CN115641721A - Multi-source traffic flow index fusion and road condition calculation method based on fusion index - Google Patents
Multi-source traffic flow index fusion and road condition calculation method based on fusion index Download PDFInfo
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- CN115641721A CN115641721A CN202211554669.6A CN202211554669A CN115641721A CN 115641721 A CN115641721 A CN 115641721A CN 202211554669 A CN202211554669 A CN 202211554669A CN 115641721 A CN115641721 A CN 115641721A
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Abstract
The invention relates to a multi-source traffic flow index fusion and a road condition calculation method based on the fusion index, wherein a discretization road network is established based on mileage piles; acquiring a floating car traffic flow index and a section detection traffic flow index of a road network closed area, wherein a mileage pile is used as an entrance or an exit in the closed area; the traffic flow indicator comprises a traffic flow speed; discretizing spatial continuous traffic flow indexes from different sources by taking the mileage stake interval as a unit; and fusing different source traffic flow indexes of each mileage pile interval. After different source traffic flow indexes are fused, the fused traffic flow speed is converted into a mileage stake interval road condition, and a discrete road network is combined to obtain a spatially continuous high-speed road condition. The method provided by the invention fully utilizes the multi-source road condition data, and realizes the whole road network road condition data fusion on any time slice based on the designed fusion algorithm, thereby outputting more accurate and more comprehensive road condition data.
Description
Technical Field
The invention belongs to the field of intelligent traffic informatization, and relates to a multi-source traffic flow index fusion and a road condition calculation method based on the fusion index.
Background
With the continuous improvement of the quantity of motor vehicles in China, the traffic jam condition of the highway is more serious, and road condition data becomes a common concern of a road manager and a large number of traffic participants. The road condition data is divided into quantitative data and qualitative data, wherein the former refers to specific traffic operation condition indexes (hereinafter referred to as traffic flow indexes) such as traffic volume, vehicle speed and the like, the latter refers to traffic operation condition descriptions such as slow traffic, congestion and severe congestion, and the latter is generally converted from the former and is easier to understand, so that the following road conditions are all designated road conditions.
The current road condition calculation mainly depends on two types of data of a floating car and section detection, the former realizes speed calculation by collecting GPS data such as a mobile phone and vehicle-mounted equipment, the latter obtains traffic flow indexes such as speed and traffic volume at the section through modes such as screen detection, image recognition, radar induction and geomagnetic induction, and the further obtains the road condition at the section through conversion. However, there are several problems and difficulties in current road condition calculation and effective application:
(1) The road condition of the floating car is based on a small sample and is not enough to represent the complete road condition
The current floating car data mainly comprise: 1) user mobile phone GPS data collected by a map service provider (map provider), 2) two-passenger one-dangerous vehicle GPS data, 3) special vehicle GPS data and the like. The GPS data of the mobile phone collected by the current map provider is the main data. However, each graph businessman can only acquire the GPS data of each user for calculating the road condition, and thus cannot describe the running condition of the complete road network, and the problem that two passengers, one dangerous vehicle, special vehicles and the like cannot express the complete road condition also exists.
(2) The space distribution of the detection points of the cross section is sparse, and the complete road condition cannot be described
The section detection needs to depend on specific facilities such as a camera, a radar, a geomagnetic coil and the like, and because the facilities are expensive and high in maintenance cost, the section detection only needs to be arranged at key points or road sections of a road network at present, the coverage of the detection data space range is insufficient, and the complete road condition cannot be described.
(3) The road condition descriptions of all sources are inconsistent
The reasons for inconsistent road condition descriptions are two reasons: 1) The traffic flow indexes calculated from all sources are inconsistent because of difference of the data and the calculation principle; 2) The manner of converting traffic flow indexes into road conditions is inconsistent. The problem of inconsistent road condition description prevents road managers and a large number of traffic participants from accurately acquiring the road running condition.
For the above problems, in the past, a single traffic data is usually selected in the service application. If in the traffic survey service, the method selects section detection data so as to obtain the running condition of a key point or a key road section of a road; in the active management and control service for road operation, the floating vehicle road condition from a single source is selected, so that the road condition data with fine granularity is obtained for precise management and control. However, the problem of insufficient spatial coverage and accuracy is caused by adopting the road condition data from a single source, and the accuracy and the service quality of the upper layer service are further affected.
Disclosure of Invention
The first objective of the present invention is to provide a method for fusing multi-source traffic flow indicators for multi-source incomplete multi-source traffic data.
