CN109872531B - Method for constructing optimal control objective function of road network controlled by road traffic signals - Google Patents
Method for constructing optimal control objective function of road network controlled by road traffic signals Download PDFInfo
- Publication number
- CN109872531B CN109872531B CN201910079914.4A CN201910079914A CN109872531B CN 109872531 B CN109872531 B CN 109872531B CN 201910079914 A CN201910079914 A CN 201910079914A CN 109872531 B CN109872531 B CN 109872531B
- Authority
- CN
- China
- Prior art keywords
- control
- traffic
- flow
- intersection
- objective function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention relates to a method for constructing an optimal control objective function of a road traffic signal control whole road network, which is characterized by comprising the following steps of: selecting an ideal control target model of an undifferentiated intersection; establishing a flow-speed function based on the SCATS signal control parameters; constructing an optimal traffic signal control objective function of a whole road network; and solving the traffic signal control whole road network optimization control objective function based on SCATS. The invention aims to solve the defects of the prior art, provides a method for controlling and optimizing an objective of urban full-road network level traffic signals aiming at the practical application requirements of traffic engineering, fills the technical blank of the signal control objective of urban large-range intersections, provides a theoretical basis for the full-road network traffic control method, can improve the traffic signal control effect and control range, improves the urban traffic management service level, reduces traffic jam and optimizes the traffic travel environment.
Description
Technical Field
The invention relates to construction of a full-road-network optimization control objective function for urban road traffic signal control in a traffic period and a solution algorithm based on SCATS (sequence characterized traffic System), belonging to the technical field of urban road traffic signal control theory and application.
Background
With the continuous development of traffic information technology, the urban traffic system accumulates a large amount of traffic flow acquisition data, system operation data and service application data, and the traffic signal control system at the core position of urban traffic management also accumulates abundant control theories and data bases, so that the urban traffic system is greatly developed in the aspects of single-point intersection signal control, linear intersection control and small-range area coordination control technology. The common signal control strategy comprises saturation balance control, minimum delay control, maximum flow control, single/two-way green wave control and the like, and is realized by signal timing methods such as green signal ratio adjustment, cycle time control, phase position reasonable setting and the like. However, the existing control theory and technology can not support the global control target of the whole road network, and when the scale of the intersection group of the control object exceeds a certain limit, the implementation effect of the methods is limited. In order to study the optimization of group control of intersection at the area level and the road network level in a wider range, an overall target of road network level traffic control needs to be established first.
Disclosure of Invention
The purpose of the invention is: the method is suitable for optimizing the global traffic control scheme which takes a large number of intersections as objects under the road traffic state.
In order to achieve the above object, the technical solution of the present invention is to provide a method for constructing an optimal control objective function for a road traffic signal control whole road network, which is characterized by comprising the following steps:
step 2, constructing input of each control traffic flowMatrix Si＝(A，B，qi，gi) 1, 2, N, will be input into the matrix SiSubstitution into
qiIndicates the phase flow at the ith intersection, giIndicating the green time, v, of the ith intersectioniExpressing the phase speed of the ith intersection, fitting N groups of SCATS traffic control data to obtain a flow-speed function, solving a constant A, B, and combining qi、giCommon substitution
Obtaining a coefficient matrix [ a, b, c, d ]]，
And 3, substituting the coefficient matrix [ a, b, c, d ] into the objective function W':
constraint s.t.0 ≤ xi；
0≤x1+x2+…+xN≤180；
Selecting an ideal control target model of the undifferentiated intersection:
step 4, solving the objective function W' by adopting a gradient ascent algorithm to obtain N independent operation traffic flow phase interval time relations f (x) with different phases and different steering directions1，x2，...，xN)。
The invention aims to solve the defects of the prior art, provides a method for controlling and optimizing an objective of urban full-road network level traffic signals aiming at the practical application requirements of traffic engineering, fills the technical blank of the signal control objective of urban large-range intersections, provides a theoretical basis for the full-road network traffic control method, can improve the traffic signal control effect and control range, improves the urban traffic management service level, reduces traffic jam and optimizes the traffic travel environment.
Drawings
FIG. 1 is a schematic diagram of a process for building a target model;
FIG. 2 is a schematic diagram of the process steps of the present invention;
fig. 3 is a layout diagram of traffic flows included in the optimization control object solved in the present example in the road network.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The invention is realized by the following technical scheme, and the invention specifically comprises the following steps:
(1) selecting an ideal control target model of an undifferentiated intersection;
(2) establishing a flow-speed function based on the SCATS signal control parameters;
(3) constructing an optimal traffic signal control objective function of a whole road network;
(4) and solving the traffic signal control whole road network optimization control objective function based on SCATS.
