CN116739169B - People flow prediction method and device - Google Patents
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Abstract
The disclosure provides a method and a device for predicting people flow, wherein the method comprises the following steps: acquiring first people flow data of a first area and second people flow data of a second area, wherein the first people flow data and the second people flow data comprise existing data, and at least one of the first people flow data and the second people flow data also comprises changed data; acquiring a distribution coefficient, wherein the distribution coefficient is used for representing the correlation between wages and traffic time; determining a traffic time between the first area and the second area; obtaining average wages corresponding to the second area; and predicting the people flow between the first area and any one of the second areas according to the distribution coefficient, the first people flow data of the first area, the traffic time between the first area and each of the second areas, the second people flow data of each of the second areas and the average wages. The method can determine the people flow between the first area and the second area after the data change, and can provide decision support for urban land utilization and traffic service management.
Description
Technical Field
The disclosure relates to the technical field of data processing, and in particular relates to a method and a device for predicting traffic flow.
Background
The traffic flow generated when urban residents go out of the house every day and return to the house is an important consideration content of urban planning construction, and can provide decision support for urban planning construction and can also provide basis for traffic service management.
At present, a method for predicting future traffic generated by commute is generally used for predicting the future traffic according to the change situation of the historical traffic, which is essentially a trend extrapolation method, when the historical traffic is not known, the method is difficult to predict the future traffic, and the method fails to relate urban commute to urban land utilization, so that the traffic after the change of urban land utilization is difficult to predict.
Disclosure of Invention
The disclosure provides a method and a device for predicting people flow, which are used for at least solving the technical problems in the prior art.
According to a first aspect of the present disclosure, there is provided a method of predicting a flow of people, the method comprising:
Acquiring first people flow data of a first area and second people flow data of a second area, wherein the first people flow data and the second people flow data comprise existing data, and at least one of the first people flow data and the second people flow data also comprises changed data; the existing data corresponding to the first person flow data is the existing number of residents, and the corresponding changed data is the number of residents expected to increase or decrease; the existing data corresponding to the second people flow data is the existing work station number, and the corresponding changed data is the work station number which is expected to be increased or decreased; the first region characterizes a residential area, and the second region characterizes a work area;
Acquiring a distribution coefficient, wherein the distribution coefficient is used for representing the correlation between wages and traffic time;
determining a traffic time between the first area and the second area;
obtaining average wages corresponding to the second area;
And predicting the traffic flow between the first area and any one of the second areas according to the distribution coefficient, the first traffic flow data of the first area, the traffic time between the first area and each of the second areas, the second traffic flow data of each of the second areas and the average wages.
In one embodiment, the flow of people between the first area and any one of the second areas is determined according to the following formula:
i denotes an ith first area, j denotes a jth second area, T ij is a traffic volume between the ith first area and the jth second area, W i is first traffic volume data of the ith first area, E j is second traffic volume data of the jth second area, θ is a distribution coefficient, For the average payroll corresponding to the jth second area, v ij is the traffic time between the ith first area and the jth second area, and N represents the total number of second areas.
In an embodiment, if the second area and the first area are the same area, the determining the traffic time between the first area and the second area includes: determining a time from each road in the second area to a specific area in the second area; and determining an average value of the time from each road to a specific area in the second area as the traffic time between the first area and the second area.
In an embodiment, if the second area and the first area are different areas, the determining the traffic time between the first area and the second area includes: determining a time corresponding to each path between the first area and the second area, wherein each path comprises at least one road; and taking the shortest time in the time corresponding to each path as the traffic time between the first area and the second area.
In an embodiment, the determining the time corresponding to each path between the first area and the second area includes: determining a road included in each path and a road grade corresponding to each road; determining the driving speed corresponding to each road according to the road grade corresponding to each road; determining the corresponding traffic time of each road according to the driving speed corresponding to each road and the corresponding distance of each road; and determining the corresponding time of each path according to the roads included in each path and the corresponding traffic time of each road.
According to a second aspect of the present disclosure, there is provided a device for predicting a flow of people, the device comprising:
The first acquisition module is used for acquiring first people flow data of a first area and second people flow data of a second area, wherein the first people flow data and the second people flow data comprise existing data, and at least one of the first people flow data and the second people flow data also comprises changed data; the existing data corresponding to the first person flow data is the existing number of residents, and the corresponding changed data is the number of residents expected to increase or decrease; the existing data corresponding to the second people flow data is the existing work station number, and the corresponding changed data is the work station number which is expected to be increased or decreased; the first region characterizes a residential area, and the second region characterizes a work area;
The second acquisition module is used for acquiring a distribution coefficient, wherein the distribution coefficient is used for representing the correlation between wages and traffic time;
A determining module for determining a traffic time between the first area and the second area;
the third acquisition module is used for acquiring average wages corresponding to the second area;
and the prediction module is used for predicting the people flow between the first area and any one of the second areas according to the distribution coefficient, the first people flow data of the first area, the traffic time between the first area and each of the second areas, the second people flow data of each of the second areas and the average wages.
