CN103093623B - Prediction method of urban road signalized intersection direct-left conflict number - Google Patents
Prediction method of urban road signalized intersection direct-left conflict number Download PDFInfo
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Abstract
The invention discloses a prediction method of an urban road signalized intersection direct-left conflict number. A plurality of direct-left conflicts and conflict flow of the direct-left conflicts and geometric characteristics of an intersection serve as data base, and an urban road signalized intersection direct-left conflict prediction model is built by aiming at four different traffic operation conditions and utilizing a mechanism generated when a generalized linear model excavates the traffic conflicts. Direct-left conflict frequency in unit time interval is obtained by substituting traffic flow parameters into the model so as to provide evidence for intersection indirect safety evaluation. According to the urban road signalized intersection direct-left conflict number obtaining method based on the direct-left conflict prediction model, traffic conflicts can be predicted by traffic flow which is easy to be collected, the defect and disadvantage that conflict collecting cost is high in present traffic conflict technology are overcome, and the prediction method is more accurate and scientific compared with manual observation conflicts of the prior art and capable of promoting the traffic conflict technology to be applied in engineers.
Description
Technical field
The invention belongs to traffic administration and traffic safety technology field, relate to the Forecasting Methodology of the straight left number of collisions of a kind of signalized intersection, be specifically related to a kind of Forecasting Methodology of the straight left number of collisions of signalized intersection based on traffic conflict forecast model.
Background technology
Along with China's path link car kilometer number and vehicle guaranteeding organic quantity increase rapidly, Road Safety Status is increasingly serious.Due to Evaluation of Traffic Safety in minimizing accident, improve the enormous benefits that road safety obtains aspect horizontal, many national government and production and operation unit drop into a huge sum of money and carry out safety evaluation.Traffic safety evaluation method comprises direct evaluation assessment and Indirect evaluation method.The safety evaluation of traditional signalized intersection adopts the expectation of traffic hazard occurrence frequency as the direct evaluation index of traffic safety conventionally.But in actual engineering practice, the traffic hazard data that meet accident micro-analysis demand are difficult to obtain, there is certain defect in the accuracy in the existing casualty data of China storehouse and publicity aspect particularly.Therefore the Evaluation of Traffic Safety system that, Chinese scholar proposes mostly adopts traffic safety Indirect evaluation method.The traffic conflict technique (Traffic Conflict Technique, be called for short TCT) is the method for traffic safety Indirect evaluation the most widely of current domestic and international application.
Traffic conflict refers to and between different traffic participants, on time and space, has produced phase mutual interference, and forces traffic participant to take a kind of traffic behavior of the behavior of dodging.The traffic conflict technique using field observation to traffic conflict carry out evaluation path Safety of Underground-Transportation Facilities situation as traffic hazard Substitute Indexes, there is data volume large, the advantage such as evaluation cycle is short.Traffic conflict collection depends on observation personnel's long-time field observation and observation personnel and need carry out strict training and distinguish standard with unified conflict, the difficulty gathering due to conflict is large, makes the application in practice of the traffic conflict technique in traffic engineering be subject to certain restriction.For head it off, within 2008, Bureau of Public Road has issued emulation conflict analysis software SSAM, and the vehicle operating trail file that this software is exported taking Microscopic Traffic Simulation Mathematic Model, as research object, carries out identification and the classification of emulation conflict.But because microscopic simulation can not reflect the driving behavior of driver in real world exactly, the accuracy of the number of collisions of SSAM simulation is under suspicion.
Summary of the invention
Goal of the invention: the problem and shortage existing for above-mentioned prior art, the object of this invention is to provide the Forecasting Methodology of the straight left number of collisions of a kind of signalized intersection, obtaining on the basis of a large amount of traffic conflict and traffic flow parameter data, contact traffic conflict generation and driving behavior, utilize negative binomial and Poisson generalized linear model to set up the forecast model of traffic conflict and traffic flow parameter under different traffic, overcoming traffic conflict, to gather difficulty large and cannot obtain according to the conflict collecting the shortcomings and deficiencies of conflict generation expectation value.
