CN108596381B - Urban parking demand prediction method based on OD data - Google Patents
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Abstract
The invention provides a parking demand prediction method based on OD data. The method comprises the following steps: obtaining OD data and parking data of a research area, and dividing parking cells of the research area according to the OD data and the parking data; performing regression analysis based on the OD data, the parking data and the parking cell partition scheme of the research area to construct a parking demand prediction model; and forecasting the parking demand of the target parking area according to the parking demand forecasting model. According to the invention, the parking demand prediction model is obtained by performing regression analysis on the OD data and the parking data of the known region, then the parking demand prediction model is used for estimating the parking demand of the unknown region, the traditional large-scale investigation is abandoned, the manpower and material resources are saved, the method has the advantages of simplicity, convenience, rapidness and accuracy, and references and technical support can be provided for parking planning, parking resource allocation and parking problem solution.
Description
Technical Field
The invention relates to the field of urban traffic and parking planning, in particular to an urban parking demand prediction method based on OD (origin, destination, starting point and ending point) data.
Background
In recent years, with the rapid increase of the holding capacity of motor vehicles, in many cities, the supply and demand of parking spaces are short, and the difficulty of parking becomes a 'growing trouble' in most cities. The parking experience also becomes a key index related to the travel happiness of people.
The accurate parking demand prediction is the premise and the foundation of urban parking facility planning construction, the land and fund waste is caused by overlarge prediction amount of the parking demand, but the serious traffic problem is caused by the limitation of social and economic development and the incapability of meeting the urban parking demand caused by undersize prediction.
At present, the method for calculating the parking demand in the prior art needs to report data according to relatively complete urban land, needs to invest a large amount of manpower, material resources and financial resources to carry out urban traffic trip investigation, and consumes relatively long time for the investigation.
Therefore, how to quickly and accurately predict the urban parking demand is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention provides an OD data-based urban parking demand prediction method, which aims to solve the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A parking demand prediction method based on OD data comprises the following steps:
Obtaining OD data and parking data of a research area, and dividing parking cells of the research area according to the OD data and the parking data;
performing regression analysis based on the OD data, the parking data and the parking cell partition scheme of the research area to construct a parking demand prediction model;
and predicting the parking demand of the target parking area according to the parking demand prediction model.
Further, the acquiring OD data and parking data of the study area includes:
obtaining OD data for a car in a study area, the OD data comprising: the method comprises the following steps of numbering, starting point O longitude and latitude of a trip, finishing point D longitude and latitude, starting time, finishing time and travel distance;
parking data of automobiles in a research area is acquired, the parking data including a parking lot ID, a peak hour, and a peak number of parking.
Further, the method further comprises the following steps:
preprocessing the errors and abnormal values in the OD data and the parking data, screening out data with empty field values, deleting data with O points or D points out of the research area according to the latitude and longitude coordinate range of the research area, deleting data with the travel distance less than 500 meters, and deleting the travel time t jData less than or equal to 0 min; delete travel speed VjData of more than or equal to 100 km/h.
Further, the dividing the parking cell of the research area according to the OD data and the parking data includes:
dividing a research area into a plurality of grids, sequentially increasing the length of the grid side from a set value, respectively counting the number of peak stops, the number of O points and the number of D points in each grid under the condition of different grid side lengths, defining the grids with the peak stops, the number of O points and the number of D points all larger than a specified value threshold as effective grids, and recording the total number of the effective grids as m;
carrying out correlation analysis on the number of peak stops, the number of O points and the number of D points in each effective grid, and calculating a correlation coefficient r of the number of the peak stops and the number of the O points under different grid side length conditionsOCalculating the correlation coefficient r of the number of the peak stops and the number of the D points under the condition of different grid side lengthsDComprehensively considering the number of effective samples m and rOAnd rDAnd determining the side length of the optimal format, and dividing the parking cell of the research area according to the side length of the optimal format.
