CN115691123A - Method, device and equipment for characterizing and evaluating traffic risks of urban road intersections - Google Patents
Method, device and equipment for characterizing and evaluating traffic risks of urban road intersections Download PDFInfo
- Publication number
- CN115691123A CN115691123A CN202211266983.4A CN202211266983A CN115691123A CN 115691123 A CN115691123 A CN 115691123A CN 202211266983 A CN202211266983 A CN 202211266983A CN 115691123 A CN115691123 A CN 115691123A
- Authority
- CN
- China
- Prior art keywords
- traffic
- risk
- intersection
- conflict
- diffusion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention provides a method, a device and equipment for characterizing and evaluating traffic risks of urban road intersections. Constructing a traffic risk index by combining traffic conflict frequency and conflict severity; introducing a smoke plume diffusion model to simulate the propagation diffusion process of the traffic risk of the conflict point to the neighborhood, and representing the risk distribution of the whole area of the intersection; based on the intersection risk distribution, a multi-dimensional hierarchical evaluation system of the intersection fine traffic risk is implemented, and the system mainly comprises an integral risk based on an area, a path traffic risk based on a single motion track and a traffic mode risk based on a plurality of tracks. According to the intersection global traffic risk quantification model and the multi-dimensional hierarchical evaluation system, risk evaluation under a single-path or group traffic mode is enabled through a risk field simulation method, and an important support effect is expected to be played for urban risk analysis in the future, so that technical support is provided for development and construction of intelligent automobiles, intelligent traffic, safe traveling and novel intelligent cities.
Description
Technical Field
The invention relates to the technical field of traffic risk assessment, in particular to a method, a device and equipment for representing and assessing traffic risks of urban road intersections.
Background
Due to the complex space-time dynamics and multi-mode characteristics of the urban road intersection, a large amount of traffic conflicts and traffic risks are involved. Road intersection traffic accidents account for approximately 20-45% of all reported traffic accidents in developed and developing countries, such as the united states, norway, japan, singapore, and thailand (golemmewsk et al, 2011 ma et al, 2021 tay et al, 2017 elvik et al, 2005. The assessment of traffic risk at urban road intersections is always a main means for identifying potential safety risk (Essa et al, 2018) of intersection design, and is very important for improving traffic conditions and guaranteeing personal safety of road users (nopan Kronprasert et al, 2021).
In the last decades, intersection traffic risk assessment has been the focus of research within the field of traffic risk assessment. (Chen et al, 2020) tracking vehicles in the intersection area by computer vision technology, and calculating the position and severity of potential collision points in the area by using TTC indexes; (Wei et al, 2019) proposes a three-dimensional cube search algorithm that discriminates traffic conflicts and conflict severity by detecting proximity between trajectories; (vitriolastarita et al, 2019) to calculate a risk value for a traffic conflict by evaluating the severity of the resulting potential collision according to accurate collision dynamics variables; (Shi et al, 2018) proposed the concept of a Key Risk Indicator (KRI) using a hybrid indicator to assess risk exposure. Developing KPI expressions to evaluate intersection traffic risk severity and likelihood based on time-integrated time to collision (TIT) and accident likelihood index (CPI) that can identify pre-accident traffic risk; (Guo et al, 2016) assessed the safety impact of signalized intersection unconventional outer left turn lanes; (Justice et al, 2020) investigated how the signalized intersection left turn collision risk varies with changing traffic conditions. The above method only focuses on the traffic risk at the conflict point. After the actual intersection scene generates traffic conflicts, the traffic of road users within a certain range of the conflict point is affected, and the traffic risk is increased. In short, the influence of traffic conflict points can be spread to the periphery, and the positions of non-conflict points also have certain risks, but the existing method cannot support the risk calculation of any positions. Meanwhile, the risk quantitative evaluation at the intersection is used for guiding the visual angle analysis of the travel of road users, and the evaluation angle has three levels of intersection level, mode level and path level. However, the above methods cannot give full consideration;
therefore, it is a technical problem to be solved urgently to realize the traffic risk assessment at any position and the traffic risk assessment at an intersection level, a mode level and a path level.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for revealing crossing space-time dynamic risk distribution and providing quantitative reference for risk decision by establishing a multi-dimensional risk quantification model, and a corresponding device and electronic equipment.
