CN107529664A - A kind of traffic based on WIFI signal is passed unimpeded grade detecting system - Google Patents
A kind of traffic based on WIFI signal is passed unimpeded grade detecting system Download PDFInfo
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Abstract
Passed unimpeded grade detecting system the invention discloses a kind of traffic based on WIFI signal, including multiple sub-networks, each sub-network includes a main frame and several extension sets, the extension set passes through wireless passive perceptual model, gather the broadcast data bag sent at random to surrounding environment based on Wifi agreements from mobile terminal device, and screen the wherein packet with mobile terminal device id information and retrieved, main frame by mobile phone to data carry out unified storage and stamp time tag, and it is uploaded in data server and stores, and traffic cross-sectional flow is obtained by data analysis, cross-sectional flow, the detected value of polymerization average hourage, and determine that the traffic in section is passed unimpeded grade.Macro-traffic infomation detection algorithm of the invention based on the unique ID of portable mobile terminal, gathered data timestamp and detection device positional information, realizes the data mining and analysis to mobile terminal gathered data, can be applied to road traffic pass unimpeded grade detection.
Description
Technical Field
The invention belongs to the mobile internet technology, and particularly relates to an improvement of a macroscopic traffic information monitoring algorithm.
Background
The traffic flow data is an important information source of a traffic operation scheduling command system, and can provide decision basis for command scheduling, traffic flow control and traffic guidance. The existing traffic flow detection technologies are various, and can be divided into a contact detection mode and a non-contact detection mode according to installation modes. Among the contact detection techniques are piezoelectric, pressure tube detection and toroidal coil detection. The main defects of the technology are that the rolling of the vehicle on the road causes the service life of the detector to be short, and when the detector is arranged, the traffic needs to be interrupted and the road surface needs to be damaged, so that the installation and the enclosure are difficult, and the use cost is high. The non-contact detection technology mainly comprises wave frequency detection and video detection. The wave frequency detection is divided into three types, namely microwave, ultrasonic wave, infrared and the like. The non-contact detector can be installed through a bracket, is convenient to maintain and long in service life, and has the main defects of being easily influenced by outdoor weather conditions, and the problems of low environmental adaptability, large data transmission quantity, low detection accuracy, high manufacturing cost and the like.
With the rapid development of the highway network in China, the application requirement of highway traffic flow detection is increased dramatically. In the expressway network, traffic flow information is also very important, and through the flow information, an expressway network management department can know the real-time vehicle quantity information of each road section in real time, provide intuitive expressway network vehicle load capacity and provide accurate data for dispatching and overall planning of the expressway network.
However, there are some special situations in the highway network, such as inconvenience in power supply on the highway, difficulty in information transmission, and failure to lay various detectors in advance in the construction process, and thus intensive monitoring and management cannot be achieved, and further design and improvement on the existing detectors are required.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a traffic smoothness grade detection system based on a WIFI signal, and the system is based on a macroscopic traffic information detection algorithm of a unique ID (identity), a data collecting timestamp and position information of detection equipment of a portable mobile terminal, realizes data mining and analysis of data collected by the mobile terminal, and can be applied to road traffic smoothness grade detection.
In order to solve the technical problem, the invention adopts the following technical scheme:
a traffic smooth grade detection system based on WIFI signals is characterized in that a detection equipment system is deployed along a traffic road and comprises a plurality of sub-networks, each sub-network comprises a host and a plurality of extension sets, each extension set (single detection equipment) collects broadcast data packets sent to the surrounding environment by mobile terminal equipment based on a Wifi protocol randomly through a wireless passive sensing mode, the data packets with ID information of the mobile terminal equipment are screened for retrieval, the extension sets are labeled and uploaded to the host, the host stores the data received by a mobile phone in a unified mode, the data tags the data received by the mobile phone are labeled and uploaded to a data server for storage, the detection values of traffic cross-section flow velocity, cross-section flow velocity and aggregate average travel time are obtained through data analysis, and the traffic smooth grade of a road section is determined.
