CN115410375A - Fusion traffic index set generation method based on fusion traffic data of thunder card - Google Patents
Fusion traffic index set generation method based on fusion traffic data of thunder card Download PDFInfo
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Abstract
The invention discloses a fusion traffic index set generation method based on mine-card fusion traffic data, which is characterized in that indirect calculation and traffic state index generation are carried out by collecting mine-card fusion traffic data, and finally a fusion traffic index set is obtained through index fusion, so that perception complementation and fine and rich index output of multi-source data are realized.
Description
Technical Field
The invention relates to the technical field of signal control, in particular to a fusion traffic index set generation method based on fusion traffic data of a thunder card.
Background
At present, the widely used detector in the field of signal control mainly includes: video detectors, radar detectors; the video detector system is flexible in setting, simple in installation, convenient to use and free of damage to the road surface. The lane does not need to be closed during maintenance, the speed measurement precision and the traffic volume counting precision are kept at higher levels, the images can be connected to a monitor of a monitoring center, traffic flow information such as vehicle speed and traffic volume can be visually displayed in real time, however, the detection result is influenced by severe weather, the accuracy of the detection result is poor in severe weather such as heavy fog, heavy rain and snow, and the stability of the detection precision is poor. After long-term use, the camera position can be deviated due to the shaking of the mounting bracket, the image quality can be deteriorated due to dust accumulation on the surface of the lens of the camera, the detection precision can be reduced, and software debugging needs to be carried out again; the product detection of radar detector does not receive the influence of greenbelt, traffic guardrail and other barriers, can detect the target vehicle who is sheltered from, has very strong interference killing feature, can carry out accurate detection under various abominable meteorological conditions such as rain, snow, haze, sand and dust storm, nevertheless, construction, maintenance cost are higher. The detection precision of the object running at low speed is not high, and the data of the static object cannot be detected.
Therefore, how to improve the reliability of the collected signals in the traffic control process is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a method for generating a fusion traffic index set based on mine-card fusion traffic data, which includes acquiring mine-card fusion traffic data to perform indirect calculation and traffic state index generation, and finally obtaining a fusion traffic index set through index fusion, so that perception complementation and fine and rich index output of multi-source data are realized, and the applicability and accuracy of control can be improved by performing traffic control calculation by using the fusion traffic index set.
In order to achieve the purpose, the invention adopts the following technical scheme:
a fusion traffic index set generation method based on a Lei-Ka fusion traffic data comprises the following steps:
step 1: collecting the thunder card fusion traffic data; the system comprises radar data, electric alarm card port data and signal control data, wherein the radar data comprises queue length, flow, position data and average vehicle speed, and the electric alarm card port data comprises flow, queue length, position data, average vehicle speed, time data and license plate data; the signaling data includes a time period scheme;
step 2: calculating indirect traffic data according to the thunder card fusion traffic data;
indirect traffic data, including flow direction queue length, flow direction flow rate, actual saturation flow rate, actual travel time, free flow travel time, flow direction-to-flow rate ratio, flow direction split, OD data, and the like;
q i the flow direction of the intersection is, i represents the ith flow direction of the intersection; q. q.s j Representing the actual flow rate of the jth lane of the ith flow direction; m represents the number of lanes in the flow direction;
Q i for the actual saturated flow of each flow direction, the maximum value of the ith flow direction flow of the intersection within 24 hours, namely the maximum number of traffic flows passing in unit time in a free flow state;
L i queuing length for flow direction; l is m The queue length of the m-th flow lane is shown, and the detection queue length of a general detector is 35-100 meters; m represents the number of lanes in the flow direction;
T i representing an actual travel time; t is 2 Representing an end of travel time; t is 1 Represents the trip start time; obtaining the travel end according to the position data and the average speedA point time and a trip start time;
T 0 =(T 0.7u ,T i ,0)
T 0 represents the free flow transit time; t is 0.7u Represents T 0 Taking the travel time of which the actual travel time of the vehicle is 70% all day; 0 represents a descending order; u represents the number of samples;
obtaining OD data according to the time data and the license plate data, and directly displaying the position, the time and the license plate of the electric police in a map to generate the OD data;
obtaining a flow direction split ratio according to a time interval scheme;
and 3, step 3: generating and calculating according to the indirect traffic data to obtain a traffic state index;
generating indexes including a flow direction unblocked index, a flow direction busy index, a flow direction congestion index, a flow direction efficiency index and the like by an algorithm;
F i the index of the busy flow direction reflects the busy degree of each flow direction traffic flow when the peak is in the morning and evening or the traffic flow is large; h i Designing saturation flow for each flow direction; k is the ratio of the actual saturation flow of each flow direction to the designed saturation flow, and the value range is 0.8-0.95; actual saturated flow and designed saturated flow of each flow direction are obtained according to the flow;
X i representing a flow direction efficiency index; the flow direction efficiency index is corrected by the principle that if L i =35，X i The value range is [3,10 ]](ii) a If L is i >35，X i The value range is [0,10 ]](ii) a The minimum value of the detected queue length is 35 due to the installation position of the detector, and the actual queue is between 0 and 35; so it is considered that when L =35, there is no actual queuing, and the X minimum is 3;
C i is the flow direction unblocked index;
and 4, step 4: performing fusion calculation on the traffic state index and the indirect traffic data to generate a fusion traffic index set;
and comprehensively evaluating the index, and acquiring a fused traffic index set through data accumulation and fusion calculation, wherein the fused traffic index set comprises indexes such as intersection congestion index, intersection efficiency index, travel delay, road section efficiency index, anti-overflow ratio, road section congestion index, regional thermal situation, signal control scheme evaluation and the like.
