization of the traffic operation condition of the hot spot area is an important way for solving the bottleneck of the travel service of rail transit and improving the commuting efficiency of the whole process, and the research of the influence of the built-up environment of the urban hot spot area on the traffic operation condition has important significance.
The method has corresponding theoretical and practical researches on the aspect of building environment at home and abroad, for example, the relation between the building environment and the walking traffic mode is analyzed through multivariate statistics, and the influence relation between the building environment and the traveling behavior of residents is clearly analyzed through meta-component analysis, but the influence of the building environment in urban hot spot areas on traffic running conditions is rarely researched and analyzed, and the main reasons are that the building environment is in shortage of data, influence factors are numerous, the data is complex in multiple sources and the traditional collection mode is difficult. Most researches and analyses only cover partial built environment influence factors when the built environment influences the traffic running condition, so that the model fitting effect is poor, and therefore, the influence of the urban hot spot area land property on the traffic running condition is difficult to accurately analyze and evaluate.
Disclosure of Invention
In order to solve the problem of optimizing traffic running conditions of urban hot spot areas, the invention provides an evaluation method for the influence of the built environment of the urban hot spot areas on the traffic running conditions, and the influence of the built environment on the traffic running conditions is quantitatively evaluated from the aspects of land development and utilization and traffic systems, so that the traffic running conditions of the hot spot areas can be guided to be optimized, and the overall structure layout and functions of the city are coordinated while the current efficient travel is adapted.
The method for evaluating the influence of the built environment of the urban hot spot area on the traffic running condition comprises the following steps:
step 1: collecting and cleaning the elements to build up environment
The built environment elements comprise land utilization elements and public transportation accessibility elements, and specifically collect POI (Point of Interest) data, land utilization attribute data, road network data, track and road public transportation site data. The POI data is obtained by calling an electronic map API (Application Programming Interface) interface crawler, and the collected data types comprise employment and residence.
Wherein employment data includes: catering service, shopping service, recreation and entertainment service, medical guarantee service, government agency, scientific, teaching and cultural service, financial insurance service, company and enterprise, and residence data mainly comprise buildings and residence areas.
The land utilization data originates from a land utilization overall planning chart, and the obtained data can be partially combined according to the national land utilization current situation classification standard and is divided into residential land, business administration land, public management and public service land, grassland/green land, special land, industrial and mining storage land and traffic transportation land, so that the classification is prevented from being too detailed and tedious, and the collection and the model analysis are not facilitated.
The road network data uses an open source OSM (Open Street Map) road network to derive a road network in Shapefile format and a necessary geographic information attribute table. The method for collecting the data of the public transportation sites of the tracks and the roads is similar to POI data, and the data is obtained through an API interface crawler of the electronic map.
The acquired POI data and the acquired track and the road public transportation site data are required to be integrated, de-duplicated and subjected to coordinate conversion and then are respectively imported into an ArcGIS platform, after land utilization data are imported into the ArcGIS platform, geographic registration tool bars (Georeferences) are used for correction registration, an OSM road network can be directly imported into the ArcGIS platform, and all the data can be directly imported into the ArcGIS platform by using a WGS-84 coordinate system and are projected by adopting a universal transverse-axis ink-Cartor.
Step 2: extraction of urban hot spot areas
Mining urban hot spot areas, namely cleaning and importing ArcGIS platform POI data based on the step 1, and combining the classified POI data into a point layer element; secondly, using a kernel density estimation clustering algorithm, and inputting the point layer elements into an ArcMAp kernel density analysis tool; then setting the output value as a function estimated value, and selecting to use a GEODESIC distance (geodetic) between the elements; and finally, outputting a visualized calculation result, wherein the kernel density estimation function is shown as follows:
wherein: f (x, y) is a kernel density estimation function; n is the number of density estimation points; k (·) is a kernel function built in ArcGIS; d, d i The distance from the point i to the average center of all elements of the point layer is estimated for the density; r is the search radius;
the output calculation result can accurately represent the integral distribution of urban employment and residence, the calculated grid nuclear density is imported into ArcScene for three-dimensional visualization and matched with road network data imported into an ArcGIS platform, and urban hot spot areas (which areas have higher density and are concentrated, namely hot spot areas are formed) and the location centers thereof are extracted through the visualization result, wherein the location centers are positioned in the grid with the maximum calculation density.
