CN111210621A - Signal green wave coordination route optimization control method and system based on real-time road condition - Google Patents

Signal green wave coordination route optimization control method and system based on real-time road condition Download PDF

Info

Publication number
CN111210621A
CN111210621A CN201911377952.4A CN201911377952A CN111210621A CN 111210621 A CN111210621 A CN 111210621A CN 201911377952 A CN201911377952 A CN 201911377952A CN 111210621 A CN111210621 A CN 111210621A
Authority
CN
China
Prior art keywords
road
coordination
green wave
road section
data
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.)
Granted
Application number
CN201911377952.4A
Other languages
Chinese (zh)
Other versions
CN111210621B (en
Inventor
谢竞成
徐甲
丁楚吟
邹开荣
吴越
郭海锋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yinjiang Technology Co.,Ltd.
Original Assignee
Enjoyor Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Enjoyor Co Ltd filed Critical Enjoyor Co Ltd
Priority to CN201911377952.4A priority Critical patent/CN111210621B/en
Publication of CN111210621A publication Critical patent/CN111210621A/en
Application granted granted Critical
Publication of CN111210621B publication Critical patent/CN111210621B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • 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
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle

Abstract

A signal green wave coordination route optimization control method based on real-time road conditions comprises the following steps: s1, acquiring road network information, real-time road condition and traffic data and signal machine control scheme data; s2, constructing a green wave coordination effectiveness comprehensive analysis model based on a fuzzy theory, and calculating the green wave coordination effectiveness of the road section; s3, clustering road sections with high green wave coordination effectiveness based on an improved hierarchical clustering algorithm, and automatically generating a coordination route; and S4, optimizing the overall coordination scheme of the coordination route based on the real-time road section speed data of the Internet, and issuing a coordination command. The invention overcomes the problem that the traditional signal system coordination scheme can only be executed based on a static solidification scheme and is difficult to adapt to real-time road conditions; a method for evaluating road green wave coordination effectiveness is provided, and theoretical support is provided for traffic signal control; the problem that the traditional signal system coordination scheme cannot dynamically change the route along with the road condition is solved.

