CN108376470A - Traffic road congestion prediction technique based on particle cluster algorithm - Google Patents
Traffic road congestion prediction technique based on particle cluster algorithm Download PDFInfo
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Abstract
The traffic road congestion prediction technique based on particle cluster algorithm that the invention discloses a kind of, including step：S1. it utilizes the sensor system of end side and the traffic surveillance and control system at intersection to acquire traffic data, is uploaded to Cloud Server；S2. cloud server traffic data is handled based on modified particle swarm optiziation, obtains prediction data；S3. the equipment that prediction data is transmitted to end side by Cloud Server；S4. the equipment of end side receives the data of Cloud Server passback, and saves it in local storage；S5. Cloud Server obtains the traffic data of terminal and traffic surveillance and control system acquisition in real time, using traffic data as new input variable, is based on modified particle swarm optiziation continuous learning, continues to optimize prediction data.The present invention has been obviously improved precision of prediction, can effectively adjust the magnitude of traffic flow, reduces traffic loading, reduces traffic delay and parking rate, improves the traffic capacity of road network, improve urban traffic conditions.
Description
Technical Field
The invention relates to the technical field of traffic, in particular to a traffic road congestion prediction method based on a particle swarm algorithm.
Background
With the development of the automobile industry, a series of problems such as traffic jam, environmental pollution, traffic accidents and the like are also created while automobiles bring various conveniences to people. The national traffic situation of China lies in the mixed running of motor vehicles and non-motor vehicles, the number of bicycles is large, the number of motor vehicles is large, the road network accommodation capacity is limited, and the like, so that the strong national strategy of traffic is implemented, and one problem to be solved is traffic jam. In the field of road traffic control, information systems for monitoring, collecting and processing traffic and the like have various problems which cannot be solved, and particularly, in a traffic control system at a road intersection, the problems of long period, vehicle delay, limited traffic capacity and the like exist.
The conventional traffic control mode takes historical traffic flow data as a regulation and control basis, time distribution is carried out in a manual mode by analyzing the change rule of traffic flow at different time, then a time distribution scheme is input into a traffic controller through a computer technology, and traffic regulation and control are carried out by calling different time distribution schemes in application.
For example, chinese patent application publication No. CN106991815A discloses a traffic congestion control method, which includes the following steps: s1, acquiring set parameters of a target road, including road traffic capacity Q, free flow speed vf and blocking density kj; s2, acquiring real-time traffic parameters of the target road, wherein the real-time traffic parameters comprise real-time traffic quantity q, real-time speed v and real-time traffic density k; and S3, constructing a traffic state judgment model, judging the influence degree of the traffic speed and the traffic flow of the current road on the traffic flow according to the model, making control measures on the traffic according to the influence degree, analyzing by combining the actual state of the traffic flow of the road and the traffic volume and the traffic speed in the traffic flow, making accurate traffic control measures, effectively relieving the traffic jam condition of the target road and effectively avoiding the waste of road resources. However, the method still has defects, such as complex algorithm design, incapability of realizing self-learning, poor real-time performance and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a traffic road congestion prediction method based on a particle swarm algorithm, can relieve the problem of traffic congestion, further improves the prediction precision, and improves the technology to enable the life of people to be more beautiful.
The purpose of the invention is realized by the following technical scheme: a traffic road congestion prediction method based on a particle swarm algorithm comprises the following steps:
s1, acquiring first traffic data by using a sensor system at a terminal side, uploading the first traffic data to a cloud server, and acquiring second traffic data by using a traffic monitoring system at a road intersection, and uploading the second traffic data to the cloud server;
s2, the cloud server receives the first traffic data and the second traffic data, and processes the first traffic data and the second traffic data based on an improved particle swarm algorithm to obtain first prediction data;
s3, the cloud server transmits the first prediction data to a device on the terminal side;
s4, the terminal side equipment receives the data returned by the cloud server and stores the data in the local storage device, and then the terminal side equipment detects whether historical road condition data are stored in the local storage device and extracts characteristic data from the historical road condition data;
and S5, the cloud server acquires the traffic data acquired by the sensor system at the terminal side in real time, acquires the second traffic data acquired by the traffic monitoring system at the road intersection in real time, takes the first traffic data and the second traffic data as new input variables, continuously learns based on the improved particle swarm algorithm, and continuously optimizes the first prediction data.
