An industrial park land planning assessment method, device and storage medium based on travel data

Abstract

The present invention discloses an industrial park land planning assessment system and method based on travel data, which belongs to the technical field of data analysis. The specific method includes: by integrating traffic node information and candidate residential point information, based on analyzing the correlation between the target traffic node and other traffic nodes within a preset time period, a set of mutually related traffic nodes can be obtained. The traffic node set calculated and analyzed in this way can accurately reflect the population and scope affected by potential candidate residential points. Planning room demand based on the traffic node set not only has good real-time performance, but also the analysis is supported by detailed data, and the conclusion formed is relatively accurate and scientific.

Description

Technical Field

[0001] The present invention belongs to the technical field of data analysis, and specifically relates to an industrial park land planning assessment method, device and storage medium based on travel data. Background Art

[0002] In urban construction planning, industrial parks or residential areas are often planned based on travel data, which helps reduce traffic congestion, reduce carbon emissions, and promote the use of sustainable transportation. The more traditional planning method is to divide the industrial park into different functional areas, such as production areas, commercial areas, office areas, and public facilities areas. This method aims to optimize the layout of different activities through spatial separation to improve efficiency and management. Or based on traditional planning concepts, design transportation networks and road systems to support internal transportation and external connections. These plans usually rely on predictions and empirical data rather than detailed real-time travel data analysis.

[0003] Although traditional land planning methods have their feasibility and empirical basis, they may appear too static and inflexible when faced with complex urbanization and modernization challenges. In current actual construction planning, how to integrate multi-source travel data (such as GPS data, traffic survey data), how to use geographic information systems (GIS) and spatial analysis techniques to perform spatial interpolation, path analysis and hotspot detection on travel data, and thus understand the spatial distribution and trend of crowd flow is a major difficulty, mainly due to the lack of effective tools and methods, and the low level of data utilization.

[0004] Therefore, in urban planning and construction, there is an urgent need for a multi-factor decision-making model that comprehensively considers travel data with factors such as land use and transportation facility planning to provide a scientific basis for the long-term development of industrial parks. Summary of the invention

[0005] In order to solve the technical problems that the current level of data utilization in urban planning is low and there is a lack of analysis technology that effectively integrates travel data, resulting in insufficient accuracy of urban planning, the present invention provides an industrial park land planning evaluation method, device and storage medium based on travel data.

[0006] First aspect

[0007] The present invention provides an industrial park land planning evaluation method based on travel data, comprising:

[0008] Acquire traffic node information and candidate residential point information, wherein the traffic node information includes traffic node coordinate information and traffic flow information of the traffic node at multiple times, the traffic flow information includes inflow flow information and outflow flow information, and the candidate residential point information includes candidate residential point coordinate information;

[0009] Traversing the candidate residential point information and obtaining a first candidate residential point, and obtaining a first transportation node closest to the first candidate residential point based on the coordinate information of the candidate residential point and the coordinate information of the transportation node;

[0010] Analyze the correlation between the first traffic node and the other traffic nodes within a preset time period, and select a set A of related first traffic nodes according to a correlation threshold, wherein the preset time period is at least one of a morning peak or an evening peak;

[0011] The room demand of the first candidate residential point is obtained according to the traffic flow information of the first traffic node set A within the preset time period.

[0012] Further, analyzing the correlation between the first traffic node and the other traffic nodes within the preset time period includes:

[0013]

[0014] Among them, Moran's I represents the measure of spatial autocorrelation, which indicates whether adjacent observations in the data set have similar values, n is the total number of observations in the traffic node, and w j is the spatial weight between the first traffic node and other traffic nodes (j), which is used to represent the strength of the spatial relationship or proximity between these units. j is the traffic flow value of other traffic nodes (j) except the first traffic node at multiple times, is the mean of the traffic flow at all traffic nodes at multiple times, K is the average of the traffic flow at the first traffic node and The sum of the squares of the differences.