Step 3: measuring and calculating built-up environment element index in hot spot area
The built-up environment element index in the hot spot area is calculated, and the area of the hot spot area cannot be directly obtained because the hot spot area is possibly irregular and is unfavorable for calculation of the index, so the built-up environment element index in the hot spot area is calculated by using the buffer influence area representation. Firstly, a round buffer influence area of the hot spot area is established according to the hot spot area and the location center thereof obtained in the step 2 (the hot spot area is divided into 9 grades according to the grid kernel density calculation result, and the buffer influence area basically covers 5 grades with larger density); then, focusing research ranges in a buffer influence area, and using ArcMap to extract and calculate built environment indexes in the buffer influence area, the method can be specifically divided into the following four types:
employment and living density representation land development strength based on POI data, and statistics of POI quantity in buffer influence area
POI data density calculations are shown below:
wherein: POI_Density is the POI data Density (Unit: personal/km) 2 )；
Is the ith 1 Number of types of POIs; n is n 1 Is the number of POI data types in the buffer influence area; buffer_area is the Area of the Buffer Area (unit: km) 2 )；
Characterizing land utilization mixedness based on land utilization entropy index, i.e. measuring diversity or complexity of land utilization categories, collecting and counting areas of different land utilization types in a buffer impact zone
The land utilization entropy index is calculated as follows:
wherein: entropy_Index is the land utilization Entropy Index;
to buffer the ith in the influence area 2 Land-like area (unit: km) 2 ) The method comprises the steps of carrying out a first treatment on the surface of the Buffer_area is the Area of the Buffer Area (unit: km) 2 )；n 2 Is the number of land utilization types in the buffer impact zone;
based on the current situation of the road network reachability represented by the road network density, the road network reachability detection method reflects the state of the neighborhood and the traffic network, and counts the road network length in the buffer influence area
The road network density is calculated as follows:
wherein: roads_Density is the road network Density (units: km) -1 )；
Is the ith 3 Length of road-like network (unit: km), n 3 Is the number of road types in the buffer impact zone; buffer_area is the Buffer Area of influence (unit: km) 2 )；
Public transportation accessibility is represented based on the coverage rate of public transportation sites, and with the improvement of public transportation passenger flow attraction, the public transportation accessibility also represents the destination accessibility of travelers to a certain extent. The station coverage rate index is calculated by firstly defining a station Service Area, namely a station buffer influence Area, selecting 800m as a Service radius for a subway station buffer Area, using 500m Service radius for a bus station, extracting the station Service Area service_area in the buffer influence Area, and calculating the station coverage rate as shown in the following formula:
wherein: coverage_rate is site Coverage; service_area is the site Service Area (unit: km) 2 ) Wherein the site buffer overlap is calculated only once; buffer_area is the Buffer Area of influence (unit: km) 2 )；
To fully incorporate the Index influence of bus stop coverage and subway stop coverage, a public transportation accessibility Index is defined and denoted as T_Index, and a correction coefficient alpha is added to the Index through bus and subway stop coverage 1 And alpha is 2 Calculation of alpha 1 And alpha is 2 Determining according to the relative proportion of bus and subway travel passenger flow sharing in the buffer influence area; the public transportation reachability index is calculated as follows:
T_Index＝α 1 ×B_Coverage_Rate+α 2 ×M_Coverage_Rate (6)
wherein: t_index is a public transportation reachability Index; alpha 1 And alpha is 2 Correction coefficients, respectively; b_coverage_rate is the bus station Coverage Rate; M_coverage_Rate is subway station Coverage Rate;
step 4: description of traffic conditions in hot spot areas
The traffic running condition mainly reflects the unblocked and blocked states of the traffic running of a road or a road network, the traffic running condition in a hot spot area buffer influence area, namely the unblocked (expected), blocked (blocked) proportion is obtained through an electronic map API interface crawler, and the area congestion proportion is used as the road network congestion mileage proportion in the corresponding crawling period.