Further, in step S2, the following steps are included:
s21, taking the data collected in the step S1 as sample data, and carrying out initialization operation, wherein the initialization comprises the size of the population, the iteration times, the weight and the threshold;
s22, constructing a neural network structure and randomly generating a population wiUsing the population wiRepresents the initial value of the neural network and,
wi＝(wi1,wi2...,wis)T
wherein,
s＝pn+pm+p+m
n is the number of input neurons of the neural network, p is the number of hidden layer neurons of the neural network, and m is the number of output neurons of the neural network;
s23, formulating evaluation parameters, creating a neural network evolution parameter, recalculating the weight and threshold of the neural network by the newly obtained particles until reaching the convergence condition, and recalculating the fitness value fitiThe definition is that,
wherein, yi′To the actual output, yiFor the desired output, n represents the population size;
s24, calculating the position of each particle according to the sample data, and taking the best position of the particle as the optimal historical position;
s25, the position and the speed of the particle are redetermined in each iteration process, new fitness value of the particle is calculated, and then the individual extreme value is determined;
and S26, when the set convergence condition is reached, bringing the optimal solution of the weight and the threshold into the neural network for training until the optimal output prediction data is obtained.
Further, in step S4, the following steps are included:
s41, on the terminal side equipment, detecting whether the local storage device stores the historical road condition data of the current road, if so, extracting the historical road condition data of the current road and marking according to the time characteristics; if not, go directly to step S44;
s42, calculating the vehicle speed mean value of the historical road condition data on the current road in the first calculation; calculating the average value of the running time of the historical road condition data on the current road, and storing the calculated data in a local storage device;
s43, extracting the average value of the vehicle speed and/or the average value of the running time of the historical road condition data of the current road in different time periods according to the marking information in the step S41;
and S44, displaying the first prediction data stored in the local storage device on a display device of the equipment at the terminal side, and predicting the road traffic jam condition based on the analysis data of the cloud particle swarm algorithm and/or the historical road condition data result at the terminal side.
Further, the first traffic data includes any one of real-time travel speed, stay time, travel time, and mileage data.
Further, the second traffic data includes any one or more of a travel speed, a travel time, a stay time, a signal light period, a green light time, and a red light time at the signal light.
Further, the terminal-side apparatus includes a vehicle, and a device provided on the vehicle.
Further, the vehicle includes an electric automobile.
Further, the vehicle comprises an unmanned vehicle.
The invention has the beneficial effects that:
(1) the improved particle swarm optimization is adopted for learning and outputting the optimal prediction data to the equipment at the terminal side, and compared with the conventional neural network algorithm, the prediction precision is further improved.
(2) The invention relieves the problem of traffic jam based on the artificial intelligence technology, has the function of self-learning, improves the efficiency of a traffic information system to be influenced by the change of the traffic flow in a road network, can effectively adjust the traffic flow, reduces the traffic load, reduces the traffic delay and the parking rate, improves the traffic capacity of the road network and improves the traffic condition of the whole city.
(3) When the historical road condition data stored by the equipment at the terminal side is processed, the processing step of marking the action is adopted, so that the program running performance is improved when the data is called, the data processing efficiency is further improved, and the running performance of the equipment is enhanced.
