CN113920730A - Signal lamp time distribution method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a signal lamp time distribution method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a distribution curve corresponding to the traffic demand indexes of each intersection in a preset time period, and selecting a plurality of first traffic demand indexes based on the distribution curve; inquiring at least two preset time periods corresponding to the first traffic demand index, and generating a first numerical combination based on the first traffic demand index and the preset time periods; generating a relation curve between the traffic demand index and the time period based on the first numerical combination; and determining the correlation degree between the distribution curve and the relation curve, and screening the first numerical combination according to the correlation degree to obtain a target numerical combination. According to the method and the device, the distribution curve of the traffic demand index and the correlation degree of the traffic demand index and the preset time period corresponding relation curve are calculated, the time of each intersection signal lamp can be accurately distributed, the time distribution efficiency is improved by means of manual adjustment, and the maintenance cost is reduced.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a method and an apparatus for time allocation of a signal lamp, an electronic device, and a storage medium.
Background
The intelligent transportation era is a new stage of transportation development and is a necessary product of social and economic development and technological progress. Since the 90 s in the 20 th century, the automobile holding capacity is rapidly increased, the road mileage is difficult to rapidly increase, and traffic jam and frequent accidents occur in all countries in the world. Each economically developed country strives to invest a large amount of research expenses, attempts to obtain great progress, and developing countries join together in various ways hopefully to solve the problems of traffic jam and the like.
In recent years, with the rapid development of GPS and LBS technologies, a large amount of trajectory data is generated, and the method can provide good solutions for traffic situation perception, smart city construction and the like. At present, the signal lamp period adjustment of urban road intersections is not self-adaptive in the true sense, and is mostly manually set, but with the intelligent development of urban traffic, the self-adaptive mode of the signal lamp adjustment of the intersections becomes a trend, manual adjustment by manual setting wastes a large amount of manpower, the configuration result is also lack of scientificity, and the maintenance cost is higher.
Disclosure of Invention
In order to solve the technical problem or at least partially solve the technical problem, the present application provides a time allocation method and apparatus for a signal lamp, an electronic device, and a storage medium.
According to an aspect of an embodiment of the present application, there is provided a time allocation method for a signal lamp, including:
the method comprises the steps of obtaining a distribution curve corresponding to traffic demand indexes of each intersection in a preset time period, and selecting a plurality of first traffic demand indexes based on the distribution curve;
inquiring at least two preset time periods corresponding to the first traffic demand index, and generating a first numerical combination based on the first traffic demand index and the preset time periods;
generating a relationship curve between the traffic demand index and the time period based on the first combination of values;
determining the correlation degree between the distribution curve and the relation curve, and screening the first numerical value combination according to the correlation degree to obtain a target numerical value combination corresponding to each intersection signal lamp, wherein the target numerical value combination comprises: the target traffic demand index and a target time period corresponding to the target traffic demand index.
Further, before obtaining a distribution curve corresponding to the traffic demand index of each intersection in a preset time period, the method further includes:
acquiring at least two target lanes corresponding to each intersection;
determining the average speed of the lane, the traffic of the lane and the occupancy of the lane corresponding to the target lane;
and calculating the traffic demand index according to the lane average speed, the lane flow and the lane occupancy.
Further, the acquiring a distribution curve corresponding to the traffic demand index of each intersection in a preset time period, and selecting a plurality of first traffic demand indexes based on the distribution curve includes:
performing smooth filtering on the traffic demand index according to a preset window length to obtain the distribution curve, wherein the preset window length is the length of a preset sliding window;
randomly selecting a plurality of traffic demand indices from the distribution curve as the first traffic demand index.
Further, the querying at least two preset time periods corresponding to the first traffic demand index includes:
acquiring the number of target lanes corresponding to each intersection;
acquiring a preset time period meeting the number of the target lanes and the first traffic demand index from a preset time period table, wherein the preset time period table comprises: the number of lanes, the traffic demand index, and the time period.
