CN107195179B - Single intersection traffic flow statistical analysis method and system based on network - Google Patents
Single intersection traffic flow statistical analysis method and system based on network Download PDFInfo
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- CN107195179B CN107195179B CN201710391954.3A CN201710391954A CN107195179B CN 107195179 B CN107195179 B CN 107195179B CN 201710391954 A CN201710391954 A CN 201710391954A CN 107195179 B CN107195179 B CN 107195179B
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
Abstract
The invention discloses a network-based single intersection traffic flow statistical analysis method and a network-based single intersection traffic flow statistical analysis system, wherein the method comprises the following steps: on-line coding, when a vehicle passes through a crossing, a random number is sent to a reader according to a certain rule, after the reader receives the random number sent by the vehicle, the reader sets a bit map B of the reader by using a hash function, and after a measurement period is finished, the reader can obtain a complete bit map B; off-line decoding, after the server receives bitmap B sent by the intersection in different measurement periods, the server performs off-line decoding work to calculate the number N of vehicles with no less than k measurement periods k Is composed of
Wherein, the bitmap obtained in t measurement periods is B 1 ，B 2 ，...，B t The number of vehicles passing through i measurement cycles is n i . On the basis of protecting the privacy of the vehicles, the invention can estimate how many vehicles pass through a certain intersection for many times in different measurement periods, and has the advantages of low hardware cost, higher recognition rate and high speed.
Description
Technical Field
The invention relates to the technical field of traffic flow statistics, in particular to a network-based single intersection traffic flow statistical analysis method and system.
Background
Statistical analysis of traffic flow at intersections is an important research direction in traffic networks, and data basis and basis are provided for many traffic plans and traffic applications. For example, whether a road is congested can be judged by counting the traffic flow of the intersection, the traffic flow in different periods or in the same time period every day is analyzed to draw the change trend of the traffic flow along with time, or how many vehicles can often pass through a certain intersection in the same time period can be further analyzed, so that an APP based on traffic analysis is designed to perform better navigation and improve riding experience. Therefore, the statistical analysis of the traffic flow at the intersection is the basis for realizing intelligent traffic and efficient traffic decision.
In addition to the traditional method of installing hardware at the intersection or calculating the traffic flow by adopting an image recognition mode according to a monitoring camera, in recent years, some researches have been made on recording the traffic flow at the intersection by adopting a vehicle-road interaction mode. The above methods each have advantages and disadvantages. The method for calculating the traffic flow by using hardware usually judges whether a vehicle passes through the method of magnetic field induction, the traditional flow statistical method has low measurement precision in a multi-lane and high vehicle speed, only can realize a simple counting function, and the cost of laid hardware is too high, so that the method is unlikely to be realized at a large number of intersections. Although the image recognition mode does not need to add extra equipment, the recognition speed is slow, and the recognition accuracy is not very high when the vehicle speed is high and the shielding between the vehicles exists. In addition, the image identification records the license plate information, and if the license plate information is directly used for scientific research or provided for enterprises, the problems of privacy disclosure of vehicles and the like exist. The network mode is used for realizing traffic flow statistics through interaction between the RFID label arranged on the vehicle and the reader at the intersection. The existing network-based single intersection traffic flow statistics mostly only statically count the number of vehicles passing through a single intersection within a certain period of time, and how to efficiently analyze how many vehicles pass through a plurality of time slices at the same time is not considered.
Therefore, in order to solve the disadvantages of the conventional traffic flow statistical method, it is necessary to provide a network-based single intersection traffic flow statistical analysis method and system.
Disclosure of Invention
In view of this, the present invention provides a method and a system for analyzing traffic flow at a single intersection based on a network, which can estimate how many vehicles pass through a certain intersection multiple times in different measurement periods on the basis of protecting vehicle privacy.
