CN104157139A - Prediction method and visualization method of traffic jam - Google Patents
Prediction method and visualization method of traffic jam Download PDFInfo
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Abstract
The invention discloses a prediction method and visualization method of traffic jam. The methods comprise the following steps: GPS data sent back by a taxicab is utilized for being associated to roads in an electronic map through the map matching method; the road section speed calculated in terms of the matched data is utilized for judging the traffic state of road sections; the evolution rule of the traffic jam, including formation and dissipation of the traffic jam, is extracted with the utilization of the historical data; the traffic jam prediction is performed with the utilization of the sliding time window scheme after a real-time traffic information database is associated; the congestion intensity and the influence range of congested road sections can be presented by a thermodynamic chart after the congestion intensity of the congested road sections is calculated based on the prediction result. The prediction method provided by the invention can realize highly accurate traffic jam prediction, and the visualization method of the thermodynamic chart enables the traffic jam to be visualized and understandable, so that the location and the influence range of the traffic jam can be discerned conveniently.
Description
Technical field
The present invention relates to field of traffic, more specifically, relate to a kind of traffic congestion Forecasting Methodology and method for visualizing.
Background technology
Traffic congestion is the key factor that affects people's go off daily quality and travel cost.Traffic congestion prediction is the core content of traffic congestion management review, and accurately real-time traffic congestion prediction can relieving traffic jam, improves the traffic capacity and the section speed of road network.
Chinese scholars has been done large quantity research to traffic congestion prediction algorithm.Some of them scholar chooses vehicle flowrate, occupation rate, wagon flow speed and vehicle density as parameter, utilizes the learning algorithm based on bayes rule, calculates the possibility that traffic congestion occurs.Also there is scholar to consider the parameters such as flow, speed, occupation rate, average track flow, use maximum entropy model training to obtain the weight of each parameter, thereby predict traffic congestion.Above-mentioned algorithm has all been considered a plurality of parameters, by training study, obtains the corresponding parameter combinations of traffic congestion.Yet the input parameter that said method needs when predicted congestion is too much, there is in actual applications significant limitation, except section speed, can utilize floating car data obtains, other parameters are mainly derived from coil checker, have the shortcomings such as installation cost is large, installation and maintenance are difficult.
As following several Forecasting Methodologies: it is more reasonable than single parameter state method of estimation that 1) multiparameter method for estimating state is used in research, more tallies with the actual situation.Consider the magnitude of traffic flow, average velocity, 3 parameters of density, by clustering method, three-dimensional parameter is converted to One-dimension Time Series, adopt BP neural network to carry out the prediction of short time traffic conditions, yet cluster result is easily subject to the impact of raw data.2) the traffic flow modes fuzzy reasoning method based on neural network.But the accuracy rate of the method can decline with the increase of continuous prediction step number.3) consider that traffic behavior itself has fuzzy uncertainty, propose based on Adaptive Fuzzy Neural-network inference system.The traffic behavior of the method is divided into 4 classes, adopts fuzzy C-means clustering to obtain every kind of corresponding cluster centre of traffic behavior, then calculates degree of membership, and the maximum corresponding traffic behavior of degree of membership is and predicts the outcome.Yet fuzzy C-means clustering is easily absorbed in local optimum, cannot guarantee that cluster centre can reflect the feature of traffic behavior well.
For convenient, traffic congestion situation is observed, have scholar to propose to express traffic congestion situation by modes such as indexes.The most of mode of service level grade or 0-10 exponentiate that adopts of Chinese scholars represents, U.S. 1985 HCM (HCM) volume of traffic of giving chapter and verse the earliest, and the sensation of road user is divided into A-F6 grade by Assessment of Serviceability of Roads; The span that some scholars define traffic congestion index is 0-10, and the larger expression of value is blocked up more serious.For the block up situation of change of intensity of presentation (or road), some scholars adopt sequential chart to represent the block up Changing Pattern of intensity of regional area at present.Yet sequential chart can only reflect a certain region, a certain congestion in road intensity time become rule, can not reflect the impact of this region (this road) on adjacent area (adjacent road).