Further, the determining the correlation between the distribution curve and the relationship curve, and screening the first numerical value combination according to the correlation to obtain a target numerical value combination corresponding to each intersection signal lamp includes:
obtaining a plurality of second numerical combinations carried in the distribution curve, wherein the second numerical combinations include: a second traffic demand index and an actual time period corresponding to the second traffic demand index;
calculating a correlation between the second combination of values and the first combination of values;
and acquiring a target numerical value combination with the correlation degree larger than a preset threshold value with the second numerical value combination from the first numerical value combination.
Further, the obtaining, from the first numerical combination, a target numerical combination having a correlation with the second numerical combination greater than a preset threshold includes:
acquiring a third numerical combination with the correlation degree larger than a preset threshold value between the third numerical combination and the second numerical combination from the first numerical combination;
acquiring a fourth numerical combination which meets a preset type in the third numerical combinations, and determining the fourth numerical combination as an initial center;
calculating a first distance between a fifth numerical combination and the initial center, determining that the numerical combination is a valid numerical combination when the first distance is smaller than or equal to a first preset threshold, and updating the initial center, wherein the fifth numerical combination is a numerical combination except a fourth numerical combination in the third numerical combination;
and calculating a second distance between the updated initial center and the effective numerical value combination, calculating a first weighted sum of the second distance, determining a second weighted sum meeting a preset condition according to the first weighted sum, and determining the updated initial center as the target array set only until the second weighted sum meets a termination condition.
Further, after determining the correlation between the distribution curve and the relationship curve and screening the first numerical combination according to the correlation to obtain a target numerical combination corresponding to each intersection signal lamp, the method further includes:
acquiring vehicle arrival data of each intersection;
inputting the vehicle arrival data into the meta-packet transmission model corresponding to each intersection to obtain a detection index corresponding to each intersection;
and determining a verification result of the target numerical value combination according to the detection index.
According to another aspect of the embodiments of the present application, there is also provided a time allocation apparatus for a signal lamp, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a distribution curve corresponding to traffic demand indexes of each intersection in a preset time period and determining a plurality of first traffic demand indexes based on the distribution curve;
the determining module is used for inquiring at least two preset time periods corresponding to the first traffic demand index and generating a first numerical combination based on the first traffic demand index and the preset time periods;
a generating module, configured to generate a relationship curve between the traffic demand index and the time period based on the candidate numerical combination;
a determining module, configured to determine a correlation between the distribution curve and the time curve, and screen the candidate value combinations according to the similarity to obtain target value combinations corresponding to each intersection signal lamp, where the target value combinations include: the target traffic demand index and a target time period corresponding to the target traffic demand index.
According to another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program that executes the above steps when the program is executed.
According to another aspect of the embodiments of the present application, there is also provided an electronic apparatus, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus; wherein: a memory for storing a computer program; a processor for executing the steps of the method by running the program stored in the memory.
Embodiments of the present application also provide a computer program product containing instructions, which when run on a computer, cause the computer to perform the steps of the above method.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: according to the method and the device, the distribution curve of the traffic demand index and the correlation degree of the traffic demand index and the preset time period corresponding relation curve are calculated, the time of each intersection signal lamp can be accurately distributed, the time distribution efficiency is improved by manual adjustment, and the maintenance cost is reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a time allocation method for a signal lamp according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a distribution curve before smoothing filtering according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a smoothed filtered distribution curve provided by an embodiment of the present application;
FIG. 4 is a schematic diagram of a traffic demand index versus time period according to an embodiment of the present disclosure;
fig. 5 is a flowchart of a method for assigning time to a signal lamp according to another embodiment of the present application;
fig. 6 is a block diagram of a time distribution device for signal lamps according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments, and the illustrative embodiments and descriptions thereof of the present application are used for explaining the present application and do not constitute a limitation to the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another similar entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiment of the application provides a signal lamp time distribution method and device, electronic equipment and a storage medium. The method provided by the embodiment of the invention can be applied to any required electronic equipment, for example, the electronic equipment can be electronic equipment such as a server and a terminal, and the method is not particularly limited herein, and is hereinafter simply referred to as electronic equipment for convenience in description.
According to an aspect of embodiments of the present application, there is provided a method embodiment of a method for time allocation of a signal lamp. Fig. 1 is a flowchart of a time allocation method for a signal lamp according to an embodiment of the present application, and as shown in fig. 1, the method includes:
step S11, a distribution curve corresponding to the traffic demand indexes of each intersection in a preset time period is obtained, and a plurality of first traffic demand indexes are selected based on the distribution curve.
In the present embodiment, there are many different types of intersections in each city, for example: the method comprises the following steps of A1-A3:
and A1, acquiring at least two target lanes corresponding to each intersection.
Step a2, the average speed of the lane, the traffic flow of the lane, and the lane occupancy rate corresponding to the target lane are determined.
In the embodiment of the present application, the calculation formula of the lane average speed of the target lane is as follows:
The calculation formula of the lane flow of the target lane is as follows:
The calculation formula of the lane occupancy of the target lane is as follows:
And A3, calculating the traffic demand index according to the average speed of the lane, the traffic flow and the lane occupancy.
In the above equation, T ∈ [1, T ], represents T sampling times, Δ T represents a time interval, pcu/h represents a road saturation flow rate per hour, Q represents a total traffic flow rate of the lane in the time interval, V represents a lane average speed in the time interval, and O represents a lane occupancy in the time interval.
Then, taking the maximum calculated traffic demand index of each lane at the same time as a final calculated traffic demand index TI, wherein the calculation formula is as follows:
Max(TIn)，n∈[1,N]，
in the formula, N represents N lanes at the intersection.
In the embodiment of the present application, in step S11, obtaining a distribution curve corresponding to the traffic demand index at each intersection in a preset time period, and selecting a plurality of first traffic demand indexes based on the distribution curve includes the following steps B1-B2:
and step B1, performing smooth filtering on the traffic demand index according to the length of a preset window to obtain a distribution curve, wherein the length of the preset window is the length of a preset sliding window.
And step B2, randomly selecting a plurality of traffic demand indexes from the distribution curve as a first traffic demand index.
Reference is made to fig. 2 and 3, wherein fig. 2 is a distribution curve before smoothing filtering, and fig. 3 is a distribution curve after smoothing filtering. The Savitzky-Golay smoothing filtering algorithm is adopted in the embodiment of the application. And (3) processing the intersection TI by using a Savitzky-Golay (S-G) smooth filtering method, and firstly, carrying out k-order polynomial fitting on data points within a preset window length so as to obtain a fitted result. S-G filtering is essentially a moving window weighted averaging algorithm, but its weighting coefficients are not simple constant windows, but are derived by a least squares fit to a given higher order polynomial over a sliding window. The formula of the filtering algorithm is as follows:
in the formula (I), the compound is shown in the specification,
represents the final smoothing result, i ∈ [ -m, m]The width of the filter window is 2m +1, CiDenotes the ith point, Yj+1Represents a weighting coefficient including edge data, and P is the total number of sample points.
In the embodiment of the application, after the distribution curve subjected to smooth filtering is obtained, a plurality of traffic demand indexes are randomly selected from the distribution curve to serve as the first traffic demand index, so that the subsequent calculation of the correlation degree is facilitated.
Step S12, at least two preset time periods corresponding to the first traffic demand index are queried, and a first numerical combination is generated based on the first traffic demand index and the preset time periods.
In the embodiment of the present application, querying at least two preset time periods corresponding to the first traffic demand index includes the following steps C1-C2:
and step C1, acquiring the number of target lanes corresponding to each intersection.
Step C2, obtaining a preset time period satisfying the number of target lanes and the first traffic demand index from a preset time period table, where the preset time period table includes: the number of lanes, the traffic demand index, and the time period.
In the embodiment of the application, in order to reasonably simplify the problem of time period generation and reduce the amount of model calculation, a preset value range is used in consideration of the traffic laws of high peak in the morning and at night, tide distribution and the like of actual traffic travel, namely information such as channeling information, lane number and the like of an intersection is collected, then the time period is divided according to the information to obtain a preset time period table, and the preset time period table can refer to table 1. And limiting the time period range according to the number of the lanes at the intersection. And a partition plate value taking method is adopted to filter invalid value combinations in the value taking range. For example, the numerical combination includes four traffic demand indices, i.e., TI1, TI2, TI3, TI4, and four C time periods, i.e., C1, C2, C3, C4.
TABLE 1
And step S13, generating a relation curve between the traffic demand index and the time period based on the first numerical combination.
In the embodiment of the present application, since the randomly selected traffic demand index is not controlled by human, the relationship curves between the traffic demand indexes and the time periods of the multiple situations are generated according to the first numerical combination (refer to fig. 4).
Step S14, determining the correlation between the distribution curve and the relation curve, and screening the first numerical combination according to the correlation to obtain a target numerical combination corresponding to each intersection signal lamp, wherein the target numerical combination comprises: the target traffic demand index and a target time period corresponding to the target traffic demand index.
In the embodiment of the present application, the target numerical combination obtained by screening the first numerical combination according to the correlation is a numerical combination corresponding to each intersection, for example: and selecting the time period with the maximum correlation degree as the target time period of the signal lamp of the intersection according to the traffic demand index of each intersection. Therefore, under the condition of excessive quantity of intersections, the time period of signal lamps at the intersections can be quickly determined according to the traffic demand index.
In this embodiment of the present application, in step S14, determining a correlation between the distribution curve and the relationship curve, and screening the first numerical combinations according to the correlation to obtain target numerical combinations corresponding to each intersection signal lamp, includes the following steps D1-D3:
step D1, obtaining a plurality of second numerical combinations carried in the distribution curve, wherein the second numerical combinations include: the second traffic demand index and an actual time period corresponding to the second traffic demand index;
step D2, calculating the correlation between the second numerical combination and the first numerical combination.
In the embodiment of the application, the preliminary screening is to calculate the correlation of the time period-TI requirement index. Specifically, Pearson Correlation Coefficients (PCCs) can be used, and the method is suitable for measuring the linear correlation relationship between two variables, and the value range is [ -1,1 ]. The algorithm formula is as follows:
in the formula, Xi and Yi respectively represent two variables of a period and TI,
represents the mean, T represents the sample point, T ∈ [1, T ∈]And r-related coefficient value field is [ -1,1]The stronger the positive correlation of the two sets of variables, the closer to 1, and vice versa, the closer to-1.
And normalizing the time period C and the TI value domain, wherein the normalization satisfies the following formula:
the formula is normalized data for calculating the time periods C and TI, ensures that the time periods C and TI are in a unified value range, and can calculate the time periods C and TI more accuratelyThe correlation between them. In the formula, xiDenotes the ith point, xminDenotes the minimum point, x, of the C and TI valuesmaxRepresenting the maximum point in the C and TI values. The correlation of the two curves was evaluated by calculating the pearson correlation coefficient and the numerical combinations were filtered.
And D3, obtaining a target value combination with the correlation degree larger than a preset threshold value with the second value combination from the first value combination.
In the embodiment of the present application, step D3, obtaining the target value combination with the correlation degree greater than the preset threshold value with the second value combination from the first value combination, includes the following steps D301 to D304:
step D301, acquiring a third numerical combination with the correlation degree between the third numerical combination and the second numerical combination larger than a preset threshold value from the first numerical combination;
step D302, acquiring a fourth numerical combination meeting the preset type in the third numerical combinations, and determining the fourth numerical combination as an initial center;
step D303, calculating a first distance between a fifth numerical combination and the initial center, determining that the numerical combination is a valid numerical combination when the first distance is less than or equal to a first preset threshold, and updating the initial center, wherein the fifth numerical combination is a numerical combination except for a fourth numerical combination in the third numerical combination;
and D304, calculating a second distance of the updated initial center and the effective numerical value combination, calculating a first weighted sum of the second distance, determining a second weighted sum meeting a preset condition according to the first weighted sum, and determining the updated initial center as a target array set only when the second weighted sum meets a termination condition.
In the embodiment of the application, the numerical combination combinations used in the intersection are limited, and one group of numerical combinations can meet the requirement when being updated every time, but the number of the numerical combination combinations finally screened by the Pearson algorithm is still large, and the screening of the numerical combination combinations by the traffic rule cannot meet the requirement, so that the numerical combination combinations finally screened by the Pearson algorithm are classified by adopting the K-means clustering algorithm, and only one group of numerical combination combinations approaching to the same distribution range is selected for use, namely 1 group of numerical combination is arbitrarily selected for field use in each class, and the secondary screening of the numerical combination combinations is completed. The number of categories is subject to the field regulations of the algorithm application.