In order to achieve the above purpose, the technical solutions provided by the embodiments of the present invention are as follows:
a network-based single intersection traffic flow statistical analysis method comprises the following steps:
on-line coding, when a vehicle passes through a crossing, a random number is sent to a reader according to a certain rule, after the reader receives the random number sent by the vehicle, the reader sets a bit map B of the reader by using a hash function, and after a measurement period is finished, the reader can obtain a complete bit map B;
off-line decoding, after receiving bitmap B sent by the intersection in different measurement periods, the server performs off-line decoding work and calculates the number N of vehicles in not less than k measurement periods k Is composed of
Wherein, bitmap obtained in t measuring periods is B 1 ，B 2 ，...，B t The number of vehicles passing through i measurement cycles is n i 。
As a further improvement of the present invention, the online encoding step of the vehicle specifically comprises:
s11, when the vehicle passes through the intersection L, the vehicle interacts with the reader to obtain the number L of the intersection;
s12, calculating a random number by a hash function
H is any random hash function, v is the license plate number of the vehicle, K v The private key of the vehicle, L is the number of the reader passing through the intersection, and m is the digit of B;
s13, random number h is added v And sending the data to a reader.
As a further improvement of the invention, the online coding step of the reader specifically comprises the following steps:
s21, judging whether a vehicle interacts with the reader or not, and if so, executing S22; otherwise, continuing to wait, and executing S21;
s22, the reader sends the intersection number L of the reader to the vehicle;
s23, judging whether the reader receives the random number h sent by the vehicle v If yes, executing S24; if not, continuing to wait, and executing S23;
s24, setting B [ h v ]＝1；
S25, judging whether the current measurement period is finished or not, if so, executing S26; otherwise, S21 is executed.
S26, sending the obtained bitmap B to a server;
and S27, setting all the bits in the bitmap B to be 0, and starting the next measurement period.
As a further improvement of the present invention, the offline decoding step of the server specifically includes:
s31, providing P' 0 ＝P 0 ＝V 0 ，P j Denotes the probability that any bit l in B is equal to j, V 0 Denotes the proportion of the number of bits equal to 0 in B to the total number of bits, P' j Is represented by B [ l ]]Probability that j is not equal to 1 for the given l-th bit of j B;
s32, calculating the total number N of vehicles passing through the intersection in all measurement periods to be
m is the number of bits of B;
s33, setting j =1;
s34, calculating the probability P of no vehicle selecting the l-th position 1 of B except given j B " j Is composed of
S35, calculating P' j Is composed of
S36, calculating the number n of vehicles passing through i measurement periods i Comprises the following steps:
wherein the content of the first and second substances,
s37, setting j = j +1;
s38, judging whether j > k-1 is true, and if so, executing S39; otherwise, executing S34;
s39, calculating the number N of vehicles in not less than k measuring periods k Is composed of
The technical scheme provided by another embodiment of the invention is as follows:
a network-based single intersection traffic flow statistical analysis system, the system comprising:
the vehicle is used for on-line coding, and when the vehicle passes through the intersection, a random number is sent to the reader according to a certain rule;
the reader is arranged at the intersection for flow statistics, the reader sends the intersection number L of the reader to the vehicle for online coding, the reader sets the position on the bitmap B of the reader by using a hash function after receiving the random number sent by the vehicle, and the reader can obtain a complete bitmap B after the measurement period is finished;
the server is used for off-line decoding, and after receiving bitmap B sent by the intersection in different measurement periods, the server performs off-line decoding work to calculate the number N of vehicles not less than k measurement periods k Is composed of
Wherein, bitmap obtained in t measuring periods is B 1 ，B 2 ，...，B t The number of vehicles passing through i measurement cycles is n i 。
As a further improvement of the invention, the vehicle is provided with an RFID label used for interacting with the reader.
The invention has the beneficial effects that:
on the basis of protecting the privacy of the vehicles, the invention can estimate how many vehicles pass through a certain intersection for many times in different measurement periods, and has the advantages of low hardware cost, higher recognition rate and high speed.