Further, the performing regression analysis based on the OD data, parking data and parking cell partition plan of the research area to construct a parking demand prediction model includes:
After the parking cells are divided into the research area according to the optimal format side length, defining each grid in the research area as a parking cell, and respectively determining the peak parking number, the O point number and the D point number of each parking cell;
establishing a unary linear fitting function P between the peak parking number and the O point number of the parking districts in the research area according to the peak parking number and the O point number of each parking districti＝αxOi；
Establishing a unary linear fitting function P between the peak parking number and the D point number of the parking districts in the research area according to the peak parking number and the D point number of each parking districti＝βxDi；
Wherein P isiFor the number of peak stops in parking cell i, xOiIs the number of O points in the parking cell, xDiThe number of D points in the parking cell is defined, and alpha and beta are regression coefficients of the parking cell about the number of O points and the number of D points respectively;
and respectively taking a unitary linear fitting function between the peak parking number corresponding to the research area and the O point number and the D point number as a parking demand prediction model.
Further, the predicting the parking demand of the target parking area according to the parking demand prediction model includes:
inputting the number of O points of a target parking area into a unitary linear fitting function between the number of peak parking and the number of O points in the parking demand prediction model to obtain the number of peak parking in the target parking area;
And/or the presence of a gas in the gas,
and inputting the number of D points of the target parking area into a unitary linear fitting function between the number of peak parking and the number of D points in the parking demand prediction model to obtain the number of peak parking in the target parking area.
According to the technical scheme provided by the embodiment of the invention, the method of the embodiment of the invention obtains the parking demand prediction model by performing regression analysis on the OD data and the parking data of the known region, then estimates the parking demand of the unknown region by using the parking demand prediction model, abandons the traditional large-scale investigation, saves manpower and material resources, has the advantages of simplicity, convenience, rapidness and accuracy, and can provide reference and technical support for parking planning, parking resource allocation and parking problem solution.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a processing flow chart of a method for predicting an urban parking demand based on OD data according to an embodiment of the present invention;
fig. 2 is a diagram illustrating a correlation analysis between the number of stops under different grid side length conditions, the number of start points, and the number of D points according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a partition of a parking cell according to an embodiment of the present invention;
fig. 4 is a scatter diagram of peak parking number and starting point number of parking lots in a parking lot according to an embodiment of the present invention;
fig. 5 is a scatter diagram of the number of peak parking and the number of D points in a parking cell according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The processing flow of the parking demand prediction method based on the OD data provided by the embodiment of the invention is shown in FIG. 1, and comprises the following processing steps:
and step S110, obtaining OD data and parking data of the research area.
And step S120, parking cell division is carried out, and an optimal parking cell division scheme is determined according to the research purpose.
And S130, performing regression analysis based on the OD data and the parking data obtained in the step S110 and the parking cell division obtained in the step two, and constructing a parking demand prediction model.
And step S140, predicting the parking demand of the target parking area according to the parking demand prediction model.
In some embodiments, the obtaining OD data and parking data of the research area in the step S110 includes:
OD data of automobile travel in a research area are extracted, wherein the OD data refer to a series of data describing travel and mainly comprise: serial numbers, O point (travel starting point) longitude and latitude, D point (travel end point) longitude and latitude, starting time, ending time and travel distance. As shown in table 1 below.
TABLE 1
And extracting parking data of automobile traveling in the research area, wherein the parking data mainly comprises a parking lot ID, peak time and peak parking number.
In some embodiments, the OD data and the parking data extracted in step 1 are preprocessed to screen out data with empty field values. And deleting data of which the starting point or the D point is not in the area according to the latitude and longitude coordinate range of the researched area. Considering that the radius of service of the public transportation station is 500m, data having a travel distance of less than 500m is deleted.
According to the starting time t of a certain trip (given the number j)OjEnd time tDjAnd a distance of travel LjThe travel time and travel speed may be calculated assuming a travel time of t jA stroke speed of VjThen t isj＝tDj-tOj，Vj＝Lj/tj。
Deleting the travel time t based on the reproduction data calculated in the above stepjData less than or equal to 0 min; delete travel speed VjData of more than or equal to 100 km/h.
The above-mentioned travel time t will be understood by those skilled in the artjStroke velocity VjBy way of example only, other travel times t that are present or may occur in the futurejStroke velocity VjSuch as may be suitable for use in embodiments of the present invention, are also intended to be encompassed within the scope of the present invention and are hereby incorporated by reference.
In some embodiments, the parking cell division in step S120 includes determining an optimal parking cell division scheme according to a research purpose, and the determining includes:
step 1, dividing a research area into a plurality of grids, wherein each grid is used as a basic research unit for parking demand prediction and is used for determining units of a travel starting point and a travel ending point.
And 2, setting the grid side length R from a set numerical value (such as 200m), increasing by taking 100m as a unit, and respectively counting the number of effective grids under different R, the number of peak stops in each grid, the number of starting points and the number of D points.