In an embodiment, the second prediction module is further configured to determine a traffic volume between the first area and any one of the second areas according to the following formula:
i denotes an ith first area, j denotes a jth second area, T ij is a traffic volume between the ith first area and the jth second area, W i is first traffic volume data of the ith first area, E j is second traffic volume data of the jth second area, θ is a distribution coefficient, For the average payroll corresponding to the jth second area, v ij is the traffic time between the ith first area and the jth second area, and N represents the total number of second areas.
In an embodiment, if the second area and the first area are the same area, the determining module includes: a first determination sub-module for determining a time of each road in the second area to a specific area in the second area; and the second determining submodule is used for determining the average value of the time from each road to a specific area in the second area as the traffic time between the first area and the second area.
In an embodiment, if the second area and the first area are different areas, the determining module includes: a third determining submodule, configured to determine a time corresponding to each path between the first area and the second area, where each path includes at least one road; and the fourth determining submodule is used for taking the shortest time in the time corresponding to each path as the traffic time between the first area and the second area.
In an embodiment, the third determining sub-module includes: a first determining unit, configured to determine a road included in each path and a road class corresponding to each road; the second determining unit is used for determining the driving speed corresponding to each road according to the road grade corresponding to each road; the third determining unit is used for determining the traffic time corresponding to each road according to the driving speed corresponding to each road and the distance corresponding to each road, and determining the time corresponding to each path according to the road included in each path and the traffic time corresponding to each road.
According to the method and the device for predicting the traffic flow, the traffic flow between the first area and the second area is predicted according to the first traffic flow data of the first area and the second traffic flow data of the second area, when urban land utilization is planned, the traffic flow between the first area and the second area after planning can be predicted according to the data such as the living population, the number of working posts and the like in the planning scheme, and decision support can be provided for urban land utilization and traffic service management.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Fig. 1 shows a schematic implementation flow diagram of a method for predicting a traffic flow according to an embodiment of the disclosure;
fig. 2 shows a second implementation flow chart of a method for predicting a traffic flow according to an embodiment of the disclosure;
fig. 3 is a schematic diagram showing a composition structure of a device for predicting a flow rate of people according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, features and advantages of the present disclosure more comprehensible, the technical solutions in the embodiments of the present disclosure will be clearly described in conjunction with the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, but not all embodiments. Based on the embodiments in this disclosure, all other embodiments that a person skilled in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.
Fig. 1 shows a schematic implementation flow diagram of a method for predicting a traffic flow according to an embodiment of the disclosure, as shown in fig. 1, according to a first aspect of an embodiment of the disclosure, there is provided a method for predicting a traffic flow, including:
step 101, acquiring first people flow data of a first area and second people flow data of a second area, wherein the first people flow data and the second people flow data comprise existing data, and at least one of the first people flow data and the second people flow data also comprises changed data; the existing data corresponding to the first person flow data are the existing number of residents, and the corresponding changed data are the number of residents expected to increase or decrease; the existing data corresponding to the second people flow data is the existing work post number, and the corresponding changed data is the work post number which is expected to increase or decrease; the first region characterizes a residential area and the second region characterizes a work area.
The first area represents a living area, the second area represents a working area, and the first area and the second area can be different areas or the same area, namely the same area can be used as the living area or the working area; the existing resident number is the actual resident number in the first area, the expected increased or decreased resident number is the increased or decreased resident number in the first area in a period of time in the future, for example, the actual resident number in the first area is fifty thousands of people, a resident district is planned to be developed and built in the first area, the district is expected to increase five thousands of resident numbers in the first area after the district is built, and the number of the resident is five thousands of resident numbers expected to be increased; the number of existing work stations is the number of existing work stations in the second area, the number of expected to be increased or decreased is the number of expected to be increased or decreased in the second area for a period of time in the future, for example, the number of existing work stations in the second area is six thousands, an office building is expected to be developed and built in the second area, two thousands of work stations are expected to be newly increased after the office building is built, and the two thousands of work stations are the expected number of increased work stations.