Summary of the invention
In order to overcome the deficiencies in the prior art, first the present invention proposes a kind of traffic congestion Forecasting Methodology, and the parameter of this method input only has section speed, and section speed can obtain by floating car data.Than traffic information collection means such as coil checker, videos, floating car technology has that data volume is large, broad covered area, low cost and other advantages.
Another object of the present invention is to propose a kind of traffic congestion method for visualizing, relative timing figure can not the impact of reflecting regional (road) on periphery road network, method for visualizing proposed by the invention is to adopt thermodynamic chart to express the distribution of traffic congestion, make traffic congestion visual and understandable, be convenient to understand traffic congestion position and coverage.
To achieve these goals, technical scheme of the present invention is:
A traffic congestion Forecasting Methodology, comprising:
Adopt Floating Car to obtain gps data, by map-matching method, gps data is associated with the road on electronic chart; According to the data estimation section speed of coupling, utilize the traffic behavior in section speed judgement section, described traffic behavior comprises and blocking up and unimpeded;
Utilize historical traffic state information to build traffic congestion model; Associated real-time traffic states information bank carries out traffic congestion prediction;
Traffic congestion model comprises traffic congestion Model I, traffic congestion modelⅱ and traffic congestion model III;
Suppose that interim one day of this week traffic behavior is similar or identical with cycle traffic behavior on the same day on it,
Building the concrete mode of traffic congestion Model I is: the traffic behavior in n cycle is trained as training sample, and the probability blocking up appears in statistics each cycle on the same day t constantly;
Target section each cycle on the same day t constantly there is the probability blocking up:
Wherein:
refer to that the probability blocking up appears in i section t constantly;
refer to that i section is in j week t section speed constantly; v
tit is congestion status discrimination threshold;
refer to that i section is at j week t traffic behavior constantly, 0 expression is smooth and easy, and 1 represents to block up;
According to above-mentioned target section, there is the probabilistic determination traffic behavior blocking up;
Building the concrete mode of traffic congestion modelⅱ is: in training sample, statistics target section is when developing the traffic behavior in the t moment from t-1 traffic behavior constantly, adjacent section is in the t-1 first traffic behavior regularity of distribution constantly, and described adjacent section refers to the section adjacent with target section;
Associated real-time traffic states information bank carries out traffic congestion prediction; Be to obtain the target section probability that blocks up according to the first traffic behavior regularity of distribution, then according to this target section, occur the traffic behavior in the probabilistic determination target section that blocks up;
Building the concrete mode of traffic congestion model III is:
In training sample, when adding up adjacent section and developing traffic behavior constantly of t from t-1 traffic behavior constantly, the section, upstream in this adjacent section is in the t-1 second traffic behavior regularity of distribution constantly, and section, described upstream refers to the section of the upstream in adjacent section;
In training sample, when adding up adjacent section and developing traffic behavior constantly of t from t-1 traffic behavior constantly, the downstream road section in this adjacent section is in t-1 the 3rd traffic behavior regularity of distribution constantly, and described downstream road section refers to the section in the downstream in adjacent section;
In training sample, when statistics target section develops the traffic behavior in the t moment from t-1 traffic behavior constantly, adjacent section is in t the 4th traffic behavior regularity of distribution constantly;
Associated real-time traffic states information bank carries out traffic congestion prediction; To obtain the constantly adjacent section probability that blocks up of t according to the second traffic behavior regularity of distribution, then according to the traffic behavior in this adjacent section of probabilistic determination that blocks up in this adjacent section; Simultaneously according to the 3rd traffic behavior regularity of distribution, obtain the t adjacent section probability that blocks up constantly, again according to the traffic behavior in this adjacent section of probabilistic determination that blocks up in this adjacent section, finally, according to the traffic behavior in the t adjacent section of the moment by second and third traffic behavior regularity of distribution prediction, and the 4th the traffic behavior regularity of distribution obtain the constantly target section probability that blocks up of t, then according to the traffic behavior in this target section of probabilistic determination that blocks up in this target section;
When traffic congestion is predicted, adopt any one model or three kinds of models couplings in above-mentioned traffic congestion Model I, traffic congestion modelⅱ or traffic congestion model III, while adopting three kinds of models couplings, with majority principle, predict.