Preferably, the intersection congestion index reflects the traffic congestion or unblocked degree of the signal-controlled intersection, is an arithmetic mean value of the busy index and the unblocked index,
wherein, Y i The flow direction congestion index is an arithmetic mean value of a flow direction busy index and a flow direction unblocked index; f i The index is a busy flow index and reflects the busy degree of each flow direction traffic flow when the peak is in the morning and evening or the traffic flow is large; c i Is the flow direction unblocked index;
the intersection congestion index expression is as follows:
wherein Y is an intersection congestion index; y is max1 And Y max2 The maximum value of the congestion indexes of the two flow directions at the intersection and the second largest value which is only next to the maximum value are respectively.
Preferably, the intersection efficiency index mainly reflects the green light signal control efficiency of the signal control intersection and is an arithmetic average value of each flow direction efficiency index; the expression is as follows:
wherein X represents an intersection efficiency index; x i Expressing a flow direction efficiency index; n represents the intersection flow number.
Preferably, the travel delay mainly refers to an average value of delay caused by signal control of the vehicle in unit time, and is mainly used for evaluating the service level of the intersection; the expression is as follows:
wherein R is D Indicating a trip delay; t is i Representing an actual travel time; t is 0 Representing the free stream travel time.
Wherein, T i Representing an actual travel time; t is 2 Representing an end of travel time; t is 1 Represents the trip start time;
T 0 =(T 0.7u ,T i ,0)
T 0 represents the free stream travel time; t is i Representing an actual travel time; t is 0.7u Represents T 0 Taking the travel time that the actual travel time of the vehicle is 70% all day; 0 represents a descending order; u represents the number of samples;
preferably, the road section efficiency index is the road section passing efficiency condition analyzed according to the traffic flow in unit time, and the comprehensive service level is analyzed; the expression is as follows:
wherein, X R The road section efficiency index is represented, and the value range is 0-10;
representing the actual flow of the road section; q R RepresentThe actual saturated flow of the road section is the maximum traffic flow passing in unit time in a free flow state.
Preferably, the anti-overflow rate is mainly to evaluate the coordination effect of the signal control scheme on the linkage control of the upstream and downstream intersections by judging the duration of the anti-overflow phenomenon at the intersections; analyzing the characteristics of traffic flow travel through the occurrence time and duration change diagram of the anti-overflow phenomenon, and making an anti-overflow control scheme according with the traffic demand; the expression is as follows:
wherein R is f Represents the anti-overflow ratio; t is f Representing the duration of the occurrence of the spill-back phenomenon in a time period or unit time; t is a unit of t Representing a time period or unit of time; setting a value of the queuing length, judging the duration of the actual flow direction queuing length being larger than or equal to the value of the set queuing length, and setting the back overflow ratio = duration/time length of the time period.
Preferably, the road section congestion index is used for analyzing road section congestion and delay conditions according to travel time and free flow travel time and analyzing comprehensive service level; the expression is as follows:
wherein Y is R The congestion index of the road section is represented, and the value range is 0-10; t is 0 Represents the free flow running time with the unit of h; t is a unit of i Representing the actual travel time in units of h.
Preferably, the electric police traffic data is formed by position data, time data and license plate data in the electric police checkpoint data; the travel route of each vehicle can be obtained, and the travel route comprises information which cannot be acquired by detectors such as a starting point, a terminal point, the travel route, the spent time length and the route length of the travel; the vehicle can be locked as a vehicle through the license plate, and the time, position, time, starting point, ending point and path of the vehicle reaching each position form the OD data of the vehicle;
the method has the advantages that travel expectation analysis is carried out according to electric police traffic data, travel habits, path hobbies and the like of citizens are known, accurate and complete reproduction of important travel demands in cities is achieved, congestion formation reasons can be analyzed accurately, more various traffic control measures are recommended, and the traffic congestion problem is relieved;
the travel expectation analysis comprises the following steps:
the regional thermodynamic situation study and judgment analysis mainly comprises intersection traffic situation study and judgment, road section traffic situation study and judgment, regional traffic situation study and judgment, traffic flow statistical analysis, traffic flow structural analysis and the like;
the study and judgment of the traffic situation at the intersection comprises the following steps: analyzing the traffic situation of the intersection, analyzing the supersaturation of the intersection, analyzing the unbalance of the intersection, and analyzing the congestion index of the intersection;
the road section traffic situation study and judgment comprises the following steps: analyzing road section traffic situation, researching road section congestion indexes, road section tides and the like;
the regional traffic situation study and judgment comprises the following steps: analyzing the regional traffic situation, and obtaining regional congestion indexes;
traffic flow statistical analysis, comprising: traffic OD data statistics, inter-area travel expectation analysis, area congestion indexes, area traffic situation prediction and the like;
traffic flow structural analysis, comprising: ingress and egress vehicle analysis, vehicle type distribution, vehicle home distribution, and the like.
Preferably, the evaluation of the signal control scheme mainly refers to the conformity degree of the green-signal ratio of the traffic flow and the timing scheme, and the adaptability of the timing scheme to the traffic flow is evaluated;
the average conformity of the timing scheme can also be judged by calculating the proportion of the time periods with consistent trends in the whole day time period; the expression is as follows:
wherein q is i The flow direction of the intersection is shown, i represents the ith flow direction of the intersection; r qxi Representing the flow ratio of each flow direction in the x-th time period; c x A period representing the x-th period; t is xi Indicating an effective green time in the ith flow direction during the xth period; r is txi Representing the green ratio of each flow direction in the x-th period; g xi Indicating the ith flow direction coincidence in the x time period; g x Representing the matching degree of the x time period timing scheme; n represents the intersection flow direction number; g represents the average conformity of the timing scheme; g represents the number of time periods; and obtaining the number of the time periods through the time period scheme, and calculating the flow direction split ratio of each time period.