At 5 minute intervals, the crawler grabs traffic running condition data of 2 periods of commute peak and peak at night respectively, averages the congestion ratio obtained in each period, and takes the congestion ratio as road network congestion mileage Ratio (RCM) in the period, and the calculation formula is as follows:
wherein: RCM is road network congestion mileage proportion; blocked ed a Is the proportion of congestion in the buffer influence area of the a-th grabbing of the hot spot area; a is the number of times of grabbing data in a grabbing interval period of 5 minutes;
the RCM can reflect the traffic jam influence range of the road network from the space distribution angle, the road traffic running index (TPI) is queried according to the reference standard of the RCM, the acquired early peak traffic running index and the acquired late peak traffic running index are averaged, and the stable daily peak average traffic running index is obtained through continuous multi-day observation, so that the traffic running condition of a hot spot area is described, and the calculation formula is as follows:
wherein: TPI_S is a stable daily peak average traffic running index; b is the number of days of continuous observation; TPI (thermoplastic polyurethane elastomer) mb Continuously observed early peak traffic running index on day b; TPI (thermoplastic polyurethane elastomer) eb Is the late peak traffic running index on day b of continuous observation.
Step 5: establishing an evaluation model of influence of built environment on traffic running conditions
And (3) based on the calculated built environment element index in the step (3) and the daily peak average traffic running index with stable traffic running condition described in the step (4), building a multiple linear regression model, namely, taking the built environment as an independent variable to explain and evaluate the characteristic change of the dependent variable traffic running condition.
The traffic running condition is described by using tpi_s obtained in step 4 as a dependent variable by comprehensively optimizing and characterizing the built environment using poi_density, entropy_index, roads_density, and t_index, respectively. In order to enable comparison of different indexes, the limitation of data units is removed through data standardization processing, data is mapped into a range of 0-1, and the calculation formula is shown as follows, because the land utilization entropy index and the public transportation accessibility index calculation result are already in a range of 0-1, the POI data density and the road network density are only subjected to the standardization processing:
wherein: x is X k Is an index after normalization processing of the kth hot spot region; x is x k The measurement and calculation value of the index of the kth hot spot area, and max is the maximum value of the corresponding measurement and calculation indexes of all extracted hot spot areas; min is the minimum value of corresponding measuring and calculating indexes of all the extracted hot spot areas;
the multiple linear regression model is built as follows:
Y＝β 0 +β 1 ×X 1 +β 2 ×X 2 +β 3 ×X 3 +β 4 ×X 4 (10)
wherein: beta 0 、β 1 、β 2 、β 3 、β 4 Is a regression coefficient; x is X 1 、X 2 、X 3 、X 4 The independent variable POI data density after normalization processing, the land utilization entropy index, the road network density and the public transportation accessibility index are respectively; y is the daily peak traffic running index with stable dependent variable;
step 6: evaluating the impact of a hot spot area build-up environment on traffic conditions
Analyzing the model R according to the multiple linear regression evaluation model established in the step 5 2 Values, e.g. R, of the model fitting effect 2 The value is 0.65, which means that the independent variable can explain the change reason of 65% of the dependent variable, the overall construction condition of the model is good (R 2 ＞0.6)。
The partial derivatives of the respective variables are calculated and combined with the statistical parameter P value to evaluate the positive and negative correlation and the significance of the influence of the built environment elements on the daily peak traffic running index of the stable dependent variables, when
Indicating the independent variable X k Is positively correlated with the dependent variable Y, when +.>
Indicating the independent variable X k Is inversely related to the dependent variable Y; the P values (statistical parameters) of the respective variables reflect the significant influence relationship (P < 0.05 level, significant correlation) of the built environment elements on the dependent variables. />
All data independent variables are normalized to eliminate dimension influence, so that the influence degree of the partial derivative on the traffic running condition, such as a certain urban hot spot area X, between the built environment elements can be directly compared by the relative magnitude of the partial derivative 2 And X 4 The partial derivatives of the (a) are-9.5 and-12.5 respectively, so that the two indexes are inversely related to the traffic operation index, the increase of the land utilization entropy index and the public transportation accessibility index can be considered to be capable of enabling the traffic operation condition in the hot spot area to be better unblocked (the TPI_S value is reduced) to a certain extent, specifically, the influence of the public transportation accessibility index in the hot spot area on the traffic operation condition is larger than the influence of the land utilization entropy index (when the two indexes are changed by 0.1 unit, the TPI_S changes differ by 0.3), and the public transportation accessibility in the urban hot spot area is improved more effectively than the optimization of the land utilization mixture degree.