And 3, defining the grids with the peak parking number, the starting point number and the D point number which are all larger than a specified numerical threshold as effective grids, counting the number of the effective grids, and recording the number as m. The grid in which the number of peak stops is not acquired is defined as an unknown grid.
Step 4, respectively calculating the peak parking under different R conditionsCorrelation coefficient r between number and starting point number, peak parking number and D point numberOAnd rD。
Step 5, according to the research purpose, considering the number m and r of effective samplesOAnd rDAnd determining the side length of the optimal format, and dividing the parking cell of the research area according to the side length of the optimal format.
And 6, defining each grid as a parking cell under the determined optimal grid side length, and respectively determining the peak parking number, the starting point number and the D point number of each parking cell.
Fig. 2 is a graph illustrating a correlation analysis between the number of stops under different grid side length conditions, the number of start points, and the number of D points according to an embodiment of the present invention, in some embodiments, the effective sample numbers m and r are obtained when the optimal grid side length is selected in step 5OAnd rDAt least the following conditions are satisfied: m is more than or equal to 10, rOAnd rD≥0.85。
Those skilled in the art will understand that m and r are as defined aboveOAnd rDThe threshold number of valid samples is only exemplary, and other threshold numbers of valid samples that are currently available or may later appear, such as may be suitable for use in embodiments of the present invention, are also included within the scope of the present invention and are hereby incorporated by reference.
In some embodiments, the implementation of performing regression analysis to construct the parking demand prediction model based on the OD data, the parking data and the parking cell partition in step S130 includes:
Establishing a unary linear fitting function P between the peak parking number and the O point number of the parking districts in the research area according to the peak parking number and the O point number of each parking districti＝αxOiThe unary linear fitting function Pi＝αxOiIs suitable for all parking districts in the research area;
establishing a unary linear fitting function P between the peak parking number and the D point number of the parking districts in the research area according to the peak parking number and the D point number of each parking districti＝βxDiThe unary linear fitting function Pi＝βxDiIs suitable forAll parking cells within the study area;
wherein P isiFor the number of peak stops in parking cell i, xOiIs the number of O points in the parking cell, xDiThe number of D points in the parking cell is defined, and alpha and beta are regression coefficients of the parking cell about the number of O points and the number of D points respectively;
and respectively taking a unitary linear fitting function between the peak parking number corresponding to the research area and the O point number and the D point number as a parking demand prediction model.
Fig. 3 is a schematic view of a parking cell according to an embodiment of the present invention, fig. 4 is a scatter diagram of the number of peak parking spots and the number of starting points of the parking cell, and fig. 5 is a scatter diagram of the number of peak parking spots and the number of D points of the parking cell. Defining each grid in the research area as a parking cell, and respectively determining the number of peak parking, the number of O points and the number of D points of each parking cell. As shown in fig. 3. The relationship among the peak parking number, the starting point number and the D point number is analyzed, as shown in fig. 4 and 5, scatter diagrams of the peak parking number, the O point number and the D point number of the parking cell are respectively observed, and a certain positive correlation among the peak parking number, the O point number and the D point number of the parking cell can be preliminarily judged, so that the correlation analysis of the next step is carried out.
Establishing a unary linear fitting function P between the peak parking number and the O point number of the parking districts according to the peak parking number and the O point number of each parking district through the correlation analysis resulti＝0.009xOiThe fitting result R of the unary linear regression model to the relationship between the number of peak stops and the number of O-points was 0.913, and the relative error was 58.45%.
Establishing a unary linear fitting function P between the peak parking number and the D point number of the parking districts according to the peak parking number and the D point number of each parking districti＝0.009xDiThe result of fitting the relationship between the number of peak stops and the number of D points by the one-dimensional linear regression model, the R-square, was 0.965 with a relative error of 40.69%.
Wherein P isiFor the number of peak stops in parking cell i, xOiIs the number of O points in the parking cell, xDiIn order to park in a districtThe number of D points, alpha and beta are regression coefficients of the parking cell about the number of O points and the number of D points respectively;
and respectively taking a unitary linear fitting function between the peak parking number corresponding to the research area and the O point number and the D point number as a parking demand prediction model.
