GB2569752A - Method for detecting human traffic in public place by using Wi-Fi probe - Google Patents
Method for detecting human traffic in public place by using Wi-Fi probe Download PDFInfo
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Abstract
A method for detecting a human traffic in a public place by using Wi-Fi probe. By comparing the detection area of the Wi-Fi probe with the area of each functional region in the public place, different layout solutions of the probe are provided; MAC layer information of a mobile device is collected, and track information of pedestrians in the public place is obtained by making comparisons on the detection results from multiple aspects.
Description
A method for detecting pedestrian volume of public areas using WI-FI probes
Technical Field
The invention belongs to the technical field of WI-FI data acquisition and pedestrian flow detection, and particularly relates to a method for detecting human flow in a public place using WI-FI probes. By comparing the detection area of the WI-FI probe with the area of each functional area in the public place, different probe layout schemes are given, and the MAC layer information of mobile devices are collected. And the detection results are analyzed in various aspects to obtain pedestrians’ trajectory information in public places.
Prior Art
Large-scale shopping malls, transportation hubs, tourist resorts and other places often have large passenger flow. Especially during the peak passenger flow, a large number of pedestrians flooding into the main roads will cause certain safety hazards and affect the operational efficiency in these places. Therefore, the detection of pedestrian traffic on the road is of great significance. According to the detection results, the actual flow of people can be estimated scientifically, and the corresponding security personnel can be provided with reliable traffic data, so that reasonable measures can be taken to ensure shopping malls, transportation hubs and tourist vacations. At present, the detection methods for human traffic are more and more diversified. According to the categories of detection technologies, they can be roughly classified into the following categories:
1) Manual survey method: Manual survey is the most traditional method for counting passenger flow. The method is simple and can be superimposed on manual judgment criteria. However, due to its high requirements for investigators, large counting errors, low data quality, heavy data collection work, and the data system is not systematic and can not provide real-time data, this method currently can not meet the growth of traffic demand.
2) Gate-type passenger flow counting: The gate is a channel blocking device (channel management equipment), which is used to manage the flow of people and regulate pedestrians' entry and exit. It is mainly used in the subway gate system and the toll gate gate system. Its most basic and core function is to realize the entrance passage of only one person at a time, which can be used for various charging and access control occasions. This method is low in cost and accurate in quantity, but in the case that the service crowd has a large amount of baggage parcels, the method is less efficient, and hinders the evacuation of pedestrians in an emergency, and is not conducive to the inconvenience of movement. Also, the method of detecting human flow data is limited to a certain section, and it is necessary to arrange a plurality of sections to grasp the distribution of people flow, which causes large floor space.
3) Pedal-type passenger flow counting: The pressure plate passenger flow detection device is installed on the ground of the inspection area, and the pressure sensor information triggered by the pedestrian is automatically recorded. This type of instrument can be roughly divided into two categories, one is based on the human treading data model mode for counting and direction judgment, and the other is based on passenger pedal profile. The method reduces the influence on the passenger flow operation and is simple to install, but the detection accuracy is low, and the components of the pressing system are easily damaged, and the maintainability is poor.
4) Infrared passenger flow counting: Infrared passenger flow counting can be divided into passive infrared passenger flow counting and active infrared passenger flow counting. Passive infrared passenger flow counting uses a infrared probe that can avoid interference from other objects and can only detect signals emitted by the human body. When someone passes, the infrared sensor can detect a certain change caused by the infrared spectrum of the human body, trigger a pulse signal, and then judge the number of people according to the number of pulse signals. The active infrared type transmits a custom wavelength infrared ray to cover a certain area through the transmitting head, and the number of passengers is recognized by the light reflected by the passenger detected by the sensor. Active infrared passenger flow counting overcomes the shortcomings of passive infrared passenger flow counting affected by environment and light. However, because it uses simple judgment of the number of pulses to determine the number of people, the accuracy of statistics is low, and the situation of many people passing at one time is even more difficult to measure. Moreover, the direction of the passenger flow cannot be discriminated by only the infrared method, and the cost of the detection device is high, and it is not suitable for a wide range of use.
