CN108648444B - Signalized intersection operation evaluation method based on grid model - Google Patents
Signalized intersection operation evaluation method based on grid model Download PDFInfo
- Publication number
- CN108648444B CN108648444B CN201810348871.0A CN201810348871A CN108648444B CN 108648444 B CN108648444 B CN 108648444B CN 201810348871 A CN201810348871 A CN 201810348871A CN 108648444 B CN108648444 B CN 108648444B
- Authority
- CN
- China
- Prior art keywords
- grid
- intersection
- road intersection
- traffic
- road
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
-
- 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
Landscapes
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a signalized intersection operation evaluation method based on a grid model, which comprises the following steps: constructing a grid model of a road intersection, and performing direction-dividing processing on track data of a floating vehicle passing through the urban road intersection based on the grid model; acquiring traffic operation parameters of the road intersection, wherein the traffic operation parameters comprise traffic flow passing through the road intersection, traffic time passing through the road intersection, free flow traffic time of the road intersection and average traffic speed of the road intersection; and calculating the traffic delay time of passing through the road intersection according to the traffic operation parameters of the road intersection, and evaluating the operation of the road intersection according to the traffic delay time. The method can traverse all signal road intersections in the selected area under the condition of abandoning map limitation, dynamically evaluate the road intersections in real time, diagnose the reason of delay of the road intersections, ensure the accuracy of evaluation and diagnosis and improve the traffic efficiency of the road intersections.
Description
Technical Field
The invention relates to the technical field of traffic control, in particular to a signalized intersection operation evaluation method based on a grid model.
Background
At present, increasingly congested road traffic becomes a serious social phenomenon, and the influence of delay caused by signal road intersections is particularly prominent. The reasonable timing scheme of the signal road intersection can effectively regulate and control the passing efficiency of the signal road intersection, however, the current timing scheme of the signal road intersection often has some problems in practical application, so that the passing efficiency of the signal road intersection is not high. Therefore, real-time evaluation and diagnosis need to be carried out on the signalized road intersection, and a reasonable timing scheme is obtained according to the evaluation result.
At present, in the method for evaluating the operation of a signal road intersection by using floating car data in the prior art, the floating car data is matched to a road network mainly through a map matching technology, so that the operation parameters of the vehicles at the signal road intersection are extracted, and the operation parameters are analyzed.
The method for evaluating the operation of the signalized road intersection by using the floating vehicle data in the prior art has the following defects: the method has large calculation amount and invisibly increases the calculation burden of the computer.
Disclosure of Invention
The embodiment of the invention provides a signalized intersection operation evaluation method based on a grid model, so as to overcome the defects of the prior art.
A signalized intersection operation evaluation method based on a grid model comprises the following steps:
constructing a grid model of an urban road intersection, and performing direction-dividing processing on track data of a floating vehicle passing through the urban road intersection based on the grid model;
acquiring traffic operation parameters of the road intersection according to the direction-dividing processing result of the track data of the floating vehicle, wherein the traffic operation parameters comprise traffic flow passing through the road intersection, traffic time passing through the road intersection, free flow traffic time of the road intersection and average traffic speed of the road intersection;
and calculating the passing delay time of the road intersection according to the traffic running parameters of the road intersection, and evaluating the running of the road intersection according to the passing delay time.
Further, the constructing of the grid model of the urban road intersection comprises:
determining a radiation area of the intersection by using central coordinate data of the intersection, taking a coordinate center point of the intersection as a reference, selecting ranges from the intersection center point to 150m in the upper direction, the lower direction, the left direction and the right direction as the radiation areas, and constructing a 3 × 3 Grid model by taking the selected radiation areas as targets, wherein each side length of the Grid model is 100m, grids in the Grid model are numbered from the lower left corner to the right side and from the lower side to the upper side, Grid _ IDs numbered from 0 to 8 are respectively Grid _ ID ═ 0, Grid _ ID ═ 1, Grid _ ID ═ 2, Grid _ ID ═ 3, Grid _ ID ═ 4, Grid _ ID ═ 5, Grid _ ID ═ 6, Grid _ ID ═ 7 and Grid _ ID ═ 8, and the Grid _ ID ═ 4 is the center Grid.
