CN103106793A - Traffic state discriminated method based on real-time driving direction and transit time quantum information - Google Patents
Traffic state discriminated method based on real-time driving direction and transit time quantum information Download PDFInfo
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Abstract
The invention relates to the technical field of traffic state discrimination, in particular to a traffic state discriminated method based on a real-time driving direction and transit time quantum information. A traffic state, as an overall indicator of evaluating an urban traffic operation state, is influenced by a plurality of factors. The traffic state discriminated method based on the real-time driving direction and the transit time quantum information puts forward a traffic state discriminated module based on the driving direction and the traffic time quantum, develops traffic state discriminated thought from the existing single mode to various comprehensive discrimination, applies a network theory with a self-adaptation characteristic to the traffic state discrimination, digs out objective data (traffic flow parameter data) rules form subjective factors (feeling of a person for the traffic state) and searches data changes which correspond to the state changes. Due to the adoption of dynamic intervals to distinguish changes of the traffic state, a parameter of the traffic state discriminated module can achieve self-adaptation adjustment, truly achieves correspondingly adjusting a traffic state discriminated threshold value according to traffic flow characters of a section of a road and achieves self-adaptation discrimination of the traffic state.
Description
Technical field
The present invention relates to the traffic state judging technical field, particularly a kind of traffic state judging method based on real-time direction of traffic and current period information.
Background technology
Traffic state judging is exactly in fact the characteristic according to traffic flow, and similar state is divided into a class.Traffic state judging can be divided into artificial cognition method and automatic distinguishing method.Although the artificial cognition method is simple, direct, convenient, is subject to the restriction of manpower, the personnel amount and the working strength that need are all larger, and therefore, the artificial cognition method is replaced by automatic distinguishing method gradually.
The traffic behavior automatic distinguishing method has a lot, for example, and California algorithm and McMaster algorithm etc.California algorithm is mainly the traffic flow parameters such as occupation rate that detect by comparing adjacent inspection stations, differentiates traffic behavior.The McMaster algorithm be the earliest with the normal logical crowded algorithm as the traffic state judging object of sexual intercourse of sending out, this algorithm is take catastrophe theory as the basis, on the basis of a large amount of historical datas, more crowded and non-crowded flow-occupation rate data, as masterplate, compare for twice by measured data and flow-occupation rate masterplate, judge whether to occur traffic congestion and congested in traffic type.
For the different sections of highway of urban road, be subject to the restriction of the conditions such as the physical condition of road own and service facility, traffic behavior shows larger difference at different sections of highway; And for same highway section, traffic behavior neither be unalterable, and the variable effect of external environment factor the driver to the sensation variation of same place traffic behavior.For example in the travel directions in highway section, possible a direction generation traffic congestion, and another direction smooth traffic; In rush-hour, the driver may be accustomed to the traffic congestion in highway section psychologically, and at non-peak period, can't accept on the contrary traffic congestion in short-term.The variation of these external environment factors when affecting the driver psychology impression, has also affected the judgement of driver to traffic behavior.
Prior art is not considered the variation of these external environment factors, directly according to the traffic behavior of a fixing judgment of standard road, tends to cause the erroneous judgement of traffic behavior.
Summary of the invention
In view of this, the present invention according to adjusting and revising, reaches the purpose of optimizing traffic state judging to traffic state judging according to the traffic stream characteristics of different roads.
The present invention adopts following scheme to realize: a kind of traffic state judging method based on real-time direction of traffic and current period information is characterized in that comprising the following steps:
Step S01: the traffic flow data that gathers the highway section, divide traffic flow data according to direction of traffic and corresponding traffic slot, different direction of traffics and traffic slot are combined into major movement traffic flow peak period, traffic flow peak period of less important direction of traffic, major movement flat peak period traffic flow and less important direction of traffic flat peak period traffic flow pattern, set up respectively the traffic flow data collection of associative mode, historical data is as training dataset, and the data of Real-time Collection are as predictive data set;
Step S02: described training dataset and predictive data set are carried out normalization, input value is distributed in the interval of [1,1], eliminate the difference on variable factor magnitude and the impact that causes;
Step S03: set up the SOM neural network, input layer quantity is determined by the number of the traffic flow parameter that classification is adopted;
Step S04: training SOM neural network, for given training dataset, the weights data set is initialized as random value, determine competition layer triumph unit, then the connection weights of triumph unit and neighborhood unit thereof to be revised, the right value update rule is:
In formula,
Connection weights for the triumph unit;
Input for the triumph unit;
,
Represent respectively the weights study factor of triumph unit and neighborhood unit thereof, span is (0,1), and
Step S05: after training satisfactory network, in predictive data set substitution network, can realize the traffic behavior classification; Simultaneously return to again step S01, predictive data set is fed back to training dataset, constantly improve the SOM neural network, the automatic adjustment of implementation model.