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 embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is also possible for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a flow chart of a network-based single intersection traffic flow statistical analysis method of the present invention;
FIG. 2 is a schematic block diagram of a network-based traffic flow statistical analysis system for a single intersection according to the present invention;
FIG. 3 is a flow chart of an on-line encoding of a vehicle according to an embodiment of the present invention;
FIG. 4 is a flowchart of an on-line encoding process of a reader according to an embodiment of the present invention;
fig. 5 is a flowchart illustrating an off-line decoding process of a server according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
Referring to fig. 1, the invention discloses a network-based single intersection traffic flow statistical analysis method, which comprises two parts of online coding and offline decoding, specifically:
on-line coding, when a vehicle passes through a crossing, a random number is sent to a reader according to a certain rule, after the reader receives the random number sent by the vehicle, the reader sets a bit map B of the reader by using a hash function, and after a measurement period is finished, the reader can obtain a complete bit map B;
off-line decoding, after the server receives bitmap B sent by the intersection in different measurement periods, the server performs off-line decoding work to calculate the number N of vehicles with no less than k measurement periods k Is composed of
Wherein, the bitmap obtained in t measurement periods is B 1 ，B 2 ，...，B t The number of vehicles passing through I measurement cycles is n i 。
Correspondingly, referring to fig. 2, the invention also discloses a single intersection traffic flow statistical analysis system based on network, the system includes:
a vehicle 10, wherein an RFID tag used for interacting with a reader is arranged on the vehicle, and when the vehicle passes through a crossing, a random number is sent to the reader according to a certain rule;
the reader 20 is arranged at the intersection for flow statistics, the reader sends the intersection number L of the reader to the vehicle for online coding, the reader sets the position on the bitmap B of the reader by using a hash function after receiving the random number sent by the vehicle, and the reader can obtain a complete bitmap B after the measurement period is finished;
the server 30 is used for off-line decoding, and after receiving bitmap B sent by the intersection in different measurement periods, the server performs off-line decoding work to calculate the number N of vehicles not less than k measurement periods k Is composed of
Wherein, the bitmap obtained in t measurement periods is B 1 ，B 2 ，...，B t The number of vehicles passing through i measurement cycles is n i 。
The online coding realizes online real-time coding through interaction between the vehicle and the intersection reader. The online coding part needs to install a reader at each intersection to be subjected to flow statistics, and an RFID tag is installed on each vehicle. When a vehicle passes through an intersection, a random number is sent to a reader according to a certain rule. After receiving the random number sent by the vehicle, the reader sets a bit map of the reader by using a hash function. Finally, after the measurement period is finished, the reader obtains a complete bitmap, and sends the bitmap to the server so as to perform off-line decoding. The specific implementation mode is as follows:
(1) At the beginning of each measurement period, the reader at the intersection L first sets all bits of its bitmap to be 0. Let it be assumed that bitmap is denoted by B, and that B has m bits. When the vehicle passes through the intersection L, a random number h is sent to the reader v . The random number is obtained by calculating a hash function, and the specific calculation formula is as follows:
wherein H can be any hash function with good randomness, v is the license plate number of the vehicle, and K v Is the private key of the vehicle and L is the number of the reader passing the intersection. Due to K v Only the vehicle knows it, so the reader cannot transmit the random number h according to the vehicle v And associating the data, thereby achieving the effect of protecting the privacy of the vehicle position. At the calculation of h v The vehicle will then transmit it to the reader.
(2) The reader receives the vehicle transmission h v Then, set B to 1 at the corresponding position of B, i.e. set B [ h ] v ]＝1。
(3) After a measurement period is finished, the reader sends bitmap B obtained in the measurement period to the server for offline decoding.
After receiving bitmaps sent by the intersection in different measurement periods, the server performs offline decoding. Suppose that there are t measurement periods to obtain bitmap, B 1 ，B 2 ，...，B t Flow statistics need to be estimated past itNumber of vehicles N in not less than k measurement periods k . Suppose that the number of vehicles that have just passed i measurement cycles is n i ，
The specific implementation method is as follows:
(1) And summing all bitmaps according to bits to obtain an array B. That is, each bit of array B is set
(2) By P j Denotes the probability that any bit l in B is equal to j, i.e. B [ l]Probability of = j. From P' j Is represented by B [ l ]]Probability that the l-th bit of = j and given j bitmaps is 1. In other words, except for a given j bitmaps, the l bits of the remaining t-j bitmaps should all be equal to 0. By V 0 Representing the ratio of the number of bits in B equal to 0 to the total number of bits. For example, the total length m =1000 of B, the number of bits equal to 0 being equal to 200, then V 0 =200/1000=0.2. First, set P' 0 ＝P 0 ＝V0。
(3) The total number of vehicles N passing through the intersection in all the measurement periods is calculated by equation (2).