Further, the analysis of the data comprises the steps of:
step 1: the data D for the complete time period are collected by the extension,wherein D is ij Indicating the ith extension data of the jth subnet; n represents the number of the sub-networks, and M represents the number of the sub-networks in the jth sub-network;
and 2, step: slicing the data and extracting the data D of the section S to be analyzed and the complete time period (T-delta T) s '，In the formula D is The data collected by the extension set with the number i under the s sub-network;
and 3, step 3: carrying out space matching on the deployed sub-network and the corresponding actual road section S to obtain the number information of the sub-network host and the corresponding road section and an extension deployment condition list of the sub-network;
and 4, step 4: sequencing the data collected by each extension according to the ID number of the mobile terminal equipment, and establishing a data matrix { Tower (i, s), t };
and 5: classifying the established ID data list according to the times of occurrence in different data matrixes: for the condition that the mobile terminal ID only appears in a single extension of the sub-network within the time period delta t, independently extracting data corresponding to the mobile terminal ID for subsequent validity analysis; for the situation that the mobile terminal ID appears in two or more extension sets of the sub-network within the time period delta t, the data corresponding to the mobile terminal ID is directly used as valid data;
step 6: for the situation that the mobile terminal ID only appears in a single extension of the sub-network in the time period delta t, the data corresponding to the mobile terminal ID is independently extracted for subsequent validity analysis: 1. when the mobile terminal ID repeatedly appears in the data matrix TOWER of a single extension within the time delta t and a plurality of mobile terminal IDs appear, marking the corresponding road section as a congestion condition, and recording the ID data as effective data; 2. when the mobile terminal ID is not found to repeatedly appear in a single extension data matrix TOWER within the time delta t, traversing whether sub-networks before and after the sub-network corresponding to the road section S have the same ID number, if not, processing the ID data as noise data, and if the ID data appear in other sub-networks, marking the ID data into an effective data list;
and 7: repeating the steps 4-6 until the data processing is finished;
and 8: merging the effective data obtained by the processing in the steps 5 and 6, dividing the ID data matrix into two directions according to the position in the physical space corresponding to the data matrix TOWER and the appearance time sequence, and performing subsequent processing on each unidirectional data;
and step 9: in the time period from T-delta T to T, the total amount of the one-way effective data is V, V = D { ID }, and the detection and calculation of the actual macroscopic section flow, the section flow rate and the aggregation average travel time are carried out by the following methods:
(1) Calculating the actual macroscopic cross-sectional flow V (T):
in the formula, ID i The data collected by the extension with the number i, F (-) is a fitting function of effective ID data, x is the total number of the extension in the subnet, and i is the number of the extension in the subnet;
(2) Cross sectional flow velocity v (L) n -L m ) The calculation of (2):
in the formula, v (L) n -L m ) For L under the subnet m To L n The average flow velocity of the road section, v' (n-m), is the average flow velocity calculated by the extensions numbered from m to n in the jth sub-network (i.e. the average flow velocity between the extensions numbered m and n); k is the number of the ID data, and x is the total number of the ID data; l is n The physical position of the extension with the number of n in the jth subnet is obtained;the moment when the kth ID number is collected by the extension with the number of n in the subnet; Δ (k) is a weight value occupied by the kth ID number in the effective data of the calculation data; i is the total number of the extension sets in the subnet, and n, m, n ', m' are all the number of the extension sets in the subnet of the number j, wherein the coverage segments from n 'to m' comprise the coverage segments from n to m; ω (n ', m') denotes the calculation of L by the matrix of the (n ', m') numbered extensions n' To L m′ A weight coefficient matrix occupied by the average velocity;
(3) Aggregate average travel time T (L) n -L m ) The calculation of (2):
in the formula, T (L) n -L m ) For L under the subnet m To L n Aggregate average travel time for the road segment; i is the total number of extension sets under the subnet;for the k ID number to be transmitted to the subnetThe time when the extension with the number n collects; n, m, n ', m' are all the number of the sub-network inner extension set of the j number; ω (n ', m') denotes the calculation of L by the (n ', m') numbered sub-matrix n' To L n Aggregating the weight coefficient matrix occupied by the average travel time;representing L calculated by product of travel time calculated by computer matrix in (n '-m') road section and number of extension intervals n' To L m′ The number of intervals of the extension of the road section;
step 10: setting the saturation flow of the road section as V 0 The speed limit speed of the road section is v 0 The mileage of the road section is D, and the traffic smooth grade is set as shown in the following formula:
by mixing V (T), V (L) n -L m )，T(L n -L m ) And comparing with the above formula to confirm the current level of smoothness of the road section.