The derivation process of the invention comprises the following parts:
model construction method of all-road-network optimization control objective function controlled by urban road traffic signals
1. Selecting an ideal control target model of an undifferentiated intersection
In the traffic signal control theory, traffic flow which is not controlled by signals runs to meet a flow-speed curve of a flow-density-speed basic theory, and according to a certified MFD theory, the traffic characteristics of a road network also accord with the flow-density speed basic theory, so that the road network also accords with the communication basic theory in the traffic stage of signal control conditions. The general and undifferentiated intersection in the urban road traffic signal control is taken as an object, and when the space traffic state reaches the optimum in a period of continuous time, namely when the control expected effect is infinitely close to the theoretical optimum flow curve, the total flow of the intersection is maximum, and the corresponding average speed is optimum. Thus, the intersection control objectives are:
Max∫q·dv (1)
in the formula (1), q represents a flow rate and v represents a velocity.
Defining the function T ═ q · dv, according to traffic flow theory, the flow q is a function of the speed v, so T transforms into:
T＝∫f(v)·dv。
2. construction of optimal traffic signal control objective function of whole road network
And selecting a control range comprising N intersection scale control ranges or N intersection scale control ranges of a whole network in a continuous time, wherein the total control target is that the expected effect in the control range is infinitely close to the theoretical optimal flow curve, the total flow of the intersections in the control range is maximum, and the corresponding average vehicle speed is optimal. The optimal traffic signal control objective function of the whole road network is constructed as follows:
in the formula (2), TiAs a function T of the ith intersection.
Second, full road network optimization control objective function solving method for urban road traffic signal control based on SCATS system data
1. SCATS-based all-road-network optimal control objective function formula for urban road traffic signal control
A flow-speed functional relation is obtained by a method for fitting Sydney self-adaptive coordination control signal system SCATS signal control parameter data:
in the formula (3), g is the green time, v is the average vehicle speed, and A, B is a constant.
Equation (2) thus translates to:
substituting the flow-speed function relationship to obtain:
Thereby to obtain
Namely to find out
2. Full-road-network optimization control objective function solving method for urban road traffic signal control based on SCATS
In the range of an urban road traffic signal control area, the number of the contained signal intersections is N, and the all-road network optimization control objective function W' controlled by the traffic signal in the area is the green light phase time g of each intersection in a signal period1,g2,g3，...，gNW' ═ f (g1, g2, g3,.., gN). To simplify the model solution complexity, let:
obtaining:
the general solution model for W 'is transformed into an optimization problem targeting W', i.e.:
an objective function: max (f (x))1，x2，...，xN) Whereinsaid:
namely, it is
Constraint s.t.0 ≤ xi；
J denotes a combination scheme where there is a set of control phases, JiThe number of control phases contained in the jth intersection is shown, m is the number of all control phases in the control range, and N is the number of N intersections in the control range.
Finding X ═ X1，x2，…，xN)
The known parameters are:
the multivariate nonlinear optimization problem is solved by adopting algorithms such as gradient rise and the like, and the detailed description is given in an embodiment.
The invention is further illustrated below with reference to specific data:
in order to better understand the method provided by the embodiment, a certain area of the Shanghai Xuhui area is selected to construct and solve the whole road network optimization objective function for urban road traffic signal control, and the method can be applied to control objects consisting of all intersections and adjacent intersections contained in different urban road networks or areas. The example requires that the detection data of the SCATS induction coil detector of the road section in a period of time is provided, and the detection data comprises information of traffic flow, phase and the like.
The specific implementation steps of the embodiment are as follows:
(1) control object selection
In order to make this embodiment include typical control scenes of the road network as much as possible, 12 independent traffic flows with different phases and different turning directions along a trunk road in the creep and merge area and related cross road intersections thereof are selected, including main direction traffic flows, cross traffic flows, coordinated phase traffic flows and traffic flows in sub-areas. Selecting 15 pm in the spring of 2013: 00-19: the SCATS traffic control data for 00 has a total of 840 groups of 70, including phase flow q, green time g, and so on.