Technical scheme: for achieving the above object, the technical solution used in the present invention is the Forecasting Methodology of the straight left number of collisions of a kind of signalized intersection, comprises the steps:
Step 1: gather traffic flow data and judge traffic behavior: add up the magnitude of traffic flow of the each entrance driveway in Permissive Left-Turn crossing, represent the magnitude of traffic flow of subtend craspedodrome with T, represent the magnitude of traffic flow of left-hand rotation with L, determine respectively the traffic capacity C of left-hand rotation direction entrance driveway
ltraffic capacity C with subtend craspedodrome direction entrance driveway
t, and calculate respectively the traffic saturation degree index vc of left-hand rotation direction
l=L/C
ltraffic saturation degree index vc with subtend craspedodrome direction
t=T/C
t;
Step 2: adopt straight left number of collisions and the magnitude of traffic flow of collecting in a period of time, respectively with vc
l=0.38 and vc
t=0.48 state cut off value for the traffic saturation degree index of left-hand rotation direction and the traffic saturation degree index of subtend craspedodrome direction, obtain the conflict prediction model (this model is negative binomial and Poisson generalized linear conflict prediction model, is called for short generalized linear conflict prediction model) of straight left number of collisions under 4 kinds of traffic behaviors and the magnitude of traffic flow:
In formula, u represents the predicted value of straight left number of collisions, T
iand L
irepresent respectively the magnitude of traffic flow that 4 kinds of subtends under traffic behavior are kept straight on and turned left, C
tiand C
lirepresent respectively the 4 kinds of traffic capacity of subtend craspedodrome direction entrance driveway and traffic capacitys of left-hand rotation direction entrance driveway under traffic behavior, wherein i=1,2,3,4;
Step 3: the predicted value of obtaining straight left number of collisions:
In described conflict prediction model by the traffic flow data substitution of the magnitude of traffic flow of left-hand rotation direction and subtend craspedodrome direction corresponding to 4 kinds of traffic behaviors, obtain the predicted value u of straight left number of collisions.
The traffic capacity of the present invention refers to the maximum vehicle number that in the unit interval, a certain section of road passes through.For a certain road in specific crossing, the traffic capacity of the traffic capacity of its left-hand rotation direction entrance driveway and subtend craspedodrome direction entrance driveway is fixed, therefore the formula in step 2 is actually classification and has enumerated all possible 4 kinds of situations, for certain road, at a certain specific crossing, the magnitude of traffic flow of same time section statistics is one of them formula of correspondence only.
Preferably, described a period of time is 15 minutes.
Preferably, in described step 1, utilize the method for playback video recording, taking 15 minutes magnitudes of traffic flow as the each entrance driveway in time interval statistics Permissive Left-Turn crossing.
Beneficial effect: the acquisition methods of the straight left number of collisions of signalized intersection based on traffic conflict forecast model that the present invention proposes, gather the magnitude of traffic flow of the craspedodrome of Permissive Left-Turn signalized intersections subtend and left-hand rotation direction, determine affiliated traffic behavior classification according to the geometrical property of crossing and the magnitude of traffic flow, by in the magnitude of traffic flow substitution generalized linear conflict prediction model of subtend craspedodrome and left-hand rotation direction, calculate the predicted value of straight left number of collisions.Advantage of the present invention is to excavate conflict mechanism of production, obey generalized linear conflict prediction model, utilize the traffic flow parameter easily obtaining to predict traffic conflict number (being aforementioned straight left number of collisions), overcome observation personnel in existing the traffic conflict technique and gathered large and high defect and the deficiency of cost of the difficulty of conflict on the spot.The present invention is adopting the traffic conflict technique to carry out having actual engineering application value aspect indirect Evaluation of Traffic Safety.
Brief description of the drawings
Fig. 1 is the schematic diagram of the collision detection grid of the embodiment of the present invention;
The modeling process flow diagram of Fig. 2 generalized linear conflict prediction model;
Fig. 3 is process flow diagram of the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, further illustrate the present invention, should understand these embodiment is only not used in and limits the scope of the invention for the present invention is described, after having read the present invention, those skilled in the art all fall within the application's claims limited range to the amendment of the various equivalent form of values of the present invention.