In an embodiment of the present invention, the unbalancedness of described direction of traffic direction distribution coefficient
Expression:
The direction of traffic distribution coefficient
Value more near 50%, shows that the volume of traffic of two direction of traffics is got over the convergence balance greater than 50%, more away from 50%, shows that the volume of traffic difference of two direction of traffics is larger.
In an embodiment of the present invention, described traffic slot is divided into peace peak period peak period, with coefficient peak period
The volume of traffic difference of expression peace peak period peak period, expression formula is as follows:
In an embodiment of the present invention, adopt the volume of traffic, average speed and traffic density as the index of classification in described step S03, input neuron is 3; The competition layer neuron number is determined by number of categories, and traffic behavior is divided into 3 classes, and competition layer has 3 neurons.
In an embodiment of the present invention, in described step S04 training process, reduce gradually the variable quantity of neighborhood and weights; The weights study factor of triumph unit
And the unit, field reduces gradually,
With the field width
Reduce gradually in iterative process, its adjustment policy expression is:
（6）
In formula,
,
Be respectively
With
Initial value,
Value between 0.2-0.5,
It is 1/2 or 1/3 width;
Be the current iteration number of times;
Be the iteration total degree of setting.
Shorter mention direction of traffic and the traffic slot relevant information impact on traffic state judging in prior art, but direct traffic behavior according to a fixing judgment of standard road, and the influence of single factor is only considered in most of quantitative analysis method.The present invention proposes the traffic state judging model based on direction of traffic and traffic slot, traffic state judging thought is differentiated to many-sided comprehensive distinguishing development from original single-mode, the self-adaptive features network theory is applied in traffic state judging, excavate the rule of objective data (traffic flow parameter data) from the factor (impression of people to traffic behavior) of subjectivity, seek the corresponding data variation of state variation.Adopt the variation of difference traffic behavior between dynamic area, the parameter of traffic discrimination model realizes adaptive adjustment, really accomplishes the traffic flow characteristics according to highway section self, and the traffic state judging threshold values is adjusted accordingly, and the self-adjusting that reaches traffic behavior is differentiated.The traffic state judging method that the present invention proposes is mainly to carry out self-adaptation for different traffic flow modes to differentiate, no matter the traffic flow modes difference in highway section is much, model can both be adjusted the traffic behavior threshold values in this highway section according to a large amount of historical datas, different sections of highway is carried out traffic state judging, make the differentiation result reach optimum.Traffic state judging model of the present invention can be realized the self-adaptation adjustment of environmental factor to external world, has very strong practicality, and application prospect is very wide.
Description of drawings
Fig. 1 is embodiment of the present invention schematic flow sheet.
Fig. 2 is that the embodiment of the present invention is based on the traffic state judging model schematic diagram of SOM.
Embodiment
Before the explanation specific embodiments of the invention, in order better to allow those skilled in the art understand the effect that the present invention realizes, the below divides 4 lower the technology used in the present invention means of simple introduction.
1. based on the traffic state analysis of direction of traffic
At the round both direction of same path, traffic flow is not to keep balance always.Due to the difference of different positions land used character and the highway section difference of the round connection of urban road, there is larger difference in the traffic flow meeting that comes and goes both direction.The unbalancedness of this direction of traffic can be used the direction distribution coefficient
Expression:
The direction of traffic distribution coefficient
Value more near 50%, shows that the volume of traffic of two direction of traffics is got over the convergence balance generally greater than 50%, more away from 50%, shows that the volume of traffic difference of two direction of traffics is larger.
2. based on the traffic state analysis of traffic slot
Same highway section, along with the time changes, traffic flow is also in continuous variation.In commuter time morning and evening, go out line density and increase, the peak can appear in the volume of traffic, for common urban road, considers that its peak hour flow stream mode can satisfy the requirement of traffic control; And Urban Advanced road more stable for traffic flow, that continue, peak period is than common urban road longer duration, therefore can not consider merely the traffic flow of peak hour, but consider as a whole with continuous traffic flow peak period, peace peak period peak period is distinguished, avoid flat peak period traffic behavior all to fall within certain lower interval.With coefficient peak period
The volume of traffic difference of expression peace peak period peak period, expression formula is as follows:
3. Multifactor Combination analysis
According to the analysis to direction of traffic and traffic slot, the separable traffic flow that goes out under different direction of traffics and traffic slot impact, there is significant difference each other in these traffic flows, and direction of traffic and traffic slot act on simultaneously on the impact of traffic flow sometimes, therefore need to make up these factors.As choose direction of traffic and two factors of traffic slot, one-tenth major movement traffic flow peak period capable of being combined, traffic flow peak period of less important direction of traffic, the flat peak period traffic flow of major movement and the flat peak period traffic flow of less important direction of traffic, namely the traffic flow in a certain highway section is divided into 4 kinds of patterns, then respectively the traffic behavior under these 4 kinds of patterns is classified, the factor of direction of traffic and traffic slot that reduces is on the impact of traffic behavior classification results generation.
4.SOM the Parameter Self of neural network
The traffic state judging process is the characteristic according to traffic flow, and similar state is divided into a class, and for the threshold values of each classification, there is no the clear and definite criteria for classifying.Due to different individualities to same state feel be not quite similar, the threshold values border of determining is nonlinear, i.e. the fixing threshold values of neither one.And use Self-Organizing Feature Maps (Self-Organizing Maps, SOM) can not use the target that the outside provides to export the classification of seeking in complex data, namely do not need to predict threshold values, the present invention adopts Self-Organizing Feature Maps that traffic behavior is differentiated.
The monolayer neural networks that the SOM neural network is comprised of input layer and competition layer, input layer are the neurons of one dimension, and neuron number is
Competition layer is the neuron of two dimension, by
Individual neuron consists of a two-dimensional planar array.The neuron of input layer has weights to be connected with the neuron of competition layer, also has local lateral inhibition to connect between the competition layer node; Have two kinds to connect weights in the SOM neural network, a kind of is the connection weights that neuron reacts the outside input, and another is the connection weights between neuron, and its size is being controlled interactive power between neuron; Therefore, in training process, not only to be adjusted in the connection weights of triumph unit in competition, also need to regulate the weights of the neighborhood unit of triumph unit.Self-Organizing Feature Maps can not use the target that the outside provides to export the classification of seeking in complex data, does not namely need to predict threshold values, is fit to mass data is processed.
Along with the development of intelligent transport technology, in time, traffic behavior classification accurately and forecast, to alleviating urban traffic blocking and dredging the urban traffic pressure and play an important role.Traffic behavior can provide for the driver decision-making foundation of driving path as an overall target estimating the urban transportation operation conditions.Yet traffic behavior is a hazy sensations amount, and not only relevant with current vehicle number on road, also psychological bearing capability and the external environment with the driver has substantial connection, and each factor is to be mutually related.Therefore, can not quantize traffic behavior with traffic flow parameter merely, but will consider to affect the various factors of traffic behavior, each factor is carried out analysis-by-synthesis and quantification.
For the different sections of highway of urban road, be subject to the restriction of the conditions such as the physical condition of road own and service facility, traffic behavior shows larger difference at different sections of highway; And for same highway section, traffic behavior neither be unalterable, and the variable effect of external environment factor the driver to the sensation variation of same place traffic behavior.For example in the travel directions in highway section, possible a direction generation traffic congestion, and another direction smooth traffic; In rush-hour, the driver may be accustomed to the traffic congestion in highway section psychologically, and at non-peak period, can't accept on the contrary traffic congestion in short-term; The variation of these external environment factors when affecting the driver psychology impression, has also affected the judgement of driver to traffic behavior.Therefore, the present invention chooses direction of traffic and traffic slot as the factor that affects the traffic behavior classification, proposes the traffic state judging method under each factor combination is seen also Fig. 1.
Basic ideas of the present invention are: arrange traffic flow data, set up the SOM neural network of traffic state judging, training SOM neural network is classified to the real-time traffic flow data according to the network that trains.Key step is as follows:
The first step: the traffic flow data that gathers the highway section, divide traffic flow data according to direction of traffic and corresponding traffic slot, different direction of traffics and traffic slot are combined into 4 kinds of patterns, set up respectively the traffic flow data collection of associative mode, historical data is as training dataset, and the data of Real-time Collection are as predictive data set.
Second step: training dataset and predictive data set are carried out normalization, input value is distributed in the interval of [1,1], eliminate the difference on variable factor magnitude and the impact that causes.