Further, in step 9, the distribution scheme of the weights Δ (x) and ω (n ', m') is obtained by self-feedback learning and multiple iterative approximation approximate fitting, and the specific steps are as follows:
first, initial values of Δ (x) and ω (n ', m') are set, and an initial value v of the output cross-sectional flow velocity is calculated 0 ；
Then, by setting the initial value v of the cross-sectional flow velocity 0 Performing self-feedback learning with a bias reference system S, performing approximate fitting by repeatedly calculating and correcting a weight function value to obtain an approximate fitting function F (-) and performing self-feedback learning with the bias reference system S,
then, the ratio S/v of the output value S of the offset reference system and the cross-sectional flow velocity v is taken as a basis for judging the termination of the feedback learning, and the S/v range is set to be more than or equal to 0.99 and less than or equal to 1.01;
finally, when the value of S/v meets the termination range, the iterative fitting is completed and the allocation schemes of Δ (x) and ω (n ', m') are output.
Further, and serves as an effective reference unit.
Further, the distance between the adjacent extensions is d, the signal coverage radius of a single extension is r, and d is greater than 2r.
Has the beneficial effects that: the invention provides a macroscopic traffic information detection algorithm based on the unique ID of a portable mobile terminal, a collected data timestamp and position information of detection equipment, realizes data mining and analysis of the collected data of the mobile terminal, provides a traffic flow detector and a detection system based on WIFI signals with deep mining and traffic flow detection algorithm implementation based on the data, fills the application blank of the data in the aspect of traffic detection, and promotes the development of the field of intelligent traffic.
Drawings
Fig. 1 is a schematic diagram illustrating a road deployment principle of a traffic flow detection system based on a WIFI signal according to the present invention;
fig. 2 is a schematic flow chart of the traffic vehicle flow detection algorithm based on the WIFI signal according to the present invention;
fig. 3 is a schematic flow chart of the detection algorithm of the cross-sectional flow velocity based on the WIFI signal according to the present invention;
fig. 4 is a schematic flow chart of the detection algorithm for the aggregate average travel time based on the WIFI signal according to the present invention;
fig. 5 is a schematic diagram illustrating a noise reduction screening process of effective data in WIFI signal detection data according to the present invention;
fig. 6 is a schematic diagram of an approximate fitting process based on a weight coefficient in a WIFI signal according to the present invention;
FIG. 7 is a schematic diagram of a deployment of a traffic information detection system in an embodiment of the present invention;
FIG. 8 is a graph comparing the ratio of the output flow rate value to the offset reference frame as a function of time for an embodiment of the present invention;
FIG. 9 is a graph comparing the ratio of the flow rate and the flow velocity respectively to the true value of the offset reference system according to the algorithm of the system of the present invention on a road section according to the present invention;
fig. 10 is a schematic view of a traffic smoothness level of a road section according to an embodiment of the present invention.
Detailed Description
The invention will be further elucidated by means of specific embodiments in the following figures. It is to be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
As shown in fig. 1, the present invention includes a plurality of sub-networks, each sub-network and an actual road segment S are in a space matching mapping relationship, and a detection device deployed on each sub-network road segment S includes a plurality of extensions and at least one host; the extension set is used as a basic sensor and is based on an 802.1b protocol (WIFI protocol) and used for monitoring and receiving a broadcast data packet sent by mobile equipment based on the WIFI signal protocol in the surrounding environment and transmitting the broadcast data packet to the data processing module, the data processing module is used for deleting, selecting and reading the broadcast data packet, obtaining an ID number of the mobile equipment and transmitting data comprising the ID number and the extension set module number to the host; and the host computer uploads the timestamp to the remote server after storing. The invention is based on the data packet containing ID number received by the extension sensor to detect and calculate, and obtains the traffic section flow velocity of the corresponding road section. Specifically, the method comprises the following steps:
the detection equipment deployment mode of the invention is as follows: in fig. 1, tower (j) represents the j-th subnet host in the deployed road network, and Tower (i, j) represents the i-th extension in the j-th subnet. Each sub-network comprises a host and a plurality of extensions, the number of the extensions can be properly increased or decreased according to road network conditions, the maximum coverage range of the sub-network is 2Km, the maximum radius of the signal coverage range of a single extension is 250m, a user can adjust the prediction range of the single extension according to actual road conditions, the distance d between the extensions is set to be the extension prediction range which is only more than two times, and flexible adjustment can be performed according to the actual road conditions. The deployment density of the sub-networks can be deployed according to the management requirements of the actual traffic environment.
