CN113706860A - Intelligent timing traffic light control method based on raspberry group - Google Patents

Intelligent timing traffic light control method based on raspberry group Download PDF

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CN113706860A
CN113706860A CN202110803564.9A CN202110803564A CN113706860A CN 113706860 A CN113706860 A CN 113706860A CN 202110803564 A CN202110803564 A CN 202110803564A CN 113706860 A CN113706860 A CN 113706860A
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time
intersection
traffic
raspberry
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CN113706860B (en
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王兴举
胡宇范
董付春
庞新乐
张雪芳
李博
赵艳峰
王晓雪
杨兴雨
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Shijiazhuang Tiedao University
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q50/40—
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention relates to the field of intelligent traffic lights, in particular to a raspberry group-based intelligent timing traffic light control method, which comprises the steps of utilizing a plurality of high-definition cameras to collect road conditions and vehicle information in real time and transmitting the road conditions and the vehicle information to a raspberry group computer; the raspberry pi computer predicts the hourly traffic flow by using a differential autoregressive moving average model; predicting the number of vehicles in a set period, and forming two time sequences through stored historical data; the predicted traffic flow and the data of detection and analysis are subjected to the minimum delay time calculation as a target by adopting a transition delay model, and the signal lamps of the intersection are accurately matched by utilizing a genetic algorithm; the method and the system predict the traffic flow on the whole road in the next time period in a short term under the condition of ensuring that the signal lamp is accurately controlled, predict and adjust in real time, and finish intelligent timing of the signal lamp.

Description

Intelligent timing traffic light control method based on raspberry group
Technical Field
The invention relates to the technical field of intelligent traffic lights, in particular to a raspberry group-based intelligent timing traffic light control method.
Background
Traffic jam is a common disease of many cities, which causes inconvenience to people living in the cities, the traditional signal lamp has unreasonable time control, faults and failures frequently occur, which causes traffic accidents, working hours loss, noise pollution, air pollution, fuel oil increase, illegal traffic rules and other problems, a plurality of traffic polices are often required to conduct on-site command in rush hours and off duty, which increases the cost of manpower and material resources, but the accidents frequently occur, so as to avoid the above situations,
for example, chinese patent application No. 201811079047.6 discloses an intelligent management system for urban traffic based on raspberry pi, comprising: the design scheme uses an artificial intelligent algorithm to control vehicle traffic by adopting a density-based method, the timing of traffic signal lamps can be automatically changed in the intersection perception traffic density state, the proposed system uses the raspberry group to combine with the high-definition camera to build a neural network analysis prediction, the time of the traffic signal lamps at road junction points is automatically changed to adapt to the movement and the sparse and dense state of vehicles, so that the unnecessary waiting time at the road junction is avoided, the scheme cannot predict the traffic flow of the whole course at the next time period, the prediction time is adjusted, and the intelligent timing of traffic lamps is completed.
Disclosure of Invention
Aiming at the defects, the intelligent timing traffic signal lamp is adopted, and under the condition that the signal lamp is accurately controlled to be timed, the signal lamp can be regulated and controlled in real time according to road conditions and vehicle conditions, namely the intelligent timing traffic signal lamp predicts the traffic flow on the whole road in the next time period in a short time period, analyzes and adjusts the effect, the vehicle passing time on the existing road is timed, and the time is predicted and adjusted in real time, so that the intelligent timing of the traffic signal lamp is completed.
In order to achieve the purpose, the invention provides the following technical scheme:
an intelligent timing traffic light control method based on raspberry dispatching comprises the following steps:
step 1, collecting road conditions and vehicle information in real time by using a plurality of high-definition cameras, compressing the collected information into an h264 format, and transmitting the compressed information to a raspberry host computer;
step 2, predicting the hourly traffic flow by utilizing a differential autoregressive moving average model in a raspberry dispatching computer system;
step 3, predicting the number of vehicles in a set period, forming two time sequences through stored historical data, wherein the two time sequences respectively adopt the model in the step 2, realizing the stable operation of the data by using a difference method, then carrying out order fixing on model parameters (p, d, q), determining an optimal model by using a BIC (building information center) criterion, then carrying out standardized residual error inspection on the model, determining the reliability of the model, predicting the traffic flow in the next time period, comparing the standardized residual errors generated by the two time sequences, and selecting the minimum result as the final result of traffic flow prediction;
and 4, accurately timing signal lamps of the intersection by using a genetic algorithm by using the data of the traffic flow and the video detection analysis predicted in the step 3 and aiming at obtaining the minimum delay time by using a transition delay model.