Further, the performing direction-based processing on the trajectory data of the floating vehicle passing through the urban road intersection based on the grids in the grid model includes:
acquiring a starting point grid in the grid model corresponding to an entrance track point of a floating vehicle passing through a road intersection, acquiring an end point grid in the grid model corresponding to an exit track point, and determining the running direction corresponding to the track data of the floating vehicle according to the direction relationship of the starting point grid and the end point grid in the 3 x 3 grid model.
Further, the determining the running direction corresponding to the track data of the floating car according to the direction relationship of the starting point grid and the end point grid in the 3 × 3 grid model includes:
the upper part of the 3 x 3 grid model corresponds to the north, the lower part corresponds to the south, the left part corresponds to the east and the right part corresponds to the west;
when the end point grid is right above the starting point grid, the running direction corresponding to the track data of the floating car is a north-direction straight-going direction;
when the end point grid is right below the starting point grid, the running direction corresponding to the track data of the floating car is a south-direction straight-going direction;
when the end point grid is positioned at the upper right side of the starting point grid, the running direction corresponding to the track data of the floating car is a north-to-right turn;
when the end point grid is positioned at the lower right side of the starting point grid, the running direction corresponding to the track data of the floating car is a south-turning right-turning direction;
when the destination grid is positioned above the left side of the starting grid, the running direction corresponding to the track data of the floating car is a north left turn;
and when the end point grid is positioned at the lower left side of the starting point grid, the running direction corresponding to the track data of the floating car is a south-turning left-turning direction.
Further, the acquiring the traffic operation parameters of the intersection according to the direction-dividing processing result of the track data of the floating vehicle, wherein the traffic operation parameters include a traffic flow passing through the intersection, a traffic time passing through the intersection, a free-flow traffic time of the intersection and a traffic average speed of the intersection, and the acquiring the traffic operation parameters includes:
according to the direction-dividing processing result of the track data of each floating vehicle, the traffic Q in each direction in the grid model of the road intersection is obtainediAnd total traffic volume (Q) at road intersections, while calculating the weight ω of each direction inside the mesh modeliAnd the weight δ of the intersectioniWherein, in the step (A),
wherein n represents the total number of road intersections:
obtaining a time T through a center mesh of a mesh modeliAnd passing through the total Time Time of the road intersection, wherein:
wherein, ttr[last]The last timestamp of the trace point is 4 by Grid _ ID; t is ttr[first]The first timestamp passes the Grid _ ID ═ 4 trace points; count [ tr)]Setting the number of tracks passing in the time for the direction; omegaiWeighting coefficients for each direction of the road intersection are as follows:
acquiring free flow time for each direction of the intersection, selecting track data of the floating car with a time period of 2:00-5:00, and passing through the intersection to designate the directionInstantaneous velocity v>Taking a 10m/s floating car track as a research object, calculating the time of passing through the road intersection, sorting the time of passing through the road intersection, and selecting a 15% quantile as the free circulation running time of the specified direction
Calculating average passing speed Avg _ v for the branch direction of the signal road intersection, selecting a central Grid _ ID (4) as a research object, and calculating the average speed passing through the central Grid, wherein,
wherein v isjThe instantaneous speed value of the trace point passing Grid _ ID is 4; count [ tr)]Setting the number of tracks passing in the time for the direction;
according to the time T of passing through the central grid of the road intersectioniAnd free stream travel time
Calculating the traffic delay D of the road intersection in different directionsiThe total Delay at the intersection is Delay, wherein,
define Delay (0, 10) → A; Delay (10, 20) → B; Delay (20, 30) → C;
Delay(30,40]→D；Delay(40,50]→E；
this definition represents a rating of the delay time.
Further, the calculating of the delay time of passing through the intersection according to the traffic operation parameters of the intersection and the operation evaluation of the intersection according to the delay time include:
a method for dividing directions of road intersections based on a grid model utilizes floating car track data with a time interval of 3s according to the floating car track dataDelay time D for passing through signal road intersection in different directionsiCalculating average traffic speed Avg _ v according to directions of signal road intersections and traffic flow Q in each direction inside the signal road intersectionsiConstructing a visual polar coordinate graph for operation evaluation of the road intersection, wherein polar coordinate points represent Delay time Delay, and the farther the distance from a central point is, the greater the Delay is; the size of each polar coordinate point represents the flow in the direction, and the larger the point is, the larger the flow is; the numerical value marked on each point represents the average speed of the road intersection, the higher the speed is, the larger the numerical value is, and the running state of the road intersection is evaluated by combining delay time, flow and speed parameters.
According to the technical scheme provided by the embodiment of the invention, the embodiment of the invention provides the operation evaluation method of the signalized road intersection based on the grid model, which can traverse all signalized road intersections in the selected area under the condition of abandoning map limitation, dynamically evaluate the road intersections in real time every 5 minutes, diagnose the reason of the delay of the road intersections and further improve the traffic efficiency of the road intersections.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a processing flow chart of a signalized intersection operation evaluation method using floating car data based on a grid model according to an embodiment of the present invention.
Fig. 2 is a logic flow diagram based on the direction of the mesh model according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a grid-based trajectory data redirection process according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a method for calculating traffic operation parameters of a road intersection based on a mesh model according to an embodiment of the present invention.
Fig. 5 is a schematic diagram for evaluating an internal portion direction of a road intersection according to an embodiment of the present invention.
Detailed Description
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The embodiment of the invention adopts the grid model, abandons the restriction of a map, utilizes floating car data with the time interval of 3s, evaluates and diagnoses the signalized road intersection in real time every 5 minutes, evaluates, diagnoses and analyzes the signalized road intersection more finely, saves the cost and improves the calculation efficiency.
The processing flow of the signalized intersection operation evaluation method based on the grid model and utilizing the floating car data is shown in fig. 1, and comprises the following processing steps:
and 11, constructing a grid model of the urban road intersection, reconstructing the road intersection by using the grid model, and performing direction-dividing processing on the track data of the floating car based on the grids in the grid model. Therefore, the limitation of applying the map is abandoned, and the calculation efficiency is improved.
And 12, acquiring traffic operation parameters according to the grid model in different directions, and acquiring the traffic operation parameters of each road intersection in the city in real time, wherein the traffic operation parameters comprise the traffic flow passing through the road intersection, the traffic time passing through the road intersection, the free flow traffic time of the road intersection, the average traffic speed of the road intersection and the signal lamp parameters of the road intersection.
And step 13, calculating delay time of passing through the road intersection according to the traffic operation parameters of the road intersection, evaluating the internal direction of the road intersection, and comprehensively evaluating the whole road intersection.
And step 14, diagnosing the road intersection, adjusting the running state of the road intersection in different traffic states, and improving the running efficiency of the road intersection.
In a specific application of the present invention, the step 11 specifically includes: FIG. 2 is a logic flow diagram of the present invention based on the orientation of the mesh model. As shown in fig. 2, the grid model adopted in the invention reconstructs the intersection, the central coordinate data of the intersection is used to determine the radiation area of the intersection, and the ranges of 150m from the intersection central point in the upper direction, the lower direction, the left direction and the right direction are selected as the radiation areas by taking the coordinate central point of the intersection as a reference. And constructing a 3 × 3 Grid model by taking the selected radiation area as a target, wherein each side length of the Grid model is 100m, the grids in the Grid model are numbered from the lower left corner to the right side and are numbered from the lower side to the upper side in sequence, and Grid _ IDs numbered from 0 to 8 are Grid _ ID ═ 0, Grid _ ID ═ 1, Grid _ ID ═ 2, Grid _ ID ═ 3, Grid _ ID ═ 4, Grid _ ID ═ 5, Grid _ ID ═ 6, Grid _ ID ═ 7 and Grid _ ID ═ 8 respectively, wherein Grid _ ID ═ 4 is a central Grid.
Fig. 3 is a schematic diagram of a grid-based floating car trajectory data direction-dividing process according to an embodiment of the present invention. The method comprises the steps of performing direction-dividing processing on track data of a floating vehicle passing through a road intersection based on a grid model, obtaining a starting point grid in the grid model corresponding to an entrance track point of the floating vehicle passing through the road intersection, obtaining an end point grid in the grid model corresponding to an exit track point, and determining the running direction corresponding to the track data of the floating vehicle according to the direction relation of the starting point grid and the end point grid in the 3 x 3 grid model.
The upper part of the 3 x 3 grid model corresponds to the north, the lower part corresponds to the south, the left part corresponds to the east and the right part corresponds to the west;
when the end point grid is right above the starting point grid, the running direction corresponding to the track data of the floating car is a north-direction straight-going direction;
when the end point grid is right below the starting point grid, the running direction corresponding to the track data of the floating car is a south-direction straight-going direction;
when the end point grid is positioned at the upper right side of the starting point grid, the running direction corresponding to the track data of the floating car is a north-to-right turn;
when the end point grid is positioned at the lower right side of the starting point grid, the running direction corresponding to the track data of the floating car is a south-turning right-turning direction;
when the destination grid is positioned above the left side of the starting grid, the running direction corresponding to the track data of the floating car is a north left turn;
and when the end point grid is positioned at the lower left side of the starting point grid, the running direction corresponding to the track data of the floating car is a south-turning left-turning direction.
For example, if a track point enters in the direction of Grid _ ID 1 and exits in the direction of Grid _ ID 7, the track direction is north-direction straight. If the track point enters in the direction of Grid _ ID being 1 and exits in the direction of Grid _ ID being 3, the track direction is north-turning left. If the track point enters in the direction of Grid _ ID being 1 and exits in the direction of Grid _ ID being 5, the track direction is north-turning right. And describing the running track direction of the track data of the floating car based on the same judgment method.
In a specific application of the present invention, the specific step of calculating the traffic operation parameters of the intersection in step 12 is shown in fig. 4, and specifically includes:
the method for dividing the direction of the road intersection of the grid model is adopted to obtain the traffic volume (Q) of each direction in the road intersectioni) And total traffic volume (Q), while calculating a weight (ω) for each direction inside the intersectioni) And the weight (delta) of each intersection in the cityi) Wherein, in the step (A),
wherein n represents the total number of intersections.
Road intersection division by adopting grid modelThe direction method comprises the steps of obtaining the time (T) of the interior of a road intersection passing through a central grid where the road intersection is locatedi) Total Time to pass through the intersection (Time), wherein,
wherein, ttr[last]The last timestamp of the trace point is 4 by Grid _ ID; t is ttr[first]The first timestamp passes the Grid _ ID ═ 4 trace points; count [ tr)]Setting the number of tracks passed in time (such as 5 minutes) for the direction; omegaiThe weight coefficients of the road intersection in all directions are obtained.
The method for dividing the direction of the road intersection by adopting the grid model obtains the free flow time for the direction division of the road intersection, selects the time period of the floating vehicle data as (2:00-5:00), and passes through the instantaneous speed (v) of the specified direction of the road intersection>10m/s) of the floating car as a research object, and calculating the time for passing through the road intersection. After the time passing through the road intersection is sequenced, a 15% quantile is selected as the free circulation running time of the specified direction
And calculating the average traffic speed (Avg _ v) of the signal road intersection according to the direction division method of the Grid model road intersection, and selecting a center Grid _ ID (4) as a research object. An average velocity through the grid is calculated, wherein,
wherein v isjThe instantaneous speed value of the trace point passing Grid _ ID is 4; count [ tr)]The number of tracks passed in time is set for the direction, and in this embodiment, the number of tracks passed in 5 minutes is set for the direction；
By adopting a method of dividing the direction of the road intersection of the grid model, the time (T) of the interior of the road intersection passing through the central grid where the road intersection is positioned is obtainedi) Free flow of travel time
Calculating the traffic delay (D) at the intersectioni) Delay is always delayed, wherein,
define Delay (0, 10) → A; Delay (10, 20) → B; Delay (20, 30) → C;
Delay(30,40]→D；Delay(40,50]→E；
this definition represents a rating of the delay time.
In a specific application of the present invention, the step 15 specifically includes: the method for evaluating the running state of the road intersection comprises the following steps of:
fig. 5 is a schematic diagram for evaluating an internal portion direction of a road intersection according to an embodiment of the present invention. The method for dividing the direction of the road intersection based on the grid model utilizes the floating car track data with the time interval of 3s and obtains the passing delay time (D) for dividing the direction of the road intersection passing the signal according to the abovei) Calculating average traffic speed (Avg _ v) according to directions of signal road intersections, and calculating traffic volume (Q) of each direction in the signal road intersectionsi) Based on this, a visualized intersection operation evaluation polar graph of the evaluation is output, as shown in fig. 5, in which polar points represent Delay times (Delay), the farther from the center point, the greater the Delay. The size of each point represents the flow in that direction, with the larger the point, the larger the flow. The numerical value marked on each point represents the average speed of the road intersection, the higher the speed is, the larger the numerical value is, and the running state of the road intersection is evaluated by combining delay time, flow and speed through a data fusion algorithm. Then, the number of signal lights at the intersection is extractedAnd matching the passing time and the passing flow in the traffic operation parameters by combining the operation state of the road intersection to obtain a reasonable signal lamp timing scheme of the road intersection.
In summary, the embodiment of the present invention provides a method for evaluating the operation of signalized road intersections based on a mesh model, which can traverse all signalized road intersections in a selected area without map restrictions, perform real-time dynamic evaluation on the road intersections every 5 minutes, diagnose the reason for the delay of the road intersections, and further improve the traffic efficiency of the road intersections.
The invention abandons the complex calculation in the map matching calculation, develops a new method to adopt the algorithm of a grid model, improves the calculation efficiency, and simultaneously adopts the floating car track data with the interval of 3s to ensure the accuracy and rationality of the evaluation diagnosis.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
Those of ordinary skill in the art will understand that: modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A signalized intersection operation evaluation method based on a grid model is characterized by comprising the following steps:
constructing a grid model of an urban road intersection, and performing direction-dividing processing on track data of a floating vehicle passing through the urban road intersection based on the grid model;
acquiring traffic operation parameters of the road intersection according to the direction-dividing processing result of the track data of the floating vehicle, wherein the traffic operation parameters comprise traffic flow passing through the road intersection, traffic time passing through the road intersection, free flow traffic time of the road intersection and average traffic speed of the road intersection;
calculating the traffic delay time of passing through the road intersection according to the traffic operation parameters of the road intersection, and performing operation evaluation on the road intersection according to the traffic delay time;
the construction of the grid model of the urban road intersection comprises the following steps:
determining a radiation area of the intersection by using central coordinate data of the intersection, taking a coordinate center point of the intersection as a reference, selecting ranges from the intersection center point to 150m in the upper direction, the lower direction, the left direction and the right direction as the radiation areas, and constructing a 3 × 3 Grid model by taking the selected radiation areas as targets, wherein each side length of the Grid model is 100m, grids in the Grid model are numbered from the lower left corner to the right side and from the lower side to the upper side, Grid _ IDs numbered from 0 to 8 are respectively Grid _ ID ═ 0, Grid _ ID ═ 1, Grid _ ID ═ 2, Grid _ ID ═ 3, Grid _ ID ═ 4, Grid _ ID ═ 5, Grid _ ID ═ 6, Grid _ ID ═ 7 and Grid _ ID ═ 8, and the Grid _ ID ═ 4 is the center Grid.
2. The method according to claim 1, wherein said steering trajectory data of floating vehicles passing through said urban road intersection based on said grids in said grid model comprises:
acquiring a starting point grid in the grid model corresponding to an entrance track point of a floating vehicle passing through a road intersection, acquiring an end point grid in the grid model corresponding to an exit track point, and determining the running direction corresponding to the track data of the floating vehicle according to the direction relationship of the starting point grid and the end point grid in the 3 x 3 grid model.
3. The method according to claim 2, wherein the determining the corresponding traveling direction of the track data of the floating car according to the directional relation of the starting point grid and the end point grid in the 3-by-3 grid model comprises:
the upper part of the 3 x 3 grid model corresponds to the north, the lower part corresponds to the south, the left part corresponds to the east and the right part corresponds to the west;
when the end point grid is right above the starting point grid, the running direction corresponding to the track data of the floating car is a north-direction straight-going direction;
when the end point grid is right below the starting point grid, the running direction corresponding to the track data of the floating car is a south-direction straight-going direction;
when the end point grid is positioned at the upper right side of the starting point grid, the running direction corresponding to the track data of the floating car is a north-to-right turn;
when the end point grid is positioned at the lower right side of the starting point grid, the running direction corresponding to the track data of the floating car is a south-turning right-turning direction;
when the destination grid is positioned above the left side of the starting grid, the running direction corresponding to the track data of the floating car is a north left turn;
and when the end point grid is positioned at the lower left side of the starting point grid, the running direction corresponding to the track data of the floating car is a south-turning left-turning direction.
4. The method according to claim 2 or 3, wherein the obtaining of traffic operation parameters of the intersection according to the direction-dividing processing result of the track data of the floating vehicle comprises the following steps of:
according to the track of each floating carThe data is processed in different directions, and the traffic volume Q of each direction in the grid model of the road intersection is obtainediAnd total traffic volume (Q) at road intersections, while calculating the weight ω of each direction inside the mesh modeliAnd the weight δ of the intersectioniWherein, in the step (A),
wherein n represents the total number of road intersections:
obtaining a time T through a center mesh of a mesh modeliAnd passing through the total Time Time of the road intersection, wherein:
wherein, ttr[last]The last timestamp of the trace point is 4 by Grid _ ID; t is ttr[first]The first timestamp passes the Grid _ ID ═ 4 trace points; count [ tr)]Setting the number of tracks passing in the time for the direction; omegaiWeighting coefficients for each direction of the road intersection are as follows:
acquiring free flow time for each direction of the road intersection, selecting track data of the floating car within a time period of 2:00-5:00, and passing through the instantaneous speed v of the road intersection in the designated direction>Taking a 10m/s floating car track as a research object, calculating the time of passing through the road intersection, sorting the time of passing through the road intersection, and selecting a 15% quantile as the free circulation running time of the specified direction
Calculating average passing speed Avg _ v for the branch direction of the signal road intersection, selecting a central Grid _ ID (4) as a research object, and calculating the average speed passing through the central Grid, wherein,
wherein v isjThe instantaneous speed value of the trace point passing Grid _ ID is 4; count [ tr)]Setting the number of tracks passing in the time for the direction;
according to the time T of passing through the central grid of the road intersectioniAnd free stream travel time
Calculating the traffic delay D of the road intersection in different directionsiThe total Delay at the intersection is Delay, wherein,
define Delay (0, 10) → A; Delay (10, 20) → B; Delay (20, 30) → C;
Delay(30,40]→D；Delay(40,50]→E；
this definition represents a rating of the delay time.
5. The method according to claim 4, wherein calculating a delay time through the intersection based on the traffic operation parameters of the intersection, and performing an operation evaluation of the intersection based on the delay time comprises:
a method for dividing directions of a road intersection based on a grid model utilizes floating car track data with a time interval of 3s to pass through delay time D according to the divided directions of a signal road intersectioniCalculating average traffic speed Avg _ v according to directions of signal road intersections and traffic flow Q in each direction inside the signal road intersectionsiBuilding visualizationsThe method comprises the following steps of (1) evaluating a polar coordinate graph in operation at a road intersection, wherein polar coordinate points represent Delay time Delay, and the Delay is larger the farther the distance is from a central point; the size of each polar coordinate point represents the flow in the direction, and the larger the point is, the larger the flow is; the numerical value marked on each point represents the average speed of the road intersection, the higher the speed is, the larger the numerical value is, and the running state of the road intersection is evaluated by combining delay time, flow and speed parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810348871.0A CN108648444B (en) | 2018-04-18 | 2018-04-18 | Signalized intersection operation evaluation method based on grid model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810348871.0A CN108648444B (en) | 2018-04-18 | 2018-04-18 | Signalized intersection operation evaluation method based on grid model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108648444A CN108648444A (en) | 2018-10-12 |
| CN108648444B true CN108648444B (en) | 2020-05-05 |
Family
ID=63746592
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810348871.0A Active CN108648444B (en) | 2018-04-18 | 2018-04-18 | Signalized intersection operation evaluation method based on grid model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108648444B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109348423B (en) * | 2018-11-02 | 2020-04-07 | 同济大学 | Sampling trajectory data-based arterial road coordination control optimization method |
| CN109509350B (en) * | 2018-12-24 | 2020-09-25 | 银江股份有限公司 | Intersection operation efficiency calculation method based on directed binary weight network |
| WO2020248197A1 (en) * | 2019-06-13 | 2020-12-17 | Beijing Didi Infinity Technology And Development Co., Ltd. | Saturation flow estimation for signalized intersections using vehicle trajectory data |
| CN110807928B (en) * | 2019-11-06 | 2021-08-24 | 南京莱斯信息技术股份有限公司 | Statistical evaluation method for bus priority effect |
| CN111292533B (en) * | 2020-02-11 | 2021-07-30 | 北京交通大学 | Method for estimating flow of arbitrary section of highway at any time period based on multi-source data |
| CN113570870A (en) * | 2021-09-27 | 2021-10-29 | 华砺智行(武汉)科技有限公司 | Distributed intersection average delay estimation method, device, equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102147260A (en) * | 2010-02-09 | 2011-08-10 | 日电（中国）有限公司 | Electronic map matching method and device |
| CN102280031A (en) * | 2011-07-22 | 2011-12-14 | 南京莱斯信息技术股份有限公司 | Crossing traffic state recognition method based on floating car data |
| CN103927873A (en) * | 2014-04-28 | 2014-07-16 | 中国航天系统工程有限公司 | Matching method for probe car and road section and method for obtaining real-time traffic status in parallel |
| CN104317583A (en) * | 2014-10-21 | 2015-01-28 | 合肥星服信息科技有限责任公司 | Road congestion optimization algorithm based on grid theory |
| CN104408918A (en) * | 2014-11-20 | 2015-03-11 | 江苏物联网研究发展中心 | Floating car data and road matching method based on MapReduce calculation frame |
| CN106530704A (en) * | 2016-11-25 | 2017-03-22 | 杭州电子科技大学 | Floating car aggregation detection method based on multivariate data fusion |
-
2018
- 2018-04-18 CN CN201810348871.0A patent/CN108648444B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102147260A (en) * | 2010-02-09 | 2011-08-10 | 日电（中国）有限公司 | Electronic map matching method and device |
| CN102280031A (en) * | 2011-07-22 | 2011-12-14 | 南京莱斯信息技术股份有限公司 | Crossing traffic state recognition method based on floating car data |
| CN103927873A (en) * | 2014-04-28 | 2014-07-16 | 中国航天系统工程有限公司 | Matching method for probe car and road section and method for obtaining real-time traffic status in parallel |
| CN104317583A (en) * | 2014-10-21 | 2015-01-28 | 合肥星服信息科技有限责任公司 | Road congestion optimization algorithm based on grid theory |
| CN104408918A (en) * | 2014-11-20 | 2015-03-11 | 江苏物联网研究发展中心 | Floating car data and road matching method based on MapReduce calculation frame |
| CN106530704A (en) * | 2016-11-25 | 2017-03-22 | 杭州电子科技大学 | Floating car aggregation detection method based on multivariate data fusion |
Non-Patent Citations (2)
| Title |
|---|
| Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data;Yang Liu等;《Sustainability》;20170331;第515-526页 * |
| Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data;Shi An等;《Information Sciences》;20161231;第1-15页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108648444A (en) | 2018-10-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108648444B (en) | Signalized intersection operation evaluation method based on grid model | |
| CN108399740B (en) | Method for predicting collision probability of motor vehicles at signalized intersection | |
| CN112789619B (en) | Simulation scene construction method, simulation method and device | |
| CN106225797B (en) | A kind of paths planning method | |
| CN106846818B (en) | road network dynamic traffic flow prediction method based on Simulink simulation | |
| CN103996289B (en) | A kind of flow-speeds match model and Travel Time Estimation Method and system | |
| Ma et al. | Estimating maximum queue length for traffic lane groups using travel times from video-imaging data | |
| WO2019047905A1 (en) | Road traffic analysis system, method and apparatus | |
| CN106052701B (en) | A kind of method of vehicle routing choice under traffic overflow state | |
| CN104615897B (en) | Road section travel time estimation method based on low-frequency GPS data | |
| CN105788298B (en) | A kind of method and device of two-way green wave control | |
| CN106097730A (en) | The method of estimation of a kind of section vehicle queue length, Apparatus and system | |
| CN102819958B (en) | Cellular simulation method for control of urban road motor vehicle traffic signals | |
| CN112078592B (en) | Method and device for predicting vehicle behavior and/or vehicle track | |
| CN107578620B (en) | Method, system and device for calculating road traffic capacity based on lane level system | |
| CN107705586B (en) | Traffic flow control method and device for road intersection | |
| CN104952248A (en) | Automobile convergence predicting method based on Euclidean space | |
| CN103177585B (en) | Road turning average travel speed calculating method based on floating car data | |
| CN103903465A (en) | Method and system for releasing reason for road congestion in real time | |
| CN108877253A (en) | A kind of public transportation lane resource dynamic sharing method and system based on Internet of Things | |
| CN112710317A (en) | Automatic driving map generation method, automatic driving method and related product | |
| CN106097734A (en) | A kind of plane perception detection method controlled for crossing traffic signal and system | |
| CN109903574A (en) | The acquisition methods and device of crossing traffic information | |
| Suh et al. | Signal design for displaced left-turn intersection using Monte Carlo method | |
| CN102890862A (en) | Traffic condition analyzing device and method based on vector mode |
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 |