According to the technical scheme, compared with the prior art, the fusion traffic index set generation method based on the mine card fusion traffic data is provided, the single-section traffic data of the radar detector and the multi-section traffic data of the electric police gate are fused to form the fusion traffic index set, the perception complementation and the fine and rich index output of the multi-source data are realized, the method is mainly applied to traffic condition research and judgment analysis, signal control optimization and diagnosis, and the problem that the common single detection mode cannot meet the requirement of collaborative development is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating a structure of a fusion traffic index set generation process provided by the present invention;
fig. 2 is a schematic diagram illustrating a network architecture and a data flow of a thundercard convergence device according to the present invention;
FIG. 3 is a schematic diagram of hourly traffic of each flow direction and total hourly traffic of an intersection of a road section and the intersection provided by the present invention;
FIG. 4 is a schematic diagram of the hourly queue length of each flow direction of the road segment and the intersection provided by the present invention;
FIG. 5 is a graph illustrating the relationship between east straight forward flow rate and flow efficiency index provided by the present invention;
FIG. 6 is a schematic diagram showing a comparison between an intersection efficiency index and an intersection total flow rate according to the present invention;
FIG. 7 is a graph showing a comparison between east straight forward flow direction flow and flow direction busy index provided by the present invention;
FIG. 8 is a schematic diagram showing the comparison between the east-straight queuing length and the flow unimpeded index provided by the present invention;
FIG. 9 is a schematic diagram of a comparison relationship among a flow direction busy index, a flow direction clear index and a flow direction congestion index provided by the invention;
fig. 10 is a schematic diagram illustrating a relationship between intersection congestion indexes and intersection total flow rates provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The embodiment of the invention discloses a fusion traffic index set generation method based on fusion traffic data of a thunder card, which comprises the following steps:
s1: collecting the thunder card fusion traffic data;
the thunder card fusion traffic data comprises single-section traffic data such as traffic flow, queue length, average vehicle speed, position data, locomotive time distance and the like; the vehicle-mounted system also comprises multi-section traffic data such as traffic flow, queue length, average vehicle speed, position data, time data, license plate data and the like;
s2: calculating indirect traffic data according to the thunder card fusion traffic data;
indirect traffic data, including flow direction queuing, intersection flow direction flow, actual saturated flow, actual travel time, free flow travel time, OD data, and the like; the intersection flow is the sum of the flows of all lanes in the intersection flow; the actual flow of the road section is the flow of the corresponding flow direction of the road section behind the canalization section;
s3: generating and calculating according to the indirect traffic data to obtain a traffic state index;
generating indexes including a smooth flow direction index, a busy flow direction index, a flow direction efficiency index and the like by an algorithm;
s4: performing fusion calculation on the traffic state indexes to generate a fusion traffic index set;
and comprehensively evaluating the index, and acquiring a fused traffic index set through data accumulation and fusion calculation, wherein the fused traffic index set comprises indexes such as intersection congestion index, intersection efficiency index, travel delay, road section efficiency index, anti-overflow ratio, road section congestion index, regional thermal situation, signal control scheme evaluation and the like.
S5: the index space granularity comprises lanes, flow directions, intersections, road sections, trunk lines, areas and the like;
s6: the index time granularity comprises a period grade, a 5 minute grade, a 15 minute grade, an hour grade, a day grade, a week grade, a month grade, a quarter grade, a year grade and the like.
In order to further optimize the technical scheme, the intersection congestion index reflects the traffic congestion or the unblocked degree of the signal control intersection, is an arithmetic average value of a busy index and an unblocked index, and has the expression:
wherein Y is intersection congestionAn index; y is max1 And Y max2 The maximum value of the congestion indexes of the two flow directions at the intersection and the second maximum value of the congestion indexes of the two flow directions at the intersection are respectively the second maximum value of the congestion indexes of the two flow directions at the intersection;
wherein, Y i Calculating the average value of the flow direction busy index and the flow direction unblocked index for each flow direction congestion index; f i The index is a busy flow index and reflects the busy degree of each flow direction traffic flow when the peak is in the morning and evening or the traffic flow is large; c i Is flow direction unimpeded index;
wherein q is i For the intersection flow direction, i represents the ith flow direction; q i The actual saturated flow of each flow direction, namely the maximum number of traffic flows passing in unit time in a free flow state; h i Designing saturated flow for each flow direction; k is the ratio of the actual saturation flow of each flow direction to the designed saturation flow, and the value is between 0.8 and 0.95;
wherein Q is i The actual saturated flow of each flow direction is the maximum value of the ith flow direction flow of the intersection within 24 hours; q. q.s i Flow direction and flow rate for the intersection;
wherein q is j Actual flow rate of a jth lane representing an ith flow direction; m represents the number of lanes in the flow direction;
wherein L is i Queuing length for flow direction;
wherein L is i Flow queue length representing the ith flow; l is m The queuing length of each lane is shown, and the queuing length detected by a general detector is 35-100 meters; m represents the number of lanes in the flow direction;
in order to further optimize the technical scheme, the intersection signal control efficiency index mainly reflects the green light signal control efficiency of the signal control intersection and is an arithmetic average value of each flow direction efficiency index; the expression is as follows:
wherein X represents an intersection signal control efficiency index; x i Expressing a flow direction efficiency index; n represents the intersection flow direction number;
wherein q is i The flow direction of the intersection is, i represents the ith flow direction of the intersection; q i The actual saturated flow of each flow direction, namely the maximum number of traffic flows passing in unit time in a free flow state; h i Designing saturation flow for each flow direction; k is the ratio of the actual saturation flow of each flow direction to the designed saturation flow, and the value is between 0.8 and 0.95; l is a radical of an alcohol i Queuing length for flow direction;
the flow direction efficiency index is corrected by the following principle if L i =35，X i The value range is [3,10 ]](ii) a If L is i >35，X i The value range is [0,10 ]]。
In order to further optimize the technical scheme, the travel delay mainly refers to the average value of delay of a vehicle caused by signal control in unit time, is mainly used for evaluating the service level of an intersection, analyzes and calculates parameters such as travel time, average speed and the like based on data such as electronic police license plate recognition, arrival time and the like, and calculates the average delay rate in unit time by comparing the actual travel time with the theoretical travel time of free flow speed under an ideal condition; the expression is as follows:
wherein R is D Indicating a trip delay; t is a unit of i Representing an actual travel time; t is 0 Representing free stream travel time.