(4) Starting from j =1, n is estimated in sequence 1 ，n 2 ，...，n k-1 . When estimating n j The specific implementation method is as follows:
first, P is calculated by the formula (3) " j Wherein P " j Indicating the probability that no vehicle will select the l-th position 1 of a bitmap other than the given j bitmaps.
Then, P 'is obtained by calculation of formula (4)' j 。
Finally, n is calculated by formula (5) j 。
Wherein the content of the first and second substances,
(5) The number N of vehicles passing through not less than k measurement cycles is calculated by formula (6) k 。
The operation of the vehicle, the reader, and the server will be described in detail below.
Referring to fig. 3, the online encoding step of the vehicle specifically includes:
s11, when the vehicle passes through the intersection L, the vehicle interacts with the reader to obtain the number L of the intersection;
s12, calculating a random number by a hash function
H is any random hash function, v is the license plate number of the vehicle, K v The vehicle is a private key of the vehicle, L is the number of a reader passing through the intersection, and m is the number of digits of B;
s13, random number h is added v And sending the data to a reader.
Referring to fig. 4, the online encoding step of the reader specifically includes:
s21, judging whether a vehicle interacts with the reader or not, and if so, executing S22; otherwise, continuing to wait, and executing S21;
s22, the reader sends the intersection number L of the reader to the vehicle;
s23, judging whether the reader receives the random number h sent by the vehicle v If yes, executing S24; if not, continuing to wait, and executing S23;
s24, setting Bh v ]＝1；
S25, judging whether the current measurement period is finished or not, if so, executing S26; otherwise, S21 is executed.
S26, sending the obtained bitmap B to a server;
s27, all the bits in bitmap B are set to be 0, and the next measurement period is started.
Referring to fig. 5, after the server receives bitmaps of t measurement periods measured by the online coding module, the offline decoding step of the server specifically includes:
s31, providing P' 0 ＝P 0 ＝V 0 ，P j Denotes the probability that any bit l in B is equal to j, V 0 Denotes the proportion of the number of bits equal to 0 in B to the total number of bits, P' j Is represented by B [ l ]]Probability that j and the l-th bit of given j B are all 1;
s32, calculating the total number N of vehicles passing through the intersection in all measurement periods to be
m is the number of bits of B;
s33, setting j =1;
s34, calculating the probability P of no vehicle selecting the l-th position 1 of B except given j B " j Is composed of
S35, calculating P' j Is composed of
S36, calculating the number n of vehicles passing through i measurement periods i Comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
s37, setting j = j +1;
s38, judging whether j > k-1 is true, and if so, executing S39; otherwise, executing S34;
s39, calculating the number N of vehicles with not less than k measuring periods k Is composed of
According to the technical scheme, on the basis of protecting the privacy of the vehicles, the invention can estimate how many vehicles pass through a certain intersection for many times in different measurement periods, and has the advantages of low hardware cost, high recognition rate and high speed.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present specification describes embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and it is to be understood that all embodiments may be combined as appropriate by one of ordinary skill in the art to form other embodiments as will be apparent to those of skill in the art from the description herein.