The acquisition methods of the straight left number of collisions of signalized intersection based on conflict prediction model, straight left conflict and traffic flow parameter to multiple Permissive Left-Turns crossing are carried out field observation, utilize Poisson and negative binomial generalized linear model, divide four kinds of traffic circulation situations according to the traffic state of saturation of subtend craspedodrome and the conflict wagon flow of turning left, because the driving behavior under every kind of state there are differences, therefore the present invention has set up the straight left conflict prediction model (being aforementioned generalized linear conflict prediction model) under four kinds of traffic circulation states.The traffic flow parameter of crossing can be brought into thus in corresponding straight left conflict prediction model, thus the straight left conflict of crossing in Obtaining Accurate a period of time interval.The modeling flow process of generalized linear conflict prediction model can be with reference to figure 2.
The first step, the traffic conflict of collection and identification Permissive Left-Turn crossing.The research range that the present invention chooses is within the scope of 30 meters of intersection parking line upstreams, and 4 lift video camera is made a video recording and covered whole survey region by high-altitude.Observe the subjective identification difference of conflict for reducing observer, in the time processing the video gathering on the spot, adopt the method that loads grid in Video processing software VideoStudio, the occurrence positions that record often collides, conflict participant is apart from the distance (d1 and d2) of conflict point and conflict participant's travel speed (V1 and V2), calculate the conflict time (TTC) with conflict distance divided by conflict speed, the grid system that conflict gathers has been realized the Measurement accuracy of conflict time (TTC), the present invention is using the standard of conflict time (TTC) as conflict identification, reduce the subjectivity of conflict identification.The grid system that conflict gathers can be with reference to figure 1.The present invention's 15 taking Kunming 20 of Permissive Left-Turn crossings entrance driveway straight left conflict of 125 hours and conflict flow thereof are the data basis of model.
Second step, set up conflict prediction model for the modal straight left conflict in Permissive Left-Turn crossing, by carrying out the statistical study of data as the flow process of Fig. 2, first the distribution of straight left number of collisions in different time interval 5min, 15min, 30min is studied, because distribution and the negative binomial matching of straight left conflict in 15min are better, therefore adopt the straight left number of collisions of collecting in 15min and the flow that conflicts thereof (be the magnitude of traffic flow of aforementioned subtend craspedodrome and left-hand rotation direction, be called for short " flow ") and crossing geometrical property to carry out the foundation of generalized linear model.Secondly determine the traffic capacity in the conflict direction 15min time interval according to the geometrical property of crossing, obtain with the ratio calculation of the traffic capacity saturation degree of direction of conflicting with the conflict flow in 15min, and calculate that subtend is kept straight on and the mean value of left-hand rotation Direction saturation degree is
determine 4 kinds of traffic circulation states based on saturation degree mean value, definition status 1 is vc
t>0.48, vc
l>0.38; Definition status 2 is vc
t<0.48, vc
l<0.38; Defining classification 3 is vc
t>0.48, vc
l<0.38; Defining classification 4 is vc
t<0.48, vc
l>0.38.
Because the operation conditions of the traffic flow that conflicts in 4 kinds of classification situations is identical, push away to such an extent that the driving behavior that occurs of impact conflict is approximate identical in 4 kinds of situations, utilize generalized linear model to set up the straight left conflict prediction model corresponding to every kind of classification.Generalized linear model is the popularization of general normal linear model, and in classical linear model, dependent variable must be normal distribution, and in generalized linear model, the occurrence frequency of dependent variable need only be obeyed Poisson or negative binomial distribution, more meets the pests occurrence rule of traffic conflict.In generalized linear model, the nonlinear relationship meeting between dependent variable and the explanatory variable of Poisson or negative binomial distribution can be converted into linear relationship by Copula, can explain that in a period of time interval, the nonlinear relationship between expectation value and its influence factor occurs in conflict.
Utilize SAS9.1 software to carry out the matching of generalized linear model to the straight left traffic conflict number of recording a video in 4 kinds of classification situations that extract and conflict flow and crossing geometric properties, software Output rusults is the Copula that straight left number of collisions meets Poisson or negative binomial distribution, i.e. relation between the expectation value of straight left conflict and its influence factor.SAS9.1 software is the business mathematics analysis software that American SAS research company limited produces, and is the large-scale integrated information system for decision support, and function of statistic analysis is important component part and the Core Feature of SAS software.SAS is combined by multiple functional modules, utilization of the present invention Genmod module wherein realizes the generalized linear model matching of straight left traffic conflict number and its influence factor, fitting result shows, in 4 kinds of conflict flow saturation degree classification situations, the logarithm value significant correlation of the expectation value of number of collisions and the flow that conflicts, T
i, L
i(i=1,2,3,4) represent respectively the magnitude of traffic flow that the subtend of 4 kinds of traffic behaviors is kept straight on and turned left, and the expectation value of the number of collisions under 4 kinds of traffic behaviors is shown below with the relational expression of the flow that conflicts.