Wherein, T i Representing an actual travel time; t is 2 Representing an end of travel time; t is a unit of 1 Represents the trip start time;
T 0 =(T 0.7u ,T i ,0)
T 0 represents the free stream travel time; t is i Representing the actual travel time; t is 0.7u Represents T 0 Taking the travel time that the actual travel time of the vehicle is 70% all day; 0 represents a descending order; u represents the number of samples;
in order to further optimize the technical scheme, the road section efficiency index is a road section passing efficiency condition analyzed according to the traffic flow in unit time, and the comprehensive service level is analyzed; the expression is as follows:
wherein X R The road section efficiency index is represented, and the value range is 0-10;
representing the actual flow of the road section; q R The actual saturated flow of the road section is represented and is the maximum number of traffic flows passing in unit time in a free flow state.
In order to further optimize the technical scheme, the anti-overflow ratio is mainly used for evaluating the coordination effect of the signal control scheme on the linkage control of upstream and downstream intersections by judging the duration of the anti-overflow phenomenon at the intersections; the anti-overflow phenomenon refers to a traffic phenomenon that vehicles at a downstream intersection queue and spread to an adjacent upstream intersection, and generally occurs when the distance between adjacent intersections is short; analyzing the characteristics of traffic flow travel through the occurrence time and duration change diagram of the anti-overflow phenomenon, and formulating an anti-overflow control scheme according with the traffic demand; the expression is as follows:
wherein R is f Representing the anti-overflow ratio; t is f Representing the duration of the occurrence of the spill-back phenomenon in a time period or unit time; t is t Representing a time period or unit of time.
In order to further optimize the technical scheme, the road section congestion index is used for analyzing road section congestion and delay conditions according to travel time and free flow travel time and analyzing comprehensive service level; the expression is as follows:
wherein, Y R The congestion index of the road section is represented, and the value range is 0-10; t is a unit of 0 Represents the free flow transit time (i.e., theoretical travel time) in units of h; t is i Representing the actual travel time in units of h;
wherein, T i Representing the actual travel time; t is 2 Representing an end of travel time; t is 1 Represents the trip start time;
T 0 =(T 0.7u ,T i ,0)
T 0 represents the free flow transit time; t is i Representing the actual travel time; t is 0 Taking the value as the actual driving of the vehicle all dayThe journey time is at 70% of the journey time; 0 represents a descending order; u represents the number of samples.
In order to further optimize the technical scheme, a complete travel path of each vehicle can be obtained through electric alarm traffic data, and the travel path comprises information which cannot be acquired by detectors such as a starting point, a terminal point, a travel path, a spent time length and a path length of travel;
travel expectation analysis is carried out according to electric police traffic data, travel habits, path hobbies and the like of citizens are known, accurate and complete reproduction of important travel demands in cities is achieved, congestion formation reasons can be analyzed accurately, more various traffic control measures are recommended, and the traffic congestion problem is relieved;
the travel expectation analysis comprises the following steps:
the regional thermodynamic situation studying, judging and analyzing method mainly comprises the steps of studying and judging intersection traffic situation, studying and judging road section traffic situation, studying and judging regional traffic situation, carrying out traffic flow statistical analysis, carrying out traffic flow structural analysis and the like;
the study and judgment of the traffic situation at the intersection comprises the following steps: analyzing the traffic situation of the intersection, analyzing the supersaturation of the intersection, analyzing the unbalance of the intersection, and analyzing the congestion index of the intersection;
the road traffic situation studying and judging method comprises the following steps: analyzing road section traffic situation, researching road section congestion indexes, road section tides and the like;
the regional traffic situation study and judgment comprises the following steps: analyzing the regional traffic situation, and regional congestion indexes;
statistical analysis of traffic flow, comprising: traffic OD data statistics, inter-area travel expectation analysis, area congestion indexes, area traffic situation prediction and the like;
traffic flow structural analysis, comprising: ingress and egress vehicle analysis, vehicle type distribution, vehicle home distribution, and the like.
In order to further optimize the technical scheme, the evaluation of the signal control scheme mainly refers to the conformity degree of the green-signal ratio of the traffic flow and the timing scheme and the adaptability of the timing scheme to the traffic flow;
the direction flow ratio is consistent with the direction split ratio trend, and the adaptability of the representative timing scheme to the traffic flow is good; the ratio range of the direction flow ratio and the direction split is 0.8-1.2, the judgment trend is consistent, the adaptability of the timing scheme to traffic flow is good, the evaluation of the time-sharing timing scheme is realized through a comparison curve chart of the flow ratio and the split, and the comparison of the time-sharing scheme with inconsistent variation trend is the key point for optimizing the signal scheme;
the average conformity of the timing scheme can also be judged by calculating the proportion of the time periods with consistent trends in the whole day time period; the expression is as follows:
wherein q is i The flow direction of the intersection is shown, i represents the ith flow direction of the intersection; r qxi Showing the flow ratio of each flow direction in the x-th time period; c x A period representing the x-th period; t is xi Indicating an effective green time in the ith flow direction during the xth period; r txi Indicating the green ratio of each flow direction in the x-th period; g xi Indicating the ith flow direction coincidence in the xth period; g x Representing the x time period timing scheme conformity; n represents the intersection flow direction number; g represents the average conformity of the timing scheme; g represents the number of time segments, the number of time segments is obtained through the time segment scheme, and the flow direction split ratio of each time segment is calculated.