According to a first aspect of the invention, the invention provides a traffic risk characterization and evaluation method for an urban road intersection, which comprises the following steps:
classifying the tracks of the moving objects in the intersection by using a track classification method of dynamic time normalization, and extracting a traffic mode;
whether two vehicles have traffic conflict or not is measured by using the collision time as a key index so as to identify track points, namely conflict points, where the traffic conflict occurs;
detecting the traffic mode to which the conflict points belong, classifying the conflict points according to the traffic mode to which the conflict points belong, and enabling different traffic modes to act on the initial values of different risk diffusion to the risk diffusion simulation of a single object;
fusing the traffic conflict frequency and the severity to construct a traffic risk index R so as to quantify the traffic risk of the conflict point;
constructing a traffic risk diffusion model according with a traffic motion mechanism by combining a Gaussian plume diffusion model;
taking the conflict point as a diffusion source, and performing traffic risk diffusion treatment according to a traffic risk diffusion model to obtain intersection traffic risk distribution;
calculating the diffusion area of the traffic risk inside the intersection so as to evaluate the traffic risk level of the intersection level;
calculating a traffic risk change curve of a traffic track in the intersection by combining the traffic risk distribution of the intersection, and evaluating the traffic risk level of the route grade by adopting the integral of the traffic risk change curve;
and calculating the average value of the traffic risk levels of all the routes in the traffic mode by combining the traffic risk levels of the route grades, so as to evaluate the traffic risk level of the traffic mode grade.
Preferably, the step of classifying the trajectory of the moving object in the intersection by using the trajectory classification method using dynamic time warping and extracting the passing mode includes:
and calculating the similarity between the passing tracks in the intersection by using dynamic time regression, setting a proper threshold value to classify the tracks, wherein the classified result meets the effect of distinguishing the passing modes in the intersection, and extracts the types of the passing modes in the intersection.
Preferably, the step of using the collision time as a key index to measure whether two vehicles have a traffic collision or not so as to identify track points with traffic collision includes:
and (4) setting a collision time threshold value by calculating collision time of all track points in the track, wherein the track points with the collision time higher than the threshold value are conflict points, and screening the conflict points.
Preferably, the step of fusing the traffic conflict frequency and severity to construct a traffic risk index R to quantify the traffic risk of the conflict point comprises:
constructing a risk index R by means of the traffic conflict frequency Sum and the severity SI in the traffic conflict technical idea;
calculating the traffic risk of various conflict points according to the risk index R, and constructing a formula as follows:
R i ＝0.5×Sum i +0.5×SI i
in the formula, R i Representing a type i conflict point traffic risk value; sum i Representing the conflict point traffic conflict frequency of the type i; SI (Standard interface) i Indicating the type i conflict point traffic conflict severity.
Preferably, the step of constructing a traffic risk diffusion model conforming to a traffic motion mechanism by combining the gaussian smoke plume diffusion model includes:
according to the Gaussian smoke plume diffusion model, the risk diffusion ratio is used as a pollutant, and the traffic risk diffusion process is simulated by the process that the pollutant diffuses to the periphery;
taking the traffic risk index R as a diffusion index, taking the main diffusion direction as the motion speed direction, taking a 180-degree angle interval of the speed direction as a diffusion interval, and constructing a traffic risk diffusion model as follows:
wherein c (x, y) represents a traffic risk value at position (x, y); delta represents the diffusion source risk intensity; μ represents the velocity of the source subject vehicle; sigma y The diffusion coefficient is indicated.
Preferably, the step of calculating the diffusion area rate of traffic risk inside the intersection comprises:
and (3) accumulating the traffic risk areas by taking the risk value P as a threshold, taking the area larger than or equal to P as a traffic risk area and the area smaller than P as a safety area, and calculating the area to obtain the traffic risk diffusion area inside the intersection, wherein the calculation formula is as follows:
S risk ＝∑s j if s j.risk ≥P
in the formula, S risk Representing the traffic risk diffusion area inside the intersection; s j Represents the area of region j; s j.risk A traffic risk value representing region j; p represents a traffic risk threshold.