The technical scheme of the invention is further improved as follows: in the step 1, OpenCV is dispatched through raspberries to operate information, vehicles in the information are captured, vehicles of continuous frames in the information are tracked, and finally the tracked vehicles are counted and classified.
The technical scheme of the invention is further improved as follows: in step 3, one of the two time sequences is a time sequence formed according to the first 10 periods of the current required prediction period, and the other is a time sequence formed by acquiring ten pieces of traffic flow data of the same time node on different dates in the database according to the current time node.
Compared with the prior art, the intelligent timing traffic light control method based on the raspberry group has the following beneficial effects:
1. the invention provides an intelligent timing traffic light control method based on raspberry assignment, which regulates and controls a traffic light according to road conditions and vehicle conditions under the condition of ensuring accurate timing of the traffic light, predicts the traffic flow on the whole road in the next time period in a short time, predicts and adjusts the time in real time, and completes intelligent timing of the traffic light.
Detailed Description
The technical solution of the present invention will be clearly and completely described by the following detailed description. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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 invention.
The intelligent timing traffic light control method based on the raspberry group comprises the following steps:
step 1, collecting road conditions and vehicle information in real time by using a plurality of high-definition cameras, compressing the collected information into an h264 format, and transmitting the compressed information to a raspberry host computer;
in the embodiment, a high-definition camera is adopted to collect the road condition video in real time, and the video is compressed into an h264 format and transmitted to a raspberry host computer; performing background deduction operation on the video stream through a raspberry serving OpenCV, capturing vehicles in the video stream, tracking the vehicles of continuous frames in the video, and finally counting and classifying the tracked vehicles;
the method comprises the following specific steps:
1. the high-definition camera module is connected with the raspberry group through the CSI interface to carry out video transmission;
2. all the latest functions and drivers are installed inside the raspberry pi computer using commands to update the operating system, enabling correct access to the internet: the command of $ sudo apt-get update # updates the operating system with the latest functions and drivers, $ sudo raspi-config raspi-config # opens the configuration tool written and maintained by the raspbian operating system;
3.$ raspivid-o video. h264-t 1000 to capture video coded in h264 for t seconds;
4. in order to detect the vehicle, a background subtraction method is adopted to subtract a road image without the vehicle from another image with the vehicle on the road, wherein the background subtraction is a process of extracting a target image from an original image; the formula is as follows:
Target(x,y)＝(1-alpha)﹒target(x,y)+alpha﹒origin(x,y)
wherein origin (x, y) is an 8-bit or 32-bit floating point number of an original image which is a color or gray image; target (x, y) is a 32-bit or 64-bit floating point of the image; alpha is the weight of the input image; the update rate is determined by alpha, which sets a lower value for this variable in the existing frame;
5. vehicle tracking, wherein the main target of tracking is to determine a target object in continuous frames of a video, match an input centroid with the centroid of an existing object for tracking a current object, and calculate the distance between each pair of objects, such as the Euclidean distance;
6. the method comprises the steps of vehicle classification counting, wherein tracked vehicles are counted when leaving an intersection or crossing an intersection stop line, in order to count the vehicles in each phase, a vehicle counting area is drawn from the stop line of each lane to the tail part of a video shot by using a counting line, each intersection is divided into 4 phases, and the phases are respectively south-north straight driving plus right turning, south-north left turning, east-west straight driving plus right turning and east-west left turning; the counted vehicles are classified based on the circumference, and if the circumference of the bounding box is less than 300, the counted vehicles are counted as bicycles; if the perimeter of the bounding box is less than 500, then it is counted as a car, and if the perimeter of the bounding box is greater than 500, then it is counted as a truck/bus;