In order to achieve the above purpose of the invention, the invention adopts the following scheme:
a multi-source traffic flow index fusion method comprises the following steps:
acquiring a floating car traffic flow index and a section detection traffic flow index of a road network closed area, wherein a mileage pile is used as an entrance or an exit in the closed area; the traffic flow indicator comprises a traffic flow speed;
discretizing spatial continuous traffic flow indexes from different sources by taking the mileage stake interval as a unit;
fusing different source traffic flow indexes of each mileage stake interval, comprising:
if the mileage pile interval is contained in the closed interval and is contained in the floating car route or is intersected with the floating car route, performing weight distribution on the floating car, and taking the weighted value of the traffic flow speed of the floating car as the traffic flow speed of the mileage pile interval;
if the mileage pile interval is only contained in the floating car route or is intersected with the floating car route, taking the average value of the traffic flow speed of the floating car as the traffic flow speed of the mileage pile interval;
if the mileage pile interval is only contained in the closed area, taking the traffic flow speed of the closed area as the traffic flow speed of the mileage pile interval;
and if the situation does not exist, judging that the traffic flow speed of the mileage stake interval is infinite.
In a preferred embodiment, the method further comprises encrypting the mileage peg by supplementing the virtual mileage peg. In traffic, the congestion starting point or ending point is not necessarily near the mile pile, and if the congestion starting point is 100.75 km from G2503, a 50-meter error is generated by using the nearest mile pile G2503K 100+700 or G2503K 100+800 instead. Therefore, in order to obtain a road condition with higher precision, the virtual mileage pile is supplemented and encrypted on the basis of the current inherent mileage pile, so that a discretization road network with higher precision is obtained.
In a preferred embodiment, a spatial linear interpolation method is adopted to supplement the virtual mileage pile, and the mileage pile is encrypted.
As a preferred embodiment, the discretization of the spatially continuous traffic flow indicators from different sources is as follows:
for a floating car data source, converting a floating car route into a coordinate system consistent with the pile number of the mileage pile, and for the mileage pile interval intersected with or contained in the floating car route, taking the corresponding traffic flow index as the traffic flow index of the associated floating car;
and for the data source of the closed area, the traffic flow index corresponding to the mileage pile interval is associated to be the traffic flow index of the closed area containing the mileage pile interval.
In a preferred embodiment, the traffic flow index further comprises traffic flow density, and the floating cars are weighted and assigned according to the traffic flow density of the closed area and the traffic flow speed of the floating cars.
As a preferred embodiment, the weight assignment principle is:
under the condition that the section traffic flow speed is greater than or equal to the average speed value of the floating cars, the weight is a monotone increasing function of the speed of the floating cars;
under the condition that the section traffic flow speed is smaller than the average value of the speeds of the floating vehicles, the weight is a monotone decreasing function of the speeds of the floating vehicles;
when the traffic flow index comprises the traffic flow density, combining the traffic flow density constraint weight, and setting the difference value of the maximum weight and the minimum weight as a monotonous decreasing function of the traffic flow density.
The multi-source traffic flow index fusion method provided by the invention fully utilizes the traffic space characteristics of the highway, establishes a discretization road network based on the mile posts, fully utilizes the traffic flow index data of multiple sources, performs fusion through variable weighted average to obtain a uniform and higher-accuracy traffic flow index, and utilizes the cross section detection traffic flow data and floating car data to complement each other so as to measure the space operation condition of the whole road network.
The second objective of the present invention is to provide a method for calculating a high-speed road condition based on a fusion index, which includes: after the traffic flow indexes from different sources are fused by the method, the fused traffic flow speed is converted into the road condition between the mileage stake intervals, and the discrete road networks are combined to obtain the spatially continuous high-speed road condition.
As a preferred embodiment, said merged discrete road network comprises: and combining the mileage pile sections with continuous space and the same road condition by taking the closed section as an upper limit.
In a preferred embodiment, the method further comprises performing spatial smoothing after combining the discrete road networks.
As a preferred embodiment, the spatial smoothing correction includes:
for the two spatially continuous mileage pile merging intervals, if the two spatially continuous mileage pile merging intervals are spatially continuous and the length ratio of the two mileage pile merging intervals meets a preset threshold, merging the two mileage pile merging intervals;
the mileage stake merge section refers to a merged mileage stake section.
According to the high-speed road condition calculation method, the multi-source road condition data are fully utilized, and the whole road network road condition data fusion on any time slice is realized based on the designed fusion algorithm, so that more accurate and more comprehensive road condition data are output.