Description

Signal green wave coordination route optimization control method and system based on real-time road condition
Technical Field
The invention belongs to the technical field of traffic, and relates to a signal green wave coordination route optimization control method and system based on real-time road conditions.
Background
Green wave coordination is a common method for urban traffic signal optimization. By adjusting the time difference of signal timing phase starting of each intersection of the coordination route, the vehicle can reach the intersection near the green light turn-on time of the downstream intersection, and the vehicle can pass through the green light on the green wave coordination route. Design elements of green wave coordination generally have cycle duration (determined by key intersections), green-to-noise ratio (determined by actual traffic conditions at each intersection), and phase difference (determined by average speed of road section and length of road section).
Although the green wave coordination can effectively improve the running speed of the vehicle, the green wave coordination has certain limitations and is generally only suitable for the following situations: (1) a road section with low traffic volume (the road section with high traffic volume can cause unstable traffic flow speed and poor green wave coordination effect); (2) trunk lines with fewer entrances and exits of the trunk line section (too many entrances and exits can influence the average speed of the road section, and vehicles at intersections cannot be emptied during signal timing, so that queuing can influence the green wave bandwidth) (3) the traffic volumes of all intersections of the trunk lines are similar (the same period is adopted for green waves, and too great traffic difference of all the intersections can cause unreasonable timing at some intersections)
Because the applicable conditions of green wave coordination are very harsh and the design is complicated, most of the current traffic signal green wave designs are fixed schemes. Namely, the historical traffic condition of a specific route is analyzed, and corresponding coordination schemes are designed for different periods of time. However, due to the strong dynamic and random characteristics of the traffic system, the green wave coordination scheme started due to the difference of road conditions cannot achieve an ideal effect, and even traffic jam is aggravated. Therefore, a method for optimizing and controlling a green wave coordination scheme based on real-time traffic conditions is needed.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a signal green wave coordination route optimization control method and system based on real-time road conditions, road sections which accord with green wave coordination design conditions in an area are screened out based on real-time road condition traffic data, the similarity of road section traffic volume is calculated, the road sections which are similar in real-time road conditions and meet the green wave conditions are clustered based on an improved hierarchical clustering algorithm, and routes which can be subjected to green wave coordination in a road network are identified; and after the phase difference parameters are optimized according to the real-time vehicle speed, the green wave coordination command is sent to the signal machine.
The technical scheme adopted by the invention is as follows:
a signal green wave coordination route optimization control method based on real-time road conditions comprises the following steps:
s1, acquiring road network information, real-time road condition and traffic data and signal machine control scheme data;
s2, constructing a green wave coordination effectiveness comprehensive analysis model based on a fuzzy theory, and calculating the green wave coordination effectiveness of the road section;
s3, clustering road sections with high green wave coordination effectiveness based on an improved hierarchical clustering algorithm, and automatically generating a coordination route;
and S4, optimizing the overall coordination scheme of the coordination route based on the real-time road section speed data of the Internet, and issuing a coordination command.
Further, the road network information in step S1 includes: the attribute of the intersection, the attribute of the road section, the attribute of the lane and the data of the dependency relationship among the three; the real-time road condition traffic data comprises road vehicle speed data, flow of each flow direction lane and saturation; the annunciator control scheme data is annunciator coordination scheme data.
Further, the specific steps of step S2 are as follows:
s2.1, establishing a dynamic factor set influencing the green wave coordination effectiveness;
the dynamic factor set is as follows:
U＝{△cm,dsm,△tm}
wherein, △ cmThe optimal cycle time length difference, ds, of the upstream and downstream intersections of the m road sectionsmSaturation of the m-segment, △ tmDesigning a difference value of the phase difference for the road section average travel time and a green wave coordination scheme;
s2.2, determining the weight of each factor on the green wave coordination effectiveness, and establishing a weight set;
the weight set of the three dynamic factors is set as follows:
W＝{qc,qds,qt}
wherein q iscWeight of theoretical period deviation of upstream and downstream junctions, qdsWeight of road section saturation, qtWeighting the road section vehicle speed and the design vehicle speed deviation;
s2.3, determining a single-factor fuzzy weight model, and establishing a membership function;
and S2.4, establishing a green wave coordination effectiveness comprehensive analysis model and calculating the green wave coordination effectiveness of the road section.
Further, the specific steps of step S2.3 are as follows:
setting the green wave coordination effectiveness to A, B, C three levels (A > B > C) according to the sequence of the levels, obtaining a green wave coordination effectiveness fuzzy set V:
V＝{A、B、C}
setting c1,c2,ds1,ds2,t1,t2Respectively, the theoretical optimal period deviation △ c of the upstream and downstream intersectionsmRoad saturation dsmDifference △ t of road section average travel time and green wave coordination scheme design phase differencemDetermining a mapping relation between a single dynamic factor and green wave coordination effectiveness grading according to a dynamic boundary of a grading threshold;
normalizing the dynamic factors to ensure that:
Figure BDA0002341525030000031
Figure BDA0002341525030000032
Figure BDA0002341525030000033
wherein, △ cmaxSetting a difference value between a maximum value and a minimum period allowed for the intersection; dsmaxThe saturation theoretical maximum boundary, typically 100; t is t0Designing a phase difference for a road section offline coordination scheme; according to the model, the membership function of the single dynamic factor is constructed as follows:
Figure BDA0002341525030000041
Figure BDA0002341525030000042
Figure BDA0002341525030000043
Figure BDA0002341525030000044
Figure BDA0002341525030000045
Figure BDA0002341525030000046
Figure BDA0002341525030000051
Figure BDA0002341525030000052
Figure BDA0002341525030000053
where phi { △ cm' → A } is △ cm' membership function for class A, and so on, where k is the slope at the critical point of the membership function.
Calculating the membership degree of the road section object about each factor, and constructing a dynamic factor fuzzy relation matrix as follows:
Figure BDA0002341525030000054
in the formula, rijRepresenting the degree of membership of the ith factor with respect to the jth ranking level;
further, the specific steps of step S2.4 are as follows:
analyzing the grading result of the road section object by adopting fuzzy transformation:
S＝W·R＝[qcqdsqt]·R＝[b1b2b3]
wherein S is a fuzzy comprehensive evaluation set, bjRepresenting the degree of membership of the road section object with respect to the jth level result;
then, taking the membership degree corresponding to each grade in the S as a weight, carrying out weighted average to obtain a fuzzy grading index α, rounding off α to obtain a grading result of the road section object, wherein the formula is as follows:
Figure BDA0002341525030000055
rank≈α。
further, the specific steps of step S3 are as follows:
s3.1, determining whether vehicles can pass between road sections according to the information of intersections on the upstream and downstream of the road sections and the lane function, and constructing a road network reachable matrix;
s3.2, counting traffic data of the nearly N periodic road sections, calculating green wave coordination effectiveness and traffic data similarity, filling a road network reachability matrix, and constructing a green wave coordination road section similarity matrix;
and S3.3, clustering road sections in the similarity matrix of the green wave coordination road sections based on a hierarchical clustering algorithm to generate a route with green wave coordination effectiveness.
Further, the specific steps of step S3.2 are as follows:
calculating the average value of theoretical period deviation of upstream and downstream roads, the average value of saturation of road sections and the average value of travel time deviation of road sections in continuous N signal periods of each road section; after normalization, a green wave coordination effectiveness comprehensive analysis model is obtained to obtain green wave coordination effectiveness of each road section;