A traffic congestion prediction method for visualizing, is the thermodynamic chart of drawing traffic congestion intensity, before drawing thermodynamic chart, need to determine hotspot location, focus coverage, traffic congestion strength retrogression's rule and scheme of colour;
Calculate the intensity C that blocks up of traffic
i;
Hotspot location, the Shi Yan section, position of focus distributes, and the terminus in each section respectively arranges a focus, from the starting point of section, often apart from M rice, a focus is set;
Focus coverage, the coverage of focus is the circle of radius M/2 rice, take section as unit, the traffic congestion intensity mean allocation in this section is to all focuses in this section; If the traffic congestion intensity in section is less than the focus number in this section, be assigned randomly to C
ion individual focus;
Traffic congestion strength retrogression's rule, for each focus, take the center of circle as starting point, traffic congestion intensity along with from distance of center circle from increase and linear attenuation;
Scheme of colour, is divided into N grade by traffic congestion intensity, and adjacent rank adopts different colours sign.
Compared with prior art, beneficial effect of the present invention is:
Traffic congestion Forecasting Methodology of the present invention, only need to obtain section data by Floating Car, and using section data as the unique input parameter of input, can realize the prediction of blocking up of high-accuracy, with the traffic information collection means such as coil checker, video, floating car technology has that data volume is large, broad covered area, low cost and other advantages.
The traffic congestion method for visualizing that the present invention proposes shows by thermodynamic chart, adopts thermodynamic chart to express the distribution of traffic congestion, makes traffic congestion visual and understandable, is convenient to understand traffic congestion position and coverage.
Accompanying drawing explanation
Fig. 1 is traffic congestion Forecasting Methodology implementation framework figure of the present invention.
Fig. 2 builds candidate road section collection schematic diagram when realizing map match.
Fig. 3 is target section and the associated schematic diagram in adjacent section.
Fig. 4 is section, upstream and the associated schematic diagram in adjacent section and target section.
Fig. 5 is downstream road section and the associated schematic diagram in adjacent section and target section.
The process flow diagram of Fig. 6 for adopting traffic congestion Forecasting Methodology of the present invention to predict.
Fig. 7 is the focus coverage schematic diagram of traffic congestion prediction method for visualizing of the present invention.
Fig. 8 is traffic congestion strength retrogression rule schematic diagram.
Fig. 9 is 8:00 Guangzhou traffic congestion thermodynamic chart on April 2
Figure 10 is 8:05 Guangzhou traffic congestion thermodynamic chart on April 2.
Embodiment
Below in conjunction with accompanying drawing, the present invention will be further described, but embodiments of the present invention are not limited to this.
As Fig. 1, the present invention utilizes the gps data of taxi passback, by map-matching method, gps data is associated with the road on electronic chart; According to the data of coupling, calculate section speed, utilize the traffic behavior in speed judgement section, section; Utilize historical data to extract the Evolution of traffic congestion, comprise formation and the dissipation of traffic congestion; Associated Real-time Traffic Information storehouse, utilizes sliding time window mechanism to carry out traffic congestion prediction; Based on predicting the outcome, calculate the intensity of blocking up in the section that blocks up, finally with thermal map, show block up intensity and the coverage in the section that blocks up.
map match
The object of map match is to determine the position in section, vehicle place.In the present embodiment map-matching method be according to road network degree adaptive select matching process (point is to line coupling and point sequence coupling), consider the factors such as distance, course, topology connectedness simultaneously, realize global path and mate.
This matching process comprises two steps, first obtains candidate road section collection, by building an error band screens possible coupling section at anchor point, also referred to as candidate road section, sees Fig. 2 around.Coupling section is determined in the course that its mid point is ordered to distance and the GPS of each candidate road section according to GPS point to line coupling; Point sequence coupling is usingd topology connectedness as the rational constraint condition of coupling, chooses the section combination of coupling weight summation maximum as final matching sequence.
