CN111951549B - Self-adaptive traffic signal lamp control method and system in networked vehicle environment - Google Patents
Self-adaptive traffic signal lamp control method and system in networked vehicle environment Download PDFInfo
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Abstract
The invention discloses a self-adaptive traffic signal lamp control method and a system under an internet vehicle environment, wherein the method comprises the following steps: acquiring data information of networked vehicles in a certain range at a road intersection; acquiring data information of a traffic signal controller at a road intersection; calculating the weighted average control delay time of the whole intersection according to the acquired data information, and judging the traffic state; if the current is free flow or stable flow, operating the signal time sequence of the traffic light in the previous period; if the current is close to the saturated current or the saturated current, self-adaptive signal control is carried out, and the green light time length and the optimal signal lamp period time length of each intersection in different directions are calculated according to the vehicle arrival rate and the vehicle queue length; and if the forced flow is adopted, carrying out timing cycle control on the traffic volume of the key lane. The invention avoids the vehicle jam phenomenon caused by fixed traffic signal lamp time when the traffic flow is in a peak, and also reduces unnecessary delay caused by small traffic flow.
Description
Technical Field
The invention belongs to the technical field of road traffic, and particularly relates to a self-adaptive traffic signal lamp control method and system in an internet vehicle environment.
Background
With the increasing automobile holding amount, the urban traffic problem becomes more and more prominent. How to effectively and adaptively control a traffic signal lamp according to the actual traffic flow, improve the traffic efficiency and reduce delay is one of the problems which are urgently needed to be solved at present.
At present, the control mode of traffic signals at urban intersections is divided into two modes of timing control and induction control. Conventional timed traffic control is well suited to dense intersections where daily traffic volume and pattern remain consistent, but cannot accommodate unplanned fluctuations in traffic flow. When the traffic volume reaches the intersection as a random variable, the vehicle passing through the intersection is often inefficient. The induction control is divided into a half induction control and a full induction control: when the semi-induction intersection signal control system sets a preset value, the speed of a main road is less than 60km/h, the traffic demand of a secondary main road crossed with the main road is low, and a detector is placed along the secondary main road. The full-induction intersection signal lamp control is characterized in that detectors are installed on crossing roads, the detector is commonly used for intersections with two main streets, and the detector is suitable for the condition that traffic flow of the two main roads fluctuates greatly in one day. Although the response system can adjust the time according to the current traffic condition, the response system can only respond through a preset loop program, and a preset program which is closest to the actual situation is found to be matched. If the traffic signal lamp control system dynamically controls the signal lamp according to the traffic flow, corresponding detection equipment is required to be installed.
The adaptive traffic light control system creates a completely new time sequence in real time that adjusts the traffic light duration and phase based on traffic volume. From the development process of the existing traffic adaptive control system, the method is divided into five stages: the first generation uses TRANSYT and MAXBAND as the representative offline multi-time-period timing control, the second generation uses SCATS of Australia and SCOOT system of UK as the representative centralized adaptive control system, the third generation uses OPAC, RHODES, TASS and BALANCE as the representative distributed adaptive control system, similar to the second generation control idea, the fourth generation is a comprehensive traffic management and control system, which can realize the comprehensive management of network flow, and the fifth generation uses INSYNC and AFT as the representative self-learning adaptive control model, based on experience information and real-time traffic condition, the calculation burden of decision optimization is reduced. At present, SCAT and SCOOT are recognized to be the best urban traffic control system at home and abroad. Because of the mixed traffic mode in China, the random fluctuation of traffic volume is large, the types of vehicles are many and other factors, the mathematical model established by historical data is inaccurate, and a relatively ideal control state cannot be realized.
The traditional timing traffic signal lamp has obvious defects, the real-time performance of the traffic signal lamp controlled by induction is poor, and the condition that the traffic flow has large fluctuation cannot be met. The intelligent control signal lamps installed at a few intersections in individual developed cities are complex in technical realization, cannot be suitable for current equipment, are high in cost and are unrealistic to popularize on a large scale at the present stage.
The existing adaptive traffic control theory, method and technology with fixed period have certain defects, and mainly have the following aspects:
(1) the existing static traffic prediction and time sequence scheme model has no learning capability. Therefore, only when the network traffic pattern changes significantly will the relevant department recalibrate the model parameters.
(2) With the continuous expansion of the traffic network scale, it is difficult to ensure the data transmission quality by adopting the large-scale regional road network with centralized control.
(3) Regional networks lack timely response to actual traffic fluctuations and are difficult to implement in real-time control.