5) Video passenger flow count: The video passenger flow count can be divided into a monocular video passenger flow count and a binocular video passenger flow count. Video passenger flow technology captures video images by installing cameras in critical channels, and uses image processing counts such as image segmentation, artificial neural networks, and stereo image analysis to capture passenger flow counts. However, the method started late and the technology is not yet mature. Moreover, the implementation cost and maintenance cost are high, and it is difficult to solve the problem of individual segmentation of people flow when the flow of people is dense, so the accuracy is low.
6) WI-FI probe passenger flow detection: WI-FI probe passenger flow detection is achieved by deploying a WI-FI network in the detection area to obtain the MAC address of the mobile devices that turns on the WI-FI function, thereby realizing the passenger flow count. The WI-FI based passenger flow statistics method is simple in operation, reasonable in equipment cost, small in impact by non-line-of-sight factors, high in flexibility, and capable of acquiring a large amount of statistical data at the same time, thus has great advantages in the detection under dense passenger flow. And in-depth analysis of the data content obtained by the probe can obtain characteristic data such as the flow stop time and streamline flow direction. And this detection method supports the cloud platform in subsequent operations, and the data application can be extended to the marketing layer. It is currently widely used in large commercial areas, tourist attractions, playgrounds and other places.
WI-FI probe technology: WI-FI probes can detect the MAC address of mobile device with WI-FI function enabled. The principle includes: WI-FI is based on IEEE802.1 la/b/g/n protocol. In the protocol, two working modes of the wireless access point and the client are defined, and various wireless data frame types such as Beacon, Ack, Data, and Probe are also specified in the protocol. When the client connects to the wireless access point, the data frame and the response frame are interacted, and the wireless access point periodically sends the Beacon. When the client is not connected to the wireless access point, the client will continue to send Probe frames to nearby wireless access points for detection. The WI-FI probe is a wireless access point that captures information about nearby clients based on various wireless data frames. It can intercept the MAC layer information of the mobile client opened by WI-FI within a certain range, including MAC address and signal, receive intensity values, timestamps, etc.
However, the manual survey method, the gate counting method, the pedal counting method, the infrared counting method, and the video counting method all have disadvantages such as requiring more manpower, expensive equipment, and a large floor space. However, the current popular WI-FI based detection method has the following problems:
1) The mobile device's unique MAC address is detected by the probe on the premise that the mobile device's WI-FI needs to be open. In reality, the proportion of mobile devices that open WI-FI in the crowd is low and unknown. Therefore, under normal circumstances, the difference between the passenger flow detected by WI-FI and the actual passenger flow is large, and the effect is not satisfactory from the detection amount.
2) The multi-path phenomenon and reflection phenomenon of the wireless detection signal emitted by the mobile device during the capture by the probe, and the multi-path phenomenon and reflection phenomenon of the wireless signal will attenuate the signal strength, resulting in the detection of the received signal strength values (RS SI) having varying degrees of attenuation and are not even detectable in severe cases. Therefore, the detection rate of WI-FI is low.
3) Due to the basic characteristics of low detection rate, it is impossible to directly use the detection result to calculate the actual traffic volume. Therefore, it is necessary to establish a suitable prediction model between the detection amount and the actual amount, thereby improving the accuracy of predicting the actual value from the detected value, and also satisfying the high accuracy of the prediction model under the continuous fluctuation of pedestrian flow.
At present, researches on WI-FI passenger flow statistics is limited, mainly focusing on the accurate positioning of the received signal strength value (RSSI) to explore the accurate positioning of indoor pedestrians, as well as the passenger flow under the existing indoor WI-FI system. Description of characteristic parameters such as density and passenger flow trajectory. There is still a lack of research on how to effectively improve the detection rate of WI-FI passenger flow statistics, how to deploy WI-FI probes to achieve better detection results, and how to improve the accuracy of passenger flow estimation through modeling.
Explanation of terms
In order to make the description of the present invention more accurate and clearer, various terms appearing in the present invention are explained as follows:
WI-FI probe: A wireless access point that captures information about nearby mobile devices based on various wireless data frames. It can intercept the MAC (Media Access Control) layer information of mobile clients opened by WI-FI within a certain range. It mainly includes MAC address, signal receiving strength value, time stamp, etc.
Detection area: The effective detection area of the WI-FI probe, which is generally a spherical area with a probe centered on the radius of 50-100 meters.
Functional area: The area of a public area that provides different functions in a public place.
Detection period: The unit detection time length used when testing pedestrians on the road using the WI-FI probes.
