CN109035772A - The recognition methods of freeway traffic operation situation and device based on charge data - Google Patents
The recognition methods of freeway traffic operation situation and device based on charge data Download PDFInfo
- Publication number
- CN109035772A CN109035772A CN201810895704.8A CN201810895704A CN109035772A CN 109035772 A CN109035772 A CN 109035772A CN 201810895704 A CN201810895704 A CN 201810895704A CN 109035772 A CN109035772 A CN 109035772A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- time
- data
- section
- journey time
- 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.)
- Pending
Links
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
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/015—Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
-
- 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
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The freeway traffic operation situation recognition methods based on networked fee collection data that the present invention relates to a kind of, belongs to traffic engineering technical field.It is characterized by: successively carrying out express highway section travel time estimation and the identification of freeway traffic situation, wherein the step of motorway journeys time Estimate includes time and the license board information that the vehicle extracted from ExpresswayNetwork Toll Collection System passes through charge station, vehicle Link Travel Time data are estimated after rejecting abnormalities data, and data are stored into database;The step of traffic status of express way identifies includes being standardized the vehicle Link Travel Time data of storage in the database, it is divided into oversize vehicle by contour of the vehicle and small vehicle carries out data extraction, freeway traffic operation situation discrimination model is constructed, and carries out the differentiation of freeway traffic operation situation using the model.The present invention effectively realizes the identification to road traffic state, can provide basic data for control of traffic and road and control and support.
Description
Technical field
The present invention relates to a kind of freeway traffic operation situation recognition methods based on networked fee collection data and use should
The device of method belongs to traffic engineering technical field.
Background technique
Highway is the important friendship of modern land transport as the strategic road between connection city and city, area
Logical facility, and there is many advantages, such as traffic capacity is big, efficient, comfortable, convenient, it can be provided for user efficient, convenient and fast
Road traffic service plays important acceleration in promoting Regional Linking process of economic development.Freeway network is not
It is disconnected to improve and mileage continues to increase, road traffic congestion is effectively alleviated, road traffic accident is reduced, is mentioned
Road traffic system service quality has been risen, has promoted China's road traffic fast-developing, Assessment of Serviceability of Roads is promoted and national society
Important positive effect can be played in economic development.However, Freeway Foundation supporting management Facilities Construction speed and driving
Mileage is increased speed compared to there are hysteresis qualitys, meanwhile, with the rapidly increase of vehicle guaranteeding organic quantity, freeway traffic demand is not
The problems such as breaking and increase, causing freeway traffic crowded takes place frequently, and induces environmental pollution exacerbation, fuel consumption increases, traffic fortune
A series of problems, such as line efficiency reduces, brings serious negative effect to social safety.In addition, highway is average due to it
Running speed higher and closed road infrastructure design, cause highway relative to other grade road traffic problems more
It is serious.According to the data that Chinese transportation portion issues, traffic congestion bring economic loss accounts for urban population disposable income
20%, it is equivalent to annual GDP (GDP) loss 5%~8%.Highway, which carries, promotes interregional exchange and society
The vital task of meeting Stable Development in Economy, the generation of congested in traffic problem seriously will restrict Chinese society, economy, environment can
Sustainable development process.
At present in correlative study present in freeway traffic congested problem, domestic and foreign scholars mainly use GPS/
GIS technology, induction coil and video equipment real time data collected differentiate road traffic state, but this rely on manually is adjusted
Look into or implantation of device mode to data, to be acquired that there are precision lower, stability is poor, the inconvenient maintenance of at high cost and equipment
Etc. more problem, the dependable with function of data information is greatly reduced, meanwhile, ExpresswayNetwork Toll Collection System is as me
State's freeway management necessary basis facility contains the information resources for enriching preciousness, and it is public effectively to describe vehicles while passing high speed
The information such as time, place, license plate number and the vehicle on road, but this resource is not adequately excavated and is utilized.Therefore, needle
It can with formulation how using networked fee collection data promotion traffic situation recognition effect to Expressway Road traffic congestion problem
The scheme leaned on alleviates traffic congestion, and Improving Expressway service quality is current urgent problem.
Summary of the invention
It is public the purpose of the present invention is aiming at the problems existing in the prior art, providing a kind of high speed based on charge data
Road traffic circulation situation recognition methods and device, to be transported the characteristics of expressway network toll data for design freeway traffic
Row situation recognition methods.
Technical solution is as follows:
A kind of freeway traffic operation situation recognition methods based on networked fee collection data successively carries out highway road
Section travel time estimation and the identification of freeway traffic situation, wherein the step of motorway journeys time Estimate includes from high speed
The vehicle extracted in Highway Network Toll Collection System passes through time and the license board information of charge station, estimates vehicle after rejecting abnormalities data
Link Travel Time data, and data are stored into database;The step of traffic status of express way identifies includes that will store
Vehicle Link Travel Time data in the database are standardized, and are divided into oversize vehicle and compact car by contour of the vehicle
Carry out data extraction, construct freeway traffic operation situation discrimination model, and using the model carry out freeway traffic
Operation situation differentiates.
