CN108681791B - People flow density prediction method, device and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a people stream density prediction method, which comprises the following steps: acquiring a time variation curve of the stream density, fitting the curve to obtain a stream density fitting curve, and obtaining a corresponding stream density fitting function according to the stream density fitting curve; acquiring people stream density data of a road to be predicted, and substituting the people stream density data of the road to be predicted into a people stream density fitting function to obtain a fitting function characteristic parameter of the road to be predicted; and according to the characteristic parameters of the road fitting function to be predicted, obtaining a pedestrian flow density time variation curve function of the road to be predicted, and predicting the pedestrian flow density of the road to be predicted according to the pedestrian flow density time variation curve function of the road to be predicted. The embodiment of the invention also provides an active interaction device and a non-transitory readable storage medium, which are used for realizing the method. The method can be widely applied to the field of people stream density prediction, and has high prediction efficiency on the people stream density.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data, in particular to a people stream density prediction method, a device and a storage medium.
Background
People stream density is important urban basic data, and plays an extremely important role in urban traffic management and urban public safety management. At present, the acquisition method of the traffic density data basically belongs to a hardware method, and comprises video detection and induction loop detection, wherein the video detection is easily influenced by light, and the induction loop needs to dig up the road, so that the popularization is very difficult. Although the obtained data is more accurate, if the data is obtained in a large range, the method has the defects of difficult implementation and low efficiency. Therefore, finding an efficient and widely applicable people stream density prediction method is an urgent problem to be solved in the industry.
Disclosure of Invention
In view of the foregoing problems in the prior art, embodiments of the present invention provide a method, an apparatus, and a storage medium for predicting a density of people stream.
In one aspect, an embodiment of the present invention provides a people stream density prediction method, including: acquiring a time variation curve of the stream density, fitting the curve to obtain a stream density fitting curve, and obtaining a corresponding stream density fitting function according to the stream density fitting curve; acquiring people stream density data of a road to be predicted, and substituting the people stream density data of the road to be predicted into a people stream density fitting function to obtain a fitting function characteristic parameter of the road to be predicted; and according to the characteristic parameters of the road fitting function to be predicted, obtaining a pedestrian flow density time variation curve function of the road to be predicted, and predicting the pedestrian flow density of the road to be predicted according to the pedestrian flow density time variation curve function of the road to be predicted.
In another aspect, an active interaction device and a non-transitory readable storage medium are provided in embodiments of the present invention. The active interaction device comprises: at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, the processor invoking the program instructions to enable execution of the one people stream density prediction method. The non-transitory readable storage medium stores program instructions for executing the method of pedestrian flow density prediction.
The embodiment of the invention provides a people stream density prediction method, a device and a storage medium, wherein a people stream density curve is fitted through people stream density to obtain a corresponding people stream density fitting function, parameters of the people stream density fitting function are obtained according to specific road conditions, and then an effective people stream density fitting function available for the specific road conditions is obtained to predict the people stream density conditions. The method, the device and the storage medium can be widely applied to the field of people stream density prediction, and the prediction efficiency of people stream density is high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flowchart of the overall method for predicting the density of people stream according to the first embodiment of the present invention;
FIG. 2 is a schematic view of a density curve of a stream of people according to a first embodiment of the present invention;
FIG. 3 is a schematic view of a multimodal fitted curve of the density of people flow in the first embodiment of the invention;
fig. 4 is a schematic diagram of the hardware device according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a people stream density prediction method, a device and a storage medium. Referring to fig. 1, fig. 1 is an overall flowchart of a people stream density prediction method in a first embodiment of the present invention, including:
s101: and obtaining a time variation curve of the stream density, fitting the curve to obtain a stream density fitting curve, and obtaining a corresponding stream density fitting function according to the stream density fitting curve.
Through statistical analysis, the people flow density of each road section is periodically changed in 24 hours (namely, the change period of the time change curve of the people flow density comprises 24 hours as a change period), and the people flow density curve in a single period is a multi-peak curve. In many engineering practical problems, there are two peaks in the graph of the probability density function of the random variable. Typical distribution functions have only one peak or no peak, and multimodal fitting can well describe such variables with bimodal or multimodal morphology. Multimodal gaussian fitting analysis using origin tool can obtain multimodal fitting curve of human stream density, the fitted gaussian function is shown as following formula:
wherein y is0Is a base line, B is a peak area, w is a full width at half maximum of the peak, and xcIs the peak position.
Referring to fig. 2, fig. 2 is a schematic view of a people flow density curve in a first embodiment of the present invention, which includes:
a traffic density axis 201, a time axis 202, an average traffic density 203, and a data smoothing curve 204.