7. the obtained vehicle data including vehicle types, quantity, vehicle distances and the like are stored in an Aliskiu MYSQL by editing tables with four phases separated and each signal period as a time unit, and then the vehicle types are subjected to unified standard conversion (4-5 seats of cars are used as standard cars), wherein the conversion coefficients are as follows:
type of vehicle Conversion factor
Bicycle with a wheel 0.4
Car (R.C.) 1.0
Truck/bus 1.5
Step 2, predicting the hourly traffic flow by utilizing a differential autoregressive moving average model in a raspberry dispatching computer system;
in the embodiment, a differential auto-regression mobile model (ARIMA) is adopted;
1. autoregressive model (AR): describing the relationship between the current value and the historical value, and predicting the variable by using the historical time data of the variable; the formula definition of the p-order autoregressive process:
Figure BDA0003163294350000041
2. moving average Model (MA): the moving average model focuses on the accumulation of error terms in the autoregressive model; the formula definition of the q-order autoregressive process:
Figure BDA0003163294350000051
3. differential autoregressive moving average model (ARIMA): AR is autoregressive, p is an autoregressive term, MA is a moving average, q is the number of terms of the moving average, and d is the number of differences made when the time series becomes stationary;
Figure BDA0003163294350000052
in the formula: y istIs the current predicted value, mu is a constant term, p, q are orders, gammaiAnd thetaiIs the autocorrelation coefficient, yt-iFor the first few data of the sequence, etIs an error;
4. the model construction steps are as follows:
4.1. data pre-processing
The original traffic flow data sequence is { B (t) }, which is generally non-stationary, and it is differenced, i.e., ordered
yn＝B(t-n)B(t),
Definition of
Figure BDA0003163294350000053
Figure BDA0003163294350000054
Figure BDA0003163294350000055
Wherein the content of the first and second substances,
Figure BDA0003163294350000056
is a combination coefficient; d-order difference of { B (t) }
Figure BDA0003163294350000057
For ARMA (p, q) sequence, we call { B (t) } a differential autoregressive moving average model of (p, d, q) order, denoted ARIMA (p, d, q);
4.2. stability determination of differential sequences
Sequencing the time series according to the time period by adopting a Daniel trend test method to obtain Y1,Y2,…,YN(serial numbers arranged by year), sorting the time values from small to large to obtain corresponding serial numbers X1,X2,…,XNThe statistical test applies a rank correlation coefficient of:
Figure BDA0003163294350000058
in the formula: di＝Xi-Yi(ii) a And N is the data volume.
The p and q orders are determined by the autocorrelation function (ACF) and the partial autocorrelation function (PACF):
4.3.1. autocorrelation function ACF:
the autocorrelation function of the ordered random variable sequence compared with itself reflects the correlation formula of the values of the same sequence in different sequences:
Figure BDA0003163294350000061
variation of a variable from itself, ytAnd yt-1To ytAnd yt-kCorrelation coefficient of (1), hysteresis point of order k, [ rho ]KIs in the range of [ -1,1 [ ]]；
4.3.2. Partial autocorrelation function (PACF):
for a stationary ar (p) model, when the lag k autocorrelation coefficient p (k) is found, it is not actually a pure correlation between x (t) and x (t-k). x (t) is simultaneously influenced by intermediate k-1 random variables x (t-1), x (t-2) and … x (t-k +1), and the k-1 random variables have a correlation relation with x (t-k), so that the influence of other variables on x (t) and x (t-k) is actually doped in the autocorrelation coefficient p (k), and the correlation degree of the influence of x (t) and x (t-k) is obtained after the interference of the intermediate k-1 random variables x (t-1), x (t-2) and … x (t-k +1) is eliminated;
4.3.3 validation methods, table below:
Figure BDA0003163294350000062
tail cutting: within the confidence interval, AR (p) sees PACE, MA (q) sees ACF;
4.4. selecting an optimal model: the subjectivity of rank determination according to the autocorrelation function and the partial autocorrelation function is compensated through a BIC criterion, a relatively optimal fitting model is searched in a limited rank range, a Bayesian information criterion is a common criterion for model selection, a model with the minimum BIC value is generally selected,
BIC is defined as: BIC (T) log (n) T-2log (L)
In the formula: t is the number of parameters in the model, and L is the maximum likelihood function of the model;
in the case of ARIMA model, from the values of P and q obtained above, P ∈ [0, P ] is obtained],Q∈[0,q]D, M can be matchedn,n is 1, …, and P is Q model, and the parameter of each candidate model is assumed to be thetanParameter θnIs Pr (theta)n|Mn) Then give data
Figure BDA0003163294350000071
Then, Mn,The posterior probability of (a) is:
Pr(Mn|B)∝Pr(Mn)Pr(B|Mn)
∝Pr(Mn)*∫Pr(B|θnMn)Pr(θn|Mn)dθn
two different models MnAnd MlWe compare their posterior probabilities:
Figure BDA0003163294350000072
if the above ratio is greater than 1, we select model MnIn the above formula
Figure BDA0003163294350000073
Referred to as bayesian factors, which measure the contribution of the training set to the posterior probability,
in general, we assume that the prior distributions for the different models are the same, which is obtained based on the so-called Laplace approximation:
Figure BDA0003163294350000074
in the formula:
Figure BDA0003163294350000075
representing maximum likelihood estimates of parameters, dnDenotes thetanThe dimension(s) of (a) is,
the BIC can be obtained by multiplying (-2), and the smaller the BIC value is, the more preferable the model is;
4.5. model normalized residual test
The residuals of the model are examined for a normal distribution with a mean of 0 and a constant variance. The residual means an observed value and a predicted value (difference between fitted values), i.e., observed value BtAnd the value predicted from the model
Figure BDA0003163294350000076
The difference, denoted by e, for the checking of the normality of epsilon can be done by analysis of the normalized residual, which is the value obtained by dividing the residual by its standard deviation, also called Pearson residual, with ReThus, the normalized residual for the ith observation is:
Figure BDA0003163294350000081
(Seis an estimate of the standard deviation of the residual error)
If the distribution of the normalized residual errors follows normal distribution, the error term epsilon follows normal distribution, and about 95% of the normalized residual errors fall between (-2, 2);
step 3, predicting the number of vehicles in a set period, forming two time sequences through stored historical data, wherein the two time sequences respectively adopt the model in the step 2, realizing the stable operation of the data by using a difference method, then carrying out order fixing on model parameters (p, d, q), determining an optimal model by using a BIC (building information center) criterion, then carrying out standardized residual error inspection on the model, determining the reliability of the model, predicting the traffic flow in the next time period, comparing the standardized residual errors generated by the two time sequences, and selecting the minimum result as the final result of traffic flow prediction;
in the embodiment, the number of vehicles in a certain period needs to be predicted, and two time sequences are formed through stored historical data, wherein one time sequence is formed according to the previous 10 periods of the current period needing to be predicted, and the other time sequence is formed by acquiring ten pieces of traffic flow data of the same time node in the database on different dates according to the current time node. And the two time sequences respectively utilize the models, utilize a difference method to realize the stable operation of data, then carry out order fixing on model parameters (p, d, q), determine an optimal model by utilizing a BIC criterion, then carry out standardized residual error test on the model, determine the reliability of the model, and then predict the traffic flow in the hour period in the future. Comparing the standardized residuals generated by the two time sequences, and selecting the minimum result as the final result of traffic flow prediction;
step 4, adopting the data of the traffic flow and the video detection analysis predicted in the step 3, and accurately timing signal lamps of the intersection by utilizing a genetic algorithm by taking the minimum delay time obtained by using a transition delay model as a target; the method comprises the following steps:
1. transition delay model:
1.1. the parameters used by the model are as follows: c: a signal period duration; gg: green light time; gr: red light time;
λ: the entrance lane direction split ratio is calculated according to the formula: λ ═ Gg/C；qa: the actual traffic arrival flow at the entrance lane; q: road traffic capacity; s: road saturation flow; y: the inlet passage traffic flow ratio is calculated according to the formula: q isa/C；Xs: and (3) the saturation in the direction of the entrance, calculating the formula:
Figure BDA0003163294350000091
Tw: observing the set time; du: average delay time of the vehicle; dr: vehicle random arrival delay time; n is a radical ofa: average number of vehicles left;
XS0: an intersection saturation critical value;
1.2. transition delay model:
the average delay time of the vehicle is calculated according to the following formula:
Figure BDA0003163294350000092
the vehicle random arrival delay time is calculated according to the following formula:
Figure BDA0003163294350000093
the average number of retained vehicles and the intersection saturation critical value are calculated according to the following formula:
Figure BDA0003163294350000094
Figure BDA0003163294350000095