The invention has the following beneficial effects:
1) In the field of traffic control, the generated fused road condition data can provide a manager with more accurate road network running conditions covering a wider area, and help the manager to improve the level of traffic operation active control, historical road condition study and judgment, personnel and control facility configuration and other services;
2) In the field of travel service, road condition data are fused more accurately and comprehensively, so that traffic participants can be helped to make travel plans, switch form routes, avoid congestion, traffic accidents and the like more reasonably;
3) In the field of data management, the invention systematically integrates and optimizes data from multiple sources, thereby improving the data management level in the aspects of data management, data release and storage, data life cycle management, data quality management and the like.
Drawings
Fig. 1 is an IDEF diagram for calculating highway conditions based on multi-source traffic flow index fusion.
Detailed Description
Example 1
This embodiment specifically illustrates a method for fusing traffic flow indicators based on multiple sources.
The scheme adopts batch processing, receives multi-source whole road network operation condition index data sliced at a certain time by calculation each time, and outputs the data as fused road condition data.
As shown in fig. 1, the technical solution of multi-source traffic flow index fusion includes the following steps:
A1. discrete road network preparation
The goal of this step is: 1) And encrypting the mileage piles, and cutting the route by using the encrypted mileage piles to obtain a discretized road network, so that the traffic flow index data of each source can be conveniently spatially aligned and fused in the follow-up process. 2) And obtaining the topological structure among the encrypted mileage piles, so that subsequent discrete road network merging and space continuity correction are facilitated.
The mileage peg is an elongated object vertically inserted into the roadside, and has a serial number, i.e., a mileage peg number, for identifying a spatial position on a route, i.e., a position of 100.7 km from a starting point, such as G2503K 100+700, which indicates a G2503 route. The highway mileage piles are characterized in that the interval is 100 meters, the density is high, the highway mileage piles are distributed on two sides of a road, and the number of the mileage piles on two sides of the road at the same position is consistent.
In the present invention, a topology structure between mileage piles is used, which requires unique topology node numbers, so that uplink and downlink mileage piles at the same position are defined as 1 direction and 0 direction respectively to distinguish, for example, at G2503K 100+700, the uplink mileage pile number is G2503K 100+700 | 1, and the downlink is G2503K 100+700 | 0.
The precondition of the step is the mileage stake interval before encryption. In a highway scene, a mileage stake interval refers to a route between two adjacent mileage stakes, and is LineString, and the mileage stake interval is specifically defined as follows:
Based on the above definitions, the following processes of performing spatial linear encryption on the mileage pile and generating an encrypted mileage pile interval are as follows.
A certain number of virtual mileage piles are required to be inserted into the system so as to ensureObtaining the encrypted milepost interval of
Rice, wherein
. For any one mileage stake interval
Let it start from
End point is
Generating the encrypted mileage piles and the mileage pile intervals:
s1: get
Is/are as follows
Bisect points from the starting point to the end point of
Wherein
；
S2: constructing a virtual milepost set
；
S3: use of
Section of will
Breaking and constructing mileage pile interval set
Constructing a correlation function
；
S4: computing
As a new set of mileposts
，
As a new set of milepost intervals
Will map to
And
merged as a new mapping
。
Wherein, the step S1 of taking the equal division point of the LineString and the step S4 of breaking the LineString have a general method, which is not described here. Traverse all mileage pile intervals
Performing the above steps A1S 1 to S4 to obtain
Then it is the set of encrypted mile posts,
the interval of the mileage piles after encryption is as follows
And (4) rice.
A2. Section traffic flow index compartmentalization
And (3) traffic flow indexes of section detection classes only represent traffic conditions near the sections. However, in the high-speed traffic practice, the road network is widely distributed, and the cross-section detection cost is high, so that only one cross-section detection point is generally arranged in a closed area (the starting point and the ending point are toll stations or hubs, and no other exit exists in the middle).
Considering that the vehicle entrance and exit are unique in the closed area, for stable flow, namely no queuing phenomenon, the vehicle flow entering the closed area is equal to the leaving vehicle flow, the traffic volume detected by the section can represent the traffic volume of any point in the area, for unstable flow, namely queuing phenomenon, the traffic volume of the section detection point is the average value of the traffic volumes in the area, therefore, the invention adopts the section detection traffic flow index to approximately replace the closed area traffic flow index. The method has rough space granularity and is not sensitive enough to the perception of the congested road condition, so the method needs to be fused with the floating car data in the steps A4 and A5.
The precondition of the step is the relation between the closed area and the section detection point, and the coordinate system of the closed area is consistent with the mileage stake area, so that the subsequent treatment is convenient, and the starting point or the end point of the closed area is required to be the mileage stake. It should be noted that one section detection point only belongs to one closed area, and one closed area includes one or more detection points, which are specifically defined as follows:
the traffic flow indexes required by the invention are as follows: slicing in enclosed space for a certain time
Density of traffic flow on
And traffic flow speed
. The section detection data directly include the driving speed of each vehicle, so that the section detection data is applied to a closed area
The speed of the traffic flow is
Wherein the content of the first and second substances,
Density of traffic flow
Can not be directly obtained from section detection data, and the calculation mode is
Wherein, the first and the second end of the pipe are connected with each other,
wherein, the first and the second end of the pipe are connected with each other,
according to the formula (1) and the formula (2), all the closed areas can be calculated
The traffic flow speed and the traffic flow density of (1) are as follows:
A3. Space continuous traffic flow index discretization
Discretizing the continuous space traffic flow indexes, including the continuous space traffic flow indexes based on the floating cars and the traffic flow indexes in the closed area obtained in A2. Because the interval traffic flow index obtained in the step A2 is an estimated value (calculated by adopting a section traffic flow index), the action of the interval traffic flow index is different from that of data directly obtained based on floating cars, specifically seen in the step A4, and therefore the interval traffic flow index is distinguished and processed in the step A.
A3.1 Floating car-based space continuous traffic flow index discretization
Is provided with
Recording all traffic flow index data obtained based on floating cars on a certain time slice as a set
(fc is floating car), for any piece of data
The route is recorded as
Recording the speed of the traffic flow as
Then, then
The discretization process of (1) is as follows.
S1: will be provided with
Converting into a coordinate system consistent with the pile number system;
s2: extracting and mixing
Intersecting or contained milepost intervals
As a set
；
S3: for all mileage stake intervals
The corresponding traffic flow rate is recorded as
；
Traverse all
If the above steps A3.1S 1 to S3 are executed, all the steps are executed
Converted to a record of the milepost dimensions, of the form:
relating to an interval set of mileage piles
；
Relates to a floating car traffic flow speed set between mileage stake intervals
And the traffic flow speed of the floating car in any mile stake interval is recorded as
；
Note that due to the existence of
The data source of the floating car, wherein one mileage stake interval may correspond to a plurality of speeds.
A3.2 Discretizing A2 closed area traffic flow index
Recording all the traffic flow index data in the closed area obtained in A2 on a certain time slice as a set
For any piece of data
（
Is estimation, i.e., an estimate), the route of which is
The speed of the traffic flow is
The density of the traffic flow is
Then, then
Is discretized by a process such asAnd (5) the following.
S1: quilt with tea leaf
Included range of mileage stake
As a set
；
S2: for all mileage stake intervals
Records the corresponding traffic flow speed as
Corresponding to a traffic flow density of
。
Traverse all
If the above steps A3.2S 1 to S2 are executed, all the steps are executed
Converted to a record of milepost dimensions, of the form:
relating to mileage stake sets
；
Relates to a speed set for estimating the section of a milepost interval
Traffic flow Density set
；
A4. Discrete road network traffic flow index fusion
Based on the step A3, the objective of this step is to merge traffic flow indexes of multiple sources in each mileage pile interval, and the specific process is as follows.
For each mileage stake interval
The velocity of the traffic flow after fusion is
Wherein:
under the condition of acquiring the traffic flow density index, the weight distribution is carried out by supplementing the following formula:
wherein:
wherein:
，
，
represent
The medium maximum value is the maximum value of the average,
to represent
The medium minimum value.
Example 2
This embodiment specifically illustrates a method for calculating a high-speed road condition based on the fusion index obtained in embodiment 1.