The realization of the invention has the following beneficial effects:
the advantages of multi-source data are fully exerted, the characteristics are fully optimized and established to form environment elements, a description analysis method of the traffic running conditions of the hot spot areas is provided, an evaluation model of the influence of the established environment on the traffic running conditions is established, the traffic running conditions of the hot spot areas are analyzed and optimized from the aspects of land development and utilization and traffic systems, and the urban overall structure layout and functions are coordinated while the urban overall structure layout is adapted to the current efficient travel. The method can be used for evaluating traffic running conditions of hot spot areas, guiding reconstruction and remodelling the built environment of the areas, thereby shaping a good traffic environment based on people, providing theoretical basis for urban traffic resource allocation reconstruction and newcastle planning construction, and having very wide application scenes.
Drawings
FIG. 1 is a flow chart of a method of assessing the impact of an urban hot spot area build-up environment on traffic conditions in accordance with the present invention;
fig. 2 is an effect diagram of integrating, de-duplicating, coordinate converting and then classifying POI data according to embodiment 1 of the present invention and importing the POI data into an ArcGIS platform;
FIG. 3 is a graph showing the results of the analysis and calculation of the density of POI data core according to embodiment 1 of the present invention;
FIG. 4 is a graph showing the result of three-dimensional visualization of the density of the obtained grid nuclei by calculation in example 1 of the present invention;
FIG. 5 is a diagram showing the result of creating a hot spot area buffer zone according to embodiment 1 of the present invention.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings:
in order to solve the problem of optimizing the traffic running condition of the urban hot spot area from the aspects of land development and utilization and traffic systems, the invention provides an evaluation method for the influence of the built environment of the urban hot spot area on the traffic running condition, which quantitatively evaluates the influence of the built environment on the traffic running condition, can guide the optimization of the traffic running condition of the hot spot area, and is suitable for the current efficient travel while keeping the coordination of the overall structure layout and the functions of the city.
As shown in fig. 1, the method for evaluating the influence of the built environment of the urban hot spot area on the traffic running condition disclosed by the embodiment of the invention extracts the urban hot spot area after collecting and cleaning the multisource built environment data such as POI, land use attribute, road network, track, public road traffic stations and the like, calculates the index of the built environment elements in the hot spot area, defines and describes the traffic running condition of the urban hot spot area, establishes an evaluation model of the influence of the built environment on the traffic running condition, and finally analyzes and evaluates the influence of the built environment of the hot spot area on the traffic running condition based on the model result. The method mainly comprises the following steps:
step 1: collecting and cleaning the elements to build up environment
The built environment elements comprise land utilization elements and public transportation accessibility elements, and specifically collect POI (Point of Interest) data, land utilization attribute data, road network data, track and road public transportation site data. The POI data is obtained by calling an electronic map API (Application Programming Interface) interface crawler, land utilization data is derived from a land utilization overall planning map, road network data is OSM (Open Street Map) road networks with open sources, and the track and road public transportation site data acquisition method is similar to the POI data and is obtained by the electronic map API interface crawler.
The acquired POI data and the acquired track and the road public transportation site data are required to be integrated, de-duplicated and subjected to coordinate conversion and then are respectively imported into an ArcGIS platform, after land utilization data are imported into the ArcGIS platform, geographic registration tool bars (Georeferences) are used for correction registration, an OSM road network can be directly imported into the ArcGIS platform, and all the data can be directly imported into the ArcGIS platform by using a WGS-84 coordinate system and are projected by adopting a universal transverse-axis ink-Cartor.