The data acquisition process of the detection equipment comprises the following steps: a single detection standby extension, namely a extension set, randomly sends WIFI broadcast type data packets to the surrounding environment through a WIFI protocol-based acquisition by mobile terminal equipment through a wireless passive sensing mode, namely a detector adopting a TI CC3 XXXXXX series chip, and screens the data packets with equipment ID information for retrieval. And uploading the extension labels to a host computer, uniformly storing the collected data and stamping time labels by the host computer, uploading the data to a data server for storage, and waiting for analysis. The algorithm principle process of the invention is explained in detail as follows:
step 1: complete data for a complete time period (T- Δ T) is collected, the complete data being expressed asWherein D ij Indicating the ith extension data of the jth subnet;
step 2: slicing the data, and extracting the complete data of the analysis road section S and the time period (T-delta T)
And step 3: mapping physical space positions, corresponding to the deployed sub-networks, and performing space matching on the sub-networks and the actual road sections S, wherein each sub-network host is provided with corresponding serial number information of the corresponding road sections and an extension deployment condition list;
and 4, step 4: sequencing the collected data according to the ID number of the collected data, and establishing a data matrix { Tower (i, S), t };
and 5: as shown in fig. 3, the established ID data list is classified according to the number of times that the ID data list appears in different Tower, IDs appearing in only one Tower are individually extracted for analysis, and ID data appearing in more than two towers are classified into one category;
step 6: the ID appearing in only one Tower was analyzed, and the cases were classified into the following two types: 1. in the time period of delta t, the ID repeatedly appears in the Tower, and the condition is occurred in a large amount, and if a certain number of IDs appear, the condition indicates that the road section is in a congestion state, and the type of ID is valid data; 2. if the ID is not found to be repeated in the Tower in the time period of delta t, the data is removed, as shown in FIG. 5;
and 7: repeating the steps 4,5 and 6, and confirming that no data is missed;
and 8: under the condition of incomplete congestion, a data matrix { Tower (i, S), t } associated with the ID number is reestablished according to the data deletion;
and step 9: the data is further classified into a bidirectional data matrix according to the relation between time t and power in the data matrix { power (i, S), t }: d L And D R And calculating it;
step 10: the road section flow rate V (T) and the flow velocity V (L) are obtained by the following calculation formula n -L m ) Average travel time for polymerization T (L) n -L m ) The detected value of (2):
in the formula, F (-) is a fitting function of effective ID data, x is the total number of extension sets in the subnet, and i is the number of the extension sets in the subnet;
according to the collected data, the ID number does not completely appear in each detection extension, certain randomness exists in the appearance of the ID number, and the collection position of the ID number is not necessarily the position of the extension but is within the position range of +/-r (r is the coverage radius of the extension) of the position of the extension due to the coverage of the extension detector. Thus, a weight matrix ω (n ', m') is introduced and the sum of weights is 1, to ensure matrix integrity, data recorded as 0 is not assigned weight coefficients, and w (1) + w (2) + \8230 ++ w (j) =1, which can be obtained according to the above formula,
by the above formula, it can be deduced that:in the formula (I), the compound is shown in the specification,the moment when the kth ID number is collected by the extension with the number of n in the subnet; n, m, n ', m' are all the number of the sub-network inner extension set of the j number.
The data of the detection system is accumulated and updated along with time, initial values of delta (x) and omega (n ', m') are set before operation is carried out, traffic cross-section flow velocity V is obtained through calculation, self-feedback learning is carried out on the traffic cross-section flow velocity obtained through the method and the traffic cross-section flow velocity S measured by a bias reference system such as a coil detector, approximate fitting is achieved through repeated iterative calculation and correction of weight functions delta (x) and omega (n ', m'), approximate fitting function F (-) is obtained, and when a ratio S/V of the bias reference system and the traffic cross-section flow velocity V obtained through calculation is obtained through the method and a termination range, iterative fitting is completed and delta (x) and omega (n ', m') in the current state are output. The invention adopts an interval sampling mode to detect the accuracy of data obtained by an algorithm, and after the data offset exceeds a threshold value or a certain time period is set, iterative fitting is carried out again to output the distribution scheme of corrected delta (x) and omega (n ', m').
The road section flow V (T) and the flow velocity V (L) are obtained by detection n -L m ) And the average travel time T (L) of the polymerization n -L m ) And then, detecting the branch machines of the traffic smooth grade. Establishing an expedite grade data matrix { flow rate by mainly depending on the prediction of traffic flow, flow speed and travel time; a flow rate; travel time }. The user can use the characteristics according to the flow saturation, the flow velocity mean value, the travel time or any combination of the three. The detection system has high deployment density, relatively accurate calculation result and data deviation controllable within 10 percent, so that the grade of smooth traffic can be further subdivided, and the detection system is expanded on the basis of the existing grade 3-4, thereby more accurately mastering the high-speed traffic state so as to prevent and control in time.
