CN103093623A - 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 the 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 the 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 usually.Yet in the engineering practice of reality, the traffic hazard data that satisfy accident micro-analysis demand are difficult to obtain, and particularly there are certain defective in the accuracy in the existing casualty data of China storehouse and publicity aspect.Therefore, the Evaluation of Traffic Safety system of Chinese scholar proposition 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 present domestic and international application.
Traffic conflict refers between different traffic participants to have produced mutual interference mutually on time and space, and forces traffic participant to take a kind of traffic behavior of the behavior of dodging.The traffic conflict technique with field observation to traffic conflict come evaluation path Safety of Underground-Transportation Facilities situation as the traffic hazard Substitute Indexes, have data volume large, the advantage such as evaluation cycle is short.The 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 that gathers due to conflict is large, makes the application in practice of the traffic conflict technique in traffic engineering be subject to certain restriction.Be head it off, Bureau of Public Road had issued emulation conflict analysis software SSAM in 2008, and this software carries out identification and the classification of emulation conflict take the vehicle operating trail file of Microscopic Traffic Simulation Mathematic Model output as research object.Yet 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: for the problem and shortage of above-mentioned prior art existence, the Forecasting Methodology that the purpose of this invention is to provide the straight left number of collisions of a kind of signalized intersection, on the basis that obtains a large amount of traffic conflicts 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 can't obtain according to the conflict that collects 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 judgement traffic behavior: the magnitude of traffic flow of each entrance driveway of statistics Permissive Left-Turn crossing, represent with T the magnitude of traffic flow that subtend is kept straight on, represent the magnitude of traffic flow of turning left 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 the traffic saturation degree index of left-hand rotation direction and 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 traffic capacity of 4 kinds of subtend craspedodrome direction entrance driveway under traffic behavior and the traffic capacity of left-hand rotation direction entrance driveway, i=1 wherein, 2,3,4;
Step 3: the predicted value of obtaining straight left number of collisions:
With the traffic flow data substitution of the magnitude of traffic flow of left-hand rotation direction and subtend craspedodrome direction corresponding to obtaining the predicted value u of straight left number of collisions in the described conflict prediction model of 4 kinds of traffic behaviors.
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, at a certain specific crossing, the magnitude of traffic flow of same time section statistics is one of them formula of correspondence only for certain road.
Preferably, described a period of time is 15 minutes.
Preferably, in described step 1, utilize the method for playback video recording, the magnitude of traffic flow of adding up each entrance driveway of Permissive Left-Turn crossing for the time interval take 15 minutes.
Beneficial effect: the acquisition methods based on the straight left number of collisions of signalized intersection of 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 geometrical property and the magnitude of traffic flow of crossing, in magnitude of traffic flow substitution generalized linear conflict prediction model with 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 the conflict mechanism of production, obey generalized linear conflict prediction model, utilize the traffic flow parameter that easily obtains that traffic conflict number (being aforementioned straight left number of collisions) is predicted, overcome observation personnel in existing the traffic conflict technique and gathered large and high defective 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.
Description of 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 only is used for explanation the present invention and is not used in and limits the scope of the invention, after having read the present invention, those skilled in the art all fall within the application's claims limited range to the modification of the various equivalent form of values of the present invention.
Acquisition methods based on the straight left number of collisions of signalized intersection of conflict prediction model, straight left conflict and traffic flow parameter to a plurality of Permissive Left-Turns crossings 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, so 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 gathers and the traffic conflict of identification Permissive Left-Turn crossing.The research range that the present invention chooses is in the scope of intersection parking line 30 meters of upstreams, and 4 lift video camera makes a video recording by the high-altitude and cover whole survey region.observe the subjective identification difference of conflict for reducing the observer, when processing the video that gathers on the spot, employing loads the method for grid in Video processing software VideoStudio, the occurrence positions that record often collides, the 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 the 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 with the standard of conflict time (TTC) as the conflict identification, reduced the subjectivity of conflict identification.The grid system that conflict gathers can be with reference to figure 1.15 take Kunming Permissive Left-Turn 20 of crossings entrance driveway straight left conflicts of 125 hours of the present invention and conflict flow thereof are the data basis of model.
Second step, set up the conflict prediction model for the modal straight left conflict in Permissive Left-Turn crossing, by carry out the statistical study of data as the flow process of Fig. 2, at first the distribution of straight left number of collisions in different time interval 5min, 15min, 30min is studied, because distribution and the negative binomial match 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, with the conflict flow in 15min and the ratio calculation of the traffic capacity saturation degree of direction that obtains conflicting, 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
T0.48, vc
L0.38; Definition status 2 is vc
T＜0.48, vc
L＜0.38; Defining classification 3 is vc
T0.48, vc
L＜0.38; Defining classification 4 is vc
T＜0.48, vc
L0.38.
Because the operation conditions of conflict traffic flow 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 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, can be converted into linear relationship with meeting the dependent variable of Poisson or negative binomial distribution and the nonlinear relationship between explanatory variable by Copula, can explain the nonlinear relationship of conflicting in a period of time interval between generation expectation value and its influence factor.