The pedestrian flow of a certain road section is selected, the pedestrian flow is counted by taking the hourly time interval as an example, the pedestrian flow density is taken as an example, the data is smoothed for five points and three times, and then a curve is obtained and is shown in fig. 2. As can be seen, the data smoothing curve 204 is consistent with the basic trend of the average people stream density 203.
Fitting analysis is carried out according to the density of the stream of people, the number of peaks is set to be 2, and y is output0，B1，B2，w1，w2，xc1，xc2Isoparametric, the fitting degree of identity is 0.961.
A Gaussian multimodal fitting curve obtained by multimodal fitting the people flow density data satisfies the following relational expression:
in the Gaussian function, xcIn formula (2), x is the peak positionc1Indicating density of night streamLow peak time of, and xc2The road is universal on roads in the whole city at noon peak or late peak time; w is a1And w2The half-height width of the corresponding peak is taken as the time length of each peak time period in the people flow density curve can be basically considered as consistent; b is1And B2The area is the peak area, i.e. the area enclosed by the peak curve and the base line, and the value is related to the difference of the road, but the statistic selected herein is the density of people stream, i.e. the number of people in the unit area, so the peak area of the same standard road has consistency.
S102: and acquiring people stream density data of the road to be predicted, and substituting the people stream density data of the road to be predicted into the people stream density fitting function to obtain the characteristic parameter of the fitting function of the road to be predicted.
Referring to fig. 3, fig. 3 is a schematic diagram of a multimodal fitted people stream density curve in a first embodiment of the invention, including:
a stream density axis 301, a time axis 302, a second peak fit curve 303, a first peak fit curve 304, a multi-peak fit curve 305, and a data smoothing curve 306. The parameter output by the two curves is B1，B2，w1，w2，xc1，xc2. Wherein, B1、w1And xc1Is the output parameter of the first peak-fitted curve 304. B is2、w2And xc2Is the output parameter of the second peak-fitted curve 303.
S103: and according to the characteristic parameters of the road fitting function to be predicted, obtaining a pedestrian flow density time variation curve function of the road to be predicted, and predicting the pedestrian flow density of the road to be predicted according to the pedestrian flow density time variation curve function of the road to be predicted.
The output parameters can obtain the characteristic curve function of the density of the stream of people of any road, and the density data (x) of the stream of people at a certain moment of the road1，y1) By substituting the formula, y can be obtained0Therefore, the people flow density y corresponding to any time x in 24 hours can be obtained through calculation, and the following formula is shown:
the second embodiment of the present invention is based on the first embodiment. Wherein, the time variation curve of people stream density of acquireing includes: the method comprises the steps of counting people stream density data of a typical road (in another embodiment, the counting of the people stream density data of the typical road comprises the steps of obtaining the people stream density data according to the length, the width, the height and the distance of an electric vehicle, wherein the distance of the electric vehicle comprises the front-rear distance and the left-right distance, in yet another embodiment, the counting of the people stream density data of the typical road comprises the steps of obtaining the people stream density data according to the social distance of people), and carrying out statistical analysis on the people stream density data of the typical road to obtain a time variation curve of the people stream density.
The third embodiment of the present invention is based on the second embodiment. Wherein, the statistics of the people flow density data of the typical road comprises the following steps:
summing the length of the electric vehicle and the front and rear vehicle distances to obtain a longitudinal occupying distance of a person, summing the width of the electric vehicle and the left and right vehicle distances to obtain a transverse occupying distance of the person, multiplying the longitudinal occupying distance of the person and the transverse occupying distance of the person, and taking the reciprocal of the product to obtain people flow density data.
Specifically, the riding electric vehicle is taken as an example in which people on the road mainly exist. The length of the electric vehicle is assumed to be 1.875m, the width is assumed to be 0.85m, the height is assumed to be 1.1m, the front-rear vehicle distance and the left-right vehicle distance are both 1m, and the number of people per square meter is 1/[ (1.875+1) × (0.85+1) calculated according to 1 person loaded on the vehicle]0.188 persons/m2。
The fourth embodiment of the present invention is based on the second embodiment. Wherein, the statistics of the people flow density data of the typical road comprises the following steps:
and taking a half of the social distance of the person as a social radius, obtaining the area of a social circle according to the social radius, and then taking the reciprocal of the area of the social circle to obtain the people flow density data.