In order to further optimize the technical scheme, the fused traffic index set is subjected to statistical analysis according to preset index space granularity and index time granularity, and the four-level diagnosis of the signal control evaluation and the display of diagnosis results are realized;
the index space granularity comprises lanes, flow directions, intersections, road sections, trunk lines, areas and the like; the method comprises the following steps that a plurality of lanes form a flow direction, a plurality of flow directions are calculated as intersections, some indexes are road sections, and the indexes of a plurality of intersections and road sections form regional indexes;
the index time granularity comprises a cycle grade, a 5 minute grade, a 15 minute grade, an hour grade, a day grade, a week grade, a month grade, a quarter grade, a year grade and the like; the index variation trend is shown, and the index variation trend can be displayed according to 5 minutes and statistically inquired according to days and months.
In order to further optimize the technical scheme, the index space granularity is determined according to the fusion traffic index set, the traffic change trend is obtained, and four-level diagnosis of signal control evaluation is carried out;
based on the above mine card fusion traffic index set, the traffic signal control evaluation diagnosis is realized, including four-level diagnosis of intersections, road sections, trunk lines and regions, and the visualization display of traffic jam, slow walking, smoothness and other situations based on an electronic map is realized.
Aiming at a fusion traffic index set such as an intersection congestion index, an intersection efficiency index, travel delay, a road section efficiency index, an anti-overflow ratio, a road section congestion index, a regional thermal situation, a signal control scheme evaluation and the like, a 7-by-24-hour index change trend and historical trend comparison are provided, problems are quickly found, a problem intersection, a road section, a trunk line and a region are positioned, and key problems are pertinently solved.
In order to further optimize the technical scheme, index time granularity is set, and display and query are carried out according to the four-level diagnosis result of the signal control evaluation;
the diagnosis results can be inquired and sorted according to fields such as time intervals, the severity of problems and the like, historical change trend comparison of the diagnosis results of a single intersection can be inquired, and evaluation diagnosis such as real-time automatic ranking of evaluation indexes, index alarm and record inquiry, index historical data comparison and the like is realized through statistical analysis;
the evaluation indexes are automatically ranked in real time, so that the real-time automatic ordering of a fusion traffic index set such as intersection congestion indexes, intersection efficiency indexes, travel delay, road section efficiency indexes, anti-overflow ratios, road section congestion indexes, regional thermal situations, signal control scheme evaluation and the like is realized, the list inquiry of intersections, road sections, trunk lines and regions ordered according to the traffic indexes at all time intervals is supported, and the inquiry and comparison of detailed index change trends are realized;
the method comprises the following steps of alarming, recording and inquiring indexes, and realizing automatic alarm of integrated traffic indexes such as intersection congestion indexes, intersection efficiency indexes, travel delay, road section efficiency indexes, anti-overflow ratio, road section congestion indexes, regional thermal situations, signal control scheme evaluation and the like, wherein alarm threshold values are preset in all the traffic indexes, and when the actual index values reach the set alarm threshold values, the indexes are automatically highlighted and flash to alarm;
index historical data comparison is achieved, fusion traffic index set historical data comparison such as intersection congestion indexes, intersection efficiency indexes, travel delay, road section efficiency indexes, anti-overflow ratios, road section congestion indexes, regional thermal situations, signal control scheme evaluation is achieved, transverse comparison of historical time section data of all indexes is supported, longitudinal data comparison of all indexes at different intersections, road sections, trunk lines and regions is supported, and comparison conclusion and signal optimization suggestions are given.
Examples
(1) The network architecture and data flow direction of the thundercard fusion device for collecting the thundercard fusion traffic data are shown in fig. 2;
the deployment position and the detection area of the millimeter wave radar detector for collecting radar data are as follows:
the millimeter wave radar detector is arranged on a signal lamp pole, and detects the traffic condition in the same direction as the signal lamp, namely, the direction of the stop line of the entrance way. The detection area of the millimeter wave radar detector is larger than 8 lanes in the transverse direction and larger than 250m in the longitudinal direction, and is calculated relative to the installation position.
The shielding of trees, walls, buildings and the like in the detection range of the millimeter wave radar detector can cause certain influence on the continuity of target detection by the radar. When the radar is installed in a typical installation height of 5 to 8 meters, if the area of interest is within the range of 30 to 280 meters, the installation pitch angle (namely the included angle between the front panel of the radar and the Z axis) is recommended to be 2 +/-1 degrees, and under any condition, the installation pitch angle is not more than 5 degrees.
The electronic police deployment position and detection area for collecting police access data are as follows:
the visual scope of the front-view camera comprises: the first ground lane marking behind the stop line can be seen clearly at the position of the crosswalk facing the entrance lane farthest.
The visual scope of back vision camera includes: the second ground lane marking behind the stop line, and the farthest visible position is 150-200 meters from the stop line.
Based on the requirement of a visual range, for a forward-looking camera, the distance from the tail end of a lane marking line (a diversion line) to a stop line of an entrance lane is 50m, the monitoring distance L2=13m, and the actual angle is based on the imaging result.