Map matching result is stored in database, as shown in table 1, tables of data comprises the fields such as the number-plate number, GPS positioning time, longitude, latitude, speed, direction, coupling road section ID, vehicle-state (wherein " 4 " represent unloadedly, and " 5 " represent carrying).
Table 1 map matching result tables of data
velocity estimation
Velocity estimation utilizes map matching result to calculate section speed.Velocity estimation algorithm is to divide single Floating Car GPS point in the distribution situation in section, according to the adaptively selected velocity estimation model of distribution situation in the present embodiment.
This algorithm adopts four kinds of velocity estimation models, respectively: distance-time model, Velocity-time trapezoidal integration model, vehicle tracking model and ground spot speed harmonic average model.
Distance-time model is chosen first and last GPS point in target section, utilizes range difference that two GPS order divided by the travel speed of mistiming estimating vehicle.
Velocity-time trapezoidal integration model utilizes instantaneous velocity sequence scores accumulated to obtain the distance that single Floating Car is passed by, divided by the travel speed of the mistiming pro form bill Floating Car of GPS point sequence first and last point.
There is crossing scope in two ends, vehicle tracking model hypothesis section, Floating Car completes even deceleration, parking and even boost phase within the scope of crossing, and Floating Car is at the uniform velocity travelled outward in crossing scope.Wherein the ground spot speed at crossing scope and edge, crossing can obtain by historical gps data.According to above hypothesis, in conjunction with real gps data, obtain the travel speed of vehicle.
Spot speed harmonic average model in ground is usingd the harmonic-mean of instantaneous velocity of all vehicles in target section as section speed.
Velocity estimation result store is in database, as shown in table 2, and tables of data comprises the fields such as road section ID, the zero hour, the finish time, speed, vehicle number.
Table 2 speed calculation result data table
traffic state judging (threshold speed)
Beijing definition average stroke speed of a motor vehicle is the section that seriously blocks up on through street lower than the section of 30 kilometers/hour, and judgement trunk roads, secondary distributor road and branch road are that the block up threshold speed in section is respectively: 20 kilometers/hour, and 15 kilometers/hour, 15 kilometers/hour.Present embodiment adopts above-mentioned speed as the threshold speed of traffic state judging.
Table 3 traffic behavior velocity range table
extract traffic behavior Evolution
Traffic congestion refers under a certain specific space-time condition, due to the traffic trapping phenomena that imbalance between supply and demand produces, comprises category of roads, number of track-lines and certain transport need constantly etc.Category of roads and number of track-lines are not easy to change, and transport need has regularity.Therefore in present embodiment, each cycle is a week, suppose some day traffic circulation state and its last week traffic circulation state on the same day same or similar, add up weekly t on the same day and constantly occur the probability blocking up.
Traffic congestion Model I, rule 1: the probability blocking up appears in t constantly on the same day weekly.
Wherein:
refer to that the probability blocking up appears in i section t constantly;
refer to that i section is in j week t section speed constantly;
refer to that i section is at j week t traffic behavior constantly, 0 expression is smooth and easy, and 1 represents to block up; N refers to all numbers of training sample.
Table 4 rule 1 storage organization
The generation of traffic congestion causes by multifactor, except road self attributes, also relevant to the traffic behavior in adjacent section, as shown in Figure 3.Therefore when prediction traffic congestion, must consider the traffic behavior in adjacent section.
Rule 2:t-1 is the traffic behavior regularity of distribution in adjacent section constantly
(t-1 traffic behavior is constantly to t traffic behavior constantly) adjacent section t-1 traffic behavior regularity of distribution constantly in rule 2 statistics target road section traffic volume state evolution processes.