filtering road sections with green wave coordination effectiveness rank B and C, and constructing a road network green wave coordination candidate road section set U; calculating the flow of the steering lanes between all the road sections in the set U, and marking the key lane with the largest flow ratio; traversing elements in a reachable matrix of the road network, screening a road section combination < X → Y > which has an upstream-downstream relation and exists in a set U, and calculating the Euclidean distance between the steering flow of the road sections from X to Y and the key lane flow of the road sections from Y:
Figure BDA0002341525030000061
wherein D isX->YIs the Euclidean distance similarity of road sections X and Y, fX->YThe steering flow rate for the X to Y road segment,
Figure BDA0002341525030000062
is the key flow of the Y road section;
if the road sections have the upstream and downstream relationship but any one of the road sections is not in the green wave coordination candidate road section set, the following steps are performed:
DX->Y＝∞
filling the calculated Euclidean distance similarity of the road traffic flow into the corresponding position of the reachable matrix of the road network to obtain a green wave coordinated road section similarity matrix KD;
further, the specific steps of step S3.3 are as follows: :
(1) determining △ D that the road segment object allows merging of the minimum Euclidean distance difference;
(2) traversing elements in the green wave coordination road section similarity matrix KD to find a minimum value;
(3) merging the road section objects corresponding to the minimum element to generate a combined road section object, wherein the upstream intersection of the combined road section object is the upstream intersection of the road section which is not connected with the upstream road section in the combination, and the downstream intersection is the downstream intersection of the road section which is not connected with the downstream road section in the combined object;
(4) calculating the Euclidean distance of traffic flow of the combined road section and other road sections, removing road section objects in the combined road section from the similarity matrix KD of the green wave coordination road section, replacing the combined road section objects, and regenerating the similarity matrix KD of the green wave coordination road section;
(5) repeating the steps (2) to (4) until no element in the matrix is less than △ D;
(6) and the road section combination object in the green wave coordination road section similarity matrix KD is a route suitable for starting green wave coordination.
Further, the specific steps of step S4 are as follows:
s4.1, designing a coordination scheme based on the offline of the current time period of the road section, and calculating a phase difference parameter of the optimized coordination scheme;
the method specifically comprises the following steps: screening the intersection with the largest demand period in the coordination route generated in the step S3, and marking the intersection as a core intersection; taking a core intersection as a center, setting the designed phase difference value of an upstream road section of the core intersection as a negative value, and setting the phase difference value of a downstream road section of the core intersection as a positive value; the period duration of the coordinated route adopts the theoretical optimal period duration of the core intersection, and the phase difference of each road section is the quotient of the road section length and the actual average vehicle speed:
Figure BDA0002341525030000071
obtaining a phase difference sequence of the whole coordinated road section:
T＝[-t1,-t2,…,-tk,tk+1…,tn-1]
wherein-t1Initiating intersections and for coordinating routesPhase difference parameter at downstream crossing, -tkCoordinating a phase difference parameter between an intersection and a core intersection for coordinating a route core intersection upstream, tk+1The phase difference parameter of the downstream intersection of the core intersection of the coordinated route and the core intersection is obtained, and n is the number of the total intersections of the coordinated route;
and S4.2, constructing a green wave coordination system command of the annunciator and issuing the green wave coordination system command to the annunciator system.
A signal green wave coordination route optimization control system based on real-time road conditions is characterized in that: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring related data required by clustering and generating a coordinated route, and the related data comprises dynamic data such as detector flow, saturation, a signal machine control scheme, internet vehicle travel speed, time and the like and static information of an urban road network;
the data storage module and the preprocessing module are used for unifying the source data format and carrying out static association processing and time granularity unification processing on the data;
the coordinated route clustering generation module is used for constructing a coordinated route effectiveness comprehensive grading model in an off-line manner, calculating road section green wave coordination effectiveness on line, and clustering and generating a coordinated route through a hierarchical clustering method based on road network reachability and traffic data similarity;
and the coordination scheme optimization control model is used for regularly receiving the coordination route clustering result, optimizing the coordination scheme on the route according to the real-time road condition, generating a control command which can be identified by the signal system and issuing the control command to the signal machine.
Further, the data acquisition module comprises:
the dynamic data acquisition unit is responsible for acquiring real-time road condition related data, including Internet road speed data, detector flow and saturation data and signal machine control scheme data;
and the static data acquisition unit is responsible for acquiring static data such as road network space geographic position information, intersection numbers, road section grades, road section lengths, road section numbers, lane functions and the like.
Further, the data storage and preprocessing module comprises:
the data storage unit is responsible for receiving the data transmitted by the data acquisition module, standardizing the format and storing the data in a database;
the data cleaning unit is responsible for carrying out abnormal value and continuity inspection on the data, eliminating abnormal values and mutation values in the data and completing missing time sequences by methods of interpolation, filtering and the like;
the data association matching unit is used for generating the association relation between the dynamic data and the static road network object by adopting a GIS technology according to the geographic information in the dynamic data;
and the data standardization unit is responsible for standardizing the data produced by the upstream data unit into a data format required by a downstream model and an algorithm, and comprises data normalization, time interval screening and format conversion.
Further, the harmonization route cluster generating module comprises:
the green wave effectiveness comprehensive analysis model construction unit is responsible for generating green wave effectiveness comprehensive analysis models of all road sections in the road network;
the green wave coordination effectiveness real-time calculation unit is responsible for receiving real-time road network dynamic data and calculating the green wave coordination effectiveness of each road section in the road network in real time based on a green wave coordination effectiveness grading model;
the road section similarity matrix construction unit is responsible for generating a road network reachable matrix, calculating the traffic data similarity between road section objects in real time and generating a green wave coordinated road section similarity matrix;
and the coordination route clustering generation unit is used for performing iterative clustering on the green wave coordination road section similarity matrix through a hierarchical clustering algorithm to automatically generate a coordination route object.
Further, the coordination scheme optimization control module comprises:
the coordination route scheme optimization unit is responsible for optimizing and adjusting the offline green wave coordination scheme according to the real-time road speed;
the coordination scheme auditing unit is responsible for sending the coordination route and the scheme generated by the algorithm to a route timing personnel for auditing, and sending the scheme to the coordination scheme generating unit after the auditing is passed;
the coordination scheme generation unit is responsible for generating corresponding control commands according to the upstream coordination scheme data and different signal machine types;
and the coordination command issuing unit is responsible for checking the reasonability of the control command of the signaler, and the command which is checked by the rule is allowed to be issued.
The invention has the beneficial effects that:
1. the problem that a traditional signal system coordination scheme can only be executed based on a static solidification scheme and is difficult to adapt to real-time road conditions is solved.
2. A method for evaluating road green wave coordination effectiveness is provided, and theoretical support is provided for traffic signal control.