For a rear view camera, the monitoring distance is L2=27m, and the actual angle is based on the imaging result.
The acquired thunder card fusion traffic data are shown in the following table 1, more traffic data such as single-section traffic data, multi-section traffic data and the like are applied to signal control, the whole data acquisition of intersections and road sections is realized, after fusion calculation, the analysis of judging, optimizing and diagnosing of the traffic conditions of the intersections, the road sections and road networks and the signal control are realized, and the green travel environment of urban traffic is assisted.
Table 1 raw data table collected by testing equipment such as a leica
(2) Data validation of intersection efficiency index and congestion index
2022.9.26 video detector data of a certain section and intersection of a certain city are selected. Raw data are flow rate per 5 minutes and queue length obtained by lane. In order to more intuitively express the change trend of the intersection efficiency index and the intersection congestion index along with the flow and the queuing length, the flow and the queuing length in every 5 minutes are counted into the hourly flow and the queuing length.
Because the number of lanes in each flow direction is large, the data of the lane is already calculated as the flow rate and the queuing length of the flow direction. The flow direction and the flow rate are the sum of the flow rates flowing to all lanes; the length of the flow direction queue is the maximum value of the length of the flow direction queue of each lane. The time unit is time; the flow data of the road section and the intersection in each flow hour are shown in the following table 2, and the flow unit is Pcu. The schematic diagram of the hourly flow rate of each flow direction and the total hourly flow rate of the intersection of the corresponding road section and intersection is shown in fig. 3
TABLE 2 hourly flow rate of each flow direction of road section and intersection
In the collected data, the data of the queuing length of each flow direction hour of the road section and the intersection are shown in the following table 3, and the unit of the queuing length is meter when the unit of time is time; the line graph of the queue length of the corresponding road section and the flow direction hour of the intersection is shown in figure 4.
TABLE 3 queuing length of each flow direction hour for road section and intersection
Taking the east-straight line as an example, the flow direction efficiency index calculation process is shown in the following table 4; the flow direction and the flow direction efficiency index are compared, and as shown in fig. 5, the flow direction efficiency index and the flow direction and the flow rate are changed in the same direction.
TABLE 4 eastern straight-through flow direction efficiency index calculation procedure
According to the same method, flow direction efficiency index values of 8 flow directions can be calculated, and intersection efficiency indexes can be calculated through arithmetic mean values, as shown in table 5. The crossing efficiency index is compared with the crossing total flow, as shown in fig. 6, the crossing efficiency index and the crossing total flow basically change in the same direction.
TABLE 5 flow direction efficiency index and intersection efficiency index for 8 flow directions
Taking east-straight-line as an example, the calculation processes of the flow direction busy index, the flow direction clear index and the flow direction congestion index are shown in table 6. The east straight line flow direction flow rate and flow direction busy index are compared as shown in fig. 7, the east straight line queuing length and flow direction clear index are compared as shown in fig. 8, and the flow direction busy index, flow direction clear index and flow direction congestion index are compared as shown in fig. 9.
TABLE 6 calculation procedure of east straight going busy flow index, smooth flow index and congestion flow index
According to the same method, the flow direction congestion index values of 8 flow directions can be calculated, and the intersection congestion index can be calculated, as shown in table 7. The intersection congestion index and the intersection total flow rate are in a comparison relationship, as shown in fig. 10, the intersection congestion index and the intersection total flow rate basically change in the same direction.
TABLE 7 Direction Congestion index values for the 8 flow directions, and intersection Congestion index
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A fusion traffic index set generation method based on a thunder card fusion traffic data is characterized by comprising the following steps:
step 1: collecting the thunder card fusion traffic data;
step 2: calculating indirect traffic data according to the thunder card fusion traffic data;
and step 3: generating and calculating according to the indirect traffic data to obtain a traffic state index;
and 4, step 4: and performing fusion calculation on the traffic state indexes to generate a fusion traffic index set.
2. The method for generating the fusion traffic index set based on the mine-card fusion traffic data according to claim 1, wherein the mine-card fusion traffic data comprises queue length, flow, position data, average vehicle speed, time data, license plate data and time period scheme; the indirect traffic data comprise flow direction queuing length, intersection flow direction, actual saturated flow, actual travel time, free flow running time, OD data, flow direction ratio and flow direction split; the traffic state index comprises a flow direction unblocked index, a flow direction busy index and a flow direction efficiency index; the fused traffic index set comprises an intersection congestion index, an intersection efficiency index, a travel delay, a road section efficiency index, an anti-overflow ratio, a road section congestion index, a regional thermodynamic situation and a signal control scheme evaluation.
3. The method for generating the fusion traffic index set based on the leica fusion traffic data according to claim 2, wherein the intersection congestion index reflects the traffic congestion or the unblocked degree of a signal-controlled intersection, is an arithmetic mean value of a busy index and an unblocked index, and has an expression as follows:
wherein Y is the intersection congestion index; y is max1 And Y max2 Respectively representing the maximum value of each flow direction congestion index of the intersection and a second maximum value next to the maximum value;
wherein Y is i Calculating the average value of the flow direction busy index and the flow direction unblocked index for each flow direction congestion index; f i Is a flow direction busy index; c i Is the flow direction unblocked index;
wherein q is i The flow direction of the intersection is, i represents the ith flow direction of the intersection; q i Actual saturation flow for each flow direction; h i Designing saturated flow for each flow direction; k is the ratio of the actual saturation flow of each flow direction to the designed saturation flow, and the value range is 0.8-0.95;
wherein q is j The actual flow rate of the flow direction corresponding to the jth lane representing the ith flow direction; m represents the number of lanes in the flow direction;
wherein L is i Queuing length for flow direction;
wherein L is i Flow queue length representing the ith flow; l is m The lane queue length of the mth flow to lane is shown.