Step 102: acquiring a distribution coefficient, wherein the distribution coefficient is used for representing the correlation between wages and traffic time;
step 103: determining a traffic time between the first area and the second area;
step 104: obtaining average wages corresponding to the second area;
Step 105: and predicting the people flow between the first area and any one of the second areas according to the distribution coefficient, the first people flow data of the first area, the traffic time between the first area and each of the second areas, the second people flow data of each of the second areas and the average wages.
The traffic time between the first area and the second area is inversely related to the traffic flow between the first area and the second area, i.e. the longer the traffic time is, the smaller the traffic flow is; the average wages corresponding to the second area are positively correlated with the traffic between the first area and the second area, i.e. the higher the average wages, the greater the traffic; the distribution coefficients corresponding to different cities are different, and can be determined according to the historic people flow and wages corresponding to the cities. Based on the above-described idea, the traffic flow between the first area and any one of the second areas is predicted from the distribution coefficient, the first traffic flow data of the first area, the traffic time between the first area and each of the second areas, the second traffic flow data of each of the second areas, and the average wages.
The traffic flow between the first area and the second area is the traffic flow between the first area and the second area due to commute; when the number of residents in the first area and/or the number of working posts in the second area is predicted to be changed, the traffic between the changed first area and any one of the second areas due to commute can be predicted according to the first traffic data of the first area and the second traffic data of all the second areas.
According to the method for predicting the traffic flow, the traffic flow between the first area and the second area is predicted according to the first traffic flow data of the first area and the second traffic flow data of the second area, when urban land utilization is planned, the traffic flow between the first area and the second area after planning can be predicted according to newly increased or decreased living population, working post number and other data in a planning scheme, and decision support can be provided for urban land utilization and traffic service management. The method is strong in universality, simple and convenient to understand, popularize and apply, and can acquire the corresponding people flow only by acquiring the first people flow data and the second people flow data of the corresponding area.
In one embodiment of the present disclosure, the flow of people between a first area and any one of the second areas is determined according to the following formula (1):
i denotes an ith first area, j denotes a jth second area, T ij is a traffic flow between the ith first area and the jth second area, W i is first traffic flow data of the ith first area, E j is second traffic flow data of the jth second area, θ is a distribution coefficient, For the average payroll corresponding to the jth second area, v ij is the traffic time between the ith first area and the jth second area, and N represents the total number of second areas.
The correlation between the people flow between the first area and the second area and the actual people flow between the first area and the second area obtained by the formula is 0.8719, and the fitting coefficient is 0.76, so that a good prediction effect is achieved.
In one embodiment of the present disclosure, if the second area and the first area are the same area, determining the traffic time between the first area and the second area includes: determining a time from each road in the second area to a specific area in the second area; an average value of the time of each road to a specific area in the second area is determined as the traffic time between the first area and the second area.
The specific area of the second area may be a government of the second area, a central urban area of the second area, etc., the time from each road to the specific area in the second area is obtained, the average value of the time from each road to the specific area is obtained, and the average value is the traffic time from the first area to the second area.
In one embodiment of the present disclosure, if the second area and the first area are different areas, determining the traffic time between the first area and the second area includes: determining a time corresponding to each path between the first area and the second area, wherein each path comprises at least one road; and taking the shortest time in the time corresponding to each path as the traffic time between the first area and the second area.
For example, 2 paths are shared between the first area and the second area, the 2 paths are a path A and a path B respectively, the path A passes through 3 roads respectively, the time for passing through the 3 roads is determined respectively, and the time for passing through the 3 roads is added to obtain the traffic time corresponding to the path A; similarly, calculating the traffic time corresponding to the path B, and selecting shorter time from the traffic time corresponding to the path A and the path B as the traffic time between the first area and the second area.
In an embodiment of the present disclosure, fig. 2 shows a second implementation flow chart of a method for predicting a traffic flow according to an embodiment of the present disclosure, and as shown in fig. 2, determining a time corresponding to each path between a first area and a second area includes: step 201, determining a road included in each path and a road grade corresponding to each road; step 202, determining a driving speed corresponding to each road according to the road grade corresponding to each road; step 203, determining a traffic time corresponding to each road according to the driving speed corresponding to each road and the distance corresponding to each road, and step 204, determining a time corresponding to each path according to the road included in each path and the traffic time corresponding to each road.
The road grades corresponding to the roads include high speed, national roads, provincial roads, county roads, rural roads and the like, and different driving speeds are given to each level of roads according to the road grades, and the exemplary road grades and the driving speeds corresponding to the road grades can be shown in table 1: the driving speed corresponding to the high speed is 120 km/h, the driving speed corresponding to national roads is 80 km/h, the driving speed corresponding to province roads is 60 km/h, the driving speed corresponding to county roads is 40 km/h, the driving speed corresponding to urban primary roads is 80 km/h, the driving speed corresponding to urban secondary roads is 60 km/h, the driving speed corresponding to urban tertiary roads is 40 km/h, and the driving speed corresponding to urban fourth roads is 30 km/h; and determining the traffic time corresponding to each road according to the distance and the driving speed corresponding to each road, and adding the traffic time corresponding to all roads included in each path to obtain the time corresponding to each path.