(4) According to the invention, real-time traffic data acquired by equipment at the terminal side and traffic data acquired by a road traffic monitoring system are combined to serve as input variables of a neural network algorithm at the cloud side, an artificial intelligence algorithm module is deployed at the cloud server, and prediction data is issued to the equipment at the terminal side after the cloud server performs learning processing, so that a large amount of data uploaded by distributed equipment or systems can be processed in a centralized manner, the cost of analysis processing on the equipment or systems at the terminal side is reduced, the prediction efficiency of traffic road congestion is improved, and the traffic travel of urban commuters is effectively regulated and controlled.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the method steps of the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following. All of the features disclosed in this specification, or all of the steps of a method or process so disclosed, may be combined in any combination, except combinations where mutually exclusive features and/or steps are used.
Any feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Specific embodiments of the present invention will be described in detail below, and it should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: it is not necessary to employ these specific details to practice the present invention. In other instances, well-known circuits, software, or methods have not been described in detail so as not to obscure the present invention.
As shown in fig. 1, a method for predicting traffic road congestion based on a particle swarm algorithm includes the following steps:
s1, acquiring first traffic data by using a sensor system at a terminal side, uploading the first traffic data to a cloud server, and acquiring second traffic data by using a traffic monitoring system at a road intersection, and uploading the second traffic data to the cloud server;
s2, the cloud server receives the first traffic data and the second traffic data, and processes the first traffic data and the second traffic data based on an improved particle swarm algorithm to obtain first prediction data;
s3, the cloud server transmits the first prediction data to a device on the terminal side;
s4, the terminal side equipment receives the data returned by the cloud server and stores the data in the local storage device, and then the terminal side equipment detects whether historical road condition data are stored in the local storage device and extracts characteristic data from the historical road condition data;
and S5, the cloud server acquires the traffic data acquired by the sensor system at the terminal side in real time, acquires the second traffic data acquired by the traffic monitoring system at the road intersection in real time, takes the first traffic data and the second traffic data as new input variables, continuously learns based on the improved particle swarm algorithm, and continuously optimizes the first prediction data.
Optionally, in step S2, the method includes the following steps:
s21, taking the data collected in the step S1 as sample data, and carrying out initialization operation, wherein the initialization comprises the size of the population, the iteration times, the weight and the threshold;
s22, constructing a neural network structure and randomly generating a population wiUsing the population wiRepresents the initial value of the neural network and,
wi＝(wi1,wi2...,wis)T
wherein,
s＝pn+pm+p+m
n is the number of input neurons of the neural network, p is the number of hidden layer neurons of the neural network, and m is the number of output neurons of the neural network;
s23, establishing evaluation parameters, creating a neural network evolution parameter, and recalculating the weight and the threshold of the neural network by the newly obtained particles until reaching the convergence conditionWill adjust the fitness value fitiThe definition is that,
wherein, yi′To the actual output, yiFor the desired output, n represents the population size;
s24, calculating the position of each particle according to the sample data, and taking the best position of the particle as the optimal historical position;
s25, the position and the speed of the particle are redetermined in each iteration process, new fitness value of the particle is calculated, and then the individual extreme value is determined;
and S26, when the set convergence condition is reached, bringing the optimal solution of the weight and the threshold into the neural network for training until the optimal output prediction data is obtained.
Optionally, in step S4, the method includes the following steps:
s41, on the terminal side equipment, detecting whether the local storage device stores the historical road condition data of the current road, if so, extracting the historical road condition data of the current road and marking according to the time characteristics; if not, go directly to step S44;
s42, calculating the vehicle speed mean value of the historical road condition data on the current road in the first calculation; calculating the average value of the running time of the historical road condition data on the current road, and storing the calculated data in a local storage device;
s43, extracting the average value of the vehicle speed and/or the average value of the running time of the historical road condition data of the current road in different time periods according to the marking information in the step S41;
and S44, displaying the first prediction data stored in the local storage device on a display device of the equipment at the terminal side, and predicting the road traffic jam condition based on the analysis data of the cloud particle swarm algorithm and/or the historical road condition data result at the terminal side.
Optionally, the first traffic data comprises any one of real-time driving speed, dwell time, driving time, mileage data.