(4) Most of the existing traffic control methods simplify control constraint conditions and establish accurate mathematical models, but the methods are different from actual traffic flow conditions and have poor control effects.
Current traffic control systems are limited in the traffic data that they acquire using induction coil detectors and other sensors. The development of information technologies such as computer science, autonomous driving, car networking technology, mobile internet and the like creates abundant means for collecting traffic data. The networked vehicles do not need infrastructure support, the network deployment is fast, and the expansion is convenient. In recent years, urban vehicles are increasing day by day, mobile networks break through increasingly, and wireless network communication technology is used in a certain area to connect vehicles and fixed infrastructures together, so that a multi-hop communication network between vehicles can be dynamically and quickly constructed on the existing road, and has the characteristics of self-organization and distributed control. Therefore, the internet vehicle has a good application prospect in the aspect of traffic. The conventional detector cannot obtain track information of the vehicle, such as speed, position, acceleration, headway and the like, the information can be easily obtained in an internet of vehicles sensing environment, the vehicle and the vehicle are networked and then transmit data to the cloud server, and the control system is based on real-time monitoring data instead of traffic prediction data and realizes automatic adjustment of a control strategy through data driving and feedback.
Disclosure of Invention
The invention aims to solve the technical problem that the intersection signal lamp control effect is poor and the defect of intersection congestion cannot be well solved in the prior art, and provides a self-adaptive traffic signal lamp control method under the internet vehicle environment, which can effectively improve the congestion condition.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the method for controlling the self-adaptive traffic signal lamp in the environment of the networked vehicles comprises the following steps:
s1, acquiring data information of networked vehicles in a certain range at a road intersection, including vehicle positions, vehicle speeds and time stamps;
s2, acquiring data information of the traffic signal controller at the intersection;
s3, calculating the weighted average control delay time of the whole intersection according to the acquired data information, and judging the traffic state including free flow, stable flow, near saturated flow, saturated flow and forced flow;
s4, if the current is free current or stable current, the signal sequence of the traffic light of the previous period is operated; if the current is close to the saturated current or the saturated current, self-adaptive signal control is carried out, and the green light time length and the optimal signal lamp period time length of each intersection in different directions are calculated according to the vehicle arrival rate and the vehicle queue length; and if the forced flow is adopted, carrying out timing cycle control on the traffic volume of the key lane.
In connection with the above technical solution, the adaptive signal control method specifically includes:
calculating the effective green light phase duration and the phase loss duration of each phase, specifically: and setting the basic phase number of the intersection signal as p, wherein p is 1,2, …, k, the phase group number is j, each phase group is taken as a stage, the stage number is i, and the phase group number corresponding to the stage i is ipThe state variable is siState variable siRepresenting the time from the start of the optimization to the end of the ith phase, decision variable xiRepresents the phase group duration assigned for the ith phase, at which time: si＝si-1+xi(ii) a For the phase group not in the current stage, the basic phase is in a red light state, and the outflow rate is 0 at the moment; djIs the delay equation of the j-th phase group, gepIs valid for the p-th phaseGreen lamp phase duration, R is phase loss duration, GminAnd GmaxIs the minimum and maximum green time duration for the p-th phase; t represents the time after the optimization begins, the queue length of the stage i is l, the outflow rate of the vehicles is Q (t), the vehicle queue length l (t) of the t time step is equal to the sum of the difference between the arriving vehicle and the departing vehicle and the vehicle queue length l (t-1) of the previous time step t-1;
assuming a saturation flow rate of sij
An objective function:
constraint conditions are as follows:
∑(gep+R)＝xi
Gmin＜gep＜Gmax；
and calculating the optimal period duration of the signal lamp according to the effective green lamp phase duration and the phase loss duration of each phase.
After one stage is finished, the recursive equation f is usedi(si)＝min{Z+fi-1(si-1) And performing rolling optimization again.
In the above technical solution, the certain range is a deceleration region.
In connection with the above technical solution, the timing cycle control of the traffic volume of the key lane in step S4 specifically includes:
the signal lamp period duration is as follows:
total green light duration: gt＝Cmin-L；
Effective green light duration for different phase critical lane groups:
wherein L is the total loss time of the intersection, VcIs the total traffic flow in the direction of travel; PHF is the peak hour coefficient; sijIs the saturation flow rate; v/c is the flow ratio; gtThe total green light duration; vc1Flow rate for the phase 1 critical lane group; ge1An effective green time period for the phase 1 critical lane group; vcpFlow rate for phase p key lane group; gepThe effective green duration for the phase p key lane group.