3. The problem that the traditional signal system coordination scheme cannot dynamically change the route along with the road condition is solved.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
2-1 to 2-3 in FIG. 2 are schematic diagrams of membership functions of the single dynamic factors constructed by the present invention.
Fig. 3 is a schematic structural diagram of a combined road segment object of the present invention.
Fig. 4 is a schematic diagram of the architecture of the system of the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are not intended to limit the invention to these embodiments. It will be appreciated by those skilled in the art that the present invention encompasses all alternatives, modifications and equivalents as may be included within the scope of the claims.
Example one
Referring to fig. 1, the invention provides a signal green wave coordination route optimization control method based on real-time road conditions, comprising the following steps:
and S1, acquiring road network information, real-time road condition and traffic data and signal machine control scheme data.
The method specifically comprises the following steps: the method comprises the steps of obtaining traffic attributes of objects such as intersections, road sections, lanes and the like, real-time road condition traffic data and signal machine control scheme data from an ITS system (intelligent traffic system).
The urban road network information specifically comprises intersection attributes, road section attributes, lane attributes and dependency relationship data among the intersection attributes, the road section attributes and the lane attributes.
The real-time traffic data comprises road vehicle speed data, flow rate of each flow direction lane, saturation and the like. Sample data are as follows:
Figure BDA0002341525030000111
the signal machine control scheme data is specifically signal machine coordination scheme data. Sample data are as follows:
Figure BDA0002341525030000112
and S2, constructing a green wave coordination effectiveness comprehensive analysis model based on a fuzzy theory, and calculating the green wave coordination effectiveness of the road section.
The green wave coordination effectiveness means that the road can implement a green wave coordination scheme in an abstract sense and obtains a good-effect probability. In order to more accurately and comprehensively analyze the green wave coordination effectiveness of the road, the invention provides a green wave coordination effectiveness comprehensive analysis method based on a fuzzy theory according to real-time road condition data, which specifically comprises the following steps:
s2.1, establishing a dynamic factor set influencing the green wave coordination effectiveness;
according to the traffic engineering theory, the green wave coordination scheme is only suitable for the road sections with small traffic volume, stable vehicle travel time and similar traffic volume conditions at the upstream and downstream intersections. Based on the above theory, the dynamic factors affecting the green wave coordination effectiveness can be summarized as: the difference value of the theoretical optimal cycle time length of the upstream and downstream road junctions, the road section saturation, the road section average travel time, the difference value of the designed phase difference and the like. Static factors such as the length of the road section, the number of road section exits and the like do not change frequently, and therefore the static factors are not used as factors for analyzing the green wave coordination effectiveness of the road section. The dynamic factor set is established as follows:
U＝{△cm,dsm,△tm}
wherein, △ cijFor the optimal cycle time length difference, ds, of the upstream and downstream intersections of the m road sectionsmSaturation of the m-segment, △ tmAnd designing a difference value of the phase difference for the road section average travel time and the green wave coordination scheme.
The theoretical optimal period time length difference value of the upstream and downstream intersections can be obtained by making difference between the optimal periods of adjacent intersections in the signal system. If the signal system can not provide theoretical optimal period data, the theoretical optimal period data can be obtained by calculation based on an F-B method provided by Webster according to the flow data of the detector.
The specific calculation process of the F-B method is as follows:
Figure BDA0002341525030000121
Figure BDA0002341525030000122
Y＝∑yk
Figure BDA0002341525030000123
wherein, C0The optimal signal period duration(s) is set, L is the total green light loss time(s) of all key traffic flows at the intersection, the total green light loss time is equal to the sum of the green light loss time of each phase, and the phase green light loss time is set as (L)s+I-A)k，LsThe time loss of starting and stopping the automobile is represented by I, namely the green light interval time, namely the yellow light time plus the total red light crossing clearing time, and A is the yellow light time. Y is the maximum traffic flow ratio Y of each phase of the intersectionkSum of (a), ykEqual to the ratio of k phase critical lane flow to lane capacity.
And the road section saturation is the average value of the saturation of the lanes passing through the key traffic flow direction of the road section. Because the green wave coordination design generally aims at the traffic flow direction with the largest traffic flow of the road section, the invention takes the lane saturation mean value corresponding to the lane function with the highest traffic flow ratio as the road section saturation index by counting the traffic flow ratio of each functional lane of the road section.
The difference value between the average travel time of the road section and the design phase difference is obtained by subtracting the average travel time of the road section from the off-line coordination scheme design phase difference of the road section at the current time period, and can be generally obtained from the data of the signal machine control scheme. If the data cannot be obtained, the data can be approximated by the following formula:
Figure BDA0002341525030000131
Figure BDA0002341525030000132
wherein, t0Designing the phase difference, v, for the road section0Design speed of vehicle for road section,. lmLength of m road sections, △ tmDifference, v, between actual travel time and design phase difference for road sectionmIs the actual speed of the road section.
S2.2, determining the weight of each factor on the green wave coordination effectiveness, and establishing a weight set;
the method specifically comprises the following steps:
the effectiveness of the green wave coordination scheme is related to the three dynamic factors, and when the green wave coordination effectiveness index is calculated, the weight of each factor on the green wave coordination effectiveness needs to be determined. In an actual traffic system, if the road section saturation degree is too high, vehicles are queued up to cause multiple times of parking and waiting for passing through the intersection, and the road section green wave starting coordination rather aggravates congestion; if the average travel time of the road section deviates from the designed phase difference too much, the green light starting time and the traffic flow arrival time are not matched, and the green light time is wasted; if the theoretical period deviation of the upstream and downstream intersections is large, the total level difference of the traffic volumes of the two intersections is shown, and the time of vehicles passing the intersections in other directions can be increased by establishing a coordination relationship.
Based on the above analysis, the weight set of three dynamic factors is set as:
W＝{qc,qds,qt}
wherein q iscWeight of theoretical period deviation of upstream and downstream junctions, qdsWeight of road section saturation, qtAnd weighting the road section vehicle speed and the design vehicle speed deviation. According to the analysis of the importance degree of each factor on the green coordination wave effectiveness, let qds>qt>qc。
S2.3, determining a single-factor fuzzy weight model, and establishing a membership function;
the method specifically comprises the following steps:
with respect to green wave coordination schemes, the industry is typically concerned only with offline design and optimization of the scheme, and there is no analysis and staging strategy with respect to its real-time runtime effectiveness. Based on common three-level classification (smooth, slow running and congestion) in road condition classification, the green wave coordination effectiveness is set to A, B, C three levels (A > B > C) according to the height in sequence to obtain a green wave coordination effectiveness fuzzy set V:
V＝{A、B、C}
in order to analyze the comprehensive influence of dynamic factors on the road section green wave coordination effectiveness, firstly, the mapping relation between single factors and road section effectiveness grading is determined:
Figure BDA0002341525030000141
wherein, c1,c2,ds1,ds2,t1,t2And setting the dynamic boundary of the grading threshold according to the traffic conditions of different road sections.
The invention comprehensively analyzes the green wave coordination effectiveness index based on a comprehensive evaluation method in a fuzzy mathematical theory. The method requires the consistency of the factor set to the evaluation set, so the normalization processing is carried out on the dynamic factors, and the order is as follows:
Figure BDA0002341525030000142
Figure BDA0002341525030000143
Figure BDA0002341525030000151
wherein, △ cmaxSetting a difference value between a maximum value and a minimum period allowed for the intersection; dsmaxThe saturation theoretical maximum boundary, typically 100; t is t0And designing a phase difference for the road section offline coordination scheme.
According to the above model, see fig. 2, a one-factor membership function is constructed as follows:
Figure BDA0002341525030000152
Figure BDA0002341525030000153
Figure BDA0002341525030000154
Figure BDA0002341525030000155
Figure BDA0002341525030000156
Figure BDA0002341525030000157
Figure BDA0002341525030000161
Figure BDA0002341525030000162
Figure BDA0002341525030000163
where phi { △ cm' → A } is △ cm"membership function for a class a, and so on, where k is the slope at the critical point of the membership function;
calculating the membership degree of the road section object about each factor, and constructing a dynamic factor fuzzy relation matrix, such as:
Figure BDA0002341525030000164
in the formula, rijIndicating the degree of membership of the ith factor with respect to the jth hierarchical level. When the data is unreliable, the associated membership may be set to 0.
In the invention, the element and the grading level in the factor set are 3, and the fuzzy relation matrix is a matrix of 3x 3:
Figure BDA0002341525030000165
and S2.4, establishing a green wave coordination effectiveness comprehensive analysis model and calculating the green wave coordination effectiveness of the road section.
The method specifically comprises the following steps:
after the fuzzy relation matrix is determined, analyzing the grading result of the road section object by adopting fuzzy transformation:
S＝W·R＝[qcqdsqt]·R＝[b1b2b3]
wherein S is a fuzzy comprehensive evaluation set, bjIndicating the degree of membership of the link object with respect to the jth level result.
Firstly, the evaluation grades are quantized, 1, 2 and 3 respectively represent A, B, C grades which are called ranks of each grading grade, then the membership grades corresponding to the grades in the S are taken as weight values to carry out weighted average to obtain a fuzzy grading index α, and after α is rounded, the fuzzy grading index is taken as the grading result of the road section object, and the formula is as follows:
Figure BDA0002341525030000171
rank≈α。
s3, clustering road sections with high green wave coordination effectiveness based on an improved hierarchical clustering algorithm, and automatically generating a coordination route;
the method specifically comprises the following steps:
s3.1, determining whether vehicles can pass between road sections according to the information of intersections on the upstream and downstream of the road sections and the lane function, and constructing a road network reachable matrix;
in order to identify a route that satisfies the green wave condition, it is necessary to determine whether the vehicle can pass between road sections. The method specifically comprises the following steps:
and acquiring the IDs of the upstream and downstream intersections of all road sections in the area, and if the IDs of the downstream intersection of the road section A and the upstream intersection of the road section B are consistent and a lane with corresponding steering exists, indicating that the road section A can reach the road section B. If the road sections can reach, setting the value of the corresponding position of the matrix to be 1, otherwise, setting the value to be infinite, and constructing a network reachable matrix.
And S3.2, counting traffic data of the nearly N periodic road sections, calculating green wave coordination effectiveness and traffic data similarity, filling the green wave coordination effectiveness and the traffic data similarity into a road network reachability matrix, and constructing a green wave effectiveness judgment matrix.
The method specifically comprises the following steps:
and calculating the average value of the deviation of the upstream theoretical period and the downstream theoretical period of the downstream road with the continuous N signal periods of each road section, the average value of the saturation degree of the road section and the average value of the deviation of the travel time of the road section. After normalization, inputting the green wave coordination effectiveness fuzzy grading indexes into a fuzzy comprehensive evaluation model to obtain the green wave coordination effectiveness fuzzy grading indexes of each road section:
β＝{α1,α2,α3,…αi,…,αk}
wherein, αiThe green wave coordination fuzzy grading index of the ith road section in the road network is represented by k, the k is the road in the road networkAnd (8) total number of segments, β, namely a road segment green wave coordination effectiveness fuzzy grading index set in a road network.
The hierarchical clustering method determines similarity by calculating Euclidean distances of data points, and since traffic volume similarity is one of important factors of whether a coordinated route can be formed between road sections, the embodiment calculates the road section traffic volume similarity by using road section traffic flow as data characteristics of the road sections. When designing a coordination route, a signal timing person needs to consider the similarity between the downstream road section key lane flow and the upstream road section turning lane flow. The key lane is a lane corresponding to the highest traffic flow ratio of the road section and the flowing direction, the turning lane flow is a lane corresponding to the turning relation between the upstream road section and the downstream road section, and if vehicles on the upstream road section pass through the intersection straight and then reach the downstream road section, the turning lane flow is the sum of the straight-going lane flow of the upstream road section, and the like. The present invention therefore calculates traffic flow similarities between road segments based on downstream road segment key lane flow and upstream road segment turn flow. The specific process is as follows:
1. and filtering road sections with green wave coordination effectiveness grades rank B and C based on the green wave coordination effectiveness fuzzy grading result β, and constructing a road network green wave coordination candidate road section set U.
2. And calculating the flow of the steering lanes between all the road sections in the set U, and marking the key lane with the largest flow ratio.
3. Traversing elements in a reachable matrix of the road network, screening a road section combination < X → Y > which has an upstream-downstream relation and exists in a set U, and calculating the Euclidean distance between the steering flow of the road sections from X to Y and the key lane flow of the road sections from Y:
Figure BDA0002341525030000181
wherein D isX->YIs the Euclidean distance similarity of road sections X and Y, fX->YThe steering flow rate for the X to Y road segment,
Figure BDA0002341525030000182
is the key flow of the Y road section.
If the road sections have the upstream and downstream relationship but any one of the road sections is not in the green wave coordination candidate road section set, the following steps are performed:
DX->Y＝∞
and filling the calculated Euclidean distance similarity of the road section traffic flow into the corresponding position of the reachable matrix of the road network to obtain a green wave coordinated road section traffic flow Euclidean distance similarity matrix KD.
And S3.3, clustering road sections in the similarity matrix of the green wave coordination road sections based on a hierarchical clustering algorithm to generate a route with green wave coordination effectiveness.
In the embodiment, road sections with approximate flow in the steering flow matrix are clustered, that is, road sections in the green wave coordinated road section traffic flow Euclidean distance similarity matrix are clustered. The hierarchical clustering method is used for creating a hierarchical nested clustering tree by calculating the similarity between different classes of data points, and the method for combining the clustering results from bottom to top is very consistent with the clustering scene of a green wave coordination route. The method comprises the following specific steps:
(1) determining △ D that the road segment object allows merging of the minimum Euclidean distance difference;
(2) traversing elements in a green wave coordinated road section traffic flow Euclidean distance similarity matrix KD to find a minimum value;
(3) and combining the road section objects corresponding to the minimum element to generate a combined road section object, wherein the upstream intersection of the combined road section object is the upstream intersection of the road section which is not connected with the upstream road section in the combination, and the downstream intersection is the downstream intersection of the road section which is not connected with the downstream road section in the combined object. As shown in fig. 3, a combined object of a link a and a link B, in which an upstream intersection as the combined object is filled with a left oblique hatching and a downstream intersection as the combined object is filled with a right oblique hatching;
(4) calculating traffic flow Euclidean distances of the combined road section and other road sections, removing road section objects in the combined road section from a green wave coordination road section traffic flow Euclidean distance similarity matrix KD, replacing the combined road section objects in a combined way, and regenerating a green wave coordination road section traffic flow Euclidean distance similarity matrix KD;