Pedestrian mobile devices: WI-FI-enabled electronic devices that are carried by pedestrians, such as smart phones, laptops, IPADs, etc.;
MAC address: The Media Access Control address, which translates to media access control, is the physical address of each mobile device, the hardware address, and the location used to define the network device. Expressed as a unique series of 12 characters consisting of numbers and letters;
Raw MAC address data: all MAC address data strips detected by the WI-FI probes;
Invalid MAC address data: MAC address which is not within the scope of the road to be studied;
Valid MAC address data: MAC address which belongs to the road to be studied;
Detected pedestrian volume data: same as valid MAC address data;
Estimating pedestrian volume data: According to a function model between the detected human flow data and the actual human flow data, there is actual pedestrian flow data estimated by detected pedestrian volume data.
Stay time: the length of time that a MAC address data stays in a functional area;
Trajectory reconstruction: Analyze the detection result of a mobile device's MAC address and reconstruct its trajectory between functional areas in public places.
Correction parameter a: a parameter obtained by multiple repeated experiments to characterize the detection rate of the probe to the mobile device;
Corrected parameters β: parameters used by the questionnaire to characterize the number of mobile devices carried by pedestrians on the road;
Summary of the invention
The present invention aims to provide a method of detecting human flow in a public place using WI-FI probes. The specific detection method is to compare the detection area of the WI-FI probe with the range of each functional area in the public place, different probe layout schemes are given, and the MAC layer information of mobile devices are collected. And the detection results are analyzed in various aspects to obtain pedestrians’ trajectory information in public places.
When the WI-FI probe is used to detect human flow, the present invention mainly solves the following three problems:
1) When using multiple probes to detect pedestrian traffic, wireless signal transmitted by the mobile device has multi-path phenomenon and reflection phenomenon during the propagation process, which may result in different degrees of attenuation of the signal received strength (RSSI) captured by the probe. Therefore, the present invention provides a plurality of superior probe placement schemes based on the influence of the spatial layout of multiple probes on the detection results of wireless signals, thereby greatly reducing the propagation of wireless signals and the effect of path phenomenon and reflection on the test results.
2) The effective detection area of the WI-FI probe is a spherical area with a certain length as a radius centered on the device. Therefore, when the detection area is larger than the road width, mobile devices outside the road (including the buildings on both sides) are also detected, resulting in the presence of such invalid data in the detection result. Therefore, the present invention needs to set scientific data screening standards to eliminate these invalid data, thereby ensuring the reliability of the detection results.
3) When the pedestrian flow volume changes, the degree of multipath and reflection of the wireless signal is different, and the detection rate under the given data screening standard also changes significantly with the change of the human flow. The invention provides a calculation model suitable for predicting the actual amount by the detection quantity under the condition that the flow rate of the human being is constantly changing, thereby improving the prediction precision.
4) For a specific MAC address, when analyzing the trajectory information between the functional areas in the public place, including when there are two functional areas detecting a certain MAC address at the same time, the probability that the MAC address actually passes through each functional area can be analyzed by a reasonable method, so that the respective probability sizes are given when the trajectory nodes cannot be visually determined.
In order to solve the problems, the technical solutions adopted by the present invention include:
1) When using multiple WI-FI probes to detect pedestrian flow, according to the geometric characteristics of the functional areas, in order to ensure more detection area as much as possible, and considering the cost of deploying the probes, a superior multiprobe layout scheme is given.
2) When collecting the original test data, the union of the test results of each probe should be taken to count the number of mobile device MAC addresses detected during a certain period of time.
3) In order to effectively eliminate invalid interference data, it is necessary to design a pre-experiment to determine the criteria for data screening. The pre-experiment is carried out on the pedestrian road to be tested, ensuring that the layout of the plurality of probes is the same as that when detecting the flow rate of the pedestrians. In the effective detection area of the probe, multiple smart devices with known MAC addresses are used, and are randomly displaced for a period of time. After that, the received signal strength value of the MAC address data detected by the probe is statistically analyzed to determine the minimum value of the received signal strength within the required detection area and is used as a data screening standard to exclude MAC addresses in areas outside the required detection area.