The invention also provides a self-adaptive traffic signal lamp control system under the networking vehicle environment, which comprises a roadside unit RSU module, a cloud server, a self-adaptive controller and a traffic lamp control module;
the roadside unit RSU module is arranged on a traffic signal lamp post, is communicated with the vehicle-mounted unit and comprises a data receiving unit and a vehicle identity recognition unit;
the cloud server receives data and is used for storing traffic flow data, traffic demands of intersections and state information of signal lamps;
the self-adaptive controller is connected with the cloud server, and controls the lighting time of the signal lamp by adopting the self-adaptive traffic signal lamp control method under the networked vehicle environment according to the traffic demand of the intersection;
the traffic light control module is connected with the self-adaptive controller and comprises a traffic light display unit, a traffic light selection unit and a traffic light timing unit.
The invention has the following beneficial effects: the invention can calculate the weighted average control delay time of the whole intersection by means of the networked vehicles, judge the traffic state including free flow, stable flow, approaching saturated flow, saturated flow and forced flow, and adaptively adjust the time length of the traffic signal lamp according to different traffic states, thereby avoiding the vehicle congestion phenomenon caused by fixed time of the traffic signal lamp when the traffic flow is in a peak, and reducing unnecessary delay caused by small traffic flow.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a block diagram of the system architecture of the present invention;
FIG. 2 is a schematic diagram of an networked vehicle environment layout;
FIG. 3 is an algorithmic modeling framework of the present invention;
FIG. 4 is a graph of the geometry and phase of the intersection of the south allen road and the great eston road, huh and Haote;
FIG. 5A is an adaptive control flow diagram;
FIG. 5B is a flow chart of a method for adaptive traffic signal control in an Internet vehicle environment;
FIG. 6 is a comparison of capacity before and after optimization;
FIG. 7 is a comparison of average control delay before and after optimization;
FIG. 8 is a comparison of v/c ratios before and after optimization;
FIG. 9 is a comparison of average queue lengths before and after optimization.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The signal lamp control algorithm in the invention takes the traffic data shared by the networked vehicles in real time as a basis, judges the traffic state through the self-adaptive control system according to the traffic flow, dynamically adjusts the lighting time of the traffic signal lamp, and timely adjusts the passing time in different directions. Compared with timing control and induction control, the self-adaptive control system can better utilize the overall traffic capacity of a road network, effectively improve the traffic efficiency of the road network, is one of important technical means for adjusting traffic flow and improving congestion, and reduces delay and emission.
The adaptive traffic signal lamp control system in the networked vehicle environment according to the embodiment of the invention, as shown in fig. 1, includes a Road Side Unit (RSU) module, a cloud server, an adaptive controller and a traffic lamp control module, wherein the RSU module is installed on a traffic signal lamp post, communicates with a vehicle-mounted Unit, and includes a data receiving Unit and a vehicle identity recognition Unit. The cloud server receives the data and is used for storing traffic flow data, traffic demands of intersections and state information of signal lamps. The self-adaptive controller adopts a self-adaptive control method to control the lighting time of the signal lamp according to the traffic demand of the intersection. The traffic light control module comprises a traffic light display unit, a traffic light selection unit and a traffic light timing unit. The cloud server is connected with the controller, and the controller is connected with the traffic light display unit, the traffic light selection unit and the traffic light timing unit respectively.
The networked vehicle environment layout is shown in fig. 2. Traffic states such as free flow (random arrival), saturated flow and forced flow can be identified through logic and algorithm, and the networked vehicle probe data set is converted or processed into a traffic signal lamp control scheme. The output values comprise real-time traffic flow parameters or variables, such as headway, traffic flow rate, vehicle type, vehicle speed, queuing length, parking time, arrival and departure modes, throughput, delay, v/c ratio and the like, and an adaptive signal control mechanism driven by the networked vehicles is developed. The above quantified variables and models were integrated into a single graphical interface GUI, with MATLAB software for this purpose. Evaluating the performance effect of the control and using a feedback mechanism to optimize the configuration requires MATLAB and VISSIM to be used together.
The self-adaptive traffic signal lamp control based on the networked vehicles comprises the following steps:
the method comprises the following steps that firstly, data such as vehicle positions, vehicle speeds, time stamps and the like are uploaded to a cloud server and an intersection RSU through a networked vehicle;
the cloud server receives the data transmitted by the RSU, and stores traffic flow data, traffic demands of intersections and state information of signal lamps;
thirdly, counting vehicle data by a computer chip built in the self-adaptive controller, and calculating the traffic demand of the intersection;
and step four, adopting a self-adaptive control method to control the duration of green light and red light of the signal lamp according to the traffic demand of the intersection.