Grading the smoothness grade: and setting the theoretical saturation flow of a certain road section as V, limiting the speed S, the mileage of the road section as D and the theoretical passing time as T. The road segments are classified into 6 levels, and the classification list can be established as follows:
| grade of smoothness (Lv) | Through flow v | Passing through uniform velocity s | Passage time t |
| Dark green (Lv 6) | v<20％V | s>80％S | t<(D/(0.8xS)) |
| Light green (Lv 5) | 20％V<v<60％V | s>60％S | t<(D/(0.6xS)) |
| Light yellow (Lv 4) | 40％V<v<80％V | 40％S<s<60％S | (D/(0.6xS))<t<(D/0.4xS) |
| Deep yellow (Lv 3) | 15％V<v<60％V | 15％S<s<40％S | (D/(0.4xS))<t<(D/0.15xS) |
| Red (Lv 2) | 5％V<v<15％V | 5％S<s<15％S | (D/(0.15xS))<t<(D/0.05xS) |
| Deep red (Lv 1) | v<5％V | s<5％S | t>(D/(0.05xV)) |
The invention is characterized in that a test is carried out on a certain section of a G42 highway in Beijing Shanghai, and traffic flow detection systems are arranged and deployed on two sides of the highway. The detector is deployed on the road as shown in fig. 7, and WIFI signal broadcast data packets sent by the mobile device on the vehicle in the surrounding environment are monitored and received. The detector, which takes 5 minutes as a time unit, calculates the speed and flow rate and detects the speed and flow rate with the coil.
As shown in fig. 8, the ratio of the cross-sectional flow velocity obtained by the algorithm of the present invention to the flow velocity measured by the coil detector at a physical location in the road section is plotted against time. As can be seen from the figure, the ratio of the two is around 1, and high consistency and stability are presented.
FIG. 9 is a graph showing the ratio of the flow and flow rate, respectively, through the algorithm of the system of the present invention to the true value of the offset reference frame. The ratio between the flows is highly consistent as can be seen from the figure; the ratio between the flow rates can be kept basically consistent, the stability of the data can be kept, and the obtained result has high reliability and accuracy.
Claims (5)
1. A traffic smooth grade detection system based on WIFI signals is characterized in that a detection equipment system is deployed along a traffic road and comprises a plurality of sub-networks, each sub-network comprises a host and a plurality of extension sets, each extension set (single detection equipment) collects broadcast data packets sent to the surrounding environment by mobile terminal equipment based on a Wifi protocol randomly through a wireless passive sensing mode, the data packets with ID information of the mobile terminal equipment are screened for retrieval, the extension sets are labeled and uploaded to the host, the host stores the data received by a mobile phone in a unified mode, the data tags the data received by the mobile phone are labeled and uploaded to a data server for storage, the detection values of traffic cross-section flow velocity, cross-section flow velocity and aggregate average travel time are obtained through data analysis, and the traffic smooth grade of a road section is determined.
2. The system according to claim 1, wherein the traffic level detection system based on WIFI signals comprises: the analysis of the data comprises the steps of:
step 1: the data D for the complete time period are collected by the extension,wherein D is ij Indicating the ith extension data of the jth subnet; n represents the number of the sub-networks, and M represents the number of the sub-networks in the jth sub-network;
and 2, step: slicing the data and extracting data D 'of a road section S to be analyzed and a complete time period (T-delta T)' s ，In the formula D is The data is collected by the extension set with the serial number i under the s sub-network;
and step 3: carrying out space matching on the deployed sub-network and the corresponding actual road section S to obtain the number information of the sub-network host and the corresponding road section and an extension deployment condition list of the sub-network;
and 4, step 4: sequencing the data collected by each extension according to the ID number of the mobile terminal equipment, and establishing a data matrix { Tower (i, s), t };
and 5: classifying the established ID data list according to the times of the ID data list appearing in different data matrixes: for the condition that the mobile terminal ID only appears in a single extension of the sub-network in the time period delta t, independently extracting data corresponding to the mobile terminal ID for subsequent validity analysis; for the condition that the mobile terminal ID appears in two or more extension sets in the sub-network within the time period delta t, the data corresponding to the mobile terminal ID is directly used as valid data;