[0015] Furthermore, the traffic node also includes a planned traffic node u, and the traffic flow of the planned traffic node u is predicted by the Kriging difference method, including:

[0016]

[0017] Among them, Z(u) is the predicted value of the traffic flow of the planned traffic node (u), λ i (u) is the interpolation weight function, which is used to estimate the traffic flow at the planned traffic node (u) and the surrounding known points u i The relevant weights are usually calculated based on spatial distance or other correlation measures, Z(u i ) is a known point u i These points are usually known points in space and close to the planned traffic node (u) to be estimated, and n is the total number of known points.

[0018] Furthermore, the traffic nodes are traversed in sequence, and traffic flow values ​​at multiple times are predicted based on traffic flow information of the traffic nodes at multiple times, including:

[0019] Z t =c+φ1Z t-1 +…+φ p Z t-p +θ1∈ t-1 +…+θ q ∈ t-q +∈ t

[0020] Among them, Z t is the traffic flow at time (t), which represents the observed value of traffic volume or density at a specific point in time, c is the constant term or intercept in the model, which represents the baseline traffic flow independent of previous values ​​or errors, φ1, φ2...φ p are the autoregressive (AR) coefficients, which quantify the previous traffic flow values ​​Z t-1 ,Z t-2 ...Z t-p For the current traffic flow Z t The influence of Z t-1 ,Z t-2 ...Z t-p are the lagged values ​​of traffic flow, which represent the traffic flow values ​​in the time period (p) before the current time (t), θ1θ2...θ q are the moving average (MA) coefficients, which measure the past errors or residuals ∈ t-1 ,∈ t-2 ...∈ t-q For the current traffic flow Z t They take into account the short-term fluctuations or noise in traffic data, ∈ t is the error term or residual at time (t), which represents the observed traffic flow Z t The difference between the traffic flow predicted by the model based on past values ​​and the error, ∈ t-1 ,∈ t-2 ...∈ t-q are the lagged errors or residuals, which are the deviations between the observed traffic flows in the previous time period and the predicted values.

[0021] Further, compare the multiple peak time points Z t The traffic flow is obtained to obtain a peak time point sequence arranged from high to low, and the peak time point sequence is used to indicate the departure time of public transportation.

[0022] Furthermore, the correlation threshold is 0.8, and the other traffic nodes having a Moran's I correlation greater than 0.8 with the first traffic node are divided into a first traffic node set A.

[0023] Furthermore, based on the traffic flow information of the first traffic node set A within the preset time period, the room demand of the first candidate residential point is obtained, including: calculating the average monthly traffic flow according to the traffic flow of the first traffic node set A during the morning peak or evening peak, and taking the product of the average monthly traffic flow and a preset matching coefficient as the room demand of the first candidate residential point, wherein the preset matching coefficient is between 0.1-0.3.

[0024] Further, compare the multiple peak time points Z t The traffic flow is obtained to obtain a peak time point sequence arranged from high to low, and the peak time point sequence is used to indicate the departure time of public transportation.

[0025] Furthermore, the method further includes: traversing the candidate residential point information and obtaining a second candidate residential point, and obtaining updated traffic node information after excluding the first traffic node and the first traffic node set A, and analyzing the correlation between the second traffic node and other traffic nodes within a preset time period based on the updated traffic node information;

[0026] Analyze the correlation between the second traffic node and other traffic nodes within a preset time period, and select a set B of related second traffic nodes according to a correlation threshold, wherein the preset time period is at least one of a morning peak or an evening peak;

[0027] The room demand of the second candidate residential point is obtained according to the traffic flow information of the second traffic node set B within the preset time period.

[0028] Second aspect

[0029] The present invention provides a computer device, characterized in that it includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the industrial park land planning evaluation method based on travel data as described in the first aspect is implemented.

[0030] The third aspect

[0031] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the industrial park land planning evaluation method based on travel data as described in the first aspect is implemented.