4. The method for generating the fusion traffic index set based on the thunder card fusion traffic data according to claim 2, wherein the intersection efficiency index reflects the green light signal control efficiency of the signal-controlled intersection and is an arithmetic average value of each flow direction efficiency index; the expression is as follows:
wherein X represents an intersection efficiency index; x i Representing a flow direction efficiency index; n represents the intersection flow direction number;
wherein q is i The flow direction of the intersection is, i represents the ith flow direction of the intersection; q i Actual saturation flow for each flow direction; h i Designing saturated flow for each flow direction; k is the ratio of the actual saturation flow of each flow direction to the designed saturation flow; l is i Queuing length for flow direction;
the flow direction efficiency index is corrected by the following principle if L i =35，X i The value range is [3,10 ]](ii) a If L is i >35，X i The value range is [0,10 ]]。
5. The method for generating the fusion traffic index set based on the mine card fusion traffic data according to claim 2, wherein the journey delay refers to an average value of delay of vehicles caused by signal control in unit time; the expression is as follows:
wherein R is D Indicating a trip delay; t is a unit of i Representing an actual travel time; t is a unit of 0 Represents the free flow transit time;
wherein, T 2 Representing an end of travel time; t is a unit of 1 Represents the trip start time;
T 0 =(T 0.7u ,T i ,0)
T 0.7u represents T 0 Taking the travel time that the actual travel time of the vehicle is 70% all day; 0 represents a descending order; u represents the number of samples.
6. The method for generating the fusion traffic index set based on the leica fusion traffic data according to claim 2, wherein the road section efficiency index is a road section traffic efficiency condition analyzed according to a traffic flow per unit time; the expression is as follows:
wherein X R Representing a road segment efficiency index;
representing the actual flow of the road section; q R Representing the actual saturated flow of the road segment.
7. The method for generating the fusion traffic index set based on the thunder card fusion traffic data according to the claim 2, wherein the anti-overflow ratio is to evaluate the coordination effect of the signal control scheme on the linkage control of the upstream and downstream intersections by judging the duration of the anti-overflow phenomenon at the intersection; the expression is as follows:
wherein R is f Representing the anti-overflow ratio; t is a unit of f The duration of the overflow phenomenon occurring in a time interval or unit time is represented; t is a unit of t Representing a time period or unit of time.
8. The method according to claim 2, wherein the road congestion index is road congestion and delay level obtained according to travel time and free traffic travel time; the expression is as follows:
wherein Y is R Representing a link congestion index; t is 0 Represents the free flow transit time; t is a unit of i Representing an actual travel time;
wherein, T 2 Representing an end of travel time; t is a unit of 1 Represents the trip start time;
T 0 =(T 0.7u ,T i ,0)
T 0.7u represents T 0 Taking the travel time of which the actual travel time of the vehicle is 70% all day; 0 represents a descending order; u represents the number of samples.
9. The method according to claim 2, wherein the position data, the time data and the license plate data in the data of the electric police dispatch gate form electric police traffic data, the OD data of each vehicle is obtained, and the regional thermal situation is displayed according to the OD data.
10. The method for generating the fusion traffic index set based on the leica fusion traffic data according to claim 2, wherein the credit control scheme evaluates the coincidence degree of the traffic flow and the green-to-credit ratio of the timing scheme, and the average coincidence degree of the timing scheme is judged by calculating the proportion of the time periods with consistent trends in the time periods of all days; the expression is as follows:
wherein q is i The flow direction of the intersection is shown, i represents the ith flow direction of the intersection; r is qxi Showing the flow ratio of each flow direction in the x-th time period; c x A period representing the x-th period; t is a unit of xi Indicating an effective green time in the ith flow direction during the xth period; r is txi Indicating the green ratio of each flow direction in the x-th period; g xi Represents the x-th periodAn ith flow direction coincidence; g x Representing the matching degree of the x time period timing scheme; n represents the intersection flow direction number; g represents the average conformity of the timing scheme; g represents the number of time periods; and obtaining the number of the time periods through the time period scheme, and calculating the flow direction split ratio of each time period.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116028892A (en) * | 2023-03-24 | 2023-04-28 | 江苏先驰物联网技术有限公司 | Data fusion algorithm of multi-police joint service control system |
| CN116386336A (en) * | 2023-05-29 | 2023-07-04 | 四川国蓝中天环境科技集团有限公司 | Road network traffic flow robust calculation method and system based on bayonet license plate data |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101702262A (en) * | 2009-11-06 | 2010-05-05 | 北京交通大学 | Data syncretizing method for urban traffic circulation indexes |