Specifically, the main existence form of the traffic flow on the road is taken as walking as an example. The social distance of the people is assumed to be 1.2m, and the number of people per square meter is 1/[3.14 x 0.6]＝0.885Human/m2。
Referring to fig. 4, fig. 4 is a schematic diagram of the operation of a hardware apparatus according to an embodiment of the present invention, where the hardware apparatus includes: a traffic density prediction apparatus 401, a processor 402, and a storage medium 403.
The crowd density predicting device 401: the people flow density prediction device 401 implements the people flow density prediction method.
The processor 402: the processor 402 loads and executes the instructions and data in the storage medium 403 to implement the people stream density prediction method.
Storage medium 403: the storage medium 403 stores instructions and data; the storage medium 403 is used for implementing a people stream density prediction method.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and 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.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods according to the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A people stream density prediction method is characterized by comprising the following steps:
acquiring a time variation curve of the pedestrian flow density of a typical road, fitting the curve to obtain a pedestrian flow density fitting curve, and obtaining a corresponding pedestrian flow density fitting function according to the pedestrian flow density fitting curve; the step of fitting the curve to obtain a fitted people stream density curve comprises the following steps: carrying out multimodal Gaussian fitting analysis on the time change curve of the pedestrian flow density to obtain a pedestrian flow density fitting curve of any road;
acquiring people stream density data of a road to be predicted, and substituting the people stream density data of the road to be predicted into a people stream density fitting function to obtain a fitting function characteristic parameter of the road to be predicted; substituting the people flow density data of the road to be predicted into the people flow density fitting function to obtain the characteristic parameters of the fitting function of the road to be predicted, and the method comprises the following steps:
substituting the people flow density data of the road to be predicted into a formula:
obtaining the characteristic parameter B of the fitting function of the road to be predicted1、B2、w1、w2、xc1And xc2Wherein B is1And B2Denotes the peak area, i.e. the area enclosed by the peak curve and the base line, w1And w2Denotes the full width at half maximum, x, of the corresponding peakc1Low peak time, x, representing night stream densityc2Indicating a noon peak or a late peakTime of day;
obtaining a pedestrian flow density time variation curve function of the road to be predicted according to the fitting function characteristic parameters of the road to be predicted, and predicting the pedestrian flow density of the road to be predicted according to the pedestrian flow density time variation curve function of the road to be predicted; the step of obtaining a pedestrian flow density time variation curve function of the road to be predicted according to the fitting function characteristic parameters of the road to be predicted comprises the following steps: fitting function characteristic parameter B based on road to be predicted1、B2、w1、w2、xc1And xc2Obtaining a pedestrian flow density characteristic curve function of any road; people stream density data (x) of a road to be predicted at a certain moment1，y1) Substituting the pedestrian flow density characteristic curve function of the arbitrary road to obtain y0Based on y0Obtaining a pedestrian flow density time variation curve function of the road to be predicted; the step of predicting the people flow density of the road to be predicted according to the people flow density time variation curve function of the road to be predicted comprises the following steps: and obtaining the people flow density corresponding to the road to be predicted at any time within 24 hours according to the people flow density time change curve function of the road to be predicted.
2. The method of claim 1, wherein the obtaining of the time variation curve of the traffic density of the typical road comprises:
and counting the people flow density data of the typical road, and carrying out statistical analysis on the people flow density data of the typical road to obtain a time variation curve of the people flow density.
3. The method of claim 2, wherein the statistical population density data for the representative road comprises:
and acquiring people stream density data according to the length, the width, the height and the distance of the electric vehicle.
4. The method of claim 3, wherein the vehicle distance comprises:
the front-rear vehicle distance and the left-right vehicle distance.
5. The method of claim 2, wherein the statistical population density data for the representative road comprises:
and acquiring people stream density data according to the social distance of people.
6. The method of claim 4, wherein the statistical population density data for the representative road comprises:
summing the length of the electric vehicle and the front and rear vehicle distances to obtain a longitudinal occupying distance of a person, summing the width of the electric vehicle and the left and right vehicle distances to obtain a transverse occupying distance of the person, multiplying the longitudinal occupying distance of the person and the transverse occupying distance of the person, and taking the reciprocal of the product to obtain people flow density data.
7. The method of claim 5, wherein the statistical population density data for the representative road comprises:
and taking a half of the social distance of the person as a social radius, obtaining the area of a social circle according to the social radius, and then taking the reciprocal of the area of the social circle to obtain the people flow density data.
8. The method of claim 1, wherein the period of change of the time profile of the people stream density comprises: 24 hours is a change period.
9. An active interaction device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 8.
10. A non-transitory readable storage medium storing program instructions for executing the method according to any one of claims 1 to 8.
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