A5. Discrete road network road condition calculation
This step is based on the result obtained in step A4
Further converting the traffic flow index of the milepost sections into the road condition of the milepost sections according to the recommended road traffic congestion degree evaluation method (GA/T115-2020) of technical supervision committee of the ministry of public Security, the conversion standard of the traffic flow speed and the road condition is as follows,
wherein
The condition is the road condition, i.e. condition, i represents severe congestion, ii represents moderate congestion, iii represents mild congestion, and iv represents unblocked. Combined formula (3) and formula (4) for all mileage stake intervals
And (3) carrying out road condition conversion to obtain the qualitative road condition of each mileage pile interval, wherein the form is as follows:
mileage pile interval set
；
Road condition set between mileage pile sections
；
A6. Discrete road network merging and spatial smoothing correction
The road condition calculation of each mileage pile section is completed by the step A5, however, the current space granularity is fine, and the section length is
Rice (see step A1), luoThe net is relatively broken, which is not convenient for subsequent analysis and display. Therefore, the objective of this step is to merge the mileage pile sections with continuous space and the same road conditions, and perform the spatial smoothing correction at the same time, for example, the first section on a certain road
To
The road condition between each mileage pile section is I (serious congestion), the second
The road condition between each mileage pile is IV (smooth), and the second is
To
The road condition in the section of the mileage stake is I (serious congestion), and the length of each section is equal to the granularity of the section of the current distance stake
Shorter, so that the section is smoothly corrected to the road condition I, i.e., the section
To is that
The serious congestion route which is combined into the road condition I is reasonable.
In addition, the upper limit of the discrete road network merging in the step is a closed area (see step A2), namely, the mileage stake areas in different closed areas are not merged even if the road conditions are consistent and the spaces are continuous. The reasons for this are the following three points: 1) In a highway scene, the closed area is long, and the problem of over-crushing of a road network can be avoided by taking the closed area as an upper limit; 2) On the management side of the highway, toll stations and hubs are important nodes in a road network, and the road network is divided by the toll stations and the hubs no matter the toll service, the traffic control service, the traffic big data analysis and the like are carried out; 3) In the public trip field, road conversion can be performed at toll stations and junctions, so that the road condition data taking the closed area as the upper limit can meet the public demand for judging the road condition.
The specific process of the step is as follows:
for any closed space
Extracting quilt
Including mileage stake interval
As a set
Then in the closed area
The inner mileage pile interval merging process is as follows:
s1: initialization ordering
Tuple of elements
The combined road condition data storage device is used for storing the combined road condition data;
s2: initialization ordering
Tuple of elements
；
S3: randomly selecting a mileage stake interval
Adding of
To aggregate
Wherein
；
S4: extraction of
The starting point and the end point of the mileage stake,
；
s5: get the set
The middle starting point is
Mileage peg interval
；
S6: if present, and
then add from the right
To order
Tuple of elements
And are combined with
End point of (1)
In substitution S5
Re-executing the steps S5 to S6;
s7: if not, then get the set
Middle endpoint is
Mileage peg interval
；
S8: if present, and
then add from the left
To order
Tuple
And are combined with
Starting point of (2)
In substitution S7
Re-executing the steps S7 to S8;
s9: if not, then
Mileage stake intervals with continuous space and same road conditions are all added to the order
Tuple of elements
In (1). Sequentially extracting
All the mileage pile intervals are combined into a route
Get it
The starting point of the mileage peg interval in the first element is marked as
And taking the end point of the mileage pile interval in the last element and recording the end point as the end point
Calculating
Number of elements (2)
Recording the road condition as
Adding ordered five-membered groups
To aggregate
；
S10: extraction of
All the mileage stake intervals are set
And are combined with
As a new set
；
S11: if it is
And if not, stopping merging.
For all closed areas
If the above-mentioned S1 to S11 are executed, the traffic information corresponding to each closed area can be obtained
Wherein each route with the same road condition and continuous space in the closed area is saved
Number of covered milepost intervals
Starting point of
End point of
Road conditions
. All the closed area road condition information is merged, and then the traffic information is obtainedObtaining the road condition information with continuous space and the same road condition in the full high-speed range, and recording as a set
Wherein
Is shown as
And road condition information corresponding to each closed area.
Then, the merged road condition data is further corrected spatially and smoothly, and it should be noted that the upper limit is still set to be the closed area.