Further, estimation vehicle Link Travel Time uses following steps:
Step S101: being defined as basic road for the express highway section between Liang Ge charge station, in charge station k=0 and k
Basic road between=1 is section 1, and the basic road between charge station k=z and k=z+1 is section z+1;
Step S102: split data into sample size it is sufficient, without vehicle registration, have vehicle registration but the less three kinds of feelings of sample size
Condition;
Situation one: when sample size abundance, vehicle Link Travel Time, calculation formula are estimated using station journey time is faced
Are as follows:WhereinWhen indicating that vehicle passes through the stroke of section k in sampling interval i
Between, it is compact car when y=c that a, which indicates that downstream extends section number, and y indicates vehicle size, and when y=b is large car;
Situation two: when without vehicle registration, vehicle Link Travel Time is estimated using across station journey time, wherein section 1
Journey time calculation formula are as follows:
WhereinIt indicates by downstream
The vehicle travel time that travel time data calculates, d indicate downstream road section, and s is the integer between [1, a], indicate
Upstream extends section number;
The journey time calculation formula of distance z+1 are as follows:
WhereinIndicate to calculate obtained vehicle travel time by upstream and downstream joint, u indicates upstream section, ε be in
Integer between [1, s], α, β,The Model Weight parameter for being value range between 0 to 1;
Situation three: when having vehicle registration but less sample size, estimating vehicle Link Travel Time using across station journey time,
The wherein journey time calculation formula in section 1 are as follows:
WhereinIt indicates by monitoring section
And the vehicle travel time that downstream data combined calculation obtains, r are the sample in section instantly when having vehicle registration but less sample size
This number;
The journey time calculation formula of distance z+1 are as follows:
WhereinIndicate actual measurement number
The journey time that vehicle passes through section k is calculated according to upstream and downstream data aggregate;
Step S103: in section 1, work as vehicle numberWhen, continuity judgement is carried out to downstream road section vehicle number,
If downstream vehicle numberThen journey time isIt otherwise is no information;
Work as vehicle numberAnd cart numberWhen, such as trolley numberThen journey time isWithOtherwise journey time isWhen cart numberWhen, journey time isWithWherein χ is section vehicle smallest sample amount threshold value;
Work as vehicle numberAndWhen, such as cart numberWhen, journey time isWithOtherwise whenWhen, journey time isWhenWhen, journey time is
WithWhenWhen, such asThen journey time isWithOtherwise it goes
The journey time isWhenWhen, journey time isWith
Step S104: in the z+1 of section, work as vehicle numberWhen, first upstream section vehicle number is judged, such asThen journey time isOtherwise just downstream road section vehicle number is judged, such asThen journey time isSuch asThen journey time is no information;
Work as vehicle numberAndWhen, such as cart numberThen journey time isOtherwise journey time isWithWhenWhen, journey time isWith
Work as vehicle numberAndWhen, such as cart numberWhen, journey time is
WithOtherwise whenWhen, journey time isWithWhenWhen, when stroke
Between beWithWhenWhen, such as cart numberThen journey time is
Otherwise journey time isWithWhenWhen, journey time isWith
Further, vehicle Link Travel Time data are standardized using following formula:
WhereinBe expressed as large and small vehicle travel speed,Two ring roads of basic road,Indicate the main line section of basic road.
Further, freeway traffic operation situation, which differentiates, uses following steps:
Step S201: judging whether the travel speed of the compact car in sampling interval i, i-1, i-2, i-3 is gradually reduced, if
Otherwise show that current highway operation situation is unimpeded, if then entering next step;
Step S202: judging whether the travel speed difference of compact car and large car in sampling interval i is less than threshold value η 1,
If otherwise showing, current highway operation situation is unimpeded, if entering next step;
Step S203: judging whether the travel speed difference of compact car and large car in sampling interval i is less than threshold value η 2,
If otherwise showing, current highway operation situation is unimpeded, if entering next step;
Step S204: judging whether the travel speed difference of compact car and large car in sampling interval i is less than threshold value η 3,
If otherwise entering step S205, if entering step S206;
Step S205: judging whether the travel speed of large car is less than or equal to threshold value Ψ 1, if then showing that high speed is public at present
Road operation situation is incidental congestion, if otherwise showing, current highway operation situation is unimpeded;
Step S206: judging whether the travel speed of large car is less than or equal to threshold value Ψ 2, if then showing that high speed is public at present
Road operation situation is blocking, if otherwise showing, current highway operation situation is recurrent congestion.
Further, threshold value η 1, η 2, η 3 are the threshold parameter of large and small vehicle speed difference in time zone, 1 > η 2 of η
> η 3;Threshold value Ψ 1, Ψ 2 are the threshold parameter of big vehicle speed in time zone, 1 > Ψ 2 of Ψ.
A kind of freeway traffic operation situation identification device based on networked fee collection data is transported based on freeway traffic
Row situation recognition methods, including express highway section travel time estimation module, for from ExpresswayNetwork Toll Collection System
The vehicle of extraction passes through time and the license board information of charge station, estimates vehicle Link Travel Time data after rejecting abnormalities data,
And data are stored into database;With freeway traffic situation identification module, for storage vehicle in the database
Link Travel Time data are standardized, and are divided into oversize vehicle by contour of the vehicle and small vehicle carries out data extraction,
Freeway traffic operation situation discrimination model is constructed, and carries out the differentiation of freeway traffic operation situation using the model.