(2) Establishing a flow-velocity function
Constructing an input matrix S for each control flowi＝(A，B，qi，gi) 1, 2, 12. Will SiTaking in formula (3), fitting the 70 sets of SCATS data to obtain a flow-speed function, solving constants A and B, and combining qi、giCommon substitution
Obtaining a coefficient matrix [ a, b, c, d ]]，
The data of this embodiment is calculated to form the following input matrix:
(3) construction of optimized traffic control objective function of whole road network
And substituting the coefficient matrix into W' to form a whole road network optimized traffic control objective function based on SCATS data: max (f (x))1，x2，...，xN) Whereinsaid:
namely, it is
Constraint s.t.0 ≤ xi；
J denotes a combination scheme where there is a set of control phases, JiThe number of control phases contained in the jth intersection is shown, m is the number of all control phases in the control range, and N is the number of N intersections in the control range.
Solving of multivariate nonlinear optimization problem representing (4) objective function
The W' function is a typical nonlinear optimization problem, the method adopts a gradient ascending algorithm to solve, and adopts C language programming to realize the solving process of 70 groups of data, and finally the phase time-sharing relation f (x) of the 12 traffic flows is obtained1，x2，...，x12) The results are as follows: f (x)1，x2，...，x12)
＝(7.86,7.37,11.44,6.36,16.00,16.00,14.49,15.51,9.58,16.00,11.60,10.81)。
Claims (1)
1. A road traffic signal control whole road network optimization control objective function construction method is characterized by comprising the following steps:
step 1, acquiring a plurality of sets of SCATS traffic control data of a target time period, wherein the SCATS traffic control data comprise phase flow q and green light time g of N independent running traffic flows with different phases and different steering directions at each road intersection of a target area;
step 2, constructing an input matrix S of each control traffic flowi＝(A，B，qi，gi)，i＝1, 2, N, will be input into the matrix SiSubstitution into
qiIndicates the phase flow at the ith intersection, giIndicating the green time, v, of the ith intersectioniExpressing the phase speed of the ith intersection, fitting N groups of SCATS traffic control data to obtain a flow-speed function, solving a constant A, B, and combining qi、giCommon substitution
Obtaining a coefficient matrix [ a, b, c, d ]]，
And 3, substituting the coefficient matrix [ a, b, c, d ] into the objective function W':
constraint s.t.0 ≤ xi；
0≤x1+x2+…+xN≤180；
Selecting an ideal control target model of the undifferentiated intersection:
step 4, solving the objective function W' by adopting a gradient ascent algorithm to obtain N independent operation traffic flow phase interval time relations f (x) with different phases and different steering directions1，x2，...，xN) The method comprises the following steps:
the intersection control target is as follows:
Max∫q·dv (1)
in the formula (1), q represents a flow rate, and v represents a velocity;
defining the function T ═ q · dv, according to traffic flow theory, the flow q is a function of the speed v, so T transforms into:
T＝∫f(v)·dv；
selecting a control range including N intersection scale control ranges or N intersection scale control ranges of a whole network in a continuous time, wherein the total control target is that an expected effect in the control range is infinitely close to a theoretical optimal flow curve, the total flow of the intersections in the control range is maximum, and the corresponding average vehicle speed is optimal; the optimal traffic signal control objective function of the whole road network is constructed as follows:
in the formula (2), TiIs a function T of the ith intersection;
obtaining a flow-speed functional relation by a method of fitting SCATS signal control parameter data of a Sydney self-adaptive coordination control signal system:
in the formula (3), g is a green time, v is an average vehicle speed, and A, B are constants respectively;
equation (2) thus translates to:
substituting the flow-speed function relationship to obtain:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910079914.4A CN109872531B (en) | 2019-01-28 | 2019-01-28 | Method for constructing optimal control objective function of road network controlled by road traffic signals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910079914.4A CN109872531B (en) | 2019-01-28 | 2019-01-28 | Method for constructing optimal control objective function of road network controlled by road traffic signals |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109872531A CN109872531A (en) | 2019-06-11 |
| CN109872531B true CN109872531B (en) | 2021-08-06 |
Family
ID=66918198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910079914.4A Active CN109872531B (en) | 2019-01-28 | 2019-01-28 | Method for constructing optimal control objective function of road network controlled by road traffic signals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109872531B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110807931B (en) * | 2019-11-08 | 2021-09-17 | 许凌 | Traffic network directed graph path model construction and solving method based on steering relation |