[0032] Compared with the prior art, the present invention has at least the following beneficial technical effects: in the present invention, by integrating traffic node information and candidate residential point information, based on analyzing the correlation between the target traffic node and other traffic nodes within a preset time period, a set of mutually related traffic nodes can be obtained. The traffic node set calculated and analyzed in this way can accurately reflect the population and scope affected by potential candidate residential points. Planning room demand based on the traffic node set not only has good real-time performance, but also the analysis is supported by detailed data, and the conclusions formed are relatively accurate and scientific. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of this specification, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 It is a flow chart of a chip defect detection method provided by the present invention.

[0035] Figure 2 It is a structural schematic diagram of a computer device provided by the present invention. DETAILED DESCRIPTION

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0037] In order to simplify the drawings, only the parts related to the invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0038] It should be further understood that the term “and / or” used in the present specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0039] In this document, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection. It can be a mechanical connection or an electrical connection. It can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0040] In addition, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0041] Example 1

[0042] In one embodiment, the reference specification Figure 1 , which shows a flow chart of an industrial park land planning evaluation method based on travel data provided by the present invention. Figure 2 , showing a schematic structural diagram of a computer device provided by the present invention.

[0043] In order to solve the technical problems that the current level of data utilization in urban planning is low and there is a lack of analysis technology that effectively integrates travel data, resulting in insufficient accuracy of urban planning, the present invention provides an industrial park land planning evaluation method, device and storage medium based on travel data.

[0044] The present invention provides an industrial park land planning evaluation method based on travel data, comprising:

[0045] Acquire traffic node information and candidate residential point information, wherein the traffic node information includes traffic node coordinate information and traffic flow information of the traffic node at multiple times, the traffic flow information includes inflow flow information and outflow flow information, and the candidate residential point information includes candidate residential point coordinate information;

[0046] Traversing the candidate residential point information and obtaining a first candidate residential point, and obtaining a first transportation node closest to the first candidate residential point based on the coordinate information of the candidate residential point and the coordinate information of the transportation node;

[0047] Analyze the correlation between the first traffic node and the other traffic nodes within a preset time period, and select a set A of related first traffic nodes according to a correlation threshold, wherein the preset time period is at least one of a morning peak or an evening peak;

[0048] The room demand of the first candidate residential point is obtained according to the traffic flow information of the first traffic node set A within the preset time period.

[0049] Further, analyzing the correlation between the first traffic node and the other traffic nodes within the preset time period includes:

[0050]

[0051] Among them, Moran's I represents the measure of spatial autocorrelation, which indicates whether adjacent observations in the data set have similar values, n is the total number of observations in the traffic node, and w j is the spatial weight between the first traffic node and other traffic nodes (j), which is used to represent the strength of the spatial relationship or proximity between these units. j is the traffic flow value of other traffic nodes (j) except the first traffic node at multiple times, is the mean of the traffic flow at all traffic nodes at multiple times, K is the average of the traffic flow at the first traffic node and The sum of the squares of the differences.

[0052] Specifically the denominator represents all (w ij ) is summed to represent the overall strength of spatial relationships in the dataset, the numerator (part I): Represents the traffic flow and mean between the first traffic node and each pair of other traffic nodes The sum of the products of the deviations, the numerator (part 2): K is the first traffic node and The sum of the squared deviations between .

[0053] Therefore, Moran's I combines these components to produce a standardized measure ranging from -1 (indicating perfect dispersion) to +1 (indicating perfect aggregation). Values ​​close to zero indicate the absence of spatial autocorrelation.

[0054] Furthermore, the traffic node also includes a planned traffic node u, and the traffic flow of the planned traffic node u is predicted by the Kriging difference method, including:

[0055]

[0056] Among them, Z(u) is the predicted value of the traffic flow of the planned traffic node (u), λ i (u) is the interpolation weight function, which is used to estimate the traffic flow at the planned traffic node (u) and the surrounding known points u i The relevant weights are usually calculated based on spatial distance or other correlation measures, Z(u i ) is a known point u iThese points are usually known points in space and close to the planned traffic node (u) to be estimated, and n is the total number of known points.