The POI data acquisition and cleaning method comprises the following steps: POI data are acquired based on an open platform API interface (https:// rest.amp.com/v 3/place/textkeywords= < data keyword > & city= < city where data are located > & output=json & offset=20 & page=1 & key= < key > & extensions of users=all), and then are respectively imported into an ArcGIS platform after integration and de-duplication and coordinate conversion, and a coordinate system is defined and projected, as shown in figure 2.
Step 2: extraction of urban hot spot areas
Mining urban hot spot areas, namely cleaning and importing ArcGIS platform POI data based on the step 1, and combining the classified POI data into a point layer element; secondly, using a kernel density estimation clustering algorithm, and inputting the point layer elements into an ArcMAp kernel density analysis tool; then, the output value is set as a function estimated value, and a GEODESIC distance (geodetic) between the elements is selected to be used, and a visualized calculation result is output.
The output calculation result can accurately represent the integral distribution of urban employment and residence, the calculated grid nuclear density is led into ArcScene for three-dimensional visualization and matched with road network data led into an ArcGIS platform, and the urban hot spot area and the location center thereof are extracted through the visualization result, wherein the location center is positioned in the grid with the maximum calculation density.
Taking the imported POI data in fig. 2 as an example, the calculation result output by the nuclear density analysis is shown in fig. 3, and the result of three-dimensional visualization by importing ArcScene into the grid nuclear density obtained by the output calculation is shown in fig. 4.
Step 3: measuring and calculating built-up environment element index in hot spot area
The index of the built-up environment elements in the hot spot area is calculated, firstly, a hot spot area buffer influence area is required to be built according to the hot spot area and the area location center obtained in the step 2, the size of the buffer influence area is calculated according to the grid kernel density analysis (divided into 9 levels), 5 levels with larger density are basically covered, and the building result of the hot spot area buffer influence area in the embodiment 1 is shown in fig. 5; then, focusing the research scope on the buffer influence area of the hot spot area, and extracting and measuring the built environment indexes in the buffer area by using ArcMAP, wherein the method can be specifically divided into the following four types:
employment and living density representation land development strength based on POI data, and statistics of POI quantity in buffer influence area
POI data density calculations are shown below:
wherein: POI_Density is the POI data Density (Unit: personal/km) 2 )；
Is the ith 1 Number of types of POIs; n is n 1 Is the POI data type in the buffer influence areaA number of; buffer_area is the Area of the Buffer Area (unit: km) 2 )；
Characterizing land utilization mixedness based on land utilization entropy index, i.e. measuring diversity or complexity of land utilization categories, collecting and counting areas of different land utilization types in a buffer impact zone
The land utilization entropy index is calculated as follows: />
Wherein: entropy_Index is the land utilization Entropy Index;
to buffer the ith in the influence area 2 Land-like area (unit: km) 2 ) The method comprises the steps of carrying out a first treatment on the surface of the Buffer_area is the Area of the Buffer Area (unit: km) 2 )；n 2 Is the number of land utilization types in the buffer impact zone;
based on the current situation of the road network reachability represented by the road network density, the road network reachability detection method reflects the state of the neighborhood and the traffic network, and counts the road network length in the buffer influence area
The road network density is calculated as follows:
wherein: roads_Density is the road network Density (units: km) -1 )；
Is the ith 3 Length of road-like network (unit: km), n 3 Is the number of road types in the buffer impact zone; buffer_area is the Buffer Area of influence (unit: km) 2 )；
Based on the public transportation station coverage rate to represent the public transportation accessibility, selecting 800m as a Service radius for a subway station buffer Area, using 500m Service radius for a bus station, and extracting station Service Area service_area in a hot spot Area buffer influence Area, wherein a station coverage rate calculation formula is as follows:
wherein: coverage_rate is site Coverage; service_area is the site Service Area (unit: km) 2 ) Wherein the site buffer overlap is calculated only once; buffer_area is the Buffer Area of influence (unit: km) 2 )；
To fully incorporate the index influence of bus stop coverage rate and subway stop coverage rate, a public transportation accessibility index is defined, and a correction coefficient alpha is added through the bus and subway stop coverage rate 1 And alpha is 2 Calculation of alpha 1 And alpha is 2 Determining according to the relative proportion of bus and subway travel passenger flow sharing in the hot spot area buffer influence area; the public transportation reachability index is calculated as follows:
T_Index＝α 1 ×B_Coverage_Rate+α 2 ×M_Coverage_Rate (6)
wherein: t_index is a public transportation reachability Index; alpha 1 And alpha is 2 Correction coefficients, respectively; b_coverage_rate is the bus station Coverage Rate; M_coverage_Rate is subway station Coverage Rate;
step 4: description of traffic conditions in hot spot areas
The traffic running condition mainly reflects the unblocked and blocked states of the traffic running of a road or a road network, the traffic running condition in a hot spot area buffer influence area, namely the unblocked (expected), blocked (blocked) proportion is obtained through an electronic map API interface crawler, and the area congestion proportion is used as the road network congestion mileage proportion in the corresponding crawling period.