4) As the pedestrian flow continues to change, the detection rate also changes. The invention directly discusses the relationship between the pedestrian detection value and the actual value when determining the flow prediction model. First, it is necessary to manually count the actual human flow while using the probe to detect the human flow, and design experiments and data processing. A variety of functions are established to determine the actual human flow rate and the detected human flow rate, and the function is used to estimate the actual value based on the detected value, thereby improving the detection accuracy.
5) When reconstructing the trajectory, some MAC address data is detected in two adjacent functional areas at a certain moment. In this case, according to the existing probe detection data, the probability of the MAC address belonging to each functional area at this moment is given, which facilitates subsequent analysis.
When exploring the layout of WI-FI probes in the public functional areas, since the probe itself has a detection area with a radius r, and the functional area can generally be regarded as a rectangle having a length a and a width b, thus there is a relationship between the detection area and the functional area, which directly affects the number of probes required and the layout form. Therefore, according to the relationship between the probe detection radius r and the length of the functional area, three layout schemes of the probes are given, as shown in FIG 1.
1) When the length and width of the functional area are smaller than the probe detection radius, that is, a < r and b < r, a WI-FI probe is disposed at the center of the functional area;
2) When only one side of the functional area is much longer than the probe detection radius, i.e. a»r or b»r, a set of WI-FI probes are arranged along the longer side of the functional area;
3) When the length and width of the functional area are much larger than the probe detection radius, i.e. a»r and b»r, a set of WI-FI probes are arranged along the diagonal of the functional area;
When the length of the functional area is smaller than the detection radius of the probe, that is, when the first probe layout scheme is adopted, since the probe detection area has a portion beyond the functional area, theoretically, MAC address data outside the functional area is detected, and these data should be considered as invalid interference data because they are not in the studied functional area.
In determining a criterion for rejecting invalid interference data, the present invention provides a data screening method based on received signal strength values: providing a pre-experiment to explore the corresponding relationship between received signal strength values (RSSI) and the distances of mobile devices and the probes, so as to determine the minimum value of the corresponding signal receiving intensity according to the spatial extent of the area to be detected in the actual test place. And the minimum value is regarded as a data screening line, filtering invalid data out of the detected area.
In the analysis of the detection results, it is necessary to count the average stay time of all valid MAC address data in each functional area. If a valid MAC address only appears once, there is no duration and no count is counted; if a valid MAC address occurs multiple times, the stay time is the time difference between the last detection time and the first detection time.
In determining the functional relationship between the actual human flow and the detected human flow, the present invention adopts one of the following three functional models between detecting the human flow data and the actual human flow data:
1) Average detection rate model: the ratio of detected pedestrian volume to corresponding actual pedestrian volume in each detection period is used as detection rate, and average detection rate weighted by detection rate of each detection period is obtained, which is used to describe the relationship between detected pedestrian volume and actual pedestrian volume;
2) Segmentation detection rate model: using said detected pedestrian volume data in each detection period as an index, said detected pedestrian volume data is divided into multiple intervals, the detection rate in each interval is obtained, and the relationship between detected pedestrian volume and detection rate is established;
3) Cubic spline interpolation model: cubic spline interpolation function is used to fit the relationship between detected pedestrian volume and actual pedestrian volume in each detection period.
Wherein, when the average detection rate model is used, and the average detection rate is obtained by weighting the detection rates of the respective detection periods, the specific weighting method is: when the actual pedestrian volume data of the detection period 1 is PT, the detection rate is W4; the actual pedestrian volume data of the detection period 2 is 72, the detection rate is VF2;...; the actual pedestrian volume data of the detection period n is Vn, the detection rate is Wn; and the weighted average detection rate is W=^—1——---T T” · 4- Vji
Wherein, when the cubic spline interpolation model is adopted, the cubic spline interpolation function S(x) given by the present invention has a natural boundary condition of 0, that is,
SOo) = 0 = 0
When the WI-FI probe is used to detect the pedestrian volume data in a public place, the detection period used needs to be determined according to the pedestrian characteristics in the functional area to be detected, and may be taken as 10 min, 30 min or 1 h as the unit time length of data collection and statistics.
When reconstructing a pedestrian trajectory, if a plurality of functional areas can detect a certain MAC address data, the trajectory of the MAC address is determined in the chronological order; if a certain MAC address data is detected by the WI-FI probes in two adjacent functional areas at the same time, the probability of the MAC address belonging to the areas is actually calculated according to the estimated pedestrian flow data and the average stay time in the two functional areas. Wherein, the estimated pedestrian flow data is calculated by the function model of the detected pedestrian flow data; the average stay time refers to the average value of the stay time of the plurality of valid MAC address data in a certain detection period.