| CN102890862A (en) * | 2012-09-29 | 2013-01-23 | 浙江大学 | Traffic condition analyzing device and method based on vector mode |
| 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 |
| CN105869405A (en) * | 2016-05-25 | 2016-08-17 | 银江股份有限公司 | Urban road traffic congestion index calculating method based on checkpoint data |
| CN108198416A (en) * | 2017-12-28 | 2018-06-22 | 金交恒通有限公司 | A kind of mobile phone signaling and the fusion method of road network big data and its application and system |
| CN108320506A (en) * | 2018-02-05 | 2018-07-24 | 青岛大学 | A kind of discovery method of the congestion period based on composite network |
| CN108335482A (en) * | 2017-01-20 | 2018-07-27 | 亚信蓝涛（江苏）数据科技有限公司 | A kind of urban transportation Situation Awareness method and method for visualizing |
| CN108629973A (en) * | 2018-05-11 | 2018-10-09 | 四川九洲视讯科技有限责任公司 | Road section traffic volume congestion index computational methods based on fixed test equipment |
| CN110807919A (en) * | 2019-10-22 | 2020-02-18 | 浙江大学 | Urban road network traffic operation situation evaluation method based on vehicle passing data |
| CN110930705A (en) * | 2019-11-28 | 2020-03-27 | 北京易华录信息技术股份有限公司 | Intersection traffic decision system, method and equipment |
| CN111091312A (en) * | 2020-03-25 | 2020-05-01 | 江苏广宇科技产业发展有限公司 | Intelligent traffic management and evaluation system based on BIM |
| CN111597700A (en) * | 2020-05-09 | 2020-08-28 | 北京百度网讯科技有限公司 | Signal control algorithm evaluation method and device, electronic equipment and readable storage medium |
| CN112950946A (en) * | 2021-02-26 | 2021-06-11 | 中冶南方城市建设工程技术有限公司 | Method for judging traffic jam reason in construction period of municipal road |
| WO2021218235A1 (en) * | 2020-04-30 | 2021-11-04 | 山东交通学院 | Road traffic jam early warning method and system |
| CN113868492A (en) * | 2021-08-04 | 2021-12-31 | 北京一通智能科技有限公司 | Visual OD (origin-destination) analysis method based on electric police and checkpoint data and application |
| CN114023073A (en) * | 2022-01-06 | 2022-02-08 | 南京感动科技有限公司 | Expressway congestion prediction method based on vehicle behavior analysis |
| CN114613151A (en) * | 2022-05-10 | 2022-06-10 | 深圳市城市交通规划设计研究中心股份有限公司 | Truck OD determination method, system, equipment and storage medium |
| CN114898555A (en) * | 2022-05-09 | 2022-08-12 | 华录易云科技有限公司 | Road intersection comprehensive evaluation method |
-
2022
- 2022-11-02 CN CN202211360452.1A patent/CN115410375A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101702262A (en) * | 2009-11-06 | 2010-05-05 | 北京交通大学 | Data syncretizing method for urban traffic circulation indexes |
| CN102890862A (en) * | 2012-09-29 | 2013-01-23 | 浙江大学 | Traffic condition analyzing device and method based on vector mode |
| 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 |
| CN105869405A (en) * | 2016-05-25 | 2016-08-17 | 银江股份有限公司 | Urban road traffic congestion index calculating method based on checkpoint data |
| CN108335482A (en) * | 2017-01-20 | 2018-07-27 | 亚信蓝涛（江苏）数据科技有限公司 | A kind of urban transportation Situation Awareness method and method for visualizing |
| CN108198416A (en) * | 2017-12-28 | 2018-06-22 | 金交恒通有限公司 | A kind of mobile phone signaling and the fusion method of road network big data and its application and system |
| CN108320506A (en) * | 2018-02-05 | 2018-07-24 | 青岛大学 | A kind of discovery method of the congestion period based on composite network |
| CN108629973A (en) * | 2018-05-11 | 2018-10-09 | 四川九洲视讯科技有限责任公司 | Road section traffic volume congestion index computational methods based on fixed test equipment |
| CN110807919A (en) * | 2019-10-22 | 2020-02-18 | 浙江大学 | Urban road network traffic operation situation evaluation method based on vehicle passing data |
| CN110930705A (en) * | 2019-11-28 | 2020-03-27 | 北京易华录信息技术股份有限公司 | Intersection traffic decision system, method and equipment |
| CN111091312A (en) * | 2020-03-25 | 2020-05-01 | 江苏广宇科技产业发展有限公司 | Intelligent traffic management and evaluation system based on BIM |
| WO2021218235A1 (en) * | 2020-04-30 | 2021-11-04 | 山东交通学院 | Road traffic jam early warning method and system |
| CN111597700A (en) * | 2020-05-09 | 2020-08-28 | 北京百度网讯科技有限公司 | Signal control algorithm evaluation method and device, electronic equipment and readable storage medium |
| CN112950946A (en) * | 2021-02-26 | 2021-06-11 | 中冶南方城市建设工程技术有限公司 | Method for judging traffic jam reason in construction period of municipal road |
| CN113868492A (en) * | 2021-08-04 | 2021-12-31 | 北京一通智能科技有限公司 | Visual OD (origin-destination) analysis method based on electric police and checkpoint data and application |
| CN114023073A (en) * | 2022-01-06 | 2022-02-08 | 南京感动科技有限公司 | Expressway congestion prediction method based on vehicle behavior analysis |
| CN114898555A (en) * | 2022-05-09 | 2022-08-12 | 华录易云科技有限公司 | Road intersection comprehensive evaluation method |
| CN114613151A (en) * | 2022-05-10 | 2022-06-10 | 深圳市城市交通规划设计研究中心股份有限公司 | Truck OD determination method, system, equipment and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116028892A (en) * | 2023-03-24 | 2023-04-28 | 江苏先驰物联网技术有限公司 | Data fusion algorithm of multi-police joint service control system |
| CN116028892B (en) * | 2023-03-24 | 2023-06-20 | 江苏先驰物联网技术有限公司 | Data fusion algorithm of multi-police joint service control system |
| CN116386336A (en) * | 2023-05-29 | 2023-07-04 | 四川国蓝中天环境科技集团有限公司 | Road network traffic flow robust calculation method and system based on bayonet license plate data |
| CN116386336B (en) * | 2023-05-29 | 2023-08-08 | 四川国蓝中天环境科技集团有限公司 | Road network traffic flow robust calculation method and system based on bayonet license plate data |
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