At 5 minute intervals, the crawler grabs traffic running condition data of 2 periods of commute peak and peak at night respectively, averages the congestion ratio obtained in each period, and takes the congestion ratio as road network congestion mileage Ratio (RCM) in the period, and the calculation formula is as follows:
wherein: RCM is road network congestion mileage proportion; blocked ed a Is the proportion of congestion in the buffer influence area of the a-th grabbing of the hot spot area; a is the number of times of grabbing data in a grabbing interval period of 5 minutes;
the traffic running condition data acquisition method in the hot spot area buffer area influence comprises the following steps: traffic running condition data is acquired based on an open platform API interface (https:// restart.amp.com/v 3/traffic/status/circulation= < location center coordinates > & radius = < radius > & key = < key of user >).
The RCM may reflect the traffic congestion influence range of the road network from the perspective of spatial distribution, and query the road traffic running index (TPI) by the RCM with reference to the specification to describe the traffic running condition of the road network, and the specific conversion method is shown in table 1:
table 1 method for converting road network congestion mileage proportion and traffic running index
Averaging the acquired early peak traffic running indexes and the late peak traffic running indexes, and calculating to obtain a stable daily peak average traffic running index through continuous multi-day observation so as to describe traffic running conditions of hot spot areas, wherein the calculation formula is as follows:
wherein: TPI_S is a stable daily peak average traffic running index; b is the number of days of continuous observation; TPI (thermoplastic polyurethane elastomer) mb Continuously observed early peak traffic running index on day b; TPI (thermoplastic polyurethane elastomer) eb Is the late peak traffic running index on day b of continuous observation.
Step 5: establishing an evaluation model of influence of built environment on traffic running conditions
And (3) based on the calculated built environment element index in the step (3) and the daily peak average traffic running index with stable traffic running condition described in the step (4), building a multiple linear regression model, namely, taking the built environment as an independent variable to explain and evaluate the characteristic change of the dependent variable traffic running condition.
The traffic running condition is described by using tpi_s obtained in step 4 as a dependent variable by comprehensively optimizing and characterizing the built environment using poi_density, entropy_index, roads_density, and t_index, respectively. In order to enable comparison of different indexes, the limitation of data units is removed through data standardization processing, data is mapped into a range of 0-1, and the calculation formula is shown as follows, because the land utilization entropy index and the public transportation accessibility index calculation result are already in a range of 0-1, the POI data density and the road network density are only subjected to the standardization processing:
wherein: x is X k Is an index after normalization processing of the kth hot spot region; x is x k The measurement and calculation value of the index of the kth hot spot area, and max is the maximum value of the corresponding measurement and calculation indexes of all extracted hot spot areas; min is the minimum value of corresponding measuring and calculating indexes of all the extracted hot spot areas;
the multiple linear regression model is built as follows:
Y＝β 0 +β 1 ×X 1 +β 2 ×X 2 +β 3 ×X 3 +β 4 ×X 4 (10)
wherein: beta 0 、β 1 、β 2 、β 3 、β 4 Is a regression coefficient; x is X 1 、X 2 、X 3 、X 4 The independent variable POI data density after normalization processing, the land utilization entropy index, the road network density and the public transportation accessibility index are respectively; y is the daily peak traffic running index with stable dependent variable;
step 6: evaluating the impact of a hot spot area build-up environment on traffic conditions
Analyzing the model R according to the multiple linear regression evaluation model established in the step 5 2 Values, e.g. R, of the model fitting effect 2 The value is 0.65, which means that the independent variable can explain the change reason of 65% of the dependent variable, the overall construction condition of the model is good (R 2 ＞0.6)。
The partial derivatives of the respective variables are calculated and combined with the statistical parameter P value to evaluate the positive and negative correlation and the significance of the influence of the built environment elements on the daily peak traffic running index of the stable dependent variables, when
Indicating the independent variable X k Is positively correlated with the dependent variable Y, when +.>
Indicating the independent variable X k Is inversely related to the dependent variable Y; the P values (statistical parameters) of the respective variables reflect the significant influence relationship (P < 0.05 level, significant correlation) of the built environment elements on the dependent variables.