The 3rd step, the measured data of organizing by inspection is carried out the precision test of conflict prediction model, and the number of collisions deviation of the number of collisions of model prediction and actual observation is less, proves applicability and the validity of model.Choose 10 of crossings, 5, the Nanjing entrance driveway straight left number of collisions of 42 hours and conflict data on flows carry out the checking of model.According to criteria for classification before, determine the affiliated classification of the conflict data on flows of straight left number of collisions and correspondence, conflict flow is brought in corresponding model formation, calculate to obtain the straight left number of collisions expectation value in 15min interval, the straight left number of collisions of itself and actual observation is compared, the difference of predicted value and measured value is very little, thereby has proved the validity of forecast model and the ubiquity of application.
The 4th step, based on the straight left conflict prediction model of generalized linear, obtains the straight left number of collisions of crossing by the magnitude of traffic flow substitution model of subtend craspedodrome and left-hand rotation direction.Invention is as shown in Figure 3 used process flow diagram, gather the conflict flow of certain crossing subtend craspedodrome and left-hand rotation direction by the time interval of 15min, the traffic circulation situation of judgement conflict direction, conflict flow is brought in the straight left conflict prediction modular form of corresponding generalized linear, and prediction obtains the straight left number of collisions of crossing.
Embodiment:
The present invention contacts traffic circulation state and the driving behavior of conflict direction, set up the generalized linear forecast model of straight left conflict for 4 kinds of different traffic circulation states, proposed a kind of acquisition methods of the straight left number of collisions of signalized intersection based on traffic conflict forecast model.Straight left conflict prediction model in 4 kinds of classification situations that the present invention sets up, taking a large amount of data as basis, proves that through the data detection of inspection group straight left conflict prediction model has higher precision of prediction.Saturation degree is the common counter of reflected signal crossing operation conditions, and in actual engineering application, saturation degree is easy to calculate, and sets it as the standard symbol of classification and the feature that straight left conflict pests occurrence rule there are differences under different ruuning situation.
The use simple and fast of model, embodiment utilizes the relatively safety case of two Permissive Left-Turn crossings of the indirect safe evaluation method of traffic conflict, and Fig. 3 is process flow diagram of the present invention.As shown in table 1, crossing 1 Permissive Left-Turn direction and the subtend craspedodrome direction traffic capacity of 15 minutes are respectively 88 and 112; Crossing 2 Permissive Left-Turn directions and the subtend craspedodrome direction traffic capacity of 15 minutes are distributed as 47 and 115.The traffic flow data that gathers two crossings in 9:00 ~ 10:00 time period, the acquisition method of traffic flow data is a lot, as artificial acquisition method, Floating Car method and mechanical count method.Traffic flow data, the saturation computation value of crossing 1 and crossing 2 are as shown in table 1.Determine the traffic behavior under traffic flow by saturation degree, bring conflict data on flows into corresponding straight left conflict prediction model, see aforesaid formula, calculate the predicted value (round) of straight left number of collisions, as shown in rightmost one row of table 1.
The predicted value of flow, saturation data and the straight left number of collisions at the 15 minutes intervals of table 1 embodiment
Quick and precisely obtaining adopting the traffic conflict technique to carry out Evaluation of Traffic Safety of straight left number of collisions is most important, the acquisition methods of the straight left number of collisions of signalized intersection based on traffic conflict forecast model, the generalized linear forecast model of the prediction straight left conflict in Permissive Left-Turn crossing is provided, can have obtained the predicted value of straight left number of collisions by conflict flow being brought into straight left conflict prediction model under corresponding traffic state classification.As shown in the Examples, because the predicted value of the straight left number of collisions that in same time section, crossing 1 occurs is greater than crossing 2, therefore the security level of crossing 1 is lower than crossing 2.The lower reason of security level of crossing 1 is that the saturation degree of its left-hand rotation direction and craspedodrome direction is all higher; the driver of left-hand rotation direction is owing to waiting for and losing patience for a long time; selection forces straightgoing vehicle to take the little gap of hedging behavior to pass through crossing; cause more straight left conflict; suggestion changes the Permissive Left-Turn of crossing 1 into protectiveness; to reduce the straight left conflict of crossing, improve the security level of crossing 1.Permissive Left-Turn in the present invention refers to turn left that direction and subtend craspedodrome direction let pass simultaneously, and protectiveness turns left to refer to turn left the clearance of staggering of direction and subtend craspedodrome direction.