And step four, controlling the duration of the green light and the red light of the signal lamp, specifically controlling the duration of the green light.
The following steps describe the process of the algorithm, which is repeated for each lane:
step one, all connected vehicles in the deceleration zone are determined, including their position, speed and acceleration.
And step two, for a pair of connected networked vehicles, determining and executing a subsequent traffic state according to the description in the flow chart 5A.
And step three, repeating the steps for each pair of connected vehicles in the deceleration area.
The detailed description of step two is as follows: after the vehicle state is determined, the required following acceleration of the vehicle is calculated from the vehicle state and then compared with the actual acceleration. For vehicles in the deceleration zone, the acceleration is typically negative, indicating that the vehicle is attempting to stop because the vehicle is queuing at the traffic light, and it is a common state to follow to a stop. Vehicle control delays are less for the free stream zone than for vehicles in the queuing or deceleration zone. The control delay refers to the delay caused by the signal control when the vehicle in one lane group decelerates or stops, compared with the delay generated under the condition of no signal control.
The invention mainly utilizes the data shared by the networked vehicles to estimate the vehicle arrival rate, judge the traffic state and optimize the traffic signal. Specifically, the adaptive traffic signal lamp control method in the internet vehicle environment according to the embodiment of the present invention, as shown in fig. 5B, includes the following steps:
s1, acquiring data information of networked vehicles in a certain range at a road intersection, including vehicle positions, vehicle speeds and time stamps; wherein the certain range may be a deceleration region.
S2, acquiring data information of the traffic signal controller at the intersection;
s3, calculating the weighted average control delay time of the whole intersection according to the acquired data information, and judging the traffic state including free flow, stable flow, near saturated flow, saturated flow and forced flow;
s41, judging whether the traffic state is free flow or stable flow;
s5, if the current is free current or stable current, the signal sequence of the traffic light of the previous period is operated;
s42, judging whether the traffic state is near saturated flow or saturated flow;
s6, if the traffic state is near to saturated flow or saturated flow, performing adaptive signal control, and calculating the green light time length and the optimal signal light cycle time length of each intersection in different directions according to the vehicle arrival rate and the vehicle queue length;
s43, judging whether the traffic state is a forced flow;
and S7, if the flow is the forced flow, performing the timing cycle control of the traffic volume of the key lane.
The self-adaptive signal control method specifically comprises the following steps:
calculating the effective green light phase duration and the phase loss duration of each phase, specifically: the basic phase number of the intersection signal is p (p is 1,2, …, k), the phase group number is j, each phase group is taken as a stage, the stage number is i, and the phase group number corresponding to the stage i is ipThe state variable is siState variable siRepresenting the time from the start of the optimization to the end of the ith phase, decision variable xiRepresents the phase group duration assigned for the ith phase, at which time: si＝si-1+xi(ii) a For the phase group not in the current stage, the basic phase is in a red light state, and the outflow rate is 0 at the moment; djIs the delay equation of the j-th phase group, gepIs the effective green phase duration for the p-th phase, R is the phase loss duration, GminAnd GmaxIs the minimum and maximum green time duration for the p-th phase; t denotes the moment after the optimization starts, phase i teamThe length of the train is l, the outflow rate of the vehicles is Q (t), the queuing length of the vehicles of the t time step l (t) is equal to the sum of the difference of the arriving vehicles and the leaving vehicles and the queuing length of the vehicles of the previous time step t-1 l (t-1);
assuming a saturation flow rate of sij
An objective function:
constraint conditions are as follows:
∑(gep+R)＝xi
Gmin＜gep＜Gmax；
and calculating the optimal period duration of the signal lamp according to the effective green lamp phase duration and the phase loss duration of each phase.
Further, after a stage is completed, the recursive equation f is usedi(si)＝min{Z+fi-1(si-1) And carrying out rolling optimization again to obtain a better effect.
The timing cycle control of the traffic volume of the key lane in step S7 specifically includes:
the signal lamp period duration is as follows:
total green light duration: gt＝Cmin-L；
Effective green light time of different phase key lane groupLength:
wherein L is the total loss time of the intersection, VcIs the total traffic flow in the direction of travel; PHF is the peak hour coefficient; sijIs the saturation flow rate; v/c is the flow ratio; gtThe total green light duration; vc1Flow rate for the phase 1 critical lane group; ge1An effective green time period for the phase 1 critical lane group; vcpFlow rate for phase p key lane group; gepThe effective green duration for the phase p key lane group.