[0057] By using the known point (u i ) at the observation value (Z(u i )) and the spatial relationship calculation weight (λ) with the planned traffic node (u) i (u)), Kriging interpolation can estimate the value (Z(u)) at the planned traffic node (u), so that the planned traffic node (u) can also be introduced into the traffic node information to realize the correlation analysis of the planned traffic nodes.

[0058] Furthermore, the traffic nodes are traversed in sequence, and traffic flow values ​​at multiple times are predicted based on traffic flow information of the traffic nodes at multiple times, including:

[0059] Z t =c+φ1Z t-1 +…+φ p Z t-p +θ1∈ t-1 +…+θ q ∈ t-q +∈ t

[0060] Among them, Z t is the traffic flow at time (t), which represents the observed value of traffic volume or density at a specific point in time, c is the constant term or intercept in the model, which represents the baseline traffic flow independent of previous values ​​or errors, φ1, φ2...φ p are the autoregressive (AR) coefficients, which quantify the previous traffic flow values ​​Z t-1 ,Z t-2 ...Z t-p For the current traffic flow Z t The influence of Z t-1 ,Z t-2 ...Z t-p are the lagged values ​​of traffic flow, which represent the traffic flow values ​​in the time period (p) before the current time (t), θ1θ2...θ q are the moving average (MA) coefficients, which measure the past errors or residuals ∈ t-1 ,∈ t-2 ...∈ t-q For the current traffic flow Z t They take into account the short-term fluctuations or noise in traffic data, ∈ t is the error term or residual at time (t), which represents the observed traffic flow Z tThe difference between the traffic flow predicted by the model based on past values ​​and the error, ∈ t-1 ,∈ t-2 ...∈ t-q are the lagged errors or residuals, which are the deviations between the observed traffic flows in the previous time period and the predicted values.

[0061] Further, compare the multiple peak time points Z t The traffic flow is obtained to obtain a peak time point sequence arranged from high to low, and the peak time point sequence is used to indicate the departure time of public transportation.

[0062] Furthermore, the correlation threshold is 0.8, and the other traffic nodes having a Moran's I correlation greater than 0.8 with the first traffic node are divided into a first traffic node set A.

[0063] Furthermore, based on the traffic flow information of the first traffic node set A within the preset time period, the room demand of the first candidate residential point is obtained, including: calculating the average monthly traffic flow according to the traffic flow of the first traffic node set A during the morning peak or evening peak, and taking the product of the average monthly traffic flow and a preset matching coefficient as the room demand of the first candidate residential point, wherein the preset matching coefficient is between 0.1-0.3.

[0064] Further, compare the multiple peak time points Z t The traffic flow is obtained to obtain a peak time point sequence arranged from high to low, and the peak time point sequence is used to indicate the departure time of public transportation.

[0065] Furthermore, the method further includes: traversing the candidate residential point information and obtaining a second candidate residential point, and obtaining updated traffic node information after excluding the first traffic node and the first traffic node set A, and analyzing the correlation between the second traffic node and other traffic nodes within a preset time period based on the updated traffic node information;

[0066] Analyze the correlation between the second traffic node and other traffic nodes within a preset time period, and select a set B of related second traffic nodes according to a correlation threshold, wherein the preset time period is at least one of a morning peak or an evening peak;

[0067] The room demand of the second candidate residential point is obtained according to the traffic flow information of the second traffic node set B within the preset time period.

[0068] In the present invention, by integrating traffic node information and candidate residential point information and analyzing the correlation between the target traffic node and other traffic nodes within a preset time period, a set of mutually related traffic nodes can be obtained. The traffic node set calculated and analyzed in this way can accurately reflect the population and scope affected by potential candidate residential points. Planning room demand based on the traffic node set not only has good real-time performance, but also the analysis is supported by detailed data, and the conclusions formed are more accurate and scientific.

[0069] Example 2

[0070] The present invention provides a computer device, characterized in that it includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the industrial park land planning evaluation method based on travel data as described in the first aspect is implemented.

[0071] Example 3

[0072] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the industrial park land planning evaluation method based on travel data as described in any one of Example 1 is implemented.