Based on the estimated pedestrian volume data and the average stay time, when a certain MAC address data appears in the detection results of two functional areas at the same time, the specific method for calculating the probability that the MAC address data belongs to the two functional areas is:
1) Mark the two functional areas as A and B respectively;
2) Calculate the estimated pedestrian volume data of A and B within the detection period in which the MAC address data appears, respectively, as QA and calculate the average stay time of the valid MAC address of A and B in the detection period in which the MAC address data appears, respectively, as TA and TB ·,
3) Calculate the probability that the MAC address data actually belongs to A is PA =
---------, and the probability of belonging to B is PB =---——.
Brief description of the drawings
FIG.l shows the three probe layout schemes based on the relationship between the probe detection radius and the length of the functional area.
FIG.2 is a schematic diagram of a probe layout scheme in a pre-experiment in which invalid data is rejected.
FIG.3 is an analysis method of detection data in a data screening standard based on received signal intensity values.
FIG.4 shows the processing flow of the raw MAC address data.
FIG. 5 is a schematic diagram of calculation of a trajectory probability distribution when a certain MAC address is simultaneously detected in an adjacent functional area during trajectory reconstruction.
Embodiments
When exploring the layout of WI-FI probes in the public functional areas, since the probe itself has a detection area with a radius r, and the functional area can generally be regarded as a rectangle having a length a and a width b, thus there is a relationship between the detection area and the functional area, which directly affects the number of probes required and the layout form. Therefore, according to the relationship between the probe detection radius r and the length of the functional area, three layout schemes of the probes are given, as shown in FIG 1.
1) When the length and width of the functional area are smaller than the probe detection radius, that is, a < r and b < r, a WI-FI probe is disposed at the center of the functional area;
2) When only one side of the functional area is much longer than the probe detection radius, i.e. a»r or b»r, a set of WI-FI probes are arranged along the longer side of the functional area;
3) When the length and width of the functional area are much larger than the probe detection radius, i.e. a»r and b»r, a set of WI-FI probes are arranged along the diagonal of the functional area;
In the third scheme, the invention adopts a method of arranging probes along a diagonal line, because when the pedestrian volume is large, the multipath and emission phenomenon of the wireless signal emitted by the mobile device during the propagation process is obvious, resulting in severe attenuation of signal strength. By arranging the probes diagonally, it is ensured that the probes are disposed in the middle area and the both sides of the functional area, which can effectively disperse the signal receiving points and more comprehensively receive data from the inner side and the outer side of the functional area, and the signal attenuation caused by multipath and reflection phenomenon is reduced to some extent; on the other hand, the probe is arranged along the diagonal line to ensure that the probe has a certain distance in the direction of the two sides of the functional area, which can effectively increase the probe detection range and detection time and effectively reduces the probability that the mobile device will not signal when the pedestrian passes through the detection area, and thus increases the detection rate.
The pre-experiment of the present invention determines the data screening standard based on the received signal strength value. The specific content of the pre-experiment is as follows: the layout of the three probes is as shown in FIG.2. The probe is taken as the center of the road with ratios equals to half of the road’s width. And In the circle region, multiple mobile devices that turn on the WI-FI function are used to simulate the movement of the pedestrian. After a period of detection, the detection results of each probe are counted.
The present invention analyzes the received signal strength value data obtained in the pre-experiment as shown in Fig.3, indicating that the received signal strength value obeys a normal distribution, and the present invention takes a 90% confidence interval to determine the final data screening line. In the raw MAC address data, the received signal strength value that is smaller than the data screening line, is considered to be invalid MAC address data.
As shown in FIG. 4, the present invention provides a process of processing the raw MAC address data of the functional area, thereby obtaining valid MAC address data, and calculating the average stay time of the functional area by counting the stay time of the valid MAC address data. Wherein, when a valid MAC address data has multiple detection records in the probe detection result of a certain functional area, the time of the stay is the time difference between the last detection time and the first detection time; and the average stay time of valid MAC address data in the function area is the arithmetic mean of all stay time.