In one embodiment of the invention, to verify the adaptive control algorithm, a virtual signal controller was used to model and experiment the intersection of huh and hao city hulun south with alctos street in VISSIM, the geometry and signal phase diagram of the intersection being shown in fig. 4. To simulate the observed data stream, a VISSIM trip chain file is created using the trajectory and Origin-Destination (OD) information, which approximates the process of converting the probe data stream into a real-time simulation. Each link file record contains a timestamp showing when the vehicle entered the network and indicates the vehicle's origin area number and destination. The total number of vehicles arriving at different routes of the intersection is determined according to the detector information transmission and the discrete space-time distribution diagram, and the signal timing and the phase sequence are determined by the data set. The comparison of the observed travel time with the simulated travel is shown in table 1.
TABLE 1 comparison of observed travel time to simulated travel time
Modeling and algorithm of traffic state:
the type of the vehicle arriving at the intersection can represent the quality of signal linkage at the intersection and is used for determining the classification of green wave quality of the signalized intersection. According to the road traffic capacity manual (2010), the road traffic capacity manual is divided into six types, wherein the arrival type 1 represents the worst intersection green wave quality, and the arrival type 6 represents the best intersection green wave quality.
The traffic state at the intersection is based on the service water balance, and the service level is defined based on the weighted average control delay at the entire intersection, as shown in table 2.
TABLE 2 service level criteria
| Service level | Mean control delay(s) | General description of the invention |
| A | ≤20 | Free flow |
| B | 30-35 | Flow stabilization |
| C | 35-55 | Near saturated flow |
| D | 55-80 | Saturated flow |
| E | ＞80 | Forced flow |
The acquisition time, the vehicle ID, the road number on which the vehicle is located, the vehicle position, the vehicle speed, the acceleration, the distance from the stop line, the offset from the center line of the lane, and the like can be easily obtained by connecting the vehicles through the internet.
The self-adaptive signal control obtains traffic flow information through the internet vehicles, the arrival condition of the vehicles in a short time and the queuing length of the intersection (expressed by delay) are presumed, an objective function is defined, and the optimal signal lamp timing scheme is obtained through calculation and solving.
The objective function minimizes master control delay, the adaptive control flow chart is shown in fig. 5A, and the real-time adaptive signal control algorithm of the patent optimizes phase duration by using a dynamic programming method in operation research, and converts a multi-stage process into a series of single-stage problems.
The embodiment shown in fig. 4 lists eight basic phases at a crossing, and possible phase combinations include east-west left turn, east-west straight, north-south left turn, north-south straight, and signal configuration schemes that determine the phase sequence and duration of each phase combination.
The mean value of the statistical distribution of the arrival of a vehicle in a section or uniform section of a roadway or road is called the arrival rate, and q is usedijAnd (4) showing. The networked vehicle trajectories are represented by vectors. In the saturated flow condition, the arrival rate of the vehicles generally follows the poisson distribution, and in the actual condition, the arrival rate of the vehicles at the intersection is often uneven, the unevenness is represented by a time parameter (the parameter can be calculated by a vehicle track vector), and the time window t1And t2The accumulated number of the arriving vehicles can be obtained by the track information and the poisson distribution of the networked vehicles. The number of traditional vehicles among the networked vehicles can be calculated through the driving-off time and the saturated headway, so that the arrival rate of the vehicles can be obtained, and the real-time traffic flow of the intersection can be estimated. These parameters, obtained or calculated, provide accurate inputs to the signal control algorithm.
The basic phase number of the intersection signal is p (p is 1,2, …, k), the phase group number is j, each phase group is taken as a stage, the stage number is i, and the phase group number corresponding to the stage i is ipThe state variable is siState variable siRepresenting the time from the start of the optimization to the end of the ith phase, decision variable xiRepresents the phase group duration assigned for the ith phase, at which time: si＝si-1+xi. For the phase group not at the current stage, the basic phase is in the red state, and the outflow rate is 0. DjIs the delay equation of the j-th phase group, gepIs the effective green phase duration for the p-th phase, R is the phase loss duration, GminAnd GmaxIs the minimum, maximum green time duration for the p-th phase. t denotes the time after the optimization begins, the queue length of the phase i is l, the outflow rate of the vehicles is Q (t), and the vehicle queue length l (t) of the t time step is equal to the sum of the difference between the arriving vehicle and the departing vehicle and the vehicle queue length l (t-1) of the previous time step t-1.