| CN113537555B (en) * | 2021-06-03 | 2023-04-11 | 太原理工大学 | Traffic sub-region model prediction sliding mode boundary control method considering disturbance |
| CN113487884B (en) * | 2021-06-15 | 2023-11-24 | 阿波罗智联(北京)科技有限公司 | Traffic volume acquisition method and device and electronic equipment |
| CN115440033B (en) * | 2022-08-16 | 2023-08-29 | 广东交通职业技术学院 | Main line coordination control method, system, device and medium based on macroscopic basic diagram |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104835335A (en) * | 2015-06-03 | 2015-08-12 | 常州市蓝涛物联网科技有限公司 | Road network traffic optimization control system and method |
| CN105206070A (en) * | 2015-08-14 | 2015-12-30 | 公安部交通管理科学研究所 | Real-time road traffic signal coordination optimization control method and control system thereof |
-
2019
- 2019-01-28 CN CN201910079914.4A patent/CN109872531B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104835335A (en) * | 2015-06-03 | 2015-08-12 | 常州市蓝涛物联网科技有限公司 | Road network traffic optimization control system and method |
| CN105206070A (en) * | 2015-08-14 | 2015-12-30 | 公安部交通管理科学研究所 | Real-time road traffic signal coordination optimization control method and control system thereof |
Non-Patent Citations (7)
| Title |
|---|
| A simulation system and speed guidance algorithms for intersection traffic control using connected vehicle technology;Shuai Liu;《Tsinghua Science and Technology》;20181231;第24卷(第2期);160-170 * |
| Signal control decision model for adjacent intersection based on coordination rate;Yanfang Shou;《2016 12th World Congress on Intelligent Control and Automation (WCICA)》;20160929;2481-2486 * |
| 城市平面交叉口信号控制的研究;孙晴;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20091215(第12期);C034-728 * |
| 城市道路交通网络过饱和状态信号控制方法研究;马旭辉;《中国博士学位论文全文数据库工程科技Ⅱ辑》;20161015(第10期);C034-23 * |
| 基于MFD的城区路网信号控制研究;伍冠桦;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20161115(第11期);C034-49 * |
| 基于空间—相位—时间网络的信号优化与路径诱导协同化研究;姚昱;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20180115(第01期);C034-2195 * |
| 非饱和状态下交通信号控制优化方法研究;田秀娟;《中国博士学位论文全文数据库工程科技Ⅱ辑》;20180115(第12期);C034-70 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109872531A (en) | 2019-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109872531B (en) | Method for constructing optimal control objective function of road network controlled by road traffic signals | |
| CN108510764B (en) | Multi-intersection self-adaptive phase difference coordination control system and method based on Q learning | |
| CN108648446B (en) | Road network traffic signal iterative learning control method based on MFD | |
| CN112700664B (en) | Traffic signal timing optimization method based on deep reinforcement learning | |
| WO2018184413A1 (en) | Green wave control method for traffic signals | |
| WO2021174374A1 (en) | Traffic signal polarization green wave control method | |
| CN108538065B (en) | Urban main road coordination control method based on adaptive iterative learning control | |
| CN104200680B (en) | The coordinating control of traffic signals method of arterial street under supersaturation traffic behavior | |
| CN108898854A (en) | A kind of Ramp cooperative control method based on Model Predictive Control | |
| CN102165501A (en) | Method for optimizing the traffic control at a traffic signal-controlled intersection in a road traffic network | |
| CN113053120B (en) | Traffic signal lamp scheduling method and system based on iterative learning model predictive control | |
| CN104766485A (en) | Traffic light optimization time distribution method based on improved fuzzy control | |
| CN114627657A (en) | Adaptive traffic signal control method based on deep graph reinforcement learning | |
| CN112837534B (en) | Multi-lane roundabout vehicle cooperative control method under intelligent network vehicle connection environment | |
| CN111126687B (en) | Single-point offline optimization system and method for traffic signals | |
| Kong et al. | Urban arterial traffic two-direction green wave intelligent coordination control technique and its application | |
| CN111429733A (en) | Road network traffic signal control method based on macroscopic basic graph | |
| CN109765801A (en) | The implementation method of car networking desin speed adjustment based on VISSIM emulation | |
| CN114995119A (en) | Urban traffic signal cooperative control method based on multi-agent deep reinforcement learning | |
| CN115019523A (en) | Deep reinforcement learning traffic signal coordination optimization control method based on minimized pressure difference | |
| CN108171997A (en) | Traffic signals iteration control optimization method based on macroscopical parent map | |
| CN114141013B (en) | Intersection signal timing method based on low-permeability network connection track data | |
| CN115472023B (en) | Intelligent traffic light control method and device based on deep reinforcement learning | |
| CN110021168B (en) | Grading decision method for realizing real-time intelligent traffic management under Internet of vehicles | |
| CN109686086A (en) | The training of fuzzy control network generates the method and device that speed is suggested at crossing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |