CN111127880A - MFD-based grid network traffic performance analysis method - Google Patents
MFD-based grid network traffic performance analysis method Download PDFInfo
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- CN111127880A CN111127880A CN201911294204.XA CN201911294204A CN111127880A CN 111127880 A CN111127880 A CN 111127880A CN 201911294204 A CN201911294204 A CN 201911294204A CN 111127880 A CN111127880 A CN 111127880A
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- 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
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- 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
Abstract
The invention discloses a grid network traffic performance analysis method based on MFD, which comprises the following steps: s1, drawing an initial MFD; s2, judging whether the initial MFD is complete, if yes, entering a step S4, and if not, entering a step S3; s3, drawing the MFD in the simulation environment, and entering step S4; s4, drawing an MFD trend line graph; s5, acquiring the average traffic density and the average traffic flow of the road section; s6, obtaining weighted average traffic density and weighted average traffic flow; s7, obtaining a basic relation graph of the average density and the average flow of the target road network; and S8, acquiring the average free flow speed, the critical flow and the critical density of the target road network, and completing the traffic performance analysis of the target road network. The invention can acquire the traffic performance of the road network through mathematical analysis, a computer simulation method and MFD characteristic consideration, find out the road section with serious congestion condition and facilitate the user to improve the layout of the road network.
Description
Technical Field
The invention relates to the field of road traffic, in particular to a grid network traffic performance analysis method based on MFD.
Background
The urban road network consists of various main roads with different functions and regional roads in the jurisdiction range of cities and towns, is a framework of urban overall planning layout, and can provide safe, rapid, economic and comfortable driving conditions for various vehicles. Urban roads play an important role in various aspects such as urban ventilation, sunshine, greening, drainage, utility pipeline laying, building appearance and the like, and the road network layout must be reasonably solved in urban overall planning. The accuracy and the reliability of the traffic demand prediction model play a vital role in urban road network evolution, state prediction and the like. MFD is an abbreviation for macroscopic fundamental Diagram, and Chinese means macroscopic basic diagram.
Disclosure of Invention
Aiming at the defects in the prior art, the traffic performance of the road network can be obtained by the MFD-based grid road network traffic performance analysis method, so that a user can conveniently improve the layout of the road network.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
the utility model provides a grid network traffic performance analysis method based on MFD, it includes the following step:
s1, drawing the initial MFD based on the real environment;
s2, judging whether the initial MFD is complete, if yes, entering a step S4, and if not, entering a step S3;
s3, discarding the initial MFD, drawing the MFD in the simulation environment based on the simulation environment, and proceeding to step S4;
s4, drawing an MFD trend line graph according to the current MFD data;
s5, acquiring average traffic density and average traffic flow of the road section in the MFD trend line graph;
s6, obtaining weighted average traffic density and weighted average traffic flow according to the road section average traffic density and the average traffic flow respectively;
s7, acquiring a basic relation graph of the average density-average flow of the target road network by using an MFD vector algorithm based on the weighted average traffic density and the weighted average traffic flow;
and S8, acquiring the average free flow speed, the critical flow and the critical density of the target road network according to the basic graph of the relation between the average density and the average flow, and completing the traffic performance analysis of the target road network.
Further, the specific method of step S1 includes the following sub-steps:
s1-1, numbering each road section in the target road network, and acquiring the road length, the road width, the number of lanes, control yielding data, signal control yielding data, setting data of one-way roads, steering limitation data, vehicle type limitation data and special road setting data of each road section;
s1-2, acquiring the average traffic density of the road section in a certain time by arranging a flow detector at the upstream of the road section intersection;
s1-3, acquiring position, direction and speed data of the taxi through GPS data of the taxi, and acquiring the average speed and the average traffic flow of the taxi on a road through which the taxi passes by combining a map matching method and a path conjecture method;
and S1-4, solving by adopting a road network average density, average speed and average traffic flow formula based on the average traffic density, average speed and average traffic flow of each road section, and drawing the solved result as a basic data point to the initial MFD based on the real environment.
Further, the specific method of step S3 includes the following sub-steps:
s3-1, abandoning the initial MFD and numbering each road section in the target road network to obtain the road length, the road width, the number of the lanes, the control yielding data, the signal control yielding data, the setting data of the one-way road, the steering limitation data, the vehicle type limitation data and the special road setting data of each road section;
s3-2, performing road network setting in simulation software according to the data acquired in the step S3-1, and setting a flow detector in a road section to obtain a simulated road network;
s3-3, inputting a starting point, a terminal point, a vehicle type proportion and a road section free form speed in the simulation road network, simulating the traffic condition of the target road network, and acquiring the average traffic density, the average vehicle speed and the average traffic flow of the road section where the flow detector is arranged within a certain time;
and S3-4, solving by adopting a road network average density, average speed and average traffic flow formula based on the average traffic density, average speed and average traffic flow of each road section, and drawing the MFD under the simulation environment by taking the solved result as a basic data point.
Further, the certain time is any time length which is longer than the signal lamp period of the road section.
Further, the specific method of step S4 is:
and fitting the current MDF data according to a least square method to obtain an MFD trend line graph.
Further, the specific method of step S6 is:
according to the formula:
respectively obtaining weighted average traffic density kwAnd weighted average traffic flow qw(ii) a Wherein q isiIs the average traffic flow for section i; liIs the length of section i; k is a radical ofiIs the average traffic density for the section i.
The invention has the beneficial effects that: the method can acquire the traffic performance of the road network through mathematical analysis, a computer simulation method and MFD characteristic consideration, and is convenient for users to improve the layout of the road network.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a basic diagram of the relationship between the average density of the road network and the average flow in the embodiment.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, the method for analyzing traffic performance of a mesh network based on MFD includes the following steps:
s1, drawing the initial MFD based on the real environment;
s2, judging whether the initial MFD is complete, if yes, entering a step S4, and if not, entering a step S3;
s3, discarding the initial MFD, drawing the MFD in the simulation environment based on the simulation environment, and proceeding to step S4;
s4, drawing an MFD trend line graph according to the current MFD data;
s5, acquiring average traffic density and average traffic flow of the road section in the MFD trend line graph;
s6, obtaining weighted average traffic density and weighted average traffic flow according to the road section average traffic density and the average traffic flow respectively;
s7, acquiring a basic relation graph of the average density-average flow of the target road network by using an MFD vector algorithm based on the weighted average traffic density and the weighted average traffic flow;
and S8, acquiring the average free flow speed, the critical flow and the critical density of the target road network according to the basic graph of the relation between the average density and the average flow, and completing the traffic performance analysis of the target road network.
The specific method of step S1 includes the following substeps:
s1-1, numbering each road section in the target road network, and acquiring the road length, the road width, the number of lanes, control yielding data, signal control yielding data, setting data of one-way roads, steering limitation data, vehicle type limitation data and special road setting data of each road section;
s1-2, acquiring the average traffic density of the road section in a certain time by arranging a flow detector at the upstream of the road section intersection; the certain time is any time length longer than the signal lamp period of the road section and is generally set to be 3-5 min;
s1-3, acquiring position, direction and speed data of the taxi through GPS data of the taxi, and acquiring the average speed and the average traffic flow of the taxi on a road through which the taxi passes by combining a map matching method and a path conjecture method;
and S1-4, solving by adopting a road network average density, average speed and average traffic flow formula based on the average traffic density, average speed and average traffic flow of each road section, and drawing the solved result as a basic data point to the initial MFD based on the real environment.
The specific method of step S3 includes the following substeps:
s3-1, abandoning the initial MFD and numbering each road section in the target road network to obtain the road length, the road width, the number of the lanes, the control yielding data, the signal control yielding data, the setting data of the one-way road, the steering limitation data, the vehicle type limitation data and the special road setting data of each road section;
s3-2, performing road network setting in simulation software according to the data acquired in the step S3-1, and setting a flow detector in a road section to obtain a simulated road network;
s3-3, inputting a starting point, a terminal point, a vehicle type proportion and a road section free form speed in the simulation road network, simulating the traffic condition of the target road network, and acquiring the average traffic density, the average vehicle speed and the average traffic flow of the road section where the flow detector is arranged within a certain time; the certain time is any time length longer than the signal lamp period of the road section and is generally set to be 3-5 min
And S3-4, solving by adopting a road network average density, average speed and average traffic flow formula based on the average traffic density, average speed and average traffic flow of each road section, and drawing the MFD under the simulation environment by taking the solved result as a basic data point.
The specific method of step S4 is: and fitting the current MDF data according to a least square method to obtain an MFD trend line graph.
The specific method of step S6 is: according to the formula:
respectively obtaining weighted average traffic density kwAnd weighted average traffic flow qw(ii) a Wherein q isiIs the average traffic flow for section i; liIs the length of section i; k is a radical ofiIs the average traffic density for the section i.
In an embodiment of the present invention, data of a certain area provided by a traffic police team in down mountain city is taken as an example (specifically, video data of 5 minutes per whole point and half point from 3/20/month to 7/night in 2018), and vehicle passing data of an intersection in the area (time from 3/20/month to 7/night in 2018).
And (3) performing data sorting on the videos of the first intersection and the last intersection of a section of road, and counting the number of automobiles on the section of road at each integral point and every minute so as to obtain the average traffic flow density of the section of video. Example (c): the density of the traffic flow of the No. 1 road section at 7 points in the morning is required, a video of the No. 1 intersection at 7 points in the morning from the west to the east and a video of the No. 2 intersection at 7 points in the morning from the east to the west are called out, the number of automobiles on the road section is respectively recorded at 7:01, 7:02, 7:03, 7:04 and 7:05, the sum of the two videos is added and then divided by 5, and the density of the No. 1 road section at 7 points can be approximately obtained. And processing the data of passing vehicles at the head intersection and the tail intersection of a section of road, namely subtracting the traffic flow leaving the section of road from the traffic flow entering the section of road at the corresponding moment. And then carrying out weighted calculation to obtain the weighted average traffic flow and the weighted average traffic flow density of the road network at each moment, and the density and the traffic flow in two directions of each road section. Further, a basic graph of the relationship between the average density and the average traffic of the network as shown in fig. 2 is obtained.
As can be seen from fig. 2, although the weighted density-weighted flow rate is discrete points, they are all attached around the trend line, and they are in the trend of ascending first and then gentle and then descending; furthermore, in the above figuresThe weighted flow-weighted density relation of the road network is distributed in a higher flow interval mostly, which shows that in a research time range (7 points early-7 points late), a case road network has congested road sections and smooth road sections at the same time; meanwhile, the high-density section samples have less scattered points, which shows that the traffic state of the roads in the road network is good, the vehicles run smoothly, and the road sections can serve more traffic demands. Specifically, we can obtain the average free flow velocity, critical flow rate and critical density data of the case road network by using the graph, respectively, vf、vl、ql、dlThe results are shown in table 1 below.
Table 1: case road network related data results
The average design speed per hour of the road sections in the case area is 45km/h, the average free flow speed is 35.77km/h, and the free flow speed level can reach 79.4% of the design speed per hour, which shows that the case road network has good traffic state and smooth vehicle running in the non-congestion period; however, the critical speed is only 16.2km/h, which is far lower than the average design speed per hour, and the congestion condition in the area is serious. And the related personnel can improve the road section with the serious congestion condition according to the result.
In summary, the present invention can obtain the traffic performance of the road network by mathematical analysis, computer simulation method and consideration of MFD characteristics, find out the road section with serious congestion, and facilitate the user to improve the layout of the road network.
Claims (6)
1. A grid network traffic performance analysis method based on MFD is characterized by comprising the following steps:
s1, drawing the initial MFD based on the real environment;
s2, judging whether the initial MFD is complete, if yes, entering a step S4, and if not, entering a step S3;
s3, discarding the initial MFD, drawing the MFD in the simulation environment based on the simulation environment, and proceeding to step S4;
s4, drawing an MFD trend line graph according to the current MFD data;
s5, acquiring average traffic density and average traffic flow of the road section in the MFD trend line graph;
s6, obtaining weighted average traffic density and weighted average traffic flow according to the road section average traffic density and the average traffic flow respectively;
s7, acquiring a basic relation graph of the average density-average flow of the target road network by using an MFD vector algorithm based on the weighted average traffic density and the weighted average traffic flow;
and S8, acquiring the average free flow speed, the critical flow and the critical density of the target road network according to the basic graph of the relation between the average density and the average flow, and completing the traffic performance analysis of the target road network.
2. The MFD-based mesh network traffic performance analysis method according to claim 1, wherein said specific method of step S1 comprises the following sub-steps:
s1-1, numbering each road section in the target road network, and acquiring the road length, the road width, the number of lanes, control yielding data, signal control yielding data, setting data of one-way roads, steering limitation data, vehicle type limitation data and special road setting data of each road section;
s1-2, acquiring the average traffic density of the road section in a certain time by arranging a flow detector at the upstream of the road section intersection;
s1-3, acquiring position, direction and speed data of the taxi through GPS data of the taxi, and acquiring the average speed and the average traffic flow of the taxi on a road through which the taxi passes by combining a map matching method and a path conjecture method;
and S1-4, solving by adopting a road network average density, average speed and average traffic flow formula based on the average traffic density, average speed and average traffic flow of each road section, and drawing the solved result as a basic data point to the initial MFD based on the real environment.
3. The MFD-based mesh network traffic performance analysis method according to claim 1, wherein said specific method of step S3 comprises the following sub-steps:
s3-1, abandoning the initial MFD and numbering each road section in the target road network to obtain the road length, the road width, the number of the lanes, the control yielding data, the signal control yielding data, the setting data of the one-way road, the steering limitation data, the vehicle type limitation data and the special road setting data of each road section;
s3-2, performing road network setting in simulation software according to the data acquired in the step S3-1, and setting a flow detector in a road section to obtain a simulated road network;
s3-3, inputting a starting point, a terminal point, a vehicle type proportion and a road section free form speed in the simulation road network, simulating the traffic condition of the target road network, and acquiring the average traffic density, the average vehicle speed and the average traffic flow of the road section where the flow detector is arranged within a certain time;
and S3-4, solving by adopting a road network average density, average speed and average traffic flow formula based on the average traffic density, average speed and average traffic flow of each road section, and drawing the MFD under the simulation environment by taking the solved result as a basic data point.
4. The MFD-based mesh network traffic performance analysis method according to claim 2 or 3, wherein said certain time is any time period longer than a signal light period of the road section.
5. The MFD-based mesh network traffic performance analysis method according to claim 1, wherein said step S4 is specifically performed by:
and fitting the current MDF data according to a least square method to obtain an MFD trend line graph.
6. The MFD-based mesh network traffic performance analysis method according to claim 1, wherein said step S6 is specifically performed by:
according to the formula:
respectively obtaining weighted average traffic density kwAnd weighted average traffic flow qw(ii) a Wherein q isiIs the average traffic flow for section i; liIs the length of section i; k is a radical ofiIs the average traffic density for the section i.
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| CN115830874A (en) * | 2023-02-10 | 2023-03-21 | 西南交通大学 | Method and system for evaluating fitting performance of traffic flow basic diagram |
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