In some embodiments, the specific implementation of the parking demand prediction for the target parking area according to the parking demand prediction model in step S140 includes:
The peak parking number P in the target parking area can be predicted by using the finally obtained parking demand prediction model and the number of the points O and D in the target parking areaiTherefore, the parking demand of the target parking area is predicted.
Inputting the number of O points of a target parking area into a unitary linear fitting function between the number of peak parking and the number of O points in the parking demand prediction model to obtain the number of peak parking in the target parking area;
and/or the presence of a gas in the gas,
and inputting the number of D points of the target parking area into a unitary linear fitting function between the number of peak parking and the number of D points in the parking demand prediction model to obtain the number of peak parking in the target parking area.
In some embodiments, the model with the larger R-square may be selected as the final parking demand prediction model by comparing the R-squares of the two unitary linear regression results.
In conclusion, the method provided by the embodiment of the invention obtains the parking demand prediction model by performing regression analysis on the OD data and the parking data of the known region, then estimates the parking demand of the unknown region by using the parking demand prediction model, abandons the traditional large-scale investigation, saves the manpower, material resources and financial resources, has the advantages of simplicity, convenience, rapidness and accuracy, and can provide reference and technical support for parking planning, parking resource allocation and parking problem solution.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (3)
1. A parking demand prediction method based on OD data is characterized by comprising the following steps:
obtaining OD data and parking data of a research area, and dividing parking cells of the research area according to the OD data and the parking data; the OD data includes: the method comprises the following steps of numbering, starting point O longitude and latitude of a trip, finishing point D longitude and latitude, starting time, finishing time and travel distance; the parking data comprises a parking lot ID, a peak time and a peak parking number; the dividing process comprises the following steps: dividing a research area into a plurality of grids, sequentially increasing the side length of the grids from a set value, and respectively counting the number of peak stops, the number of O points and the number of D points in each grid under the condition of different grid side lengthsDefining grids with the peak parking number, the O point number and the D point number larger than a specified numerical threshold as effective grids, and recording the total number of the effective grids as m; carrying out correlation analysis on the number of peak stops, the number of O points and the number of D points in each effective grid, and calculating a correlation coefficient r of the number of the peak stops and the number of the O points under different grid side length conditions OCalculating the correlation coefficient r of the number of the peak stops and the number of the D points under the condition of different grid side lengthsDComprehensively considering the number of effective samples m and rOAnd rDDetermining the side length of an optimal format, and dividing parking cells of the research area according to the side length of the optimal format;
performing regression analysis based on the OD data, the parking data and the parking cell partition scheme of the research area to construct a parking demand prediction model, wherein the parking demand prediction model comprises the following steps:
after the parking cells are divided into the research area according to the optimal format side length, defining each grid in the research area as a parking cell, and respectively determining the peak parking number, the O point number and the D point number of each parking cell;
according to the peak parking number and the O point number of each parking cell, a unary linear fitting function P between the peak parking number and the O point number of the parking cells in the research area is establishedi＝αxOi；
Establishing a unary linear fitting function P between the peak parking number and the D point number of the parking districts in the research area according to the peak parking number and the D point number of each parking districti＝βxDi；
Wherein P isiFor the number of peak stops in parking cell i, xOiIs the number of O points in the parking cell, xDiThe number of D points in the parking cell is alpha and beta, and the alpha and beta are regression coefficients of the parking cell about the number of the O points and the D points respectively;
Taking a unitary linear fitting function between the peak parking number corresponding to the research area and the O point number and the D point number respectively as a parking demand prediction model;
and forecasting the parking demand of the target parking area according to the parking demand forecasting model.
2. The method of claim 1, further comprising:
preprocessing errors and abnormal values in the OD data and the parking data, screening out data with empty field values, deleting data with O points or D points not in the research area according to the longitude and latitude coordinate range of the research area, deleting data with the travel distance less than 500 meters, and deleting the travel time tjData less than or equal to 0 min; delete travel speed VjData of more than or equal to 100 km/h.
3. The method according to claim 1 or 2, wherein the predicting of the parking demand for the target parking area according to the parking demand prediction model comprises:
inputting the number of O points in a target parking area into a unitary linear fitting function between the number of peak parking and the number of O points in the parking demand prediction model to obtain the number of peak parking in the target parking area;
and/or the presence of a gas in the gas,
and inputting the number of the D points of the target parking area into a unitary linear fitting function between the number of the peak parking areas and the number of the D points in the parking demand prediction model to obtain the number of the peak parking areas.
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