All the data independent variables are normalized to eliminate the dimension influence, so that the influence degree of the environment elements on the traffic running condition can be directly built through the relative magnitude comparison of the partial derivatives. For example X 1 、X 2 、X 3 、X 4 The partial derivative and P value of (C) are 0.015 (P) 1 ＝0.018)、-8.840(P 2 ＝0.023)、-0.865(P 3 ＝0.042)、-12.050(P 4 =0.004), indicating that: the POI data density is positively correlated with the traffic running index, while the land utilization entropy index, the road network density, the public transportation accessibility index are negatively correlated with the traffic running index, and it is considered that the increase of the land development intensity (POI data density) can change the traffic running condition in the hot spot area to a certain extentMore congestion (increased tpi_s value), but the increase of the land utilization entropy index, road network density, public transportation accessibility index can make the traffic running condition in the hot spot area become better and smoother (decreased tpi_s value); specifically, the 4 built environment indexes and the traffic running condition indexes have obvious correlation on the level P < 0.05, and the program for influencing the traffic running condition is ordered as follows: the public transportation accessibility index is greater than the land utilization entropy index is greater than the road network density is greater than the POI data density, and when the index is changed by 0.1 unit at the same time, the traffic operation index changes as follows: -1.025, -0.884, -0.0865, +0.0015, poi data density and road network density have relatively weak impact on the traffic conditions in the hot spot area, and it is more effective to improve the public transportation accessibility and land utilization mix in the hot spot area to optimize the traffic conditions in the area.
The invention also provides an evaluation device for the influence of the built-up environment of the urban hot spot area on the traffic running condition, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the evaluation method for the influence of the built-up environment of the urban hot spot area on the traffic running condition when executing the computer program.
The invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor implements the steps of the method for evaluating the influence of the urban hot spot area build-up environment on traffic running conditions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchronization Link) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
While the invention has been described with respect to the preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions may be made without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. The method for evaluating the influence of the built environment of the urban hot spot area on the traffic running condition is characterized by mainly comprising the following steps:
step 1: collecting and cleaning to build environmental elements;
step 2: extracting a city hot spot area;
step 3: establishing a buffer influence area of the urban hot spot area, and measuring and calculating indexes of built environment elements in the buffer influence area;
the indexes of the built environment elements are four indexes of POI data density, land utilization entropy index, road network density and public transportation accessibility index;
the calculation formula of the POI data density is as follows:
wherein: poi_density is POI data Density; />
Is the ith 1 Number of types of POIs; n is n 1 Is the number of POI data types in the buffer influence area; buffer_area is a Buffer shadowThe area of the sound area;
the calculation formula of the land utilization entropy index is as follows:
wherein: entropy_Index is the land utilization Entropy Index; />
To buffer the ith in the influence area 2 The land-like utilization area; n is n 2 Is the number of land utilization types in the buffer impact zone;
the calculation formula of the road network density is as follows:
wherein: roads_Density is road network Density; />
Is the ith 3 Length of road-like network, n 3 Is the number of road types in the buffer impact zone;
the public transportation reachability index calculation formula is as follows: t_index=α 1 ×B_Coverage_Rate+α 2 X m_coverage_rate, wherein: t_index is a public transportation reachability Index;
b_service_area is the Service Area of the bus stop for the coverage rate of the bus stop;
for subway station coverage, M_Service_area is the subway station Service Area; alpha 1 And alpha beta 2 The correction coefficients of the B_coverage_Rate and the M_coverage_Rate are respectively determined according to the proportion between bus and subway travel passenger flow sharing in the buffer influence area;
step 4: describing traffic running conditions of a hot spot area;