(5) repeating the steps (2) to (4) until no element in the matrix is less than △ D;
(6) and the road section combination object in the green wave coordination road section traffic flow Euclidean distance similarity matrix KD is a route suitable for starting green wave coordination.
The minimum link object merging distance △ D is set to avoid merging links with too large flow difference between upstream and downstream coordinated links into the same coordinated route, and to ensure similar traffic volumes of the links in the coordinated route.
And S4, optimizing the overall coordination scheme of the coordination route based on the real-time road section speed data of the Internet, and issuing a coordination command. Under the actual traffic condition, a certain deviation exists between the average speed of a road section and the design speed of an offline coordination scheme, and in order to guarantee the real-time effectiveness of the green wave coordination scheme, the invention provides a coordination scheme phase difference optimization method based on internet real-time vehicle speed data. The method specifically comprises the following steps:
and S4.1, designing a coordination scheme based on the offline of the current time period of the road section, and calculating a phase difference parameter of the optimized coordination scheme. The method specifically comprises the following steps:
the intersection with the largest demand period in the coordination route generated in the screening step S3 is marked as a core intersection. The core intersection is taken as the center, the designed phase difference value of the upstream road section of the core intersection is set as a negative value, and the phase difference value of the downstream road section of the core intersection is set as a positive value. The period duration of the coordinated route adopts the theoretical optimal period duration of the core intersection, and the phase difference of each road section is the quotient of the road section length and the actual average vehicle speed:
Figure BDA0002341525030000201
obtaining a phase difference sequence of the whole coordinated road section:
T＝[-t1,-t2,…,-tk,tk+1…,tn-1]
wherein-t1To coordinate the phase difference parameter between the initial intersection and the downstream intersection of the route, -tkCoordinating a phase difference parameter between an intersection and a core intersection for coordinating a route core intersection upstream, tk+1Core intersection downstream intersection and for coordinating routesPhase difference parameters of the core intersection. n is the number of coordinated route intersections.
And S4.2, constructing a green wave coordination system command of the annunciator and issuing the green wave coordination system command to the annunciator system.
Generally, the signal green wave coordination command needs to specify a phase difference parameter, a period duration parameter and a coordination reference phase of the intersection. The phase difference parameter can be obtained from T, the period parameter is set as a theoretical period of the core intersection, and the coordination scheme reference phase can be obtained from an offline design coordination scheme. According to the green wave coordination route generated, the green wave coordination system commands of the annunciator are constructed from the initial intersection and are issued one by one, and the real-time optimization control of the green wave coordination route is realized.
The method is based on real-time traffic data to evaluate the green wave effectiveness of the road, dynamically identifies the routes in the road network meeting the green wave coordination condition, and realizes the optimal control of the green wave coordination scheme based on real-time road section vehicle speed data.
Example two
Referring to fig. 4, the present embodiment provides a system for implementing the method for optimizing and controlling a green wave coordinated signal route based on real-time road conditions in the first embodiment, including:
and the data acquisition module is used for acquiring related data required by the coordinated route clustering generation, including dynamic data such as detector flow, saturation, signal machine control scheme, internet vehicle travel speed, time and the like, and urban road network static information.
And the data storage module and the preprocessing module are used for unifying the source data format, and performing static association processing, time granularity unification processing and the like on the data.
The coordinated route clustering generation module is used for constructing a coordinated route validity comprehensive analysis model in an off-line manner, calculating road section green wave validity on line, and clustering and generating a coordinated route through a hierarchical clustering method based on road network reachability and traffic data similarity;
and the coordination scheme optimization control model is used for regularly receiving the coordination route clustering result, optimizing the coordination scheme on the route according to the real-time road condition, generating a control command which can be identified by the signal system and issuing the control command to the signal machine.
The data acquisition module of this embodiment includes dynamic data acquisition unit and static data acquisition unit.
And the dynamic data acquisition unit is responsible for acquiring relevant data of real-time road conditions, including Internet road section speed data (a Gade map API and a Baidu map API), detector flow and saturation data (a SCATS detector) and signal machine control scheme data.
And the static data acquisition unit is responsible for acquiring static data such as road network space geographic position information, intersection numbers, road section grades, road section lengths, road section numbers, lane functions and the like.
The data storage and preprocessing module in this embodiment includes a data storage unit, a data cleaning unit, a data association matching unit, and a data standardization unit.
The data storage unit is responsible for receiving the data transmitted by the data acquisition module, standardizing the format and storing the data in a database;
the data cleaning unit is responsible for carrying out abnormal value and continuity inspection on the data, eliminating abnormal values and mutation values in the data and completing missing time sequences by methods of interpolation, filtering and the like;
the data association matching unit is used for generating the association relation between the dynamic data and the static road network object by adopting a GIS technology according to the geographic information in the dynamic data;
and the data standardization unit is responsible for standardizing the data produced by the upstream data unit into a data format required by a downstream model and an algorithm, and comprises data normalization, time interval screening, format conversion and the like.
The coordination route clustering generation module comprises a green wave effectiveness hierarchical model construction unit, a green wave effectiveness real-time calculation unit, a road section similarity matrix construction unit and a coordination route clustering generation unit.
And the green wave effectiveness grading model construction unit is responsible for generating green wave coordination effectiveness grading models of all road sections in the road network.
The green wave effectiveness real-time calculation unit is responsible for receiving real-time road network dynamic data and calculating the green wave coordination effectiveness of each road section in the road network in real time based on a green wave effectiveness comprehensive analysis model;
and the road section similarity matrix construction unit is responsible for generating a road network reachable matrix, calculating the traffic data similarity between road section objects in real time and generating a green wave coordinated road section traffic flow Euclidean distance similarity matrix.
And the coordination route clustering generation unit is used for performing iterative clustering on the green wave coordination road section similarity matrix through a hierarchical clustering algorithm to automatically generate a coordination route object.
The coordination scheme optimization control module of this embodiment includes a coordination route scheme optimization unit, a coordination scheme auditing unit, a coordination scheme generation unit, and a coordination command issuing unit.
And the coordination route scheme optimization unit is responsible for optimizing and adjusting the offline green wave coordination scheme according to the real-time road speed.
And the coordination scheme auditing unit is responsible for sending the coordination route and the scheme generated by the algorithm to a route timing person for auditing. And after the verification is passed, sending the scheme to a coordination scheme generation unit.
And the coordination scheme generation unit is responsible for generating corresponding control commands according to the upstream coordination scheme data and different signal machine types.
And the coordination command issuing unit is responsible for checking the reasonability of the control command of the signaler, and the command which is checked by the rule is allowed to be issued.