The input data of the K-means clustering algorithm is a numerical combination of C and TI screened by the Pearson algorithm, the preset category number is used as an initial clustering center, the distance between each point and the central points is calculated according to the mean value (namely the central point) of each clustering point, and the corresponding points are divided again according to the minimum distance. After the re-division, the mean value of each changed point and the changed cluster center point is re-calculated, and whether the termination condition is met or not is judged after each change. If the square error can not be reduced any more, the function is converged, the algorithm is terminated, and if the termination condition is not met, the minimum mean value is continuously calculated to update the central point until the function is converged.
According to the method and the device, the distribution curve of the traffic demand index and the correlation degree of the traffic demand index and the preset time period corresponding relation curve are calculated, the time of each intersection signal lamp can be accurately distributed, the time distribution efficiency is improved by manual adjustment, and the maintenance cost is reduced.
In this embodiment of the application, after determining the correlation between the distribution curve and the relationship curve, and screening the first numerical combination according to the correlation to obtain the target numerical combination corresponding to each intersection signal lamp, as shown in fig. 5, the method further includes:
step S21, acquiring vehicle arrival data of each intersection;
step S22, inputting vehicle arrival data into the meta-packet transmission model corresponding to each intersection to obtain a detection index corresponding to each intersection;
and step S23, determining the verification result of the target numerical value combination according to the detection index.
In the embodiment of the application, the intersections are divided according to types according to road network topology data, intersection lane distribution conditions and intersection passing rules obtained through analysis. The types comprise 4 types of crossroads, T-shaped intersections, Y-shaped intersections and annular intersections. And (4) using a cellular transmission model according to each type, and applying the real vehicle arrival data of the intersection to realize the verification of the target numerical value combination. Through simulating the crossing traffic condition, the numerical combinations to be checked are configured to calculate the indexes of the crossing such as vehicle queuing length, congestion index and the like, so that the traffic efficiency of each group of numerical combinations under the same traffic condition is evaluated.
Specifically, the vehicle queue length of each lane can be calculated, and the validity of the combination of the values to be verified is analyzed. The verification unit evaluates the combination of the numerical values to be verified by controlling the input variables, guarantees the safety of intersection periodic configuration, and has the advantages of light model, easiness in deployment, high verification efficiency and the like.
Firstly, the crossing traffic law, namely the arrival number of vehicles passing through the single lane all day per minute is analyzed.
The number of vehicle arrivals obeys a poisson distribution:
where k is the number of vehicles passing through a given time interval, λ is the average value of traffic flows passing through a given time interval, t is a time interval in seconds, Q represents the traffic volume (vehicles/h), and the base of the natural number e is 2.71828.
The average arrival rate of the vehicles is averaged by using weighting, and the following formula is satisfied:
where i ∈ (0, N), a discrete distribution, representing the number of vehicle arrivals per unit time, fiIndicating the number of times (frequency) that the vehicle arrival number occurs.
The embodiment of the application verifies the scientificity of threshold selection by dividing the intersection types and simulating according to the intersection types. The method can well control the input variables to evaluate the numerical combination, ensures the safety of intersection periodic configuration, and has the advantages of light model, easy deployment, high verification efficiency and the like.
Fig. 6 is a block diagram of a time distribution apparatus for signal lamps according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device through software, hardware, or a combination of the two. As shown in fig. 6, the apparatus includes:
the acquisition module 61 is configured to acquire a distribution curve corresponding to the traffic demand index at each intersection in a preset time period, and determine a plurality of first traffic demand indexes based on the distribution curve;
the determining module 62 is configured to query at least two preset time periods corresponding to the first traffic demand index, and generate a first numerical combination based on the first traffic demand index and the preset time periods;
a generating module 63, configured to generate a relation curve between the traffic demand index and the time period based on the candidate numerical combination;
a determining module 64, configured to determine a correlation between the distribution curve and the time curve, and filter the candidate value combinations according to the similarity to obtain a target value combination corresponding to each intersection signal lamp, where the target value combination includes: the target traffic demand index and a target time period corresponding to the target traffic demand index.