in the step 4, the crawler is connected through an API of the electronic mapAcquiring traffic running conditions in a hot spot area buffer zone: taking 5 minutes as an interval, respectively crawling the traffic running condition data of 2 periods of commute peak and peak at night by a crawler, averaging the congestion proportion obtained in each period, and taking the congestion proportion as the road network congestion mileage proportion RCM in the period, wherein the calculation formula is as follows:
wherein: RCM is road network congestion mileage proportion; blocked ed a Is the congestion ratio in the buffer influence area of the a-th grabbing of the hot spot area; a is the number of times of grabbing data in a grabbing interval period of 5 minutes;
the road traffic running index TPI is inquired through the RCM, the obtained road traffic running indexes of the early peak and the late peak are averaged, and the road average traffic running index of the stable daily peak is calculated through continuous multi-day observation, so that the traffic running condition of a hot spot area is described, and the calculation formula is as follows:
wherein: TPI_S is a stable daily peak-to-average road traffic running index; b is the number of days of continuous observation; TPI (thermoplastic polyurethane elastomer) mb The road traffic operation index of the early peak on the b th day of continuous observation; TPI (thermoplastic polyurethane elastomer) eb Road traffic running index of late peak on day b of continuous observation; />
Step 5: establishing an evaluation model of the influence of the index of the built environment element on the traffic running condition;
the step 5 specifically comprises the following steps: based on the index of the built environment element in the step 3 and the road traffic running index of the stable daily peak average in the step 4, a multiple linear regression model is built: y=β 0 +β 1 ×X 1 +β 2 ×X 2 +β 3 ×X 3 +β 4 ×X 4 Wherein: beta 0 、β 1 、β 2 、β 3 、β 4 Are regression coefficients; x is X 1 、X 2 、X 3 、X 4 Respectively represent independent variables after normalization processing, namely POI data density,A land utilization entropy index, a road network density, and a public transportation reachability index; y is a dependent variable, namely a stable daily peak average road traffic running index TPI_S;
step 6: and (3) respectively solving partial derivatives of the respective variables according to the multiple linear regression model established in the step (5), and further evaluating the influence of indexes of built environment elements of the urban hot spot area on traffic running conditions.
2. The method according to claim 1, characterized in that: the built environment elements in the step 1 comprise POI data, land utilization attribute data, road network data, track and road public transportation site data;
and after the constructed environmental elements are subjected to cleaning treatment including de-duplication and coordinate conversion, the constructed environmental elements are imported into an ArcGIS platform.
3. The method according to claim 1, characterized in that: the step 3 specifically comprises the following steps:
firstly, establishing a buffer influence area of a hot spot area according to the hot spot area and the location center thereof obtained in the step 2, wherein the buffer influence area is a circle with the location center as a circle center and a set radius;
and then, extracting and measuring the built environment indexes in the buffer influence area by using Arcmap.
4. The method according to claim 1, characterized in that: in the step 6, according to the multiple linear regression model established in the step 5, partial derivatives of the respective variables are respectively calculated to evaluate the positive and negative correlation between the index of the built environment element and the road traffic running index of the stable daily peak average as the dependent variable:
when (when)
Indicating the independent variable X k Is positively correlated with the dependent variable Y, when +.>
Indicating the independent variable X k With a negative correlation to the dependent variable Y, k=1, 2,3,4.
5. An apparatus for evaluating the impact of a built-in urban hot spot area environment on traffic conditions, comprising a memory and a processor, said memory storing a computer program, characterized in that said processor, when executing said computer program, implements the steps of the method for evaluating the impact of a built-in urban hot spot area environment on traffic conditions according to any one of claims 1 to 4.
6. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method for evaluating the impact of the urban hotspot area build-up environment according to any one of claims 1 to 4 on traffic conditions.
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