Table 1 road class and corresponding driving speed
| Road grade | Speed (kilometer/hour) |
| High speed | 120 |
| National road | 80 |
| Province way | 60 |
| County road | 60 |
| Rural road | 40 |
| Urban first-grade road | 80 |
| Urban two-stage road | 60 |
| Urban three-level road | 40 |
| Urban four-level road | 30 |
As shown in fig. 3, an embodiment of the present disclosure provides a device for predicting a traffic flow, including:
A first obtaining module 301, configured to obtain first people traffic data of a first area and second people traffic data of a second area, where the first people traffic data and the second people traffic data include existing data, and at least one of the first people traffic data and the second people traffic data further includes changed data; the existing data corresponding to the first person flow data is the existing number of residents, and the corresponding changed data is the number of residents expected to increase or decrease; the existing data corresponding to the second people flow data is the existing work post number, and the corresponding changed data is the work post number which is expected to increase or decrease; the first region characterizes a residential area and the second region characterizes a work area;
a second obtaining module 302, configured to obtain a distribution coefficient, where the distribution coefficient is used to characterize a correlation between wages and traffic time;
a determining module 303, configured to determine a traffic time between the first area and the second area;
a third obtaining module 304, configured to obtain an average wages corresponding to the second area;
the prediction module 305 is configured to predict the traffic between the first area and any one of the second areas according to the distribution coefficient, the first traffic data of the first area, the traffic time between the first area and each of the second areas, the second traffic data of each of the second areas, and the average wages.
In one embodiment of the present disclosure, the prediction module 305 is further configured to determine the traffic between the first area and any one of the second areas according to the following formula:
i denotes an ith first area, j denotes a jth second area, T ij is a traffic flow between the ith first area and the jth second area, W i is first traffic flow data of the ith first area, E j is second traffic flow data of the jth second area, θ is a distribution coefficient, For the average payroll corresponding to the jth second area, v ij is the traffic time between the ith first area and the jth second area, and N represents the total number of second areas.
In one embodiment of the present disclosure, if the second area and the first area are the same area, the determining module 303 includes: a first determining submodule 3031, configured to determine a time from each road in the second area to a specific area in the second area; the second determining submodule 3032 is used for determining the average value of the time from each road to a specific area in the second area as the traffic time between the first area and the second area.
In one embodiment of the present disclosure, if the second area and the first area are different areas, the determining module 303 includes: a third determining submodule 3033, configured to determine a time corresponding to each path between the first region and the second region, where each path includes at least one road; the fourth determination submodule 3034 takes the shortest time in the time corresponding to each path as the traffic time between the first area and the second area.
In one embodiment of the present disclosure, the third determining submodule 3033 includes: a first determining unit 30331, configured to determine a road included in each path and a road class corresponding to each road; a second determining unit 30332, configured to determine a driving speed corresponding to each road according to the road class corresponding to each road; a third determining unit 30333, configured to determine a traffic time corresponding to each road according to the driving speed corresponding to each road and the distance corresponding to each road; the third determining unit 30333 is further configured to determine a time corresponding to each path according to the link included in each path and the traffic time corresponding to each link.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
The foregoing is merely specific embodiments of the disclosure, but the protection scope of the disclosure is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the disclosure, and it is intended to cover the scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (8)
1. A method of predicting traffic, the method comprising:
Acquiring first people flow data of a first area and second people flow data of a second area, wherein the first people flow data and the second people flow data comprise existing data, and at least one of the first people flow data and the second people flow data also comprises changed data;
The existing data corresponding to the first person flow data is the existing number of residents, and the corresponding changed data is the number of residents expected to increase or decrease;
the existing data corresponding to the second people flow data is the existing work station number, and the corresponding changed data is the work station number which is expected to be increased or decreased;
the first region characterizes a residential area, and the second region characterizes a work area;
Acquiring a distribution coefficient, wherein the distribution coefficient is used for representing the correlation between wages and traffic time;
determining a traffic time between the first area and the second area;
obtaining average wages corresponding to the second area;
Predicting the traffic between the first area and any one of the second areas according to the distribution coefficient, the first traffic data of the first area, the traffic time between the first area and each of the second areas, the second traffic data of each of the second areas and the average wages;
and determining the people flow between the first area and any one of the second areas according to the following formula:
i denotes an ith first area, j denotes a jth second area, T ij is a traffic volume between the ith first area and the jth second area, W i is first traffic volume data of the ith first area, E j is second traffic volume data of the jth second area, θ is a distribution coefficient, For the average payroll corresponding to the jth second area, v ij is the traffic time between the ith first area and the jth second area, and N represents the total number of second areas.