Claims (14)

1. A signal green wave coordination route optimization control method based on real-time road conditions comprises the following steps:
s1, acquiring road network information, real-time road condition and traffic data and signal machine control scheme data;
s2, constructing a green wave coordination effectiveness comprehensive analysis model based on a fuzzy theory, and calculating the green wave coordination effectiveness of the road section;
s3, clustering road sections with high green wave coordination effectiveness based on an improved hierarchical clustering algorithm, and automatically generating a coordination route;
and S4, optimizing the overall coordination scheme of the coordination route based on the real-time road section speed data of the Internet, and issuing a coordination command.
2. The signal green wave coordination route optimization control method based on the real-time road condition as claimed in claim 1, characterized in that: in step S1, the road network information includes: the attribute of the intersection, the attribute of the road section, the attribute of the lane and the data of the dependency relationship among the three; the real-time road condition traffic data comprises road vehicle speed data, flow of each flow direction lane and saturation; the annunciator control scheme data is annunciator coordination scheme data.
3. The signal green wave coordination route optimization control method based on the real-time road condition as claimed in claim 1, characterized in that: the specific steps of step S2 are as follows:
s2.1, establishing a dynamic factor set influencing the green wave coordination effectiveness;
the dynamic factor set is as follows:
U＝{△cm,dsm,△tm}
wherein, △ cmFor the optimal cycle time length difference, ds, of the upstream and downstream intersections of the m road sectionsmSaturation of the m-segment, △ tmDesigning a difference value of the phase difference for the road section average travel time and a green wave coordination scheme;
s2.2, determining the weight of each factor on the green wave coordination effectiveness, and establishing a weight set;
the weight set of the three dynamic factors is set as follows:
W＝{qc,qds,qt}
wherein q iscWeight of theoretical period deviation of upstream and downstream junctions, qdsWeight of road section saturation, qtWeighting the road section vehicle speed and the design vehicle speed deviation;
s2.3, determining a single-factor fuzzy weight model, and establishing a membership function;
and S2.4, establishing a green wave coordination effectiveness comprehensive analysis model and calculating the green wave coordination effectiveness of the road section.
4. The signal green wave coordination route optimization control method based on the real-time road condition as claimed in claim 1, characterized in that: the specific steps of step S2.3 are as follows:
setting the green wave coordination effectiveness to A, B, C three levels according to the sequence of the levels, obtaining a green wave coordination effectiveness fuzzy set V:
V＝{A、B、C}
setting c1,c2,ds1,ds2,t1,t2Respectively, the theoretical optimal period deviation △ c of the upstream and downstream intersectionsmRoad section saturation dsmDifference △ t of road section average travel time and green wave coordination scheme design phase differencemDetermining a mapping relation between a single dynamic factor and green wave coordination effectiveness grading according to a dynamic boundary of a grading threshold;
normalizing the dynamic factors to ensure that:
Figure FDA0002341525020000021
Figure FDA0002341525020000022
Figure FDA0002341525020000023
wherein, △ cmaxSetting a difference value between a maximum value and a minimum period allowed for the intersection; dsmaxThe saturation theoretical maximum boundary, typically 100; t is t0Designing a phase difference for a road section offline coordination scheme;
according to the model, the membership function of the single dynamic factor is constructed as follows:
Figure FDA0002341525020000031
Figure FDA0002341525020000032
Figure FDA0002341525020000033
Figure FDA0002341525020000034
Figure FDA0002341525020000035
Figure FDA0002341525020000036
Figure FDA0002341525020000041
Figure FDA0002341525020000042
Figure FDA0002341525020000043
where phi { △ cm' → A } is △ cm"membership function for a class a, and so on, where k is the slope at the critical point of the membership function;
calculating the membership degree of the road section object about each factor, and constructing a dynamic factor fuzzy relation matrix as follows:
Figure FDA0002341525020000044
in the formula, rijIndicating the degree of membership of the ith factor with respect to the jth hierarchical level.
5. The signal green wave coordination route optimization control method based on the real-time road condition as claimed in claim 4, wherein: the specific steps of step S2.4 are as follows:
analyzing the grading result of the road section object by adopting fuzzy transformation:
S＝W·R＝[qcqdsqt]·R＝[b1b2b3]
wherein S is a fuzzy comprehensive evaluation set, bjRepresenting the degree of membership of the road section object with respect to the jth level result;
then, taking the membership degree corresponding to each grade in the S as a weight, carrying out weighted average to obtain a fuzzy grading index α, rounding off α to obtain a grading result of the road section object, wherein the formula is as follows:
Figure FDA0002341525020000051
rank≈α。
6. the signal green wave coordination route optimization control method based on the real-time road condition as claimed in claim 1, characterized in that: the specific steps of step S3 are as follows:
s3.1, determining whether vehicles can pass between road sections according to the information of intersections on the upstream and downstream of the road sections and the lane function, and constructing a road network reachable matrix;
s3.2, counting traffic data of the nearly N periodic road sections, calculating green wave coordination effectiveness and traffic data similarity, filling a road network reachability matrix, and constructing a green wave coordination road section similarity matrix;
and S3.3, clustering road sections in the similarity matrix of the green wave coordination road sections based on a hierarchical clustering algorithm to generate a route with green wave coordination effectiveness.
7. The signal green wave coordination route optimization control method based on real-time road conditions as claimed in claim 6, characterized in that: the specific steps of step S3.2 are as follows:
calculating the average value of theoretical period deviation of upstream and downstream roads, the average value of saturation of road sections and the average value of travel time deviation of road sections in continuous N signal periods of each road section; after normalization, inputting the green wave coordination effectiveness comprehensive analysis model into the green wave coordination effectiveness comprehensive analysis model to obtain the green wave coordination effectiveness of each road section;
filtering road sections with green wave coordination effectiveness rank B and C, and constructing a road network green wave coordination candidate road section set U; calculating the flow of the steering lanes between all the road sections in the set U, and marking the key lane with the largest flow ratio; traversing elements in a reachable matrix of the road network, screening a road section combination < X → Y > which has an upstream-downstream relation and exists in a set U, and calculating the Euclidean distance between the steering flow of the road sections from X to Y and the key lane flow of the road sections from Y:
Figure FDA0002341525020000052
wherein D isX->YIs the Euclidean distance similarity of road sections X and Y, fX->YThe steering flow rate for the X to Y road segment,
Figure FDA0002341525020000053
is the key flow of the Y road section;
if the road sections have the upstream and downstream relationship but any one of the road sections is not in the green wave coordination candidate road section set, the following steps are performed:
DX->Y＝∞
and filling the calculated Euclidean distance similarity of the road traffic flow into the corresponding position of the reachable matrix of the road network to obtain a green wave coordinated road section similarity matrix KD.
8. The signal green wave coordination route optimization control method based on real-time road conditions as claimed in claim 7, wherein: the specific steps of step S3.3 are as follows:
(1) determining △ D that the road segment object allows merging of the minimum Euclidean distance difference;
(2) traversing elements in the green wave coordination road section similarity matrix KD to find a minimum value;
(3) merging the road section objects corresponding to the minimum element to generate a combined road section object, wherein the upstream intersection of the combined road section object is the upstream intersection of the road section which is not connected with the upstream road section in the combination, and the downstream intersection is the downstream intersection of the road section which is not connected with the downstream road section in the combined object;
(4) calculating the Euclidean distance of traffic flow of the combined road section and other road sections, removing road section objects in the combined road section from the similarity matrix KD of the green wave coordination road section, replacing the combined road section objects, and regenerating the similarity matrix KD of the green wave coordination road section;
(5) repeating the steps (2) to (4) until no element in the matrix is less than △ D;
(6) and the road section combination object in the green wave coordination road section similarity matrix KD is a route suitable for starting green wave coordination.
9. The signal green wave coordination route optimization control method based on the real-time road condition as claimed in claim 1, characterized in that: the specific steps of step S4 are as follows:
s4.1, designing a coordination scheme based on the offline of the current time period of the road section, and calculating a phase difference parameter of the optimized coordination scheme;
the method specifically comprises the following steps: screening the intersection with the largest demand period in the coordination route generated in the step S3, and marking the intersection as a core intersection; taking a core intersection as a center, setting the designed phase difference value of an upstream road section of the core intersection as a negative value, and setting the phase difference value of a downstream road section of the core intersection as a positive value; the period duration of the coordinated route adopts the theoretical optimal period duration of the core intersection, and the phase difference of each road section is the quotient of the road section length and the actual average vehicle speed:
Figure FDA0002341525020000071
obtaining a phase difference sequence of the whole coordinated road section:
T＝[-t1,-t2,…,-tk,tk+1…,tn-1]
wherein-t1To coordinate the phase difference parameter between the initial intersection and the downstream intersection of the route, -tkCoordinating a phase difference parameter between an intersection and a core intersection for coordinating a route core intersection upstream, tk+1The phase difference parameter of the downstream intersection of the core intersection of the coordinated route and the core intersection is obtained, and n is the number of the total intersections of the coordinated route;
and S4.2, constructing a green wave coordination system command of the annunciator and issuing the green wave coordination system command to the annunciator system.
10. A signal green wave coordination route optimization control system based on real-time road conditions is characterized in that: the method comprises the following steps:
the data acquisition module is used for acquiring related data required by the coordinated route clustering generation, wherein the related data comprises dynamic data such as detector flow, saturation, signal machine control scheme, internet vehicle travel speed, time and the like, and static information of an urban road network;
the data storage module and the preprocessing module are used for unifying the source data format and carrying out static association processing and time granularity unification processing on the data;
the coordinated route clustering generation module is used for constructing a coordinated route validity comprehensive analysis model in an off-line manner, calculating road section green wave coordinated validity on line, and clustering and generating a coordinated route through a hierarchical clustering method based on road network reachability and traffic data similarity;
and the coordination scheme optimization control model is used for regularly receiving the coordination route clustering result, optimizing the coordination scheme on the route according to the real-time road condition, generating a control command which can be identified by the signal system and issuing the control command to the signal machine.
11. The system according to claim 10, wherein the system comprises: the data acquisition module comprises:
the dynamic data acquisition unit is responsible for acquiring real-time road condition related data, including Internet road speed data, detector flow and saturation data and signal machine control scheme data;
and the static data acquisition unit is responsible for acquiring static data such as road network space geographic position information, intersection numbers, road section grades, road section lengths, road section numbers, lane functions and the like.
12. The system according to claim 10, wherein the system comprises: the data storage and preprocessing module comprises:
the data storage unit is responsible for receiving the data transmitted by the data acquisition module, standardizing the format and storing the data in a database;
the data cleaning unit is responsible for carrying out abnormal value and continuity inspection on the data, eliminating abnormal values and mutation values in the data and completing missing time sequences by methods of interpolation, filtering and the like;
the data association matching unit is used for generating the association relation between the dynamic data and the static road network object by adopting a GIS technology according to the geographic information in the dynamic data;
and the data standardization unit is responsible for standardizing the data produced by the upstream data unit into a data format required by a downstream model and an algorithm, and comprises data normalization, time interval screening and format conversion.
13. The system according to claim 10, wherein the system comprises: the coordinated route cluster generation module comprises:
the green wave effectiveness comprehensive analysis model construction unit is responsible for generating green wave coordination effectiveness comprehensive analysis models of all road sections in the road network;
the green wave coordination effectiveness real-time calculation unit is responsible for receiving real-time road network dynamic data and calculating the green wave coordination effectiveness of each road section in the road network in real time based on a green wave effectiveness grading model;
the road section similarity matrix construction unit is responsible for generating a road network reachable matrix, calculating the traffic data similarity between road section objects in real time and generating a green wave coordinated road section similarity matrix;
and the coordination route clustering generation unit is used for performing iterative clustering on the green wave coordination road section similarity matrix through a hierarchical clustering algorithm to automatically generate a coordination route object.
14. The system according to claim 10, wherein the system comprises: the coordination scheme optimization control module comprises:
the coordination route scheme optimization unit is responsible for optimizing and adjusting the offline green wave coordination scheme according to the real-time road speed;
the coordination scheme auditing unit is responsible for sending the coordination route and the scheme generated by the algorithm to a route timing personnel for auditing, and sending the scheme to the coordination scheme generating unit after the auditing is passed;
the coordination scheme generation unit is responsible for generating corresponding control commands according to the upstream coordination scheme data and different signal machine types;
and the coordination command issuing unit is responsible for checking the reasonability of the control command of the signaler, and the command which is checked by the rule is allowed to be issued.
CN201911377952.4A 2019-12-27 2019-12-27 Signal green wave coordination route optimization control method and system based on real-time road condition Active CN111210621B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911377952.4A CN111210621B (en) 2019-12-27 2019-12-27 Signal green wave coordination route optimization control method and system based on real-time road condition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911377952.4A CN111210621B (en) 2019-12-27 2019-12-27 Signal green wave coordination route optimization control method and system based on real-time road condition