Further, freeway traffic situation identification module includes that expressway oversize vehicle link travel speed calculates
Submodule passes through road row according to oversize vehicle based on the time that vehicle on database high speed highway passes through charge station
Sail the travel speed of mileage calculation oversize vehicle operation;Highway small vehicle link travel speed computational submodule, with number
It is small by road driving mileage calculation according to small vehicle based on the time for passing through charge station according to vehicle on the high speed highway of library
The travel speed of type vehicle operation;Freeway traffic operating status differentiates submodule, with expressway oversize vehicle section row
The road that Cheng Sudu computational submodule and highway small vehicle link travel speed computational submodule obtain runs large car
And small vehicle travel speed be data basis, by oversize vehicle and small vehicle road traffic operating parameter statistical
Traffic status of express way discrimination model is established in analysis, realizes the differentiation to freeway traffic operation situation.
A kind of freeway traffic operation situation identification device based on networked fee collection data, is set using a kind of computing terminal
It is standby, including one or more processors and computer readable storage medium;Processor is computer-readable to deposit for realizing each instruction
Storage media is loaded by the processor of terminal device for storing a plurality of instruction, instruction and executes following processing: being joined from highway
The vehicle extracted in net charging system passes through time and the license board information of charge station, estimates vehicle section row after rejecting abnormalities data
Journey time data, and data are stored into database;The vehicle Link Travel Time data of storage in the database are carried out
Standardization is divided into oversize vehicle by contour of the vehicle and small vehicle carries out data extraction, building freeway traffic operation
Situation discrimination model, and the differentiation of freeway traffic operation situation is carried out using the model.
The utility model has the advantages that
1) the present invention overcomes expressway network toll data development and utilization problems not yet in effect, collect Free Way Networking and receive
Expense data processing, Link Travel Time Estimation and road section traffic volume condition discrimination are integrated.
2) it effectively realizes the identification to road traffic state, basic data branch can be provided for control of traffic and road and control
It holds.
Detailed description of the invention
Fig. 1 is freeway traffic operation situation identification process schematic diagram;
Fig. 2 is freeway network schematic diagram;
Fig. 3 is estimation vehicle Link Travel Time flow diagram;
Fig. 4 is travel time estimation experiment statistics figure;
Fig. 5 is the large and small vehicle operating flux of highway and density relationship figure;
Fig. 6 is the large and small vehicle speed of service of highway and density relationship figure;
Fig. 7 is section Z+1 burst traffic accident schematic diagram;
Fig. 8 is that the large and small vehicle of section Z+1 traffic accident spends time diagram;
Fig. 9 is traffic state judging flow diagram;
Figure 10 is accidental traffic jam condition discrimination experiment statistics figure;
Figure 11 is the judgement algorithm flow schematic diagram of suicide protection circuit.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings:
A kind of freeway traffic operation situation recognition methods based on networked fee collection data as shown in Figure 1, successively carries out
Express highway section travel time estimation and the identification of freeway traffic situation, wherein the step of motorway journeys time Estimate
Vehicle including extracting from ExpresswayNetwork Toll Collection System passes through time and the license board information of charge station, rejecting abnormalities data
Vehicle Link Travel Time data are estimated afterwards, and data are stored into database;The step of traffic status of express way identifies
Including being standardized the vehicle Link Travel Time data of storage in the database, it is divided into large car by contour of the vehicle
And small vehicle carry out data extraction, construct freeway traffic operation situation discrimination model, and carry out using the model high
Fast highway communication operation situation differentiates.
As shown in figure 3, estimation vehicle Link Travel Time uses following steps:
Step S101: being defined as basic road for the express highway section between Liang Ge charge station as shown in Figure 2, is charging
The basic road stood between k=0 and k=1 is section 1, and the basic road between charge station k=z and k=z+1 is section z+1;
Step S102: split data into sample size it is sufficient, without vehicle registration, have vehicle registration but the less three kinds of feelings of sample size
Condition;
Situation one: when sample size abundance, vehicle Link Travel Time, calculation formula are estimated using station journey time is faced
Are as follows:WhereinExpression vehicle in sampling interval i passes through the journey time of section k,
A indicates that downstream extends section number, and y indicates vehicle size, is compact car when y=c, and when y=b is large car;
Situation two: when without vehicle registration, vehicle Link Travel Time is estimated using across station journey time, wherein section 1
Journey time calculation formula are as follows:
WhereinIt indicates by downstream
The vehicle travel time that travel time data calculates, d indicate downstream road section, and s is the integer between [1, a], indicate
Upstream extends section number;
The journey time calculation formula of distance z+1 are as follows:
WhereinIndicate to calculate obtained vehicle travel time by upstream and downstream joint, u indicates upstream section, ε be in
Integer between [1, s], α, β,The Model Weight parameter for being value range between 0 to 1;
Situation three: when having vehicle registration but less sample size, estimating vehicle Link Travel Time using across station journey time,
The wherein journey time calculation formula in section 1 are as follows:
WhereinIt indicates by monitoring section
And the vehicle travel time that downstream data combined calculation obtains, r are the sample in section instantly when having vehicle registration but less sample size
This number;
The journey time calculation formula of distance z+1 are as follows:
WhereinIndicate actual measurement number
The journey time that vehicle passes through section k is calculated according to upstream and downstream data aggregate;
Step S103: in section 1, work as vehicle numberWhen, continuity judgement is carried out to downstream road section vehicle number,
If downstream vehicle numberThen journey time isIt otherwise is no information;
Work as vehicle numberAnd cart numberWhen, such as trolley numberThen journey time isWithOtherwise journey time isWhen cart numberWhen, journey time isWithWherein χ is section vehicle smallest sample amount threshold value;
Work as vehicle numberAndWhen, such as cart numberWhen, journey time isWithOtherwise whenWhen, journey time isWhenWhen, journey time is
WithWhenWhen, such asThen journey time isWithOtherwise it goes
The journey time isWhenWhen, journey time isWith
Step S104: in the z+1 of section, work as vehicle numberWhen, first upstream section vehicle number is judged, such asThen journey time isOtherwise just downstream road section vehicle number is judged, such asThen journey time isSuch asThen journey time is no information;
Work as vehicle numberAndWhen, such as cart numberThen journey time isOtherwise journey time isWithWhenWhen, journey time isWith
Work as vehicle numberAndWhen, such as cart numberWhen, journey time is
WithOtherwise whenWhen, journey time isWithWhenWhen, when stroke
Between beWithWhenWhen, such as cart numberThen journey time is
Otherwise journey time isWithWhenWhen, journey time isWith
Vehicle Link Travel Time data are standardized using following formula:
WhereinBe expressed as large and small vehicle travel speed,Two ring roads of basic road,Indicate the main line section of basic road.
As shown in figure 9, freeway traffic operation situation, which differentiates, uses following steps:
Step S201: judging whether the travel speed of the compact car in sampling interval i, i-1, i-2, i-3 is gradually reduced, if
Otherwise show that current highway operation situation is unimpeded, if then entering next step;
Step S202: judging whether the travel speed difference of compact car and large car in sampling interval i is less than threshold value η 1,
If otherwise showing, current highway operation situation is unimpeded, if entering next step;
Step S203: judging whether the travel speed difference of compact car and large car in sampling interval i is less than threshold value η 2,
If otherwise showing, current highway operation situation is unimpeded, if entering next step;
Step S204: judging whether the travel speed difference of compact car and large car in sampling interval i is less than threshold value η 3,
If otherwise entering step S205, if entering step S206;
Step S205: judging whether the travel speed of large car is less than or equal to threshold value Ψ 1, if then showing that high speed is public at present
Road operation situation is incidental congestion, if otherwise showing, current highway operation situation is unimpeded;
Step S206: judging whether the travel speed of large car is less than or equal to threshold value Ψ 2, if then showing that high speed is public at present
Road operation situation is blocking, if otherwise showing, current highway operation situation is recurrent congestion.
Threshold value η 1, η 2, η 3 are the threshold parameter of large and small vehicle speed difference in time zone, 1 > η of η, 2 > η 3;Threshold value Ψ
1, Ψ 2 is the threshold parameter of big vehicle speed in time zone, 1 > Ψ 2 of Ψ.
A kind of freeway traffic operation situation identification device based on networked fee collection data is transported based on freeway traffic
Row situation recognition methods, including express highway section travel time estimation module, for from ExpresswayNetwork Toll Collection System
The vehicle of extraction passes through time and the license board information of charge station, estimates vehicle Link Travel Time data after rejecting abnormalities data,
And data are stored into database;With freeway traffic situation identification module, for storage vehicle in the database
Link Travel Time data are standardized, and are divided into oversize vehicle by contour of the vehicle and small vehicle carries out data extraction,
Freeway traffic operation situation discrimination model is constructed, and carries out the differentiation of freeway traffic operation situation using the model.
Freeway traffic situation identification module includes expressway oversize vehicle link travel speed computational submodule, with
Based on the time that vehicle passes through charge station on database high speed highway, road driving mileage calculation is passed through according to oversize vehicle
The travel speed of oversize vehicle operation;Highway small vehicle link travel speed computational submodule, with database high speed
Based on the time that vehicle passes through charge station on highway, run according to small vehicle by road driving mileage calculation small vehicle
Travel speed;Freeway traffic operating status differentiates submodule, with the calculating of expressway oversize vehicle link travel speed
The road operation oversize vehicle and compact car that submodule and highway small vehicle link travel speed computational submodule obtain
Travel speed is data basis, by establishing high to oversize vehicle and small vehicle road traffic operating parameter statistical analysis
Fast highway traffic state discrimination model realizes the differentiation to freeway traffic operation situation.
A kind of freeway traffic operation situation identification device based on networked fee collection data, is set using a kind of computing terminal
It is standby, including one or more processors and computer readable storage medium;Processor is computer-readable to deposit for realizing each instruction
Storage media is loaded by the processor of terminal device for storing a plurality of instruction, instruction and executes following processing: being joined from highway
The vehicle extracted in net charging system passes through time and the license board information of charge station, estimates vehicle section row after rejecting abnormalities data
Journey time data, and data are stored into database;The vehicle Link Travel Time data of storage in the database are carried out
Standardization is divided into oversize vehicle by contour of the vehicle and small vehicle carries out data extraction, building freeway traffic operation
Situation discrimination model, and the differentiation of freeway traffic operation situation is carried out using the model.
Express highway section travel time estimation passes through the quasi- identification road of expressway tol lcollection equipment automatic collection traffic situation
The current data of section associated vehicle, and historical traffic data library is uploaded to by data-interface, it realizes to road vehicle operation ID's
Record and extraction, and then on the basis of considering that abnormal data influences traffic state judging precision, between adjacent charge station
Section is Research foundation, obtains the travel time data that vehicle passes through each section.Since the vehicle of different automobile types is in monitoring section
The consumed journey time of traveling has apparent otherness, and there may be whole or individual for collected travel time data
Model data amount is insufficient the phenomenon that being even not present, and leads to not accurately obtain travel time data in some period, therefore
According to the quantity of database sample size data, be divided into sample size it is sufficient, without vehicle registration, have vehicle registration but sample size
Less three kinds of situations, and according to monitoring section and its adjacent segments by vehicle fleet size situation, design is based on networking charging system
Link Travel Time Estimation method.Motorway journeys time Estimate module is mainly responsible for passing through expressway network toll
On the basis of the vehicle that system is extracted is by the time of charge station and license board information processing, Link Travel Time data are obtained, and
Travel time data is stored using the hard disk of cloud storage space or big storage volume.It is mentioned by using highway is practical
The highway traffic data taken has carried out validity check, and experimental result based on expressway network toll data as shown in figure 4, obtained
The Link Travel Time Estimation result taken is generally maintained at 5% hereinafter, illustrating that this method can be limited with true value relative error
Realize the estimation to Link Travel Time.
Freeway traffic situation identification module mainly includes oversize vehicle link travel speed calculation module, small vehicle
Link travel speed calculation module and freeway traffic operating status discrimination module three parts.According to different automobile types vehicle to road
The otherness that road traffic circulation influences, is divided into two class of small vehicle and oversize vehicle for highway driving vehicle, middle-size and small-size
Vehicle refers to 7 manned cars below.
Expressway oversize vehicle link travel speed calculation module and highway small vehicle link travel speedometer
Module is calculated with the database of the express highway section travel time estimation result supplement obtained based on the estimation of networked fee collection data
Based on information, the travel speed that road driving mileage calculation oversize vehicle and small vehicle are run is passed through according to oversize vehicle,
And it is updated in motorway journeys speed storage device.
Freeway traffic operating status discrimination module with expressway oversize vehicle link travel speed calculation module and
The road operation oversize vehicle and small vehicle travel speed that highway small vehicle link travel speed calculation module obtains
Basis is supplied for data, on the basis of to oversize vehicle and small vehicle road traffic operating parameter statistical analysis, design
Traffic status of express way discrimination model realizes the differentiation to freeway traffic operation situation.The model uses highway
The highway traffic data of actual extracting has carried out validity check, and experimental result is as shown in Figure 10 and Figure 11.
When traffic status of express way discrimination module is arranged, expressway oversize vehicle and small vehicle fortune have been comprehensively considered
The otherness of capable time response and spatial character designs Expressway Road traffic state judging algorithm, realizes and hands over road
The monitoring of logical state and the data supplement on backstage.
In conjunction with Fig. 5 and Fig. 6 to traffic flow difference running state analysis.In Fig. 5, (0, ρc) curve and oblique line in section
Flow-the density relationship for respectively indicating trolley and cart, in (ρc,ρj) flow-density relationship of trolley and cart is then in section
It is indicated with same oblique line, and two interval curve slopes respectively indicate cart and small vehicle speed, i.e.,
1) when turnpike driving is in unimpeded state, density changes in the section (0, ρ '), at this time road traffic flow
It interferes with each other that situation is less, therefore runs the travel speed u of cart and trolley on roadyMore level off to the freedom of each self-operating
Flow velocity degreeThe travel speed difference of cart and trolley is basicly stable in a certain range;
2) with the growth of highway vehicle flowrate, traffic current density moves closer to critical density ρc, road traffic flow phase
Mutual disturbed condition gradually increases, and the traffic flow speed of service reduces on road at this time, but since cart is different with trolley travelling characteristic,
Therefore the speed range of decrease difference.In general, highway operation trolley speed it is higher (maintain essentially in 95km/h~
Within the scope of 135km/h) and cart since itself restricted speed can maintain it is lower it is horizontal (maintain essentially in 68km/h~
Within the scope of 87km/h), when road traffic flow density is close to critical density, shown according to Fig. 6, the trolley speed range of decrease it is larger and by
Gradually close to the free stream velocity of cartAnd big vehicle speed is held essentially constant, and then the travel speed of cart and trolley is poor
Value reduces even 0.
3) when Expressway Traffic Flow Density is in (ρc,ρj) in range when, with the increase of road traffic current density, vehicle
It is also all the more fierce that situation is interfered with each other between, and road traffic flow operation conditions is caused to run down.Cart and small on road at this time
Vehicle speed is far below cart free stream velocityThe travel speed difference of large and small vehicle can decrease and almost 0 simultaneously.
The shadow region Fig. 7 indicates Coherent traffic noise generation area, 1., 2. respectively indicates and is done in section z+1 institute overlay area
Disturb the traffic zone formed after a separation.Region 1,3 (2,4) are respectively that 1., 2. interior small (big) vehicle slows down in region in Fig. 7 in Fig. 8
With the process of acceleration, and the region between region 1,3 (2,4) be then expressed as small (big) vehicle interference region fortune signal namely scheme
Shadow region in 7.
When monitor section z+1 occur traffic accident when, region 1. in vehicle have queuing phenomena generation, traffic flow operation
Speed can decreased significantly trend, and the small vehicle speed range of decrease is greater than cart, and large and small vehicle keeps identical and lower speed substantially at this time
Degree is advanced.And when wagon flow it is steady when entering region 2. by emergency area, the speed of cart and trolley will appear apparent increase
Trend and close to respective free stream velocity, meanwhile, the speed difference of large and small vehicle can also increase therewith.Comprehensive entire traffic
Operation conditions is flowed, there are certain differences for speed in the process of running for large and small vehicle, and the travel speed of cart and trolley is less than respectively
From desired speed, this is also that it is different from place of the unimpeded state of traffic flow.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to restrict the invention, all in original of the invention
Then with any modifications, equivalent replacements, and improvements made within spirit etc., should all be included in the protection scope of the present invention.
Claims (8)
1. the freeway traffic operation situation recognition methods based on charge data, it is characterised in that: successively carry out highway
Link Travel Time Estimation and the identification of freeway traffic situation, wherein the step of motorway journeys time Estimate includes from height
The vehicle extracted in fast Highway Network Toll Collection System passes through time and the license board information of charge station, estimates vehicle after rejecting abnormalities data
Link Travel Time data, and data are stored into database;The step of traffic status of express way identifies includes that will deposit
Storage vehicle Link Travel Time data in the database are standardized, and are divided into oversize vehicle and small-sized by contour of the vehicle
Vehicle carries out data extraction, constructs freeway traffic operation situation discrimination model, and carry out highway friendship using the model
Logical operation situation differentiates.
2. freeway traffic operation situation recognition methods as described in claim 1, it is characterised in that: estimation vehicle section row
The journey time uses following steps:
Step S101: being defined as basic road for the express highway section between Liang Ge charge station, in charge station k=0 and k=1
Between basic road be section 1, basic road between charge station k=z and k=z+1 is section z+1;
Step S102: split data into sample size it is sufficient, without vehicle registration, have vehicle registration but the less three kinds of situations of sample size;
Situation one: when sample size abundance, vehicle Link Travel Time, calculation formula are estimated using station journey time is faced are as follows:WhereinIndicate journey time of the vehicle by section k, a in sampling interval i
Indicate that downstream extends section number, it is compact car when y=c that y, which indicates vehicle size, and when y=b is large car;
Situation two: when without vehicle registration, vehicle Link Travel Time is estimated using across station journey time, wherein the stroke in section 1
Time calculation formula are as follows:
WhereinIt indicates by downstream stroke
The vehicle travel time that time data calculate, d indicate downstream road section, and s is the integer between [1, a], indicate upstream
Extend section number;
The journey time calculation formula of distance z+1 are as follows:
WhereinIndicate to calculate obtained vehicle travel time by upstream and downstream joint, u indicates upstream section, ε be in
Integer between [1, s], α, β,The Model Weight parameter for being value range between 0 to 1;
Situation three: when having vehicle registration but less sample size, estimating vehicle Link Travel Time using across station journey time, wherein
The journey time calculation formula in section 1 are as follows:
WhereinIndicate by monitoring section and under
The vehicle travel time that is calculated of trip data aggregate, r are the sample number in section instantly when having vehicle registration but less sample size;
The journey time calculation formula of distance z+1 are as follows:
WhereinIndicate measured data and
The journey time that vehicle passes through section k is calculated in upstream and downstream data aggregate;
Step S103: in section 1, work as vehicle numberWhen, continuity judgement is carried out to downstream road section vehicle number, if
Downstream vehicle numberThen journey time isIt otherwise is no information;
Work as vehicle numberAnd cart numberWhen, such as trolley numberThen journey time isWithOtherwise journey time isWhen cart numberWhen, journey time isWithWherein χ is section vehicle smallest sample amount threshold value;
Work as vehicle numberAndWhen, such as cart numberWhen, journey time isWithOtherwise whenWhen, journey time isWhenWhen, journey time is
WithWhenWhen, such asThen journey time isWithOtherwise it goes
The journey time isWhenWhen, journey time isWith
Step S104: in the z+1 of section, work as vehicle numberWhen, first upstream section vehicle number is judged, such asThen journey time isOtherwise just downstream road section vehicle number is judged, such asThen journey time isSuch asThen journey time is no information;
Work as vehicle numberAndWhen, such as cart numberThen journey time isIt is no
Then journey time isWithWhenWhen, journey time isWith
Work as vehicle numberAndWhen, such as cart numberWhen, journey time isWithOtherwise whenWhen, journey time isWithWhenWhen, journey time
ForWithWhenWhen, such as cart numberThen journey time isIt is no
Then journey time isWithWhenWhen, journey time isWith
3. freeway traffic operation situation recognition methods as described in claim 1, it is characterised in that: the vehicle section
Travel time data is standardized using following formula:
WhereinBe expressed as large and small vehicle travel speed,Two ring roads of basic road,Indicate the main line section of basic road.
4. freeway traffic operation situation recognition methods as described in claim 1, it is characterised in that: the highway
Traffic circulation situation, which differentiates, uses following steps:
Step S201: judging whether the travel speed of the compact car in sampling interval i, i-1, i-2, i-3 is gradually reduced, if otherwise
Show that current highway operation situation is unimpeded, if then entering next step;
Step S202: judging whether the travel speed difference of compact car and large car in sampling interval i is less than threshold value η 1, if not
Then show that current highway operation situation is unimpeded, if then entering next step;
Step S203: judging whether the travel speed difference of compact car and large car in sampling interval i is less than threshold value η 2, if not
Then show that current highway operation situation is unimpeded, if then entering next step;
Step S204: judging whether the travel speed difference of compact car and large car in sampling interval i is less than threshold value η 3, if not
S205 is then entered step, if then entering step S206;
Step S205: judging whether the travel speed of large car is less than or equal to threshold value Ψ 1, if then showing current highway fortune
Row situation is incidental congestion, if otherwise showing, current highway operation situation is unimpeded;
Step S206: judging whether the travel speed of large car is less than or equal to threshold value Ψ 2, if then showing current highway fortune
Row situation is blocking, if otherwise showing, current highway operation situation is recurrent congestion.
5. freeway traffic operation situation recognition methods as claimed in claim 4, it is characterised in that: threshold value η 1, the η
2, η 3 is the threshold parameter of large and small vehicle speed difference in time zone, 1 > η of η, 2 > η 3;Threshold value Ψ 1, Ψ 2 are in time zone
The threshold parameter of big vehicle speed, 1 > Ψ 2 of Ψ.
6. a kind of freeway traffic operation situation identification device based on charge data, it is characterised in that: the device base
In any freeway traffic operation situation recognition methods of claim 1-5, including express highway section journey time
Estimation module, is picked at time and license board information of the vehicle by charge station for extracting from ExpresswayNetwork Toll Collection System
Except estimation vehicle Link Travel Time data after abnormal data, and data are stored into database;
With freeway traffic situation identification module, for being carried out to storage vehicle Link Travel Time data in the database
Standardization is divided into oversize vehicle by contour of the vehicle and small vehicle carries out data extraction, building freeway traffic operation
Situation discrimination model, and the differentiation of freeway traffic operation situation is carried out using the model.
7. freeway traffic operation situation identification device as claimed in claim 6, it is characterised in that: the highway is handed over
On-state gesture identification module includes expressway oversize vehicle link travel speed computational submodule, on database high speed highway
Based on the time that vehicle passes through charge station, the stroke that road driving mileage calculation oversize vehicle is run is passed through according to oversize vehicle
Speed;
Highway small vehicle link travel speed computational submodule passes through charge station with vehicle on database high speed highway
Time based on, according to small vehicle pass through road driving mileage calculation small vehicle run travel speed;
Freeway traffic operating status differentiates submodule, with expressway oversize vehicle link travel speed computational submodule and
The road operation oversize vehicle and small vehicle stroke speed that highway small vehicle link travel speed computational submodule obtains
Degree is data basis, by establishing highway friendship to oversize vehicle and small vehicle road traffic operating parameter statistical analysis
Logical condition discrimination model, realizes the differentiation to freeway traffic operation situation.
8. a kind of freeway traffic operation situation identification device based on charge data, it is characterised in that: use a kind of calculating
Terminal device, including one or more processors and computer readable storage medium;Processor is for realizing each instruction, computer
Readable storage medium storing program for executing is loaded by the processor of terminal device for storing a plurality of instruction, instruction and executes following processing: from high speed
The vehicle extracted in Highway Network Toll Collection System passes through time and the license board information of charge station, estimates vehicle after rejecting abnormalities data
Link Travel Time data, and data are stored into database;To the vehicle Link Travel Time number of storage in the database
According to being standardized, it is divided into oversize vehicle by contour of the vehicle and small vehicle carries out data extraction, building highway is handed over
Logical operation situation discrimination model, and the differentiation of freeway traffic operation situation is carried out using the model.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810895704.8A CN109035772A (en) | 2018-08-08 | 2018-08-08 | The recognition methods of freeway traffic operation situation and device based on charge data |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810895704.8A CN109035772A (en) | 2018-08-08 | 2018-08-08 | The recognition methods of freeway traffic operation situation and device based on charge data |
Publications (1)
Publication Number | Publication Date |
---|---|
CN109035772A true CN109035772A (en) | 2018-12-18 |
Family
ID=64649465
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201810895704.8A Pending CN109035772A (en) | 2018-08-08 | 2018-08-08 | The recognition methods of freeway traffic operation situation and device based on charge data |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109035772A (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2020151287A1 (en) * | 2019-01-22 | 2020-07-30 | 江苏智通交通科技有限公司 | Highway travel time estimation method based on gm car following model |
CN111932877A (en) * | 2020-08-07 | 2020-11-13 | 公安部交通管理科学研究所 | Road section traffic abnormal state identification method based on license plate data |
CN114973659A (en) * | 2022-05-12 | 2022-08-30 | 山东高速集团有限公司创新研究院 | Method, device and system for detecting indirect event of expressway |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102081846A (en) * | 2011-02-22 | 2011-06-01 | 交通运输部公路科学研究所 | Expressway charge data track matching based traffic state recognition method |
EP2447924A1 (en) * | 2010-10-29 | 2012-05-02 | Siemens Aktiengesellschaft | System for detecting the traffic situation on a stretch of road |
CN105913661A (en) * | 2016-06-15 | 2016-08-31 | 北京航空航天大学 | Highway road section traffic state discrimination method based on charging data |
-
2018
- 2018-08-08 CN CN201810895704.8A patent/CN109035772A/en active Pending
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP2447924A1 (en) * | 2010-10-29 | 2012-05-02 | Siemens Aktiengesellschaft | System for detecting the traffic situation on a stretch of road |
CN102081846A (en) * | 2011-02-22 | 2011-06-01 | 交通运输部公路科学研究所 | Expressway charge data track matching based traffic state recognition method |
CN105913661A (en) * | 2016-06-15 | 2016-08-31 | 北京航空航天大学 | Highway road section traffic state discrimination method based on charging data |
Non-Patent Citations (1)
Title |
---|
杨庆芳,等: "基于收费数据的高速公路交通状态判别方法", 《华南理工大学学报(自然科学版)》 * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2020151287A1 (en) * | 2019-01-22 | 2020-07-30 | 江苏智通交通科技有限公司 | Highway travel time estimation method based on gm car following model |
CN111932877A (en) * | 2020-08-07 | 2020-11-13 | 公安部交通管理科学研究所 | Road section traffic abnormal state identification method based on license plate data |
CN111932877B (en) * | 2020-08-07 | 2022-06-17 | 公安部交通管理科学研究所 | Road section traffic abnormal state identification method based on license plate data |
CN114973659A (en) * | 2022-05-12 | 2022-08-30 | 山东高速集团有限公司创新研究院 | Method, device and system for detecting indirect event of expressway |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN103839409B (en) | Based on the traffic flow modes method of discrimination of multibreak facial vision sensing cluster analysis | |
CN105590346B (en) | The traffic information collection of turn pike net and inducible system based on path identifying system | |
CN101650876B (en) | Method for obtaining average speed of traffic flow of urban road sections | |
CN105261212B (en) | A kind of trip space-time analysis method based on GPS data from taxi map match | |
CN205721998U (en) | Turn pike net traffic information collection based on path identifying system and inducible system | |
CN103440764A (en) | Urban road network vehicle travel path reconstruction method based on vehicle automatic identification data | |
CN109035772A (en) | The recognition methods of freeway traffic operation situation and device based on charge data | |
CN103500504A (en) | Urban road traffic parameter estimation and road situation discrimination method based on RFID (Radio Frequency Identification) data and application system | |
CN101964941A (en) | Intelligent navigation and position service system and method based on dynamic information | |
CN104318770A (en) | Method for detecting traffic jam state of expressway in real time based on mobile phone data | |
Jiang et al. | A multi-period analysis of taxi drivers' behaviors based on GPS trajectories | |
CN103632541B (en) | Traffic incident road chain detection and data filling method | |
CN104750963A (en) | Intersection delay time estimation method and device | |
CN109493449A (en) | A kind of lorry loading method for estimating state based on lorry GPS track data and high speed transaction data | |
CN102867406B (en) | Traffic network generation method applying vehicle detection data | |
CN114005275B (en) | Highway vehicle congestion judging method based on multi-data source fusion | |
CN113442791B (en) | Electric energy supplementing method for electric automobile | |
CN108269411B (en) | Expressway ETC traffic flow prediction method | |
CN104616495A (en) | Bus GPS (Global Positioning System) probe vehicle based urban traffic state recognition method | |
CN109752590B (en) | Electric bus energy consumption estimation method based on data driving | |
CN103700264B (en) | Based on the express highway section travel speed computing method of ETC charge data | |
CN105070060A (en) | Method for determining urban road traffic state based bus-mounted GPS data | |
Qiang et al. | Measuring variability of arterial road traffic condition using archived probe data | |
Ma et al. | An influence analytical model of dedicated bus lane on network traffic by macroscopic fundamental diagram | |
Yue et al. | Urban road travel speed estimation based on low sampling floating car data |
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 | ||
WD01 | Invention patent application deemed withdrawn after publication | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20181218 |