In an embodiment of the present application, the apparatus further includes: the calculation module is used for acquiring at least two target lanes corresponding to each intersection; determining the average speed of a lane, the traffic of the lane and the occupancy of the lane corresponding to the target lane; and calculating the traffic demand index according to the average speed of the lane, the traffic flow and the lane occupancy.
In the embodiment of the present application, the obtaining module 61 is configured to perform smooth filtering on the traffic demand index according to a preset window length to obtain a distribution curve, where the preset window length is a length of a preset sliding window; a plurality of traffic demand indexes are randomly selected from the distribution curve as a first traffic demand index.
In the embodiment of the present application, the determining module 62 is configured to obtain the number of target lanes corresponding to each intersection; acquiring a preset time period meeting the number of target lanes and a first traffic demand index from a preset time period table, wherein the preset time period table comprises: the number of lanes, the traffic demand index, and the time period.
In this embodiment of the present application, the determining module 64 is configured to obtain a plurality of second numerical combinations carried in the distribution curve, where the second numerical combinations include: the second traffic demand index and an actual time period corresponding to the second traffic demand index; calculating the correlation between the second numerical combination and the first numerical combination; and acquiring a target numerical value combination with the correlation degree larger than a preset threshold value with the second numerical value combination from the first numerical value combination.
In this embodiment of the present application, the determining module 64 is configured to obtain, from the first numerical combinations, third numerical combinations with a correlation degree greater than a preset threshold with the second numerical combinations; acquiring a fourth numerical combination meeting the preset type in the third numerical combination, and determining the fourth numerical combination as an initial center; calculating a first distance between a fifth numerical combination and the initial center, determining the numerical combination as a valid numerical combination when the first distance is smaller than or equal to a first preset threshold, and updating the initial center, wherein the fifth numerical combination is a numerical combination except for a fourth numerical combination in the third numerical combination; and calculating a second distance of the updated initial center and the effective numerical value combination, calculating a first weighted sum of the second distance, determining a second weighted sum meeting a preset condition according to the first weighted sum, and determining the updated initial center as a target array set only until the second weighted sum meets a termination condition.
In an embodiment of the present application, the apparatus further includes: the verification module is used for acquiring vehicle arrival data of each intersection; inputting vehicle arrival data into the meta-packet transmission model corresponding to each intersection to obtain detection indexes corresponding to each intersection; and determining a verification result of the target numerical value combination according to the detection index.
An embodiment of the present application further provides an electronic device, as shown in fig. 7, the electronic device may include: the system comprises a processor 1501, a communication interface 1502, a memory 1503 and a communication bus 1504, wherein the processor 1501, the communication interface 1502 and the memory 1503 complete communication with each other through the communication bus 1504.
A memory 1503 for storing a computer program;
the processor 1501 is configured to implement the steps of the above embodiments when executing the computer program stored in the memory 1503.
The communication bus mentioned in the above terminal may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the terminal and other equipment.
The Memory may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In another embodiment provided by the present application, a computer-readable storage medium is further provided, in which instructions are stored, and when the instructions are executed on a computer, the instructions cause the computer to execute the time allocation method of the signal lamp in any one of the above embodiments.
In a further embodiment provided by the present application, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the method for time allocation of signal lights as described in any of the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk), among others.
The above description is only for the preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for assigning time to a signal, comprising:
the method comprises the steps of obtaining a distribution curve corresponding to traffic demand indexes of each intersection in a preset time period, and selecting a plurality of first traffic demand indexes based on the distribution curve;
inquiring at least two preset time periods corresponding to the first traffic demand index, and generating a first numerical combination based on the first traffic demand index and the preset time periods;
generating a relationship curve between the traffic demand index and the time period based on the first combination of values;
determining the correlation degree between the distribution curve and the relation curve, and screening the first numerical value combination according to the correlation degree to obtain a target numerical value combination corresponding to each intersection signal lamp, wherein the target numerical value combination comprises: the target traffic demand index and a target time period corresponding to the target traffic demand index.