Assuming a saturation flow rate of sij
An objective function:
constraint conditions are as follows:
∑(gep+R)＝xi
Gmin＜gep＜Gmax
when the signal lamp is green, when the number of queued vehicles in the time step t-1 and the number of arriving vehicles in the time step t are greater than the maximum number of departing vehicles, the vehicle outflow rate is equal to the saturation flow rate; otherwise, the number of vehicles leaving is equal to the sum of the number of vehicles in line in the time step t-1 and the number of vehicles arriving in the time step t.
To make full use of the vehicle arrival data, after a stage has ended, the recursive equation f can be usedi(si)＝min{Z+fi-1(si-1) And carrying out rolling optimization again to obtain a better effect.
According to fig. 3, fig. 5A and fig. 5B, the arrival rate and the queue length are input, the traffic state is judged, and the plan is selected. Scheme 1 is the signal timing of the last cycle of operation: ci+1＝Ci；
Scheme 2 is to implement an adaptive signal control algorithm. Inputting: basic phase number p, phase group number j, arrival rate qijSaturation flow rate sijPhase loss duration R and signal minimum and maximum period duration Gmin、Gmax. And (3) outputting: effective green time period g for phase pepFurther, the optimal period duration C of the signal lamp is obtainedopt. i represents the stage of optimization; p is the phase number, if corresponding to the case of fig. 4, p has two straight rows and left turns in a certain phase group, so p is 1, 2; j refers to the phase group, two groups: north-south direction, east-west direction;
the signal lamp period duration is as follows:
total green light duration: gt＝Clength-L；
Effective green duration for each phase:
wherein L is the total loss time of the intersection, VcIs the total traffic flow in the direction of travel, vehicle/h; PHF is the peak hour coefficient; sijIs the saturation flow rate, vehicle/h; v/c is the flow ratio; gtTotal green duration, s; vc1Flow rate, vehicle/h, for phase 1 critical lane group; ge1An effective green time duration, s, for the phase 1 key lane group; vcpFlow rate for phase p key lane group, vehicle/h; gepThe effective green duration, s, for the phase p key lane group.
The performance of adaptive control is measured by traffic capacity, control delay, v/c ratio, queue length.
The traffic capacity of the invention is measured according to the maximum sum of the flow of the key lanes which can be accommodated by the intersection.
In the formula, csumMaximum sum of critical lane flow, pcu/h; gepEffective green duration, s, for phase p; c is the signal lamp period duration, s; sijIs the saturation flow rate, vehicle/h.
Control delay is the most important measurement index of green wave quality of signalized intersections and is used for evaluating service level and intersection design.
d＝d1+d2+d3
In the formula, d is the average control delay of each vehicle, s/vehicle; d1For uniform delay, s/vehicle; d2For incremental delay, s/vehicle; d3For initial queuing delay, s/vehicle.
The v/c ratio refers to the ratio of the flow rate to the capacity of the transportation facility. The critical v/c ratio for an entire intersection may be defined as the ratio of the critical lane flow to the traffic capacity of the entire intersection (all critical lane groups).
Xc＝∑vcp/csum
In the formula, XcThe critical v/c ratio of the intersection; v. ofcpThe traffic of the key lane group is pcu/h; c. CsumThe traffic capacity of all key lane groups is pcu/h.
VISSIM allows the user to identify the queue counter position, which is the distance from the point furthest in front of the queue. If the queue backs up on multiple links, the longest distance is recorded. If the front of the queue begins to clear, the VISSIM will track all the way to the back of the queue until there are no vehicles in line between the queue counter position and the back of the current queue.
AVEQ＝∑il(t)/I
In the formula, AVEQ is the average queue waiting length in the analysis period, m; l (t) is the queue rear length at the end of step time t, m; i is the total time step in the analysis cycle.
The adaptive traffic signal optimization results of the embodiment based on the networked vehicles are shown in fig. 6-9.