1.3. optimizing the transition model: according to the evaluation indexes of the road condition of the intersection, in order to alleviate the traffic pressure and obtain the optimal signal timing, on one hand, the traffic capacity of the road needs to be improved, and on the other hand, the average delay time needs to be reduced, so that the following objective function is obtained:
Figure BDA0003163294350000101
for the multi-objective problem, generally, the objective is converted into a general objective for optimization, and because the units of two parameter indexes are different, the two parameter indexes are subjected to non-dimensionalization processing to obtain a multi-objective joint optimization function:
Figure BDA0003163294350000102
in the formula: d0The initial total average delay time of the vehicle at the intersection; q0The initial traffic capacity of the intersection; beta is the weight of the average delay time of the intersection;
1.4. and (3) modifying the optimization model: in order to reduce the stopping time of vehicles at each intersection, a group of adjacent signalized intersections on a trunk road are connected for coordination control, and a trunk intersection coordination control system, called drive-by-wire for short, is generated. Based on the thought, the multi-target joint optimization function is corrected, and the purpose of line control is achieved. Introducing a variable parameter gamma to correct the original average delay time so as to achieve the purpose of emphasizing the importance of the trunk line, wherein the corrected average delay time model has the following calculation formula:
Figure BDA0003163294350000103
in the formula: n is the number of elements of the set N capable of evacuating the phases of the vehicles on the main road, gammaiAs a set, the correction coefficients for phase i in N;
modified objective function:
Figure BDA0003163294350000104
1.5. model constraints comprising the steps of:
1.5.1. period of signal
If the signal period is too short, the vehicle is caused to start and stop frequently at the intersection, and if the period is too long, the vehicle waiting time is caused to be too long, the delay time of the vehicle is increased, so that the constraint is that:
Cmin≤C≤Cmax
1.5.2. split constraint
The higher the green ratio of a certain signal phase, the longer the green time of the phase, the shorter the green time of other phases, and the upper and lower limits are set so as to coordinate the smooth passing of vehicles at each phase through the intersection, and the upper limit cannot be larger than 1:
λmin≤λj≤λmax<1,λj＝Gg,j/Cj
in the formula: gg,jGreen time for jth, phase;
1.6. in summary, the calculation formula of the modified transition delay model is as follows:
Figure BDA0003163294350000111
Figure BDA0003163294350000112
1.7. the correction involves a phase correction parameter γ, calculated by the formula γ ═ e μ +1, where μ is the ratio;
1.8. solving the weight beta to be 0.5 by using a genetic algorithm to obtain a timing optimization scheme;
2. solving by using a genetic algorithm:
2.1. and randomly generating chromosomes with a preset population number by adopting a binary coding method according to the number of the vehicles entering and leaving in the next two adjacent intersections A and B in each direction predicted in the first stage. Setting a period value, a population number, a chromosome length and an iteration total algebra;
2.2. setting a fitness function, setting an objective function as J, representing the average delay time of the intersection and needing to obtain the minimum value, wherein the fitness function is represented by Z, and the expression is Z-Omax-J, wherein OmaxIs the maximum estimated value;
2.3. calculating and screening adaptive function, and solving the fitness Z contacting the current populationtAnd t represents the current generationAnd (4) counting. To ZtThe data in (1) is processed,
Figure BDA0003163294350000113
wherein
Figure BDA0003163294350000114
Is the maximum value of fitness in the t generation population;
2.4. according to a proportional selection method, for the processed ZtProcessing corresponding chromosome, selecting chromosome group as new population, and screening out nth chromosome with probability of
Figure BDA0003163294350000121
2.5. Performing cross operation on the candidate solution according to a random extraction number in the candidate solution group at a preset cross rate;
2.6. extracting candidate solutions according to a predetermined hybridization and mutation rate to carry out crossover and mutation operations;
2.7. calculating the objective function value of each candidate solution, and eliminating the worst objective function value in the candidate solutions according to the selected elimination rate, wherein the lacking position is generated from the better solution of the objective function;
2.8. judging whether the preset iteration times are reached, if so, continuing the next step, and otherwise, turning to the step 2.5 to repeat the step for calculation;
2.9. and calculating the optimal timing of each phase.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the appended claims.