and 6: for the situation that the mobile terminal ID only appears in a single extension of the sub-network in the time period delta t, the data corresponding to the mobile terminal ID is independently extracted for subsequent validity analysis: 1. when the mobile terminal ID repeatedly appears in the data matrix TOWER of a single extension within the time delta t and a plurality of mobile terminal IDs appear, marking the corresponding road section as a congestion condition, and recording the ID data as effective data; 2. when the mobile terminal ID is not found to repeatedly appear in a single extension data matrix TOWER within the time delta t, traversing whether sub-networks before and after the sub-network corresponding to the road section S have the same ID number, if not, processing the ID data as noise data, and if the ID data appear in other sub-networks, marking the ID data into an effective data list;
and 7: repeating the steps 4-6 until the data processing is finished;
and step 8: merging the effective data obtained by the processing in the steps 5 and 6, dividing the ID data matrix into two directions according to the position in the physical space corresponding to the data matrix TOWER and the appearance time sequence, and performing subsequent processing on each unidirectional data;
and step 9: in a time period from T-delta T to T, the total amount of the one-way effective data is V, V = D { ID }, and the detection and calculation of the actual macroscopic section flow, the section flow velocity and the polymerization average travel time are carried out by the following methods:
(1) Calculation of the actual macroscopic section flow V (T):
in the formula, ID i The data collected by the extension with the number i, F (-) is a fitting function of effective ID data, x is the total number of the extension in the subnet, and i is the number of the extension in the subnet;
(2) Cross sectional flow velocity v (L) n -L m ) The calculation of (2):
in the formula, v (L) n -L m ) For L under the subnet m To L n The average flow speed of the road section, v' (n-m), is the average flow speed calculated by the extension numbers m to n in the jth sub-network (i.e. the average flow speed between the extension numbers m and n); k is the number of the ID data, and x is the total number of the ID data; l is a radical of an alcohol n The physical position of the extension with the number of n in the jth subnet is obtained;the moment when the kth ID number is collected by the extension with the number of n in the subnet; Δ (k) is a weight value occupied by the kth ID number in the effective data of the calculation data; i is the total number of the extension sets in the subnet, and n, m, n ', m' are all the number of the extension sets in the subnet of the number j, wherein the coverage segments from n 'to m' comprise the coverage segments from n to m; ω (n ', m') denotes the calculation of L by the (n ', m') numbered sub-matrix n' To L m′ A weight coefficient matrix occupied by the set of results when averaging the velocity;
(3) Aggregate average travel time T (L) n -L m ) The calculation of (c):
in the formula, T (L) n -L m ) For L under subnet m To L n Aggregate average travel time for the road segments; i is the total number of extension sets under the subnet;the moment when the kth ID number is collected by the extension with the number of n in the subnet; n, m, n ', m' are all the number of the sub-network inner extension set of the j number; ω (n ', m') denotes the calculation of L by the matrix of the (n ', m') numbered extensions n' To L m′ Aggregating the weight coefficient matrix occupied by the group of results when the average travel time is obtained;
step 10: setting the saturation flow of the road section as V 0 The speed limit speed of the road section is v 0 The mileage of the road section is D, and the traffic smooth grade is set as shown in the following formula:
by mixing V (T), V (L) n -L m )，T(L n -L m ) And comparing the current grade with the formula to confirm the current grade of smoothness of the road section.
3. The system of claim 2, wherein the traffic level detection system based on WIFI signals is characterized in that: in step 9, the distribution scheme of the weights Δ (x) and ω (n ', m') is obtained through multiple iterative approximation approximate fitting through self-feedback learning, and the specific steps are as follows:
first, initial values of Δ (x) and ω (n ', m') are set, and an initial value v of the output cross-sectional flow velocity is calculated 0 ；
Then, by setting the initial value v of the cross-sectional flow velocity 0 Performing self-feedback learning with a bias reference system S, performing approximate fitting by calculating and correcting a weight function value through multiple iterations to obtain an approximate fitting function F (-),
then, the ratio S/v of the output value S of the offset reference system and the cross-section flow velocity v is taken as a basis for judging the termination of the feedback learning, and the range of S/v is set to be more than or equal to 0.99 and less than or equal to 1.01;
finally, when the value of S/v meets the termination range, the iterative fitting is complete and the assignment schemes for Δ (x) and ω (n ', m') are output.
4. The WIFI signal based traffic penetration level detection system according to claim 3, wherein: and serves as an effective reference unit.
5. The system according to claim 3, wherein the system comprises: the distance between the adjacent extensions is d, the signal coverage radius of a single extension is r, and d is larger than 2r.
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