Publications (2)

Publication Number Publication Date
CN111210621A true CN111210621A (en) 2020-05-29
CN111210621B CN111210621B (en) 2021-04-06

Family

ID=70789445

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911377952.4A Active CN111210621B (en) 2019-12-27 2019-12-27 Signal green wave coordination route optimization control method and system based on real-time road condition

Country Status (1)

Country Link
CN (1) CN111210621B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112037546A (en) * 2020-09-07 2020-12-04 青岛海信网络科技股份有限公司 Traffic signal control method and device
CN112885089A (en) * 2021-01-25 2021-06-01 合肥学院 Main line green wave intelligent diagnosis model based on multi-dimensional indexes
CN113240925A (en) * 2021-04-21 2021-08-10 郑州大学 Travel path determination method considering random delay influence of intersection signal lamps
CN113345230A (en) * 2021-06-02 2021-09-03 江苏智通交通科技有限公司 Optimization method and optimization system for researching and judging coordination trunk management and control problem
CN113851010A (en) * 2021-10-11 2021-12-28 武汉理工大学 Non-linear path green wave control method, device, equipment and storage medium
CN115424460A (en) * 2022-08-10 2022-12-02 上海宝康电子控制工程有限公司 Road green wave optimization algorithm and system

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101281685A (en) * 2008-01-30 2008-10-08 吉林大学 Coordination control method for area mixed traffic self-adaption signal
CN101639978A (en) * 2009-08-28 2010-02-03 华南理工大学 Method capable of dynamically partitioning traffic control subregion
CN103578273A (en) * 2013-10-17 2014-02-12 银江股份有限公司 Road traffic state estimation method based on microwave radar data
CN103927890A (en) * 2014-04-29 2014-07-16 北京建筑大学 Artery coordination signal control method based on dynamic O-D matrix estimation
CN105551250A (en) * 2016-01-13 2016-05-04 东南大学 Method for discriminating urban road intersection operation state on the basis of interval clustering
CN106297334A (en) * 2016-10-27 2017-01-04 东南大学 Main line section division methods under Philodendron ‘ Emerald Queen'
CN106448196A (en) * 2016-05-16 2017-02-22 江苏智通交通科技有限公司 Solution selection type trunk green wave configuration method and system
CN106530684A (en) * 2015-09-11 2017-03-22 杭州海康威视系统技术有限公司 Method and device of processing traffic road information
US20180233037A1 (en) * 2017-02-08 2018-08-16 Weiping Meng Traffic Signal 2D-Jam-Relief Control Mode
WO2018184413A1 (en) * 2017-04-07 2018-10-11 孟卫平 Green wave control method for traffic signals
CN110164128A (en) * 2019-04-23 2019-08-23 银江股份有限公司 A kind of City-level intelligent transportation analogue system

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101281685A (en) * 2008-01-30 2008-10-08 吉林大学 Coordination control method for area mixed traffic self-adaption signal
CN101639978A (en) * 2009-08-28 2010-02-03 华南理工大学 Method capable of dynamically partitioning traffic control subregion
CN103578273A (en) * 2013-10-17 2014-02-12 银江股份有限公司 Road traffic state estimation method based on microwave radar data
CN103927890A (en) * 2014-04-29 2014-07-16 北京建筑大学 Artery coordination signal control method based on dynamic O-D matrix estimation
CN106530684A (en) * 2015-09-11 2017-03-22 杭州海康威视系统技术有限公司 Method and device of processing traffic road information
CN105551250A (en) * 2016-01-13 2016-05-04 东南大学 Method for discriminating urban road intersection operation state on the basis of interval clustering
CN106448196A (en) * 2016-05-16 2017-02-22 江苏智通交通科技有限公司 Solution selection type trunk green wave configuration method and system
CN106297334A (en) * 2016-10-27 2017-01-04 东南大学 Main line section division methods under Philodendron ‘ Emerald Queen'
US20180233037A1 (en) * 2017-02-08 2018-08-16 Weiping Meng Traffic Signal 2D-Jam-Relief Control Mode
WO2018184413A1 (en) * 2017-04-07 2018-10-11 孟卫平 Green wave control method for traffic signals
CN110164128A (en) * 2019-04-23 2019-08-23 银江股份有限公司 A kind of City-level intelligent transportation analogue system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
杨立才: "城市道路交通智能控制策略的研究", 《中国博士论文全文数据库》 *
王兹林: "基于道路交通状态判别的干线动态协调控制优化方法研究", 《中国优秀硕士学位论文全文数据库》 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112037546A (en) * 2020-09-07 2020-12-04 青岛海信网络科技股份有限公司 Traffic signal control method and device
CN112037546B (en) * 2020-09-07 2021-09-14 青岛海信网络科技股份有限公司 Traffic signal control method and device
CN112885089A (en) * 2021-01-25 2021-06-01 合肥学院 Main line green wave intelligent diagnosis model based on multi-dimensional indexes
CN112885089B (en) * 2021-01-25 2022-06-07 合肥学院 Main line green wave intelligent diagnosis method based on multi-dimensional indexes
CN113240925A (en) * 2021-04-21 2021-08-10 郑州大学 Travel path determination method considering random delay influence of intersection signal lamps
CN113345230A (en) * 2021-06-02 2021-09-03 江苏智通交通科技有限公司 Optimization method and optimization system for researching and judging coordination trunk management and control problem
CN113345230B (en) * 2021-06-02 2023-01-03 江苏智通交通科技有限公司 Optimization method and optimization system for researching and judging coordination trunk management and control problem
CN113851010A (en) * 2021-10-11 2021-12-28 武汉理工大学 Non-linear path green wave control method, device, equipment and storage medium
CN115424460A (en) * 2022-08-10 2022-12-02 上海宝康电子控制工程有限公司 Road green wave optimization algorithm and system
CN115424460B (en) * 2022-08-10 2024-02-09 上海宝康电子控制工程有限公司 Road green wave optimization method and system

Also Published As

Publication number Publication date
CN111210621B (en) 2021-04-06

Similar Documents

Publication Publication Date Title
CN111210621B (en) Signal green wave coordination route optimization control method and system based on real-time road condition
CN108629974B (en) Traffic operation index establishing method considering urban road traffic network characteristics
CN102087788B (en) Method for estimating traffic state parameter based on confidence of speed of float car
Fang et al. FTPG: A fine-grained traffic prediction method with graph attention network using big trace data
CN103871246B (en) Based on the Short-time Traffic Flow Forecasting Methods of road network spatial relation constraint Lasso
CN110634287B (en) Urban traffic state refined discrimination method based on edge calculation
CN109785618B (en) Short-term traffic flow prediction method based on combinational logic
WO2022083166A1 (en) Method and system for reconstructing vehicle&#39;s driving trajectory when checkpoint data is missing
CN114049765B (en) Urban road network traffic flow OD estimation method based on automatic vehicle number plate identification data
CN111091295B (en) Urban area boundary control system
CN106447119A (en) Short-term traffic flow prediction method and system based on convolutional neural network
CN108765961B (en) Floating car data processing method based on improved amplitude limiting average filtering
CN112037539B (en) Method and system for recommending signal control scheme for saturated urban traffic network
CN110836675A (en) Decision tree-based automatic driving search decision method
CN113140114B (en) Vehicle travel path reconstruction method based on travel time estimation
CN114093168A (en) Urban road traffic running state evaluation method based on toughness view angle
CN111523706A (en) Section lane-level short-term traffic flow prediction method based on deep learning combination model
CN111311907B (en) Identification method for uncertain basic graph parameter identification based on cellular transmission model
CN113033899A (en) Unmanned adjacent vehicle track prediction method
CN114202120A (en) Urban traffic travel time prediction method aiming at multi-source heterogeneous data
CN111899511A (en) Bus arrival time prediction method for AVL data of collinear line
CN105139328B (en) Hourage real-time predicting method and device towards license plate identification data
Shenghua et al. Road traffic congestion prediction based on random forest and DBSCAN combined model
Dong et al. An identification model of urban critical links with macroscopic fundamental diagram theory
CN115691140A (en) Analysis and prediction method for space-time distribution of automobile charging demand

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
CP01 Change in the name or title of a patent holder

Address after: 310012 1st floor, building 1, 223 Yile Road, Hangzhou City, Zhejiang Province

Patentee after: Yinjiang Technology Co.,Ltd.

Address before: 310012 1st floor, building 1, 223 Yile Road, Hangzhou City, Zhejiang Province

Patentee before: ENJOYOR Co.,Ltd.

CP01 Change in the name or title of a patent holder