Claims (5)
1. A network-based single intersection traffic flow statistical analysis method is characterized by comprising the following steps:
on-line coding, when a vehicle passes through a crossing, a random number is sent to a reader according to a certain rule, after the reader receives the random number sent by the vehicle, the reader sets a bit map B of the reader by using a hash function, and after a measurement period is finished, the reader can obtain a complete bit map B;
off-line decoding, after the server receives bitmap B sent by the intersection in different measurement periods, the server performs off-line decoding work to calculate the number N of vehicles with no less than k measurement periods k Is composed of
Wherein, the bitmap obtained in t measurement periods is B 1 ，B 2 ，...，B t The number of vehicles passing through i measurement cycles is n i ；
The off-line decoding step of the server specifically comprises the following steps:
s31, providing P' 0 ＝P 0 ＝V 0 ，P j Denotes the probability that any bit l in B is equal to j, V 0 Representing the ratio of the number of bits equal to 0 in B to the total number of bits, P' j Is represented by B [ l ]]Probability that j is not equal to 1 for the given l-th bit of j B;
s32, calculating the total number N of vehicles passing through the intersection in all measurement periods to be
m is the number of bits of B;
s33, setting j =1;
s34, calculating the probability P 'that no vehicle selects the l-th position 1 of B except given j B' j Is composed of
S35, calculating P' j Is composed of
S36, calculating the number n of vehicles passing through i measurement periods i Comprises the following steps:
wherein the content of the first and second substances,
s37, setting j = j +1;
s38, judging whether j is greater than k-1, and if so, executing S39; otherwise, executing S34;
s39, calculating the number N of vehicles with not less than k measuring periods k Is composed of
2. The network-based single intersection traffic flow statistical analysis method according to claim 1, wherein the vehicle online coding step specifically comprises:
s11, when the vehicle passes through the intersection L, the vehicle interacts with the reader to obtain the number L of the intersection;
s12, calculating a random number by a hash function
H is any random hash function, upsilon is the license plate number of the vehicle, and K v The vehicle is a private key of the vehicle, L is the number of a reader passing through the intersection, and m is the number of digits of B;
s13, random number h υ And sending the data to a reader.
3. The single intersection traffic flow statistical analysis method based on the network as claimed in claim 1, wherein the on-line encoding step of the reader specifically is:
s21, judging whether a vehicle interacts with the reader or not, and if so, executing S22; otherwise, continuing to wait, and executing S21;
s22, the reader sends the intersection number L of the reader to the vehicle;
s23, judging whether the reader receives the random number h sent by the vehicle υ If yes, executing S24; if not, continuing to wait, and executing S23;
s24, setting B [ h υ ]＝1；
S25, judging whether the current measurement period is finished or not, if so, executing S26; otherwise, executing S21;
s26, sending the obtained bitmap B to a server;
s27, all the bits in bitmap B are set to be 0, and the next measurement period is started.
4. A network-based statistical analysis system for traffic flow at a single intersection, the system comprising:
the vehicle is used for on-line coding, and when the vehicle passes through the intersection, a random number is sent to the reader according to a certain rule;
the reader is arranged at the intersection for flow statistics, the reader sends the intersection number L of the reader to the vehicle for online coding, the reader sets the position on the bitmap B of the reader by using a hash function after receiving the random number sent by the vehicle, and the reader can obtain a complete bitmap B after the measurement period is finished;
the server is used for off-line decoding, and after receiving bitmap B sent by the intersection in different measurement periods, the server can perform off-line decoding work and calculate the number N of vehicles in not less than k measurement periods k Is composed of
Wherein, the bitmap obtained in t measurement periods is B 1 ，B 2 ，...，B t The number of vehicles passing through i measurement cycles is n i ；
The off-line decoding step of the server specifically comprises the following steps:
s31, providing P' 0 ＝P 0 ＝V 0 ，P j Represents an arbitrary bit in BProbability that l equals j, V 0 Denotes the proportion of the number of bits equal to 0 in B to the total number of bits, P' j Is represented by B [ l ]]Probability that j and the l-th bit of given j B are all 1;
s32, calculating the total number N of vehicles passing through the intersection in all measurement periods to be
m is the number of bits of B;
s33, setting j =1;
s34, calculating the probability P 'that no vehicle selects the l-th position 1 of B except given j B' j Is composed of
S35, calculating P' j Is composed of
S36, calculating the number n of vehicles passing through i measurement periods i Comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
s37, setting j = j +1;
s38, judging whether j is greater than k-1, and if so, executing S39; otherwise, executing S34;
s39, calculating the number N of vehicles in not less than k measuring periods k Is composed of
5. The network-based intersection traffic flow statistical analysis system of claim 4, wherein said vehicles have mounted thereon RFID tags for interaction with readers.
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