Preferably, the step of calculating a traffic risk variation curve of a traffic track in the intersection by combining the traffic risk distribution at the intersection and evaluating the traffic risk level at the route level by using the integral of the traffic risk variation curve includes:
calculating a traffic risk change curve of a traffic track in the intersection according to traffic risk distribution of the intersection, and evaluating the traffic risk level of a single object passing through the intersection by adopting traffic risk integration along a path, wherein the calculation formula is as follows:
in the formula, RL l The traffic risk of the trajectory i is represented,
represents the sum of all track traffic risks in the track l, t represents the transit time of the track l, and alpha is an adjustment coefficient.
Preferably, the step of calculating the average value of the traffic risk levels of all the routes in the traffic pattern in combination with the traffic risk level of the route level to evaluate the traffic risk level of the traffic pattern level comprises:
the traffic risk of the traffic mode or a certain turning mode is evaluated by adopting the average value of the traffic risk levels of the same type of traffic modes including the tracks, and the calculation formula is as follows:
in the formula, RM m Representing the traffic risk of the transit mode m,
representing the sum of all the trajectory traffic risks in mode m.
According to a second aspect of the present invention, the present invention provides a traffic risk characterization and assessment device for an urban road intersection, comprising the following modules:
the track classification module is used for classifying the tracks of the moving objects in the intersection by using a track classification method of dynamic time normalization and extracting a passing mode;
the conflict point identification module is used for measuring whether two vehicles conflict with each other by using the collision time as a key index so as to identify track points, namely conflict points, where the traffic conflicts occur;
the conflict point classification module is used for detecting the traffic mode to which the conflict point belongs and classifying the conflict point according to the traffic mode to which the conflict point belongs, and different traffic modes apply different initial values of risk diffusion to the risk diffusion simulation of a single object;
the risk index construction module is used for fusing the traffic conflict frequency and the severity degree and constructing a traffic risk index R so as to quantify the traffic risk of the conflict point;
the risk diffusion model building module is used for building a traffic risk diffusion model according with a traffic motion mechanism by combining the Gaussian plume diffusion model;
the risk diffusion processing module is used for performing traffic risk diffusion processing by taking the conflict point as a diffusion source according to the traffic risk diffusion model to obtain intersection traffic risk distribution;
the intersection level risk evaluation module is used for calculating the diffusion area of the traffic risk inside the intersection so as to evaluate the traffic risk level of the intersection;
the path level risk evaluation module is used for calculating a traffic risk change curve of a traffic track in the intersection by combining intersection traffic risk distribution and evaluating the traffic risk level of the path level by adopting the integral of the traffic risk change curve;
and the mode level risk evaluation module is used for calculating the mean value of the traffic risk levels of all the paths in the traffic mode by combining the traffic risk levels of the path levels so as to evaluate the traffic risk level of the traffic mode level.
According to a third aspect of the present invention, the present invention provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps of the method for characterizing and evaluating traffic risk at an urban intersection when executing the program.
The technical scheme provided by the invention has the following beneficial effects:
a smoke plume diffusion model is introduced to simulate the propagation diffusion process of the traffic risk of the conflict point to the neighborhood, represent the risk distribution of the whole domain of the intersection and serve the requirements of high concurrency, low delay, whole domain traffic and high-precision real-time digital mapping of all elements. Based on the intersection risk distribution, a multi-dimensional hierarchical evaluation system of the intersection fine traffic risk is implemented, and the system mainly comprises an integral risk based on an area, a path traffic risk based on a single motion track and a traffic mode risk based on a plurality of tracks. According to the intersection global traffic risk quantification model and the multi-dimensional hierarchical evaluation system constructed by the invention, risk evaluation under a single-path or group traffic mode is enabled by a risk field simulation method, and an important support effect is expected to be played for urban risk analysis in the future, so that technical support is provided for intelligent automobiles, intelligent traffic, safe traveling and development and construction of novel intelligent cities in China.
Drawings
The invention will be further described with reference to the following drawings and examples, wherein:
FIG. 1 is a flow chart of a method for characterizing and