Claims (6)

1. A raspberry group-based intelligent timing traffic light control method is characterized in that: the method comprises the following steps:
step 1, collecting road conditions and vehicle information in real time by using a plurality of high-definition cameras, compressing the collected information into an h264 format, and transmitting the compressed information to a raspberry host computer;
step 2, predicting the hourly traffic flow by utilizing a differential autoregressive moving average model in a raspberry dispatching computer system;
step 3, predicting the number of vehicles in a set period, forming two time sequences through stored historical data, wherein the two time sequences respectively adopt the model in the step 2, realizing the stable operation of the data by using a difference method, then carrying out order fixing on model parameters (p, d, q), determining an optimal model by using a BIC (building information center) criterion, then carrying out standardized residual error inspection on the model, determining the reliability of the model, predicting the traffic flow in the next time period, comparing the standardized residual errors generated by the two time sequences, and selecting the minimum result as the final result of traffic flow prediction;
and 4, accurately timing signal lamps of the intersection by using a genetic algorithm by using the data of the traffic flow and the video detection analysis predicted in the step 3 and aiming at obtaining the minimum delay time by using a transition delay model.
2. The raspberry-based intelligent timing traffic light control method according to claim 1, characterized in that: in the step 1, OpenCV is dispatched through raspberries to operate information, vehicles in the information are captured, vehicles of continuous frames in the information are tracked, and finally the tracked vehicles are counted and classified.
3. The raspberry-based intelligent timing traffic light control method according to claim 1, characterized in that: in step 2, the differential autoregressive moving average model is calculated and constructed by adopting an autoregressive model (AR), a moving average Model (MA) and a differential autoregressive moving average model (ARIMA).
4. The raspberry-based intelligent timing traffic light control method according to claim 1, characterized in that: in step 3, one of the two time sequences is a time sequence formed according to the first 10 periods of the current required prediction period, and the other is a time sequence formed by acquiring ten pieces of traffic flow data of the same time node on different dates in the database according to the current time node.
5. The raspberry-based intelligent timing traffic light control method according to claim 1, characterized in that: in step 4, the transition delay model includes the following steps:
5.1. the parameters used by the model are as follows: c: a signal period duration; gg: green light time; gr: red light time;
λ: the entrance lane direction split ratio is calculated according to the formula: λ ═ Gg/C；qa: the actual traffic arrival flow at the entrance lane; q: road traffic capacity; s: road saturation flow; y: the inlet passage traffic flow ratio is calculated according to the formula: q isa/C；Xs: and (3) the saturation in the direction of the entrance, calculating the formula:
Figure FDA0003163294340000021
Tw: observing the set time; du: average delay time of the vehicle; dr: vehicle random arrival delay time; n is a radical ofa: average number of vehicles left;
XS0: an intersection saturation critical value;
5.2. transition delay model:
the average delay time of the vehicle is calculated according to the following formula:
Figure FDA0003163294340000022
the vehicle random arrival delay time is calculated according to the following formula:
Figure FDA0003163294340000023
the average number of retained vehicles and the intersection saturation critical value are calculated according to the following formula:
Figure FDA0003163294340000024
Figure FDA0003163294340000025
5.3. optimizing the transition model: according to the evaluation indexes of the road condition of the intersection, in order to alleviate the traffic pressure and obtain the optimal signal timing, on one hand, the traffic capacity of the road needs to be improved, and on the other hand, the average delay time needs to be reduced, so that the following objective function is obtained:
Figure FDA0003163294340000031
for the multi-objective problem, generally, the objective is converted into a general objective for optimization, and because the units of two parameter indexes are different, the two parameter indexes are subjected to non-dimensionalization processing to obtain a multi-objective joint optimization function:
Figure FDA0003163294340000032
in the formula: d0The initial total average delay time of the vehicle at the intersection; q0The initial traffic capacity of the intersection; beta is the weight of the average delay time of the intersection;
5.4. and (3) modifying the optimization model: in order to reduce the stopping time of a vehicle at each intersection, a group of adjacent signalized intersections on a trunk road are connected for coordination control to generate a trunk intersection coordination control system, called drive-by-wire for short, based on the thought, a multi-target joint optimization function is corrected to achieve the purpose of drive-by-wire, a variable parameter gamma is introduced to correct the original average delay time to achieve the purpose of emphasizing the importance of the trunk road, and the corrected average delay time model has the following calculation formula:
Figure FDA0003163294340000033
in the formula: n is the number of elements of the set N capable of evacuating the phases of the vehicles on the main road, gammaiAs a set, the correction coefficients for phase i in N;
modified objective function:
Figure FDA0003163294340000034
5.5. model constraints comprising the steps of:
5.5.1. period of signal
If the signal period is too short, the vehicle is caused to start and stop frequently at the intersection, and if the period is too long, the vehicle waiting time is caused to be too long, the delay time of the vehicle is increased, so that the constraint is that:
Cmin≤C≤Cmax
5.5.2. split constraint
The higher the green ratio of a certain signal phase, the longer the green time of the phase, the shorter the green time of other phases, and the upper and lower limits are set so as to coordinate the smooth passing of vehicles at each phase through the intersection, and the upper limit cannot be larger than 1:
λmin≤λj≤λmax＜1，λj＝Gg，j/Cj
in the formula: gg，jGreen time for the j-th phase;
5.6. in summary, the calculation formula of the modified transition delay model is as follows:
Figure FDA0003163294340000041
Figure FDA0003163294340000042
5.7. the correction involves a phase correction parameter γ, calculated by the formula γ ═ e μ +1, where μ is the ratio;
5.8. and (5) solving by using a genetic algorithm by making the weight beta equal to 0.5 to obtain a timing optimization scheme.
6. The raspberry-based intelligent timing traffic light control method according to claim 1, characterized in that: in step 4, the genetic algorithm comprises the following steps:
6.1. according to the number of vehicles entering and exiting from the adjacent two intersections A and B in the next period in all directions, which is obtained by prediction in the first stage, a binary coding method is adopted, chromosomes of a preset population number are randomly generated, and a period value, the population number, the length of the chromosomes and an iteration total algebra are set;
6.2. setting a fitness function, setting an objective function as J, representing the average delay time of the intersection and needing to obtain the minimum value, wherein the fitness function is represented by Z, and the expression is Z-Omax-J, wherein OmaxIs the maximum estimated value;
6.3. calculating and screening adaptive function, and solving the fitness Z contacting the current populationtT represents the current population generation, for ZtThe data in (1) is processed,
Figure FDA0003163294340000043
wherein
Figure FDA0003163294340000044
Is the maximum value of fitness in the t generation population;
6.4. according to a proportional selection method, for the processed ZRProcessing corresponding chromosome, selecting chromosome group as new population, and screening out nth chromosome with probability of
Figure FDA0003163294340000051
6.5. Performing cross operation on the candidate solution according to a random extraction number in the candidate solution group at a preset cross rate;
6.6. extracting candidate solutions according to a predetermined hybridization and mutation rate to carry out crossover and mutation operations;
6.7. calculating the objective function value of each candidate solution, and eliminating the worst objective function value in the candidate solutions according to the selected elimination rate, wherein the lacking position is generated from the better solution of the objective function;
6.8. judging whether the preset iteration times are reached, if so, continuing the next step, and if not, switching to the 6.5 th step to repeat the calculation of the step;
6.9. and calculating the optimal timing of each phase.
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