Optionally, the second traffic data comprises any one or more of a travel speed, a travel time, a dwell time, a signal light period, a green light time, a red light time at a signal light.
Alternatively, the terminal-side apparatus includes a vehicle, a device provided on the vehicle.
Optionally, the vehicle comprises an electric car.
Optionally, the vehicle comprises an unmanned vehicle.
A general particle swarm algorithm comprises the steps of:
step 1: initializing parameter conditions, and setting the number of particles and the initial position of each particle;
step 2: calculating a fitness function of each particle;
and step 3: for each particle, the calculated fitness function value is compared with the previous better position, and if the new fitness function is better, the new fitness function is used for replacing the original better position;
and 4, step 4: determining a termination condition, setting a threshold value when the algorithm stops, finishing the algorithm if the termination condition is met, and otherwise, continuously and iteratively calculating the position and the speed until the requirement of the termination condition is met.
The technical personnel in the field can set the neural network structure, the learning rate, the particle swarm algorithm scale and the like by themselves, for example, a three-layer neural network structure can be adopted, the learning rate can be set to 0.02, the particle swarm algorithm population scale can be set to 50, the iterative evolution algebra can be set to 150 times, and the established particle swarm algorithm is utilized to be applied to traffic data vehicle flow prediction and can realize prediction of traffic road congestion. However, it will be apparent to one of ordinary skill in the art that: it is not necessary to employ these specific details to practice the present invention. In other instances, well-known algorithms, methods or systems have not been described in detail so as not to obscure the present invention, and are within the scope of the present invention as defined by the claims.
For simplicity of explanation, the foregoing method embodiments are described as a series of acts or combinations, but those skilled in the art will appreciate that the present application is not limited by the order of acts, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and elements referred to are not necessarily required in this application.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The disclosed systems, modules, and methods may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be referred to as an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may also be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It will be understood by those skilled in the art that all or part of the processes in the methods for implementing the embodiments described above can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a ROM, a RAM, etc.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A traffic road congestion prediction method based on a particle swarm algorithm is characterized by comprising the following steps:
s1, acquiring first traffic data by using a sensor system at a terminal side, uploading the first traffic data to a cloud server, and acquiring second traffic data by using a traffic monitoring system at a road intersection, and uploading the second traffic data to the cloud server;
s2, the cloud server receives the first traffic data and the second traffic data, and processes the first traffic data and the second traffic data based on an improved particle swarm algorithm to obtain first prediction data;
s3, the cloud server transmits the first prediction data to a device on the terminal side;
s4, the terminal side equipment receives the data returned by the cloud server and stores the data in the local storage device, and then the terminal side equipment detects whether historical road condition data are stored in the local storage device and extracts characteristic data from the historical road condition data;
and S5, the cloud server acquires the traffic data acquired by the sensor system at the terminal side in real time, acquires the second traffic data acquired by the traffic monitoring system at the road intersection in real time, takes the first traffic data and the second traffic data as new input variables, continuously learns based on the improved particle swarm algorithm, and continuously optimizes the first prediction data.
2. The method for predicting the traffic road congestion based on the particle swarm optimization according to claim 1, wherein in step S2, the method comprises the following steps:
s21, taking the data collected in the step S1 as sample data, and carrying out initialization operation, wherein the initialization comprises the size of the population, the iteration times, the weight and the threshold;
s22, constructing a neural network structure and randomly generating a population wiUsing the population wiRepresents the initial value of the neural network and,
wi＝(wi1,wi2...,wis)T
wherein,
s＝pn+pm+p+m
n is the number of input neurons of the neural network, p is the number of hidden layer neurons of the neural network, and m is the number of output neurons of the neural network;
s23, formulating evaluation parameters, creating a neural network evolution parameter, recalculating the weight and threshold of the neural network by the newly obtained particles until reaching the convergence condition, and recalculating the fitness value fitiThe definition is that,
wherein, yi′To the actual output, yiFor the desired output, n represents the population size;
s24, calculating the position of each particle according to the sample data, and taking the best position of the particle as the optimal historical position;
s25, the position and the speed of the particle are redetermined in each iteration process, new fitness value of the particle is calculated, and then the individual extreme value is determined;
and S26, when the set convergence condition is reached, bringing the optimal solution of the weight and the threshold into the neural network for training until the optimal output prediction data is obtained.