In conclusion, the traffic flow information can be collected in real time, data sharing is realized, self-adaptive control of the traffic signal lamps of the single intersection is carried out according to the traffic state, and control delay, congestion, fuel consumption and pollutant emission can be effectively reduced. The invention also fully utilizes the existing equipment, realizes the accurate acquisition of traffic flow information by combining the camera and the RSU of the intersection, and provides guidance for realizing the cooperative control of the signal lamps of the multi-intersection in the future.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (5)
1. A self-adaptive traffic signal lamp control method under the environment of networked vehicles is characterized by comprising the following steps:
s1, acquiring data information of networked vehicles in a certain range at a road intersection, including vehicle positions, vehicle speeds and time stamps;
s2, acquiring data information of the traffic signal controller at the intersection;
s3, calculating the weighted average control delay time of the whole intersection according to the acquired data information, and judging the traffic state including free flow, stable flow, near saturated flow, saturated flow and forced flow;
s4, if the current is free current or stable current, the signal sequence of the traffic light of the previous period is operated; if the current is close to the saturated current or the saturated current, self-adaptive signal control is carried out, and the green light time length and the optimal signal lamp period time length of each intersection in different directions are calculated according to the vehicle arrival rate and the vehicle queue length; if the forced flow is adopted, the timing period control of the traffic volume of the key lane is carried out;
the self-adaptive signal control method specifically comprises the following steps:
calculating the effective green light phase duration and the phase loss duration of each phase, specifically: and setting the basic phase number of the intersection signal as p, wherein p is 1,2, …, k, the phase group number is j, each phase group is taken as a stage, the stage number is i, and the phase group number corresponding to the stage i is ipThe state variable is siState variable siRepresenting the time from the start of the optimization to the end of the ith phase, decision variable xiRepresents the phase group duration assigned for the ith phase, at which time: si＝si-1+xi(ii) a For the phase group not in the current stage, the basic phase is in a red light state, and the outflow rate is 0 at the moment; djIs the delay equation of the j-th phase group, gepIs the effective green phase duration for the p-th phase, R is the phase loss duration, GminAnd GmaxIs the minimum and maximum green time duration for the p-th phase; t represents the time after the optimization begins, the queue length of the stage i is l, the outflow rate of the vehicles is Q (t), the vehicle queue length l (t) of the t time step is equal to the sum of the difference between the arriving vehicle and the departing vehicle and the vehicle queue length l (t-1) of the previous time step t-1;
assuming a saturation flow rate of sij
An objective function:
constraint conditions are as follows:
∑(gep+R)＝xi
Gmin<gep<Gmax；
and calculating the optimal period duration of the signal lamp according to the effective green lamp phase duration and the phase loss duration of each phase.
2. The method of claim 1, wherein the recursive equation f is used to control the traffic signal after a phase is completedi(si)＝min{Z+fi-1(si-1) And performing rolling optimization again.
3. The adaptive traffic signal light control method in an on-grid vehicle environment according to claim 1, wherein the certain range is a deceleration region.
4. The adaptive traffic signal light control method under the networked vehicle environment of claim 1, wherein the timing cycle control of the key lane traffic volume in step S4 is specifically:
the signal lamp period duration is as follows:
total green light duration: gt＝Cmin-L；
Effective green light duration for different phase critical lane groups:
wherein L is the total loss time of the intersection, VcIs the total traffic flow in the direction of travel; PHF is the peak hour coefficient; sijIs the saturation flow rate; v/c is the flow ratio; gtThe total green light duration; vc1Flow rate for the phase 1 critical lane group; ge1An effective green time period for the phase 1 critical lane group; vcpFlow rate for phase p key lane group; gepThe effective green duration for the phase p key lane group.
5. A self-adaptive traffic signal lamp control system under an internet vehicle environment is characterized by comprising a roadside unit RSU module, a cloud server, a self-adaptive controller and a traffic lamp control module;
the roadside unit RSU module is arranged on a traffic signal lamp post, is communicated with the vehicle-mounted unit and comprises a data receiving unit and a vehicle identity recognition unit;
the cloud server receives data and is used for storing traffic flow data, traffic demands of intersections and state information of signal lamps;
the self-adaptive controller is connected with the cloud server, and controls the lighting time of a signal lamp by adopting the self-adaptive traffic signal lamp control method under the networked vehicle environment according to the traffic demand of an intersection, wherein the self-adaptive traffic signal lamp control method is as defined in any one of claims 1 to 4;
the traffic light control module is connected with the self-adaptive controller and comprises a traffic light display unit, a traffic light selection unit and a traffic light timing unit.