The traffic behavior in t-1 moment target section can be divided into 4 kinds of situations to the traffic behavior in t moment target section, is respectively:
Table 5 target road section traffic volume state
The traffic behavior of adding up t-1 adjacent section of the moment in every kind of situation, statistics is as shown in table 6
Table 6 rule 2 storage organizations
The traffic behavior of considering target section is not only relevant to adjacent section, also may be subject to the impact in the section, upstream (or downstream road section) in adjacent section, during target of prediction road section traffic volume state, should consider the impact in section, upstream, adjacent section (or downstream road section).As shown in Figure 4, Figure 5, dotted lines represents the section blocking up, the dotted line possible coverage in section that represents to block up.The definition in section, upstream is except adjacent section and target section, is positioned at the section of upstream, adjacent section; The definition of downstream road section is except adjacent section and target section, is positioned at the section in downstream, adjacent section.
Rule 3, rule 4 and rule 5 are considered is the impact on target section of the section, upstream (or downstream road section) in adjacent section.Suppose that section, upstream (or downstream road section) can be embodied the impact in adjacent section and target section within a time interval (being Δ t), as blocking up appears in t-1 moment downstream road section, the traffic behavior in t adjacent section of the moment and target section becomes and blocks up thereupon.Section, upstream (or downstream road section) first affects adjacent section, then affect target section, this process comprises two rules: (1) t-1 is the traffic behavior regularity of distribution of section, upstream (or downstream road section) constantly, be rule 4, rule 5, for judging the t traffic behavior in adjacent section constantly; (2) the traffic behavior regularity of distribution in t adjacent section of the moment, rule 3, for judging the t traffic behavior in target section constantly.
Rule 3:t is the traffic behavior regularity of distribution in adjacent section constantly
(t-1 traffic behavior is constantly to t traffic behavior constantly) adjacent section t traffic behavior regularity of distribution constantly in rule 3 statistics target road section traffic volume state evolution processes.
Equally with rule 2 divide 4 kinds of situations, add up in every kind of situation the t traffic behavior in adjacent section constantly, statistics is as shown in table 7.
Table 7 rule 3 storage organizations
Rule 4:t-1 is the traffic behavior regularity of distribution in section, upstream constantly
(t-1 traffic behavior is constantly to t traffic behavior constantly) section, upstream t traffic behavior regularity of distribution constantly in the adjacent road section traffic volume state evolution process of rule 4 statistics.
Equally with rule 2 divide 4 kinds of situations, add up in every kind of situation the t-1 traffic behavior in section, upstream constantly, statistics is as shown in table 8.
Table 8 rule 4 storage organizations
Rule 5:t-1 is the traffic behavior regularity of distribution of downstream road section constantly
(t-1 traffic behavior is constantly to t traffic behavior constantly) downstream road section t traffic behavior regularity of distribution constantly in the adjacent road section traffic volume state evolution process of rule 5 statistics.
Equally with rule 2 divide 4 kinds of situations, add up in every kind of situation the t-1 traffic behavior of downstream road section constantly, statistics is as shown in table 9.
Table 9 rule 5 storage organizations
traffic congestion Forecasting Methodology
Traffic congestion Forecasting Methodology process flow diagram as shown in Figure 6.This Forecasting Methodology is comprised of three methods, and each method obtains the traffic behavior predicted value in target section, and the traffic behavior that employing is obtained by most methods is as the traffic behavior in target section.
1) method 1
Step 1-1: laws of use 1, search t and constantly occur the probability blocking up
Step 1-2: if
be more than or equal to 50%, judge traffic behavior TS
ifor blocking up, otherwise be smooth and easy.
2) method 2
Step 2-1: obtain adjacent section t-1 traffic behavior constantly.
Step 2-2: obtain target section t-1 traffic behavior constantly.
Step 2-3: the probability that blocks up that calculates target section
here laws of use 2 calculates the probability that blocks up, as shown in table 10, and t-1 traffic behavior constantly in hypothetical target section is smooth and easy, adjacent section t-1 traffic behavior is constantly respectively and blocks up, smooth and easy, block up, block up, smooth and easy, smooth and easy.
Table 10 calculates the rule (rule 2) of using when target section blocks up probability
The target section probability that blocks up
for:
The probability that blocks up is greater than 50%, and prediction t traffic behavior is constantly for blocking up.