[0073] A computer-readable storage medium provided by the present invention can implement the steps and effects of the chip defect detection method in the above-mentioned embodiment 1. To avoid repetition, the present invention will not go into details.

[0074] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0075] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for evaluating land planning of industrial parks based on travel data, characterized in that: include: Acquire traffic node information and candidate residential point information, wherein the traffic node information includes traffic node coordinate information and traffic flow information of the traffic node at multiple times, the traffic flow information includes inflow flow information and outflow flow information, and the candidate residential point information includes candidate residential point coordinate information; Traversing the candidate residential point information and obtaining a first candidate residential point, and obtaining a first transportation node closest to the first candidate residential point based on the coordinate information of the candidate residential point and the coordinate information of the transportation node; Analyze the correlation between the first traffic node and the other traffic nodes within a preset time period, and select a set A of related first traffic nodes according to a correlation threshold, wherein the preset time period is at least one of a morning peak or an evening peak; According to the traffic flow information of the first traffic node set A in the preset time period, the room demand of the first candidate residence point is obtained; Wherein, analyzing the correlation between the first traffic node and the other traffic nodes within the preset time period includes: ， Among them, Moran's I represents the measure of spatial autocorrelation, which indicates whether adjacent observations in the data set have similar values, n is the total number of observations in the traffic node, is the spatial weight between the first traffic node and other traffic nodes (j), which is used to represent the strength of the spatial relationship or proximity between these units, is the traffic flow value of other traffic nodes (j) except the first traffic node at multiple times, is the mean of the traffic flow at all traffic nodes at multiple times, K is the average of the traffic flow at the first traffic node and The sum of the squares of the differences; The traffic nodes also include planning traffic nodes , the planned traffic node The traffic flow is predicted by the Kriging difference method, including: ， in, is the predicted value of the traffic flow at the planned traffic node (u), is the interpolation weight function, which is used to estimate the traffic flow at the planned traffic node (u) and the surrounding known points The relevant weights, For known points Traffic flow value at Traversing the traffic nodes in sequence, and predicting the traffic flow values ​​at multiple times based on the traffic flow information of the traffic nodes at multiple times, including: ， in, is the traffic flow at time (t), which represents the observed value of traffic volume or density at a specific point in time, c is the constant term or intercept in the model, which represents the baseline traffic flow independent of previous values ​​or errors, are the autoregressive (AR) coefficients, which quantify the previous traffic flow values The current traffic flow The impact of are the lagged values ​​of traffic flow, which represent the traffic flow values ​​in the time period (p) before the current time (t). are the moving average (MA) coefficients, which measure the past errors or residuals The current traffic flow The impact of is the error term or residual at time (t), which represents the observed traffic flow The difference between the traffic flow predicted by the model based on past values ​​and errors, are the lagged errors or residuals, which are the deviations between the observed traffic flows in the previous time period and the predicted values; Compare multiple peak time points The traffic flow is obtained to obtain a sequence of peak time points arranged from high to low, wherein the sequence of peak time points is used to indicate the departure time of public transportation; The correlation threshold is 0.8, and the other traffic nodes having a Moran's I correlation greater than 0.8 with the first traffic node are divided into a first traffic node set A; Based on the traffic flow information of the first traffic node set A within the preset time period, the room demand of the first candidate residential point is obtained, including: calculating the average monthly traffic flow according to the traffic flow of the first traffic node set A during the morning peak or evening peak, and using the product of the average monthly traffic flow and a preset matching coefficient as the room demand of the first candidate residential point, wherein the preset matching coefficient is between 0.1-0.

3.

2. The industrial park land planning evaluation method based on travel data according to claim 1 is characterized in that: Compare multiple peak time points The traffic flow is obtained to obtain a peak time point sequence arranged from high to low, and the peak time point sequence is used to indicate the departure time of public transportation.

3. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the industrial park land planning assessment method based on travel data as described in any one of claims 1 to 2 when executing the computer program.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the industrial park land planning evaluation method based on travel data as described in any one of claims 1 to 2 is implemented.

Images