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| CN113112812B (en) * | 2021-05-20 | 2022-05-20 | 青岛海信网络科技股份有限公司 | Traffic signal adaptive optimization prejudging method, device, equipment and medium |
| CN113538936B (en) * | 2021-05-28 | 2022-07-22 | 东南大学 | Signal cooperative control method and device under vehicle-road cooperative environment and storage medium |
| CN113506443A (en) * | 2021-09-10 | 2021-10-15 | 华砺智行(武汉)科技有限公司 | Method, device and equipment for estimating queuing length and traffic volume and readable storage medium |
| CN113936454B (en) * | 2021-09-22 | 2023-02-21 | 上海宝康电子控制工程有限公司 | Information processing method and system based on track fusion |
| CN113851008B (en) * | 2021-10-25 | 2022-06-07 | 深圳技术大学 | Traffic signal lamp control method, device, equipment and storage medium |
| CN114267185A (en) * | 2021-12-09 | 2022-04-01 | 北京交通大学 | Recoverable self-adaptive traffic signal lamp timing method |
| CN114155724B (en) * | 2021-12-23 | 2022-11-22 | 连云港杰瑞电子有限公司 | Intersection traffic signal control method in Internet of vehicles environment |
| CN114464000B (en) * | 2022-02-21 | 2023-04-25 | 上海商汤科技开发有限公司 | Intersection traffic light control method, device, equipment and storage medium |
| CN114399912B (en) * | 2022-03-24 | 2022-07-22 | 华砺智行(武汉)科技有限公司 | Adaptive signal control method and system under intelligent networking environment |
| CN115273499B (en) * | 2022-06-30 | 2023-11-10 | 华东师范大学 | Traffic flow-based signal lamp dynamic timing method and system |
| TWI849874B (en) * | 2023-05-01 | 2024-07-21 | 睿星科技股份有限公司 | V2x traffic signal controlling system, method and computer program product thereof |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2379761C1 (en) * | 2008-07-01 | 2010-01-20 | Государственное образовательное учреждение высшего профессионального образования "Уральский государственный технический университет УПИ имени первого Президента России Б.Н.Ельцина" | Method of controlling road traffic at intersection |
| CN101707000A (en) * | 2009-10-26 | 2010-05-12 | 北京交通大学 | Urban road traffic multiobjective optimization control method |
| CN101976510A (en) * | 2010-10-26 | 2011-02-16 | 隋亚刚 | Method for optimally controlling crossing vehicle signal under high definition video detection condition |
| CN103593545A (en) * | 2012-08-16 | 2014-02-19 | 同济大学 | Intersection multi-mode integrated service level evaluation method |
| CN103985261A (en) * | 2014-04-21 | 2014-08-13 | 东南大学 | Traffic signal light control method and system based on vehicle queuing length measurement |
| CN104933872A (en) * | 2014-03-19 | 2015-09-23 | 北京航天长峰科技工业集团有限公司 | Single-intersection traffic signal optimization control method |
| CN106652493A (en) * | 2016-12-29 | 2017-05-10 | 东南大学 | Intersection signal optical control method in environment of Internet of vehicles |
| CN107016861A (en) * | 2017-05-31 | 2017-08-04 | 电子科技大学 | Traffic lights intelligent control system based on deep learning and intelligent road-lamp |
| CN107730926A (en) * | 2017-11-24 | 2018-02-23 | 信利光电股份有限公司 | A kind of intellectual traffic control method, apparatus and system |
| 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 |
-
2020
- 2020-08-04 CN CN202010772034.8A patent/CN111951549B/en not_active Expired - Fee Related
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2379761C1 (en) * | 2008-07-01 | 2010-01-20 | Государственное образовательное учреждение высшего профессионального образования "Уральский государственный технический университет УПИ имени первого Президента России Б.Н.Ельцина" | Method of controlling road traffic at intersection |
| CN101707000A (en) * | 2009-10-26 | 2010-05-12 | 北京交通大学 | Urban road traffic multiobjective optimization control method |
| CN101976510A (en) * | 2010-10-26 | 2011-02-16 | 隋亚刚 | Method for optimally controlling crossing vehicle signal under high definition video detection condition |
| CN103593545A (en) * | 2012-08-16 | 2014-02-19 | 同济大学 | Intersection multi-mode integrated service level evaluation method |
| CN104933872A (en) * | 2014-03-19 | 2015-09-23 | 北京航天长峰科技工业集团有限公司 | Single-intersection traffic signal optimization control method |
| CN103985261A (en) * | 2014-04-21 | 2014-08-13 | 东南大学 | Traffic signal light control method and system based on vehicle queuing length measurement |
| CN106652493A (en) * | 2016-12-29 | 2017-05-10 | 东南大学 | Intersection signal optical control method in environment of Internet of vehicles |
| 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 |
| CN107016861A (en) * | 2017-05-31 | 2017-08-04 | 电子科技大学 | Traffic lights intelligent control system based on deep learning and intelligent road-lamp |
| CN107730926A (en) * | 2017-11-24 | 2018-02-23 | 信利光电股份有限公司 | A kind of intellectual traffic control method, apparatus and system |
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