3. The method for predicting the traffic road congestion based on the particle swarm optimization according to claim 1, wherein in step S4, the method comprises the following steps:
s41, on the terminal side equipment, detecting whether the local storage device stores the historical road condition data of the current road, if so, extracting the historical road condition data of the current road and marking according to the time characteristics; if not, go directly to step S44;
s42, calculating the vehicle speed mean value of the historical road condition data on the current road in the first calculation; calculating the average value of the running time of the historical road condition data on the current road, and storing the calculated data in a local storage device;
s43, extracting the average value of the vehicle speed and/or the average value of the running time of the historical road condition data of the current road in different time periods according to the marking information in the step S41;
and S44, displaying the first prediction data stored in the local storage device on a display device of the equipment at the terminal side, and predicting the road traffic jam condition based on the analysis data of the cloud particle swarm algorithm and/or the historical road condition data result at the terminal side.
4. The method for predicting the traffic road congestion based on the particle swarm optimization according to any one of claims 1 to 3, wherein the first traffic data comprises any one of real-time driving speed, residence time, driving time and mileage data.
5. The method for predicting traffic road congestion based on particle swarm optimization according to claim 4, wherein the second traffic data comprises any one or more of driving speed, driving time, staying time, signal light period, green light time and red light time at signal light.
6. The method for predicting the traffic road congestion based on the particle swarm optimization according to any one of claims 1 to 3, wherein the equipment at the terminal side comprises a vehicle and a device arranged on the vehicle.
7. The method for predicting the traffic road congestion based on the particle swarm optimization according to any one of claims 1 to 3, wherein the vehicle comprises an electric automobile.
8. The method for predicting the traffic road congestion based on the particle swarm optimization according to any one of claims 1 to 3, wherein the vehicles comprise unmanned vehicles.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10757485B2 (en) | 2017-08-25 | 2020-08-25 | Honda Motor Co., Ltd. | System and method for synchronized vehicle sensor data acquisition processing using vehicular communication |
| US11163317B2 (en) | 2018-07-31 | 2021-11-02 | Honda Motor Co., Ltd. | System and method for shared autonomy through cooperative sensing |
| US11181929B2 (en) | 2018-07-31 | 2021-11-23 | Honda Motor Co., Ltd. | System and method for shared autonomy through cooperative sensing |
| CN117373263A (en) * | 2023-12-08 | 2024-01-09 | 深圳市永达电子信息股份有限公司 | A traffic flow prediction method and device based on quantum pigeon swarm algorithm |
-
2018
- 2018-01-17 CN CN201810045762.1A patent/CN108376470A/en not_active Withdrawn
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10757485B2 (en) | 2017-08-25 | 2020-08-25 | Honda Motor Co., Ltd. | System and method for synchronized vehicle sensor data acquisition processing using vehicular communication |
| US11163317B2 (en) | 2018-07-31 | 2021-11-02 | Honda Motor Co., Ltd. | System and method for shared autonomy through cooperative sensing |
| US11181929B2 (en) | 2018-07-31 | 2021-11-23 | Honda Motor Co., Ltd. | System and method for shared autonomy through cooperative sensing |
| CN117373263A (en) * | 2023-12-08 | 2024-01-09 | 深圳市永达电子信息股份有限公司 | A traffic flow prediction method and device based on quantum pigeon swarm algorithm |
| CN117373263B (en) * | 2023-12-08 | 2024-03-08 | 深圳市永达电子信息股份有限公司 | A traffic flow prediction method and device based on quantum pigeon swarm algorithm |
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Application publication date: 20180807 |