Claims (3)
1. a Forecasting Methodology for the straight left number of collisions of signalized intersection, comprises the steps:
Step 1: gather traffic flow data and judge traffic behavior: add up the magnitude of traffic flow of the each entrance driveway in Permissive Left-Turn crossing, represent the magnitude of traffic flow of subtend craspedodrome with T, represent the magnitude of traffic flow of left-hand rotation with L, determine respectively the traffic capacity C of left-hand rotation direction entrance driveway
ltraffic capacity C with subtend craspedodrome direction entrance driveway
t, and calculate respectively the traffic saturation degree index vc of left-hand rotation direction
l=L/C
ltraffic saturation degree index vc with subtend craspedodrome direction
t=T/C
t;
Step 2: adopt straight left number of collisions and the magnitude of traffic flow of collecting in a period of time, respectively with vc
l=0.38 and vc
t=0.48 is the state cut off value of the traffic saturation degree index of left-hand rotation direction and the traffic saturation degree index of subtend craspedodrome direction, obtains the conflict prediction model of 4 kinds of straight left number of collisions under traffic behavior:
In formula, u represents the predicted value of straight left number of collisions, T
iand L
irepresent respectively the magnitude of traffic flow that 4 kinds of subtends under traffic behavior are kept straight on and turned left, C
tiand C
lirepresent respectively the 4 kinds of traffic capacity of subtend craspedodrome direction entrance driveway and traffic capacitys of left-hand rotation direction entrance driveway under traffic behavior, wherein i=1,2,3,4;
Step 3: the predicted value of obtaining straight left number of collisions:
In described conflict prediction model by the traffic flow data substitution of the magnitude of traffic flow of left-hand rotation direction and subtend craspedodrome direction corresponding to 4 kinds of traffic behaviors, obtain the predicted value u of straight left number of collisions.
2. the Forecasting Methodology of the straight left number of collisions of signalized intersection according to claim 1, is characterized in that: described a period of time is 15 minutes.
3. the Forecasting Methodology of the straight left number of collisions of signalized intersection according to claim 1, is characterized in that: in described step 1, utilize the method for playback video recording, the magnitude of traffic flow of adding up the each entrance driveway in Permissive Left-Turn crossing for the time interval taking 15 minutes.
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| CN108711288A (en) * | 2018-06-07 | 2018-10-26 | 郑州大学 | Joint intersection non-motor vehicle, which is turned right, is connected the method for quantitatively evaluating of safety problem |
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| CN106205172B (en) * | 2016-09-07 | 2018-09-21 | 东南大学 | Unsignalized intersection conflict resolution method and system |
| CN106504527B (en) * | 2016-10-19 | 2018-12-28 | 东南大学 | A kind of signalized intersections directly left conflict and its impact analysis method |
| CN110136437B (en) * | 2019-05-14 | 2021-03-19 | 青岛海信网络科技股份有限公司 | Method and device for determining left-right interference problem of intersection entrance lane |
| CN110232822B (en) * | 2019-06-24 | 2022-03-08 | 上海理工大学 | Plane intersection traffic order evaluation parameter solving method based on track data |
| CN110930700A (en) * | 2019-11-21 | 2020-03-27 | 南通大学 | Method for building traffic conflict prediction model based on normal distribution theory |
| CN115273467B (en) * | 2022-07-15 | 2023-07-04 | 南京莱斯信息技术股份有限公司 | Method for identifying traffic flow interaction event based on multi-region detection data |
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| CN108711288A (en) * | 2018-06-07 | 2018-10-26 | 郑州大学 | Joint intersection non-motor vehicle, which is turned right, is connected the method for quantitatively evaluating of safety problem |
| CN108711288B (en) * | 2018-06-07 | 2020-11-20 | 郑州大学 | Quantitative evaluation method for right turn connection safety problem of non-motor vehicles at connection intersection |
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