2. The method of claim 1, wherein if the second area is the same area as the first area, the determining the traffic time between the first area and the second area comprises:
determining a time from each road in the second area to a specific area in the second area;
And determining an average value of the time from each road to a specific area in the second area as the traffic time between the first area and the second area.
3. The method of claim 1, wherein if the second area and the first area are different areas, the determining the traffic time between the first area and the second area comprises:
Determining a time corresponding to each path between the first area and the second area, wherein each path comprises at least one road;
And taking the shortest time in the time corresponding to each path as the traffic time between the first area and the second area.
4. A method according to claim 3, wherein said determining the time for each path between the first region and the second region comprises:
Determining a road included in each path and a road grade corresponding to each road;
Determining the driving speed corresponding to each road according to the road grade corresponding to each road;
Determining the corresponding traffic time of each road according to the driving speed corresponding to each road and the corresponding distance of each road;
And determining the corresponding time of each path according to the roads included in each path and the corresponding traffic time of each road.
5. A device for predicting a flow of people, the device comprising:
The first acquisition module is used for acquiring first people flow data of a first area and second people flow data of a second area, wherein the first people flow data and the second people flow data comprise existing data, and at least one of the first people flow data and the second people flow data also comprises changed data;
The existing data corresponding to the first person flow data is the existing number of residents, and the corresponding changed data is the number of residents expected to increase or decrease;
the existing data corresponding to the second people flow data is the existing work station number, and the corresponding changed data is the work station number which is expected to be increased or decreased;
the first region characterizes a residential area, and the second region characterizes a work area;
The second acquisition module is used for acquiring a distribution coefficient, wherein the distribution coefficient is used for representing the correlation between wages and traffic time;
A determining module for determining a traffic time between the first area and the second area;
the third acquisition module is used for acquiring average wages corresponding to the second area;
The prediction module is used for predicting the flow of people between the first area and any one of the second areas according to the distribution coefficient, the first flow data of the first area, the traffic time between the first area and each of the second areas, the second flow data of each of the second areas and the average wages;
The prediction module is further configured to determine a traffic flow between the first area and any one of the second areas according to the following formula:
i denotes an ith first area, j denotes a jth second area, T ij is a traffic volume between the ith first area and the jth second area, W i is first traffic volume data of the ith first area, E j is second traffic volume data of the jth second area, θ is a distribution coefficient, For the average payroll corresponding to the jth second area, v ij is the traffic time between the ith first area and the jth second area, and N represents the total number of second areas.
6. The apparatus of claim 5, wherein the determining module comprises, if the second region is the same region as the first region:
A first determination sub-module for determining a time of each road in the second area to a specific area in the second area;
And the second determining submodule is used for determining the average value of the time from each road to a specific area in the second area as the traffic time between the first area and the second area.
7. The apparatus of claim 5, wherein the determining module comprises:
A third determining submodule, configured to determine a time corresponding to each path between the first area and the second area, where each path includes at least one road;
And the fourth determining submodule is used for taking the shortest time in the time corresponding to each path as the traffic time between the first area and the second area.
8. The apparatus of claim 7, wherein the third determination submodule comprises:
A first determining unit, configured to determine a road included in each path and a road class corresponding to each road;
The second determining unit is used for determining the driving speed corresponding to each road according to the road grade corresponding to each road;
the third determining unit is used for determining the traffic time corresponding to each road according to the driving speed corresponding to each road and the distance corresponding to each road;
and the third determining unit is further used for determining the time corresponding to each path according to the roads included in each path and the traffic time corresponding to each road.
Priority Applications (1)
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| CN202310699382.0A CN116739169B (en) | 2023-06-13 | People flow prediction method and device |
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| CN202310699382.0A CN116739169B (en) | 2023-06-13 | People flow prediction method and device |
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| CN116739169A CN116739169A (en) | 2023-09-12 |
| CN116739169B true CN116739169B (en) | 2024-07-12 |
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| Title |
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| 城市轨道交通客流预测方法;马超群;陈宽民;王玉萍;;长安大学学报(自然科学版);第30卷(第05期);第69-74页 * |
| 基于互联网大数据的区域多层次空间结构分析研究;牛方曲等;地球信息科学;第18卷(第6期);第720-726页 * |
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