For any enclosed space
Corresponding to the road condition of
Then, the spatial smoothing correction process of the closed region is as follows:
s1: number of initialization iterations
；
S2: if it is
Get the first
An ordered five-membered group
Otherwise, the loop is terminated, and
is a closed space
Spatially smoothing the corrected result;
s3: get the set
The middle starting point is
Ordered pentamer of (1)
；
S4: if present, remember
。
1) If it is
Then, it is determined
Can be covered
Smooth correction, use
Substitution
And returning to execute the step S1 again;
2) If it is
Then use contrary to 1)
Substitution
And returning to execute the step S1 again;
3) If it is
If no space smooth correction is needed, continue to execute S6 and the subsequent steps
Wherein
In order to set the threshold value in advance,
representing a route
And
spatial merging of (2);
s5: if not, then get the set
Middle endpoint is
Ordered pentamer of (1)
；
S6: if present, note
。
1) If it is
Then, it is judged
Can be covered
Smooth correction using
Substitution
And returning to execute the step S1 again;
2) If it is
Then, contrary to 1), use
Substitution
And returning to execute the step S1 again;
3) If it is
Then, without spatial smoothing correction, use
+1 substitution
Returning to the step S2 to continue execution;
s7: if not, using
+1 substitution
And returning to the step S2 to continue the execution.
For each closed area
Performing the above S1 to S7, correspondingly obtained
Is that
The final road condition data set is the road condition information after the spatial smoothing correction
The road condition information of the full-high speed road network after the discrete road network combination and the spatial smoothing is described, wherein each element is information of a route, a road condition grade, a starting point and an end point.
Claims (10)
1. A multi-source traffic flow index fusion method is characterized by comprising the following steps:
establishing a discretization road network based on the mileage pile;
acquiring a floating car traffic flow index and a section detection traffic flow index of a road network closed area, wherein the closed area takes a mileage pile as an entrance or an exit; the traffic flow indicator comprises a traffic flow speed;
discretizing spatial continuous traffic flow indexes from different sources by taking the mileage stake interval as a unit;
fusing different source traffic flow indexes of each mileage stake interval, comprising:
if the mileage pile interval is contained in the closed interval and is contained in the floating car route or is intersected with the floating car route, performing weight distribution on the floating car, and taking the weighted value of the traffic flow speed of the floating car as the traffic flow speed of the mileage pile interval;
if the mileage pile interval is only contained in the floating car route or is intersected with the floating car route, taking the average value of the traffic flow speed of the floating car as the traffic flow speed of the mileage pile interval;
if the mileage pile interval is only contained in the closed area, taking the traffic flow speed of the closed area as the traffic flow speed of the mileage pile interval;
and if the situation does not exist, judging that the traffic flow speed of the mileage stake interval is infinite.
2. The method of claim 1, further comprising, supplementing the virtual milepost encrypts the milepost.
3. The method of claim 2, wherein the virtual milepost is supplemented by a spatial linear interpolation method to encrypt the milepost.
4. The method according to claim 1, wherein the spatially continuous traffic flow indicators from different sources are discretized by:
for a floating car data source, converting a floating car route into a coordinate system consistent with the pile number of the mileage pile, and for the mileage pile interval intersected with or contained in the floating car route, taking the corresponding traffic flow index as the traffic flow index of the associated floating car;
for the data source of the closed area, the traffic flow index corresponding to the mileage stake interval is related to the traffic flow index of the closed area containing the mileage stake interval.
5. The method of claim 1, wherein the traffic flow indicators further include traffic flow density, and wherein floating cars are weighted using traffic flow density in the enclosed area and traffic flow speed of floating cars.
6. The method according to claim 1 or 5, wherein the weight assignment rule is:
under the condition that the section traffic flow speed is greater than or equal to the average speed value of the floating cars, the weight is a monotone increasing function of the speed of the floating cars;
under the condition that the section traffic flow speed is smaller than the average value of the speeds of the floating vehicles, the weight is a monotone decreasing function of the speeds of the floating vehicles;
when the traffic flow index comprises the traffic flow density, combining the traffic flow density constraint weight, and setting the difference value of the maximum weight and the minimum weight as a monotonous decreasing function of the traffic flow density.
7. A high-speed road condition calculation method based on fusion indexes is characterized by comprising the following steps: after the traffic flow indexes from different sources are fused by the method of any one of claims 1 to 6, the fused traffic flow speed is converted into a road condition of a mileage stake interval, and a discrete road network is combined to obtain a spatially continuous highway condition.
8. The method of claim 7, wherein said merging discrete road networks comprises: and combining the mileage pile sections with continuous space and the same road condition by taking the closed section as an upper limit.
9. The method of claim 7 or 8, further comprising performing spatial smoothing after merging the discrete road networks.
10. The method of claim 9, wherein the spatially smooth modification comprises:
for two mileage pile merging intervals with continuous space, if the two mileage pile merging intervals have continuous space and the length ratio of the two mileage pile merging intervals meets a preset threshold value, merging the two mileage pile merging intervals;
the mileage stake merge section refers to a merged mileage stake section.
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