2. The method according to claim 1, wherein before obtaining the distribution curve corresponding to the traffic demand index at each intersection in the preset time period, the method further comprises:
acquiring at least two target lanes corresponding to each intersection;
determining the average speed of the lane, the traffic of the lane and the occupancy of the lane corresponding to the target lane;
and calculating the traffic demand index according to the lane average speed, the lane flow and the lane occupancy.
3. The method according to claim 1, wherein the obtaining of a distribution curve corresponding to traffic demand indexes at each intersection in a preset time period and the selecting of a plurality of first traffic demand indexes based on the distribution curve comprises:
performing smooth filtering on the traffic demand index according to a preset window length to obtain the distribution curve, wherein the preset window length is the length of a preset sliding window;
randomly selecting a plurality of traffic demand indices from the distribution curve as the first traffic demand index.
4. The method of claim 1, wherein the querying for at least two predetermined time periods corresponding to the first traffic demand index comprises:
acquiring the number of target lanes corresponding to each intersection;
acquiring a preset time period meeting the number of the target lanes and the first traffic demand index from a preset time period table, wherein the preset time period table comprises: the number of lanes, the traffic demand index, and the time period.
5. The method of claim 1, wherein the determining the correlation between the distribution curve and the relationship curve and screening the first numerical combinations according to the correlation to obtain target numerical combinations corresponding to signal lamps at each intersection comprises:
obtaining a plurality of second numerical combinations carried in the distribution curve, wherein the second numerical combinations include: a second traffic demand index and an actual time period corresponding to the second traffic demand index;
calculating a correlation between the second combination of values and the first combination of values;
and acquiring a target numerical value combination with the correlation degree larger than a preset threshold value with the second numerical value combination from the first numerical value combination.
6. The method according to claim 5, wherein the obtaining a target value combination having a correlation greater than a preset threshold with the second value combination from the first value combination comprises:
acquiring a third numerical combination with the correlation degree larger than a preset threshold value between the third numerical combination and the second numerical combination from the first numerical combination;
acquiring a fourth numerical combination which meets a preset type in the third numerical combinations, and determining the fourth numerical combination as an initial center;
calculating a first distance between a fifth numerical combination and the initial center, determining that the numerical combination is a valid numerical combination when the first distance is smaller than or equal to a first preset threshold, and updating the initial center, wherein the fifth numerical combination is a numerical combination except a fourth numerical combination in the third numerical combination;
and calculating a second distance between the updated initial center and the effective numerical value combination, calculating a first weighted sum of the second distance, determining a second weighted sum meeting a preset condition according to the first weighted sum, and determining the updated initial center as the target array set only until the second weighted sum meets a termination condition.
7. The method of claim 1, wherein after determining a correlation between the distribution curve and the relationship curve and screening the first numerical combination according to the correlation to obtain a target numerical combination corresponding to each intersection signal lamp, the method further comprises:
acquiring vehicle arrival data of each intersection;
inputting the vehicle arrival data into the meta-packet transmission model corresponding to each intersection to obtain a detection index corresponding to each intersection;
and determining a verification result of the target numerical value combination according to the detection index.
8. A time distribution device for a signal lamp, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a distribution curve corresponding to traffic demand indexes of each intersection in a preset time period and determining a plurality of first traffic demand indexes based on the distribution curve;
the determining module is used for inquiring at least two preset time periods corresponding to the first traffic demand index and generating a first numerical combination based on the first traffic demand index and the preset time periods;
a generating module, configured to generate a relationship curve between the traffic demand index and the time period based on the candidate numerical combination;
a determining module, configured to determine a correlation between the distribution curve and the time curve, and screen the candidate value combinations according to the similarity to obtain target value combinations corresponding to each intersection signal lamp, where the target value combinations include: the target traffic demand index and a target time period corresponding to the target traffic demand index.
9. A storage medium, characterized in that the storage medium comprises a stored program, wherein the program is operative to perform the method steps of any of the preceding claims 1 to 7.
10. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus; wherein:
a memory for storing a computer program;
a processor for performing the method steps of any of claims 1-7 by executing a program stored on a memory.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102142197A (en) * | 2011-03-31 | 2011-08-03 | 汤一平 | Intelligent traffic signal lamp control device based on comprehensive computer vision |
| US20140210645A1 (en) * | 2013-01-30 | 2014-07-31 | Wavetronix Llc | Traffic flow through an intersection by reducing platoon interference |
| CN105761517A (en) * | 2016-05-18 | 2016-07-13 | 杭州智诚惠通科技有限公司 | Signal light timing method |
| CN108734354A (en) * | 2018-05-23 | 2018-11-02 | 吉林大学 | A kind of urban road signalized intersections multiple target timing designing method |
| CN108806283A (en) * | 2017-05-02 | 2018-11-13 | 中移物联网有限公司 | A kind of control method and car networking platform of traffic lights |
| CN109584551A (en) * | 2018-11-27 | 2019-04-05 | 南京航空航天大学 | A kind of design method that the related grouped intersections based on hierarchical clustering divide |
| CN110738863A (en) * | 2018-07-19 | 2020-01-31 | 北京嘀嘀无限科技发展有限公司 | Method and device for determining signal lamp period, timing change time and passing duration |
| CN110782667A (en) * | 2019-10-30 | 2020-02-11 | 北京百度网讯科技有限公司 | Signal lamp time-sharing timing method and device, electronic equipment and storage medium |
| US10643465B1 (en) * | 2019-11-18 | 2020-05-05 | Iteris, Inc. | Dynamic advanced traffic detection from assessment of dilemma zone activity for enhancement of intersection traffic flow and adjustment of timing of signal phase cycles |
| CN112071065A (en) * | 2020-09-16 | 2020-12-11 | 山东理工大学 | Traffic flow prediction method based on global diffusion convolution residual error network |
| CN113096414A (en) * | 2021-03-10 | 2021-07-09 | 东南大学 | Intersection timing method, system and device based on traffic conflict analysis |
-
2021
- 2021-10-11 CN CN202111183472.1A patent/CN113920730B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102142197A (en) * | 2011-03-31 | 2011-08-03 | 汤一平 | Intelligent traffic signal lamp control device based on comprehensive computer vision |
| US20140210645A1 (en) * | 2013-01-30 | 2014-07-31 | Wavetronix Llc | Traffic flow through an intersection by reducing platoon interference |
| CN105761517A (en) * | 2016-05-18 | 2016-07-13 | 杭州智诚惠通科技有限公司 | Signal light timing method |
| CN108806283A (en) * | 2017-05-02 | 2018-11-13 | 中移物联网有限公司 | A kind of control method and car networking platform of traffic lights |
| CN108734354A (en) * | 2018-05-23 | 2018-11-02 | 吉林大学 | A kind of urban road signalized intersections multiple target timing designing method |
| CN110738863A (en) * | 2018-07-19 | 2020-01-31 | 北京嘀嘀无限科技发展有限公司 | Method and device for determining signal lamp period, timing change time and passing duration |
| CN109584551A (en) * | 2018-11-27 | 2019-04-05 | 南京航空航天大学 | A kind of design method that the related grouped intersections based on hierarchical clustering divide |
| CN110782667A (en) * | 2019-10-30 | 2020-02-11 | 北京百度网讯科技有限公司 | Signal lamp time-sharing timing method and device, electronic equipment and storage medium |
| US10643465B1 (en) * | 2019-11-18 | 2020-05-05 | Iteris, Inc. | Dynamic advanced traffic detection from assessment of dilemma zone activity for enhancement of intersection traffic flow and adjustment of timing of signal phase cycles |
| CN112071065A (en) * | 2020-09-16 | 2020-12-11 | 山东理工大学 | Traffic flow prediction method based on global diffusion convolution residual error network |
| CN113096414A (en) * | 2021-03-10 | 2021-07-09 | 东南大学 | Intersection timing method, system and device based on traffic conflict analysis |
Non-Patent Citations (2)
| Title |
|---|
| CHUANDONG DING ET AL.: "Study on Dynamic and Switched Control System of Arterial Traffic Signal Based on Multi-Agent", 《2018 THE INTERNATIONAL CONFERENCE OF INTELLIGENT ROBOTIC AND CONTROL ENGINEERING》 * |
| 刘红元等: "信号交叉口机非冲突临界流量研究", 《交通信息与安全》 * |
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