Utilize straight left traffic conflict number in 4 kinds of classification situations that SAS9.1 software extracts video recording and conflict flow and crossing geometric properties to carry out the match of generalized linear model, the 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 a plurality of functional modules, utilization of the present invention Genmod module wherein realizes the generalized linear model match 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 goes on foot, and carries out the precision test of conflict prediction model by the measured data of check group, 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 the 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, the conflict flow is brought in corresponding model formation, calculate to get the straight left number of collisions expectation value in the 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.
In the 4th step, based on the straight left conflict prediction model of generalized linear, subtend is kept straight on and the magnitude of traffic flow substitution model of left-hand rotation direction obtains the straight left number of collisions of crossing.Process flow diagram is used in invention as shown in Figure 3, gather by time interval of 15min that certain crossing subtend is kept straight on and the conflict flow of left-hand rotation direction, the traffic circulation situation of judgement conflict direction, the conflict flow is brought in the corresponding straight left conflict prediction modular form of 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 the traffic conflict forecast model.Straight left conflict prediction model in 4 kinds of classification situations that the present invention sets up has higher precision of prediction take a large amount of data as the basis through the straight left conflict prediction model of the data detection of check group proof.Saturation degree is the common counter of reflected signal crossing operation conditions, and saturation degree is easy to calculate in the engineering of reality is used, with its characteristics that there are differences under different ruuning situation as standard symbol and the straight left conflict pests occurrence rule of classification.
The use simple and fast of model, embodiment utilize 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.Gather the traffic flow data of 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.The traffic flow data of crossing 1 and crossing 2, saturation computation are worth as shown in table 1.Determine traffic behavior under traffic flow by saturation degree, the data on flows of conflicting is brought corresponding straight left conflict prediction model into, sees aforesaid formula, calculates 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, acquisition methods based on the straight left number of collisions of signalized intersection of traffic conflict forecast model, the generalized linear forecast model of the prediction straight left conflict in Permissive Left-Turn crossing is provided, has brought by the flow that will conflict the predicted value that straight left conflict prediction model under corresponding traffic state classification can obtain straight left number of collisions into.As shown in the Examples, the predicted value of the straight left number of collisions that occurs due to crossing 1 in the same time section is greater than crossing 2, so the security level of crossing 1 is lower than crossing 2.The reason that the security level of crossing 1 is lower is that the saturation degree of its left-hand rotation direction and craspedodrome direction is all higher; the driver of left-hand rotation direction loses patience owing to waiting for for a long time; selection forces straightgoing vehicle to take the little gap of hedging behavior to pass through the 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. the Forecasting Methodology of the straight left number of collisions of signalized intersection, comprise the steps:
Step 1: gather traffic flow data and judgement traffic behavior: the magnitude of traffic flow of each entrance driveway of statistics Permissive Left-Turn crossing, represent with T the magnitude of traffic flow that subtend is kept straight on, represent the magnitude of traffic flow of turning left 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 the traffic saturation degree index of left-hand rotation direction and subtend craspedodrome direction obtains straight left number of collisions under 4 kinds of traffic behaviors and the conflict prediction model of 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 traffic capacity of 4 kinds of subtend craspedodrome direction entrance driveway under traffic behavior and the traffic capacity of left-hand rotation direction entrance driveway, i=1 wherein, 2,3,4;
Step 3: the predicted value of obtaining straight left number of collisions:
With the traffic flow data substitution of the magnitude of traffic flow of left-hand rotation direction and subtend craspedodrome direction corresponding to obtaining the predicted value u of straight left number of collisions in the described conflict prediction model of 4 kinds of traffic behaviors.
2. the Forecasting Methodology of the straight left number of collisions of signalized intersection according to claim 1, it 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 each entrance driveway of Permissive Left-Turn crossing for the time interval take 15 minutes.
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| CN110930700A (en) * | 2019-11-21 | 2020-03-27 | 南通大学 | Method for building traffic conflict prediction model based on normal distribution theory |
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| CN106205172A (en) * | 2016-09-07 | 2016-12-07 | 东南大学 | Unsignalized intersection conflict resolution method and system |
| CN106504527A (en) * | 2016-10-19 | 2017-03-15 | 东南大学 | A kind of signalized intersections directly left conflict and its impact analysis method |
| 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 |
| CN110136437A (en) * | 2019-05-14 | 2019-08-16 | 青岛海信网络科技股份有限公司 | A kind of determination method and device of the left straight interference problem in crossing inlet road |
| 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 |
| CN110232822A (en) * | 2019-06-24 | 2019-09-13 | 上海理工大学 | Intersection accidents order evaluation parameter method for solving 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 |
| CN115273467A (en) * | 2022-07-15 | 2022-11-01 | 南京莱斯信息技术股份有限公司 | Traffic flow mutual influence event identification method based on multi-region detection data |
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