When the average detection rate model is used, and the average detection rate is obtained by weighting the detection rates of the respective detection periods, the specific weighting method is: when the actual pedestrian volume data of the detection period 1 is ΙΑ, the detection rate is VIA; the actual pedestrian volume data of the detection period 2 is V2, the detection rate is VIA;...; the actual pedestrian volume data of the detection period n is Vn, the detection rate is Wn; and the weighted average detection rate is W=^—1——---T Τ’' ’ T V72
The present invention establishes a cubic spline interpolation function to fit the relationship between the actual value of the pedestrian flow and the detected value. The specific method is as follows:
In the experiment, n groups of data are obtained, and the number of mobile device MAC addresses detected in each group of data is respectively recorded as x0> χι> 'χη> corresponding to each node in the interval [x0, xn\-. and the actual person flow corresponding to each node is manually counted as y0' 7ι> ’Τη· Thus determine the correspondence at each node as f(xn) = yn. Then, the cubic spline interpolation function S(x) can be constructed as follows.
Remember, there is
Mark hj = Xj — Xj-r, S(x7) = Mj, then 'j
Mj hj J (1) (3)
6hj 1
6hj 1
-M; + c, x + c?
6hj J 1 2
6hj hj (2) (*—7—)2 M yr*,- _ h
2hj ' 1 2/1, J hj 6 '
2h.j hj (4)
PjMj-j + 2Mj + YjMj+1 = dj, j = 1,2 ...,n - 1
In the formula (5):
hj+i γ· = ----J hj + hj+y (5) (6) (7) hj (8)
Combined with the natural boundary conditions S(x0) = Mo = 0 and S(xn) = Mn = 0, (5) can be written in matrix form:
yx d-L
| ZU 2 γ2 | m2 | d2 | ||
| Μη-2 2 Yn-2 | Mn-2 | dn-2 |
Pn-l n-1 n-1 (9)
According to equations (1)-(9), the cubic spline interpolation function can be calculated as follows:
sn(x), [xn—1, M.1
The invention uses the correction parameter a to correct the established relationship between the detected human flow and the actual human flow, and the correction parameter a is obtained by experimental results of a single mobile device repeatedly passing through a certain WI-FI detection area. The WI-FI function of the mobile device in the experiment is open and the MAC address of the mobile device and the number of times it has been repeated is recorded. And the experimental results are processed as follows:
If the mobile device passes N times in the detection area of the probes, and the MAC address of the mobile device exists n times in the probe detection result, that is, n times is detected, it is considered that the WI-FI probe has a detection rate a = - of devices with WI-FI activated. And the correction parameter a needs to be added before the final cubic spline interpolation function model S(x), that is, the corrected cubic spline interpolation function is S(x)' = S(x)/a.
The invention uses the correction parameter β to correct the established relationship between the detected human flow and the actual human flow, and the correction parameter β is obtained through questionnaire survey on the pedestrians on the road to be tested. The main content of the questionnaire is to investigate the number of devices carried by pedestrians. And the specific correction method is:
If the result of the questionnaire shows that the proportion of pedestrians carrying two mobile devices is a, then the correction parameters β = 1 + a, and it needs to be added before the final cubic spline interpolation function model S(x), that is, the modified cubic spline interpolation function is S(x)' = S(x) β.
As shown in FIG. 5, when reconstructing a pedestrian trajectory, when a certain MAC address data appears in the detection results of two functional areas at the same time, the specific method for calculating the probability that the MAC address data belongs to the two functional areas is:
1) Mark the two functional areas as A and B respectively;
2) Calculate the estimated pedestrian volume data of A and B within the detection period in which the MAC address data appears, respectively, as QA and calculate the average stay time of the valid MAC address of A and B in the detection period in which the MAC address data appears, respectively, as TA and TB·,
3) Calculate the probability that the MAC address data actually belongs to A is PA = ——, and the probability of belonging to B is PB = ——.
