CN104658252B - Method for evaluating traffic operational conditions of highway based on multisource data fusion - Google Patents
Method for evaluating traffic operational conditions of highway based on multisource data fusion Download PDFInfo
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- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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Abstract
The invention discloses a method for evaluating traffic operational conditions of a highway based on multisource data fusion and relates to the technical field of evaluation of the traffic operational conditions. The traffic operational conditions of the highway based on multisource data fusion are evaluated in combination with a large amount of GPS (global positioning system) data acquired and accumulated by a floating car acquisition system and a large amount of toll data acquired and accumulated by a highway toll system by utilizing data of fixed car detectors, and an obtained evaluation result of the traffic operational condition of the highway is more accurate and wider in coverage range, so that a defect of insufficient arrangement of car detectors can be made up for, road network monitoring range and system can be performed, traffic jams on roads can be accurately discovered in time, and the safety and efficiency of road traffic can be guaranteed; moreover, the toll data and floating car data are all based on existing toll system and floating car GPS management system, and the additional data acquisition cost is zero, so that the method provided by the embodiment of the invention is good in economy and promotion prospect.
Description
Technical field
The present invention relates to traffic circulation state assessment technology field, more particularly to a kind of high speed based on multisource data fusion
The appraisal procedure of highway communication running status.
Background technology
The assessment of traffic circulation state has material impact to the safety and efficiency of road traffic.Traffic circulation state is entered
Go and monitor and predict, traffic congestion present on timely, accurate discovery road, is always that traffic circulation state evaluation areas are ground
The focus and difficult point studied carefully, since the sixties in last century, the assessment to freeway traffic running status has turned into dynamic friendship
One of focus of siphunculus reason research field, and emerged in large numbers substantial amounts of achievement.
At present, to the research of the assessment of traffic circulation state, fixed wagon detector data are depended on more, but due to receiving
The limitation of cost, the laying quantity of vehicle checker is extremely limited, then the vehicle checker data for obtaining are extremely limited, and traffic data be into
Row traffic circulation state monitors the basis with prediction, so, in the prior art, using limited vehicle checker data to traffic circulation
State is estimated so that traffic behavior is monitored and receives serious influence with the economy and spatial coverage of prediction, from
And traffic congestion present on road cannot be accurately and in time found, influence the safety and efficiency of road traffic.
The content of the invention
It is an object of the invention to provide a kind of assessment of the freeway traffic running status based on multisource data fusion
Method, so as to solve foregoing problems present in prior art.
To achieve these goals, the technical solution adopted by the present invention is as follows：
A kind of appraisal procedure of the freeway traffic running status based on multisource data fusion, comprises the following steps：
Step 1, obtains the zone-to-zone travel operational factor based on multi-source data；
Step 2, merges, the zone-to-zone travel for being merged to the zone-to-zone travel operational factor based on multi-source data
Operational factor；
Step 3, the zone-to-zone travel operation ginseng of the fusion obtained using zone-to-zone travel running status index system and step 2
Number, is estimated to zone-to-zone travel running status, obtains the assessment result of zone-to-zone travel running status；
Step 4, using section traffic circulation state index system and the place traffic circulation parameter of fixed vehicle checker, to solid
Section traffic circulation state where determining vehicle checker is estimated, and obtains the assessment result of section traffic circulation state；
Step 5, the section traffic that the assessment result and step 4 of the zone-to-zone travel running status obtained using step 3 are obtained
The assessment result of running status, obtains the assessment result of freeway traffic running status.
Preferably, in step 1, the multi-source data includes fixed vehicle checker data, charge data and floating car data.
Preferably, in step 1, the zone-to-zone travel operation ginseng between the vehicle checker section based on the fixed vehicle checker data
Number, is obtained using Cell Transmission Model.
Preferably, in step 1, the zone-to-zone travel operational factor between the toll station based on the charge data, by right
The charge data carries out statistics acquisition.
More preferably, it is necessary to the charge data of statistics includes：Outlet charge station numbering, exit lane numbering, outlet
In time, entrance charge station numbering, entrance lane number, entry time, vehicle, passenger-cargo classification, charge action type and/or traveling
Journey；It is described that the charge data is counted, specially：To in identical Outlet time section there is identical to export charge station
Numbering, the vehicle of entrance charge station numbering are counted, and obtain the interval vehicle flowrate between toll station；Obtained by equation below
The interval speed of service of single car：
The interval speed of service=distance travelled/(Outlet time-entry time)
Preferably, in step 1, the section zone-to-zone travel operational factor based on the floating car data, using point-to-point
Map-matching method is obtained.
Specifically, the map-matching method of the point-to-point is implemented in accordance with the following steps：
Search the driving path of Floating Car；
The physical location of Floating Car is determined in the driving path；
The data of the map-matching method input of the point-to-point include GPS location point data and GIS path space data.
Preferably, in step 2, by BP neural network model to the zone-to-zone travel operational factor based on multi-source data
Merged.
It is highly preferred that the BP neural network model is made up of input layer, output layer and some hidden layers；
The input data of the input layer includes：Zone-to-zone travel operational factor and sample size based on various data sources；
The output data of the output layer is the zone-to-zone travel operational factor after fusion.
Preferably, in step 5, the assessment result of freeway traffic running status is obtained using equation below：
In formula：The assessment result of-highway running status t；
Assessed value of-the i-th kind of algorithm in t；
wi(t)—Weight.
The beneficial effects of the invention are as follows：Technical scheme provided in an embodiment of the present invention, using fixed vehicle checker data, with reference to
A large amount of gps datas that Floating Car acquisition system is gathered and accumulated, and the substantial amounts of receipts that highway tolling system is gathered and accumulated
Take data, the freeway traffic running status based on multisource data fusion is estimated, the freeway traffic fortune for obtaining
The assessment result of row state more accurate, coverage rate is wider, can make up vehicle checker and lay not enough defect, improves road network monitoring
Scope and system, it is possible to achieve find traffic congestion present on road accurately and in time, it is ensured that the safety of road traffic and effect
Rate；And, charge data, floating car data are all based on existing Fare Collection System, Floating Car GPS management systems, and data are extra
Acquisition cost is zero, so, the economy and promotion prospect using method provided in an embodiment of the present invention are good.
Brief description of the drawings
Fig. 1 is appraisal procedure schematic flow sheet provided in an embodiment of the present invention.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with accompanying drawing, the present invention is entered
Row is further described.It should be appreciated that specific embodiment described herein is only used to explain the present invention, it is not used to
Limit the present invention.
As shown in figure 1, the embodiment of the invention provides a kind of freeway traffic operation shape based on multisource data fusion
The appraisal procedure of state, comprises the following steps：
Step 1, obtains the zone-to-zone travel operational factor based on multi-source data；
Step 2, merges, the zone-to-zone travel for being merged to the zone-to-zone travel operational factor based on multi-source data
Operational factor；
Step 3, the zone-to-zone travel operation ginseng of the fusion obtained using zone-to-zone travel running status index system and step 2
Number, is estimated to zone-to-zone travel running status, obtains the assessment result of zone-to-zone travel running status；
Step 4, using section traffic circulation state index system and the place traffic circulation parameter of fixed vehicle checker, to solid
Section traffic circulation state where determining vehicle checker is estimated, and obtains the assessment result of section traffic circulation state；
Step 5, the section traffic that the assessment result and step 4 of the zone-to-zone travel running status obtained using step 3 are obtained
The assessment result of running status, obtains the assessment result of freeway traffic running status.
With it is of the prior art it is single carry out the appraisal procedure of traffic circulation state using vehicle checker data compared with, this reality
Apply in example, traffic circulation state assessment is carried out using the fusion to the traffic circulation parameter based on multi-source data, the high speed for obtaining
The assessment result of highway communication running status more accurate, coverage rate is wider, can make up vehicle checker and lay not enough defect, complete
Kind road network monitoring range and system, it is possible to achieve find traffic congestion present on road accurately and in time, it is ensured that road traffic
Safety and efficiency.
Wherein, in step 1, the multi-source data can include fixed vehicle checker data, charge data and floating car data.
Because charge data is the Fare Collection System based on existing highway, floating car data is based on existing floating
Car GPS management systems, so using appraisal procedure provided in an embodiment of the present invention, when being estimated to traffic circulation state, nothing
The acquisition system of charge data and floating car data need to be additionally set up, without extra collection charge data and floating car data,
Only need to extract data related to assessment traffic circulation state in charge data from Fare Collection System, by Floating Car number
The data related to assessment traffic circulation state are extracted from GPS management systems in, it is possible to directly used, so,
Compared with the appraisal procedure that traffic circulation state is carried out using vehicle checker data in the prior art, side provided in an embodiment of the present invention
Method, its data acquisition cost is zero, good economy performance, with good popularizing application prospect.
In the embodiment of the present invention, in step 1, the zone-to-zone travel between the vehicle checker section based on the fixed vehicle checker data
Operational factor, is obtained using Cell Transmission Model.Can specifically adopt with the following method：
Section a is divided into λ by time discretization by CTMaIndividual equidistant cellular, the length of wherein each cellular is equal to
The distance that vehicle is travelled in a time step δ under the conditions of free flow.Wherein, cellular 1 is to cellular λa- 1 is Hun Hang areas cellular,
The vehicle of different destinations mixes row in the region；Cellular λaIt is canalization area's cellular, is divided into not according to downstream road section direction
Same queue area, vehicle is queued up according to the downstream purpose section of traveling into different tracks.Traffic in CTM can be obtained
Stream temporal-spatial evolution equation：
Due to ni(k)=ρiK () ν δ, then have
Simultaneously because flow conservation, then have
WhereinFor on a of section cellular i period k influx,It is the cellular i of section a period k's
Rate of inflow,For section a cellular i period k vehicle density,It is the cellular i of section a period k's
Maximum flow rate,For section a cellular i period k maximum stream flow,It is the cellular i of section a period k's
Maximum load-carrying capacity,For section a cellular i period k vehicle number.
Calculate the method congestion delay similar with calculating of the average instantaneous velocity of the average instantaneous velocity and network in section
Method is similar to.In period k, for any cellular i on any section a, it is assumed that the vehicle that the cellular all flows out is with maximum
Speed νaOutflow section, and remaining vehicle is trapped in first intracellular, this Some vehicles speed is 0.Then, road-section average is instantaneously fast
The method of degree is calculated as follows：
In simulation process, the trip information such as time, position of all vehicles is all recorded.Therefore, according to entrance
And time in section is left, average, the real-time travel time and each paths phase in each section in the same time can be extrapolated not
Average, the real-time travel time answered.Wherein, road-section average travel time τaK () can obtain by the following method：
Wherein, MrsIt has been the set up to point all traveler compositions between r, s；It is realities of the traveler z by section a
Border travel time；Whether description traveler z enters section a within the k periods, is thenOtherwise
WhenWhen,
Wherein,It is free flow travel time of the vehicle by section a.The Section 1 explanation vehicle of (4-25) formula passes through road
The travel time of section a can not possibly be less than the travel time under the conditions of free flow, i.e.,Section 2 is by FIFO conditionsRelease.
Traveler enters road network and passes through path p={ a at the k moment1,a2,…,anTrip, then during its Actual path traveling
BetweenCan calculate in the following manner：
During actual emulation, the average travel time information in section needs just be obtained after emulation terminates.
For the ITS applications under real-time traffic conditions, can only be obtained by having run out the Traveler Information in section, then section is real-time
Travel time τ 'aK () can be calculated as follows：
WhereinWhether description traveler z leaves section a within the k periods, is thenOtherwise
WhenWhen,
Therefore k moment paths p={ a1,a2,…,anReal-time route travel timeCan calculate in the following manner：
The instantaneous traffic flow modes at a certain moment can not embody the real driving condition in section and traffic.Therefore,
The overall traffic in section is weighed using road-section average traveling speed.The average travel time of section a is given belowWith
Average travel speedComputational methods：
Wherein, LaRepresent the length of section a.
In the embodiment of the present invention, in step 1, the zone-to-zone travel operation ginseng between the toll station based on the charge data
Number, statistics acquisition is carried out by the charge data.
Needing the charge data of statistics includes：Outlet charge station numbering, exit lane numbering, Outlet time, entrance
Charge station's numbering, entrance lane number, entry time, vehicle, passenger-cargo classification, charge action type and/or distance travelled；It is described right
The charge data is counted, specially：To having identical outlet charge station numbering, entrance in identical Outlet time section
The vehicle of charge station's numbering is counted, and obtains the interval vehicle flowrate between toll station；Single car is obtained by equation below
The interval speed of service：
The interval speed of service=distance travelled/(Outlet time-entry time) (13)
G=[M0.25-1.5R,M0.25+1.5R] (14)
R=M0.75-M0.25 (15)
In formula (14), (15), G represents effective data intervals, it is every fall data outside G be required for filtering；M0.75、
M0.25Arranged by order from small to large respectively by all journey times and be divided into the quartering, in the first, the 3rd cut-point
The value of position；R represents quartile extreme difference.Cleaned using formula (14), the set of (15) to the interval speed of service, removed
Irrational data.
In the embodiment of the present invention, in step 1, the section zone-to-zone travel operational factor based on the floating car data is utilized
The map-matching method of point-to-point is obtained.
Wherein, the matching process of point-to-point refer to anchor point is matched it is nearest with electronically map point geometric distance
Node or shape point process.
In the embodiment of the present invention, implemented to realize the ground to object Floating Car in certain time window in accordance with the following steps
Figure matching：
Search the driving path of Floating Car；
The physical location of Floating Car is determined in the driving path.
Wherein, the main purpose for searching driving path is to determine the specific road that the Floating Car is passed through in the time window
Section, by will determine specific on GPS location spot projection to corresponding floating vehicle travelling path the step of determine physical location
Specific locus residing for moment Floating Car.
In the embodiment of the present invention, the data of the map-matching method of point-to-point input include GPS location point data and
GIS path space data.
In the embodiment of the present invention, the specific method of floating vehicle travelling transitional search can be：It is assumed that in time window i
M-th GPS location point data of n Floating Car beWherein, x, y are respectively the latitude and longitude coordinates of location point,
M=1,2 ..., M, M represent n-th all GPS location point data total number of Floating Car in time interval i.Because on follow-up road
Footpath calculates at least needs two location points just to can confirm that a driving trace for Floating Car, if M=1, represents this Floating Car n
A positional information is only uploaded in i-th time window, then the detection data of the Floating Car as invalid data at
Reason, the treatment that algorithm skips the Floating Car directly carries out (n+1)th map match of Floating Car.
After confirming that Floating Car n possesses the GPS location point data no less than in current time window, the Floating Car is entered
The selection of row initial candidate section collection.Due to the influence of the factors such as signal, there is certain error in the position data of Floating Car, its
Generally in a certain region around actual position, the region is referred to as error band to location point.The choosing of initial candidate section collection
Selection method is to carry out frame choosing to road using GPS error region, the section covered by error band as initial candidate road
Section.
Error band is generally assumed specific shape, such as circular.By taking circle as an example, it is all fall in GPS location pointCircular error regions in k bars section constitute the location point initial candidate section setK=1,
2 ..., K, K represent included in initial section Candidate Set section sum.If K=0, and error range is not covered with any road
Section, then mean the location point of the Floating Car not on target road network or the location point be abnormal data, and the data are added
To remove filtering.
The transport information such as travel speed are carried out into collection meter according to the time window of 5min, is transported by the cycle of above-mentioned time window
Row map match, often runs and once just completes all GPS location Point matchings in this window time, according to the GPS of Floating Car
Positional information processed simultaneously with recognize floating vehicle travelling path and finally obtain particular location of the Floating Car on section and
Corresponding traffic parameter information, has been processed after all location point information of the Floating Car in the time window under start to process again
One Floating Car, do not consider when Floating Car Path Recognition in the time window is carried out on corresponding Floating Car in a time window
Driving trace.
Under normal circumstances, the approximate Normal Distribution of vehicle section mean speed on road, is designated as N (va,σ2).According to number
Sampling theorem in reason statistics, the section mean speed of n table flotation motor-carsNormal Distribution N (va,σ2/ n), and：
If the section mean speed of n table flotation motor-carsWith actual section mean speed vaError be less than the limits of error
The probability of ε is not less than 1- α, i.e.,
Can obtain
φ (x) is Standard Normal Distribution, φ in formula-1X () is the inverse function of φ (x).
As can be seen from the above equation, Floating Car quantity reaches minimum samplesWhen, meter
Calculate result just more accurate.But in a practical situation, calculated in time interval (such as 5min) at one, the floating on any section
Car quantity can not possibly entirely reach minimum samples requirement.For this problem, the embodiment of the present invention proposes to use self adaptation
Weighted index exponential smoothing carrys out computation interval average speed, and its Mathematical Modeling is
In formula,It is the current section mean speed estimate for calculating time interval；During for previous calculating
Between be spaced section mean speed estimate；F (k) is adaptive weighting.
In the embodiment of the present invention, in step 2, by BP neural network model to the zone-to-zone travel based on multi-source data
Operational factor is merged.
Wherein, the BP neural network model is made up of input layer, output layer and some hidden layers；
The input data of the input layer includes：Zone-to-zone travel operational factor and sample size based on various data sources；
The output data of the output layer is the zone-to-zone travel operational factor after fusion.
In a preferred embodiment of the invention, using BP neural network method, to fixed vehicle checker data, charge data and
The zone-to-zone travel operation average speed of floating car data carries out information fusion.The input data of the input layer includes：Floating Car
Section mean speed, the sample size of floating car data, the charge data section mean speed for calculating, number of charging that data are calculated
According to sample size data, the section mean speed that fixed vehicle checker data are calculated and fixed vehicle checker data sample size.Therefore,
The input data of neutral net is above-mentioned 6 parameters, and the output data of output layer is the section mean speed after fusion.According to god
Through the input data and output data of network, determine that the input layer of neutral net includes 6 nodes (area that floating car data is calculated
Between the section mean speed, the sample size data of charge data, solid that calculates of average speed, Floating Car sample size, charge data
Determine the section mean speed of vehicle checker data calculating and the sample size of fixed vehicle checker data), output layer only includes a node
(section mean speed after output fusion), hidden layer node number is 9.
In the embodiment of the present invention, traffic circulation state index system is promulgated according to Department of Transportation《Network of highways traffic is transported
Row monitoring and the provisional technical requirements of service》In to congested in traffic degree index norm-setting.
In the embodiment of the present invention, after the zone-to-zone travel operational factor for being merged, implementation steps 3, using zone-to-zone travel
The zone-to-zone travel operational factor of the fusion that running status index system and step 2 are obtained, comments zone-to-zone travel running status
Estimate, obtain the assessment result of zone-to-zone travel running status.
In the preferred embodiment of the present invention, zone-to-zone travel running status index system (express highway section crowding index) is such as
Shown in table 1.
The express highway section crowding index (overall travel speed index) of table 1
Judge the section mean speed of step 2 output where in the zone-to-zone travel running status index system shown in table 1
Interval range, obtain section traffic circulation state, export it is unimpeded, substantially unimpeded, general, crowded, block etc. zone-to-zone travel
Running status.
In the embodiment of the present invention, section traffic circulation state index system (express highway section crowding index) such as table 2
It is shown.
In the preferred embodiments of the present invention, implementation steps 4 with the following method can be adopted.
Using place traffic circulation parameter (5 minutes the vehicle checker section of statistics point directional flow, the speed of fixed vehicle checker
Degree, occupation rate), the traffic circulation state of section where vehicle checker is assessed by the section traffic circulation state index system of table 2,
It is interval according to the residing speed in table 2 of vehicle checker profile data, occupation rate, judge the traffic circulation of section representated by vehicle checker
State (congestion, jogging, unimpeded).
The section traffic circulation state index system of table 2 (section speed-occupation rate index)
In the embodiment of the present invention, what the assessment result and step 4 of the zone-to-zone travel running status obtained using step 3 were obtained
The assessment result of section traffic circulation state, obtains the assessment result of freeway traffic running status.Specifically can be using such as
Lower formula obtains the assessment result of freeway traffic running status：
In formula：
The assessment result of-highway running status t；
Assessed value of-the i-th kind of algorithm in t；
wi(t)—Weight.
In the present embodiment, in above-mentioned formula, according to different data sources algorithm (including algorithm, base based on floating car data
Algorithm in charge data, the algorithm based on fixed vehicle checker data) assessment resultWith reference to the sample according to the algorithm
This amount accounts for the weighted value w that the ratio of total sample size is calculatediT (), final highway state estimation is obtained by weighted average
As a result
By using above-mentioned technical proposal disclosed by the invention, following beneficial effect has been obtained：：The embodiment of the present invention is carried
The technical scheme of confession, using fixed vehicle checker data, a large amount of gps datas for gathering and accumulating with reference to Floating Car acquisition system, and
The substantial amounts of charge data that highway tolling system is gathered and accumulated, to the freeway traffic fortune based on multisource data fusion
Row state is estimated, and the assessment result more accurate, coverage rate of the freeway traffic running status for obtaining is wider, can be more
Mend vehicle checker and lay not enough defect, improve road network monitoring range and system, it is possible to achieve find to be deposited on road accurately and in time
Traffic congestion, it is ensured that the safety and efficiency of road traffic.
Each embodiment in this specification is described by the way of progressive, what each embodiment was stressed be with
The difference of other embodiment, between each embodiment identical similar part mutually referring to.
Those skilled in the art should be understood that the sequential of the method and step that above-described embodiment is provided can be entered according to actual conditions
Row accommodation, is concurrently carried out also dependent on actual conditions.
All or part of step in the method that above-described embodiment is related to can be instructed by program correlation hardware come
Complete, described program can be stored in the storage medium that computer equipment can read, for performing the various embodiments described above side
All or part of step described in method.The computer equipment, for example：Personal computer, server, the network equipment, intelligent sliding
Dynamic terminal, intelligent home device, wearable intelligent equipment, vehicle intelligent equipment etc.；Described storage medium, for example：RAM、
ROM, magnetic disc, tape, CD, flash memory, USB flash disk, mobile hard disk, storage card, memory stick, webserver storage, network cloud storage
Deng.
Finally, in addition it is also necessary to explanation, herein, such as first and second or the like relational terms be used merely to by
One entity or operation make a distinction with another entity or operation, and not necessarily require or imply these entities or operation
Between there is any this actual relation or order.And, term " including ", "comprising" or its any other variant meaning
Covering including for nonexcludability, so that process, method, commodity or equipment including a series of key elements not only include that
A little key elements, but also other key elements including being not expressly set out, or also include for this process, method, commodity or
The intrinsic key element of equipment.In the absence of more restrictions, the key element limited by sentence "including a ...", does not arrange
Except also there is other identical element in the process including the key element, method, commodity or equipment.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
Depending on protection scope of the present invention.
Claims (1)
1. a kind of appraisal procedure of the freeway traffic running status based on multisource data fusion, it is characterised in that including such as
Lower step：
Step 1, obtains the zone-to-zone travel operational factor based on multi-source data；
Step 2, merges to the zone-to-zone travel operational factor based on multi-source data, the zone-to-zone travel operation merged
Parameter；
Step 3, the zone-to-zone travel operational factor of the fusion obtained using zone-to-zone travel running status index system and step 2 is right
Zone-to-zone travel running status is estimated, and obtains the assessment result of zone-to-zone travel running status；
Step 4, using section traffic circulation state index system and the place traffic circulation parameter of fixed vehicle checker, to fixed car
Section traffic circulation state where inspection device is estimated, and obtains the assessment result of section traffic circulation state；
Step 5, the section traffic circulation that the assessment result and step 4 of the zone-to-zone travel running status obtained using step 3 are obtained
The assessment result of state, obtains the assessment result of freeway traffic running status；
In step 1, the multi-source data includes fixed vehicle checker data, charge data and floating car data；
In step 1, the zone-to-zone travel operational factor between the vehicle checker section based on the fixed vehicle checker data is passed using cellular
Defeated model is obtained；
In step 2, the zone-to-zone travel operational factor based on multi-source data is merged by BP neural network model；
The BP neural network model is made up of input layer, output layer and some hidden layers；
The input data of the input layer includes：Zone-to-zone travel operational factor and sample size based on various data sources；
The output data of the output layer is the zone-to-zone travel operational factor after fusion.
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| CN105702041A (en) * | 2016-04-21 | 2016-06-22 | 东南大学 | Highway multisource data fusion state estimation system based on neural network and method thereof |
| CN106017496A (en) * | 2016-05-25 | 2016-10-12 | 南京信息职业技术学院 | Real-time navigation method based on road condition |
| CN106251630B (en) * | 2016-10-13 | 2018-09-07 | 东南大学 | A kind of progressive Extended Kalman filter traffic status of express way method of estimation based on multi-source data |
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