3) method 3
Step 3-1: obtain adjacent section t-1 traffic behavior constantly.
Step 3-2: laws of use 4 and the adjacent section t of rule 5 prediction traffic behavior constantly.As shown in table 11, suppose that adjacent section t-1 traffic behavior is constantly smooth and easy, t-1 traffic behavior constantly in section, upstream is respectively smooth and easy, blocks up, smooth and easy.
Table 11 calculates the rule (rule 4,5) of using when adjacent section blocks up probability
The adjacent section probability that blocks up
for:
The probability that blocks up is less than 50%, and prediction t traffic behavior is constantly smooth and easy.
Step 3-3: obtain target section t-1 traffic behavior constantly.
Step 3-4: the probability that blocks up that calculates target section
here laws of use 3 calculates the probability that blocks up, as shown in table 12, and t-1 traffic behavior constantly in hypothetical target section is smooth and easy, and adjacent section t traffic behavior is constantly respectively smooth and easy, blocks up, blocks up, blocks up, blocks up, blocks up.
Table 12 calculates the rule (rule 3) of using when adjacent section blocks up probability
The target section probability that blocks up
for:
The probability that blocks up is greater than 50%, and prediction t traffic behavior is constantly for blocking up.
traffic congestion intensity is calculated
The calculation process of the traffic congestion intensity of present embodiment is as follows:
Wherein, C
itraffic congestion intensity for section i.
for the discriminant function that blocks up,
for the average speed of section i within t the time interval, v
tfor congestion status discrimination threshold, a
ifor the number of track-lines of section i,
for the block up length of section i within t the time interval, s
tit is the duration in t the time interval.
draw traffic congestion intensity thermodynamic chart
For the traffic behavior expression way (index that blocks up, sequential chart) of current main flow can only reflect regional area traffic behavior time become rule, can not reflect the problems such as whole traffic behavior Changing Pattern, adopt thermodynamic chart to express the distribution of traffic congestion herein, and by the synthetic Dynamic Graph of several thermodynamic charts.Thermodynamic chart can show the occurrence positions of traffic congestion and the intensity of traffic congestion on map, make traffic congestion visual and understandable, Dynamic Graph is convenient to study the impact of jam road (congestion regions) on peripheral path (neighboring area), grasps the Evolution of road net traffic state.Draw before thermodynamic chart, need to first determine hotspot location, focus coverage, traffic congestion strength retrogression's rule and scheme of colour.
Hotspot location
Because traffic congestion occurs on section, so the Shi Yan section, position of focus distributes, and the terminus in each section respectively arranges a focus, and from the starting point of section, 100 meters of every distances arrange a focus.
Focus coverage
As shown in Figure 7, the coverage of focus is the circle of 50 meters of radiuses.Take section as unit, and the traffic congestion intensity mean allocation in this section is to all focuses in this section.If the traffic congestion intensity in section is less than the focus number in this section, (traffic congestion intensity is C
i), be assigned randomly to C
ion individual focus.
Traffic congestion strength retrogression's rule
For each focus, take the center of circle as starting point, traffic congestion intensity along with from distance of center circle from increase and linear attenuation.Effect as shown in Figure 8.
Scheme of colour
Traffic congestion intensity is divided into 5 grades, and 1 represents that traffic congestion intensity is minimum, and 5 represent that traffic congestion intensity is maximum, and the corresponding color of grade 1-5 is: transparent (colourless) → blueness → redness → yellow → white.
When carrying out map match, also can adopt other high-precision matching process to realize, as point-to-point coupling, map-matching method based on topological analysis.
Same, during velocity estimation practical application, also can also select other high-precision velocity estimation algorithms, as the section velocity estimation model based on neural network.
The division of traffic behavior definition to the section that seriously blocks up with reference to Beijing in the present invention, during practical application, also can select other traffic behavior division rules, as the < < urban traffic control assessment indicator system > > (2008) of Ministry of Public Security's issue.
The comparison of traffic congestion prognostic experiment result
1) traffic congestion prediction
In table 13, method 1 represents the only accuracy of using method 1, method 2, the rest may be inferred for method 3, accuracy after the comprehensive three kinds of methods of method representation, first use three kinds of methods to obtain the judged result of three traffic behaviors, using the traffic behavior that obtained by the most methods traffic behavior as target section.As shown in Table 13, the accuracy of traffic congestion judgement reaches more than 85% substantially, and precision of prediction is satisfactory.
The analysis that predicts the outcome of table 13 traffic congestion
2) traffic congestion is visual
Fig. 9, Figure 10 are Guangzhou traffic congestion intensity thermodynamic charts, thermodynamic chart can intuitively show traffic congestion position and coverage thereof, by the synthetic traffic congestion dynamic changes of strength figure of thermodynamic chart, traffic congestion thermodynamic chart more in the same time, is not convenient to grasp the Evolution that urban road network traffic blocks up.
Above-described embodiments of the present invention, do not form limiting the scope of the present invention.Any modification of having done within spiritual principles of the present invention, be equal to and replace and improvement etc., within all should being included in claim protection domain of the present invention.
Claims (9)
1. a traffic congestion Forecasting Methodology, is characterized in that, comprising:
Adopt Floating Car to obtain gps data, by map-matching method, gps data is associated with the road on electronic chart; According to the data estimation section speed of coupling, utilize the traffic behavior in section speed judgement section, described traffic behavior comprises and blocking up and unimpeded;
Utilize historical traffic state information to build traffic congestion model; Associated real-time traffic states information bank carries out traffic congestion prediction;
Traffic congestion model comprises traffic congestion Model I, traffic congestion modelⅱ and traffic congestion model III;
Suppose that interim one day of this week traffic behavior is identical with cycle traffic behavior on the same day on it,
Building the concrete mode of traffic congestion Model I is: the traffic behavior in n cycle is trained as training sample, and the probability blocking up appears in statistics each cycle on the same day t constantly;
Target section each cycle on the same day t constantly there is the probability blocking up:
Wherein:
refer to that the probability blocking up appears in i section t constantly;
refer to that i section is in j week t section speed constantly; v
tit is congestion status discrimination threshold;
refer to that i section is at j week t traffic behavior constantly, 0 expression is smooth and easy, and 1 represents to block up;
According to above-mentioned target section, there is the probabilistic determination traffic behavior blocking up;
Building the concrete mode of traffic congestion modelⅱ is: in training sample, statistics target section is when developing the traffic behavior in the t moment from t-1 traffic behavior constantly, adjacent section is in the t-1 first traffic behavior regularity of distribution constantly, and described adjacent section refers to the section adjacent with target section;
Associated real-time traffic states information bank carries out traffic congestion prediction; Be to obtain the target section probability that blocks up according to the first traffic behavior regularity of distribution, then according to this target section, occur the traffic behavior in the probabilistic determination target section that blocks up;
Building the concrete mode of traffic congestion model III is:
In training sample, when adding up adjacent section and developing traffic behavior constantly of t from t-1 traffic behavior constantly, the section, upstream in this adjacent section is in the t-1 second traffic behavior regularity of distribution constantly, and section, described upstream refers to the section of the upstream in adjacent section;
In training sample, when adding up adjacent section and developing traffic behavior constantly of t from t-1 traffic behavior constantly, the downstream road section in this adjacent section is in t-1 the 3rd traffic behavior regularity of distribution constantly, and described downstream road section refers to the section in the downstream in adjacent section;
In training sample, when statistics target section develops the traffic behavior in the t moment from t-1 traffic behavior constantly, adjacent section is in t the 4th traffic behavior regularity of distribution constantly;
Associated real-time traffic states information bank carries out traffic congestion prediction; To obtain the constantly adjacent section probability that blocks up of t according to the second traffic behavior regularity of distribution, then according to the traffic behavior in this adjacent section of probabilistic determination that blocks up in this adjacent section; Simultaneously according to the 3rd traffic behavior regularity of distribution, obtain the t adjacent section probability that blocks up constantly, again according to the traffic behavior in this adjacent section of probabilistic determination that blocks up in this adjacent section, finally, according to the traffic behavior in the t adjacent section of the moment by second and third traffic behavior regularity of distribution prediction, and the 4th the traffic behavior regularity of distribution obtain the constantly target section probability that blocks up of t, then according to the traffic behavior in this target section of probabilistic determination that blocks up in this target section;
When traffic congestion is predicted, adopt any one model or three kinds of models couplings in above-mentioned traffic congestion Model I, traffic congestion modelⅱ or traffic congestion model III, while adopting three kinds of models couplings, with majority principle, predict.
2. traffic congestion Forecasting Methodology according to claim 1, is characterized in that, builds in traffic congestion mould, and the evolution process that develops t traffic behavior constantly from t-1 traffic behavior is constantly:
T-1 traffic behavior is constantly when blocking up, and the traffic behavior in the t moment is for blocking up or smooth and easy;
When t-1 traffic behavior is constantly smooth and easy, t traffic behavior is constantly for blocking up or smooth and easy.
3. traffic congestion Forecasting Methodology according to claim 1 and 2, is characterized in that, according to the probability that blocks up, carries out traffic behavior judgement, when the probability that blocks up is more than or equal to 50%, and traffic behavior TS
ifor blocking up, otherwise be smooth and easy.
4. traffic congestion Forecasting Methodology according to claim 3, is characterized in that, described map-matching method is specially: obtain candidate road section collection, by building an error band screens possible coupling section at anchor point, also referred to as candidate road section around; Coupling section is determined in the course that its mid point is ordered to distance and the GPS of each candidate road section according to GPS point to line coupling; Point sequence coupling is usingd topology connectedness as the rational constraint condition of coupling, chooses the section combination of coupling weight summation maximum as final matching sequence.
5. traffic congestion Forecasting Methodology according to claim 3, it is characterized in that, according to the adaptively selected velocity estimation model of matching result, carry out section velocity estimation, the appraising model of employing is: distance-time model, Velocity-time trapezoidal integration model, vehicle tracking model or ground spot speed harmonic average model.
6. a method for visualizing of applying the traffic congestion Forecasting Methodology described in the claims 1 to 5 any one, it is characterized in that, it is the thermodynamic chart of drawing traffic congestion intensity, before drawing thermodynamic chart, need to determine hotspot location, focus coverage, traffic congestion strength retrogression's rule and scheme of colour;
Calculate the intensity C that blocks up of traffic
i;
Hotspot location, the Shi Yan section, position of focus distributes, and the terminus in each section respectively arranges a focus, from the starting point of section, often apart from M rice, a focus is set;
Focus coverage, the coverage of focus is the circle of radius M/2 rice, take section as unit, the traffic congestion intensity mean allocation in this section is to all focuses in this section; If the traffic congestion intensity C in section
ithe focus number that is less than this section, is assigned randomly to C
ion individual focus;
Traffic congestion strength retrogression's rule, for each focus, take the center of circle as starting point, traffic congestion intensity along with from distance of center circle from increase and linear attenuation;
Scheme of colour, is divided into N grade by traffic congestion intensity, and adjacent rank adopts different colours sign.
7. traffic congestion prediction method for visualizing according to claim 6, is characterized in that the intensity C that blocks up of traffic
irealize in the following ways:
Wherein, C
ifor the traffic congestion intensity of section i,
for the discriminant function that blocks up,
for the average speed of section i within t the time interval, v
tfor congestion status discrimination threshold, a
ifor the number of track-lines of section i,
for the block up length of section i within t the time interval, s
tit is the duration in t the time interval.
8. according to the traffic congestion prediction method for visualizing described in claim 6 or 7, it is characterized in that, described scheme of colour is that traffic congestion intensity is divided into 5 grades, and 1 represents that traffic congestion intensity is minimum, and 5 represent that traffic congestion intensity is maximum.
9. traffic congestion prediction method for visualizing according to claim 8, is characterized in that, the corresponding color of grade 1-5 is: transparent (colourless) → blueness → redness → yellow → white.
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