QaTa+QbTb’ 1 J ΰ QaTa+QbTb
Claims (9)
1) Data collecting: deploying WI-FI probes in the public functional areas to detect raw MAC address data of pedestrian mobile devices in each detection period in the detection area of said WI-FI probes; said public functional areas are regarded as rectangles, each with a length a and a width 6; there are three probe layout schemes, according to the relationship between probe detection radius r and the length (a or b) of said public functional area:
la) when the length and width of said public functional area are smaller than said probe detection radius, that is a < r and b < r, a WI-FI probe is disposed at the center of said functional area;
lb) when only one side of said public functional area is much longer than said probe detection radius, that is a»r or b»r, a set of WI-FI probes are arranged along the longer side of said functional area;
lc) When the length and width of said public functional area are much larger than said probe detection radius, that is a»r and b»r, a set ofWI-FI probes are arranged along the diagonal of said functional area;
2) Data screening: screening said raw MAC address data according to received signal strength value; deleting invalid MAC address data, obtaining valid MAC address data of pedestrian mobile devices, which is regarded as detected pedestrian volume data; in the meantime, recording stay time of each said valid MAC address data in said functional area;
3) Data processing: based on said valid MAC address data of pedestrian mobile devices, establishing a function model between detected pedestrian volume data and actual pedestrian volume data;
4) Trajectory reconstruction: based on detection result of said valid MAC address data of pedestrian mobile devices, reconstructing the trajectory of pedestrians moving between different said functional areas, according one of the following methods:
4a) if a certain MAC address data is detected in a plurality of said functional areas, the trajectory of said MAC address is determined in the chronological order;
4b) if a certain MAC address data is detected by WI-FI probes in two adjacent functional areas at the same time, the probability of said MAC address belonging to each said functional area is calculated according to detected pedestrian volume data and the average stay time of said MAC address in each of the two functional areas.
2. A method according to claim 1, wherein, when adopting layout scheme la), the method of data screening in step 2) according to received signal strength value is: designing a preexperiment to determine the criteria for data screening; determining the minimum value of said received signal strength value; the minimum value is used as a data screening standard to exclude MAC addresses, the received signal strength value of which is less than said data screening standard.
3. A method according to claim 1, wherein said function model in step 3) is one of the following three models:
3a) average detection rate model: the ratio of detected pedestrian volume to corresponding actual pedestrian volume in each detection period is used as detection rate, and average detection rate weighted by detection rate of each detection period is obtained, which is used to describe the relationship between detected pedestrian volume and actual pedestrian volume;
3b) segmentation detection rate model: using said detected pedestrian volume data in each detection period as an index, said detected pedestrian volume data is divided into multiple intervals, the detection rate in each interval is obtained, and the relationship between detected pedestrian volume and detection rate is established;
3c) cubic spline interpolation model: cubic spline interpolation function is used to fit the relationship between detected pedestrian volume and actual pedestrian volume in each detection period.
4. A method according to claim 1, wherein said detection period is determined by pedestrian characteristics in said public functional areas to be detected, and equals to 10 min, 30 min or 1 hour.
5. A method according to claim 2, wherein layout scheme la) is adopted in said preexperiment, the pre-experiment uses a plurality of devices with known MAC addresses, and they moves in a circle centered on the probe and having a diameter of the short side of the functional areas, then the detection result of the probe is counted.
6. A method according to claim 1, wherein said stay time is the time difference between the last detection time and the first detection time.
7. A method according to claim 1, wherein said average stay time when using method 4b) to reconstruct trajectory of pedestrians, refers to the average value of the stay time of the plurality of valid MAC address data in a certain detection period; if a valid MAC address data appears only once, it is not counted in calculation of said average stay time.
8. A method according to claim 3, wherein when adopting 3a) average detection rate model, weighted average detection rate W = (ViW) + V2W2 + ... + VnWn) I (Vi + V2 + ... + K), where Vn is actual pedestrian volume in detection period n, Wn is detection rate in detection period n.
9. A method according to any of claims 1 to 8, wherein when adopting 4b) to reconstruct trajectory of pedestrians, when said certain MAC address data is detected by WI-FI probes in two adjacent functional areas at the same time, the probability of said MAC address belonging to each said functional area is calculated according to the following steps:
4b 1) marking the two adjacent functional areas as area A and area B respectively;
4b2) calculating the estimated pedestrian volume data of area A and area B within the detection period in which said certain MAC address data appears, respectively, marked as Qa and Qb, calculate average stay time of valid MAC address of area A and area B in said detection period, respectively, marked as Ta and Tb,
4b3) calculating the probability of said MAC address data belonging to area A is Pa = (Qa Ta) / (Qa Ta + Qb Tb), and the probability of said MAC address data belonging to area B is Pb = (Qb Tb) / (Qa Ta + Qb Tb).
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