The 3rd step: set up the SOM neural network, input layer quantity is determined by the number of the traffic flow parameter that classification is adopted.The present embodiment adopts the volume of traffic, average speed and traffic density as the index of classification, so input neuron is 3.The competition layer neuron number is determined by number of categories, and traffic behavior is divided into 3 classes, so competition layer has 3 neurons.
The 4th step: training SOM neural network.For given training dataset, the weights data set is initialized as random value, determine competition layer triumph unit, then the connection weights of triumph unit and neighborhood unit thereof to be revised, the right value update rule is:
（3）
（4）
In formula,
Connection weights for the triumph unit;
Input for the triumph unit;
,
Represent respectively the weights study factor of triumph unit and neighborhood unit thereof, span is (0,1), and
In learning process, reduce gradually the variable quantity of neighborhood and weights.The weights study factor of triumph unit
And the unit, field reduces gradually,
With the field width
Reduce gradually in iterative process, its adjustment policy expression is:
（6）
In formula,
,
Be respectively
With
Initial value,
Value between 0.2-0.5,
Be generally 1/2 or 1/3 width;
Be the current iteration number of times;
Be the iteration total degree of setting.
The 5th step: after training satisfactory network, can with in predictive data set substitution network, can realize the traffic behavior classification; Simultaneously return to again the first step, predictive data set is fed back to training dataset, constantly improve the SOM neural network, the automatic adjustment of implementation model.
By above-mentioned learning process, can classify to the traffic flow under various combination, thereby obtain traffic behavior under different affecting factors, model is as shown in Figure 2.
The above is only preferred embodiment of the present invention, and all equalizations of doing according to the present patent application the scope of the claims change and modify, and all should belong to covering scope of the present invention.
Claims (5)
1. traffic state judging method based on real-time direction of traffic and current period information is characterized in that comprising the following steps:
Step S01: the traffic flow data that gathers the highway section, divide traffic flow data according to direction of traffic and corresponding traffic slot, different direction of traffics and traffic slot are combined into major movement traffic flow peak period, traffic flow peak period of less important direction of traffic, major movement flat peak period traffic flow and less important direction of traffic flat peak period traffic flow pattern, set up respectively the traffic flow data collection of associative mode, historical data is as training dataset, and the data of Real-time Collection are as predictive data set;
Step S02: described training dataset and predictive data set are carried out normalization, input value is distributed in the interval of [1,1], eliminate the difference on variable factor magnitude and the impact that causes;
Step S03: set up the SOM neural network, input layer quantity is determined by the number of the traffic flow parameter that classification is adopted;
Step S04: training SOM neural network, for given training dataset, the weights data set is initialized as random value, determine competition layer triumph unit, then the connection weights of triumph unit and neighborhood unit thereof to be revised, the right value update rule is:
（3）
In formula,
Connection weights for the triumph unit;
Input for the triumph unit;
,
Represent respectively the weights study factor of triumph unit and neighborhood unit thereof, span is (0,1), and
Step S05: after training satisfactory network, in predictive data set substitution network, can realize the traffic behavior classification; Simultaneously return to again step S01, predictive data set is fed back to training dataset, constantly improve the SOM neural network, the automatic adjustment of implementation model.
2. the traffic state judging method based on real-time direction of traffic and current period information according to claim 1, is characterized in that: the unbalancedness of described direction of traffic direction distribution coefficient
Expression:
（1）
The direction of traffic distribution coefficient
Value more near 50%, shows that the volume of traffic of two direction of traffics is got over the convergence balance greater than 50%, more away from 50%, shows that the volume of traffic difference of two direction of traffics is larger.
3. the traffic state judging method based on real-time direction of traffic and current period information according to claim 1 is characterized in that: described traffic slot is divided into peace peak period peak period, with coefficient peak period
The volume of traffic difference of expression peace peak period peak period, expression formula is as follows:
4. the traffic state judging method based on real-time direction of traffic and current period information according to claim 1, it is characterized in that: adopt the volume of traffic, average speed and traffic density as the index of classification in described step S03, input neuron is 3; The competition layer neuron number is determined by number of categories, and traffic behavior is divided into 3 classes, and competition layer has 3 neurons.
5. the traffic state judging method based on real-time direction of traffic and current period information according to claim 1, is characterized in that: in described step S04 training process, reduce gradually the variable quantity of neighborhood and weights; The weights study factor of triumph unit
And the unit, field reduces gradually,
With the field width
Reduce gradually in iterative process, its adjustment policy expression is:
In formula,
,
Be respectively
With
Initial value,
Value between 0.2-0.5,
It is 1/2 or 1/3 width;
Be the current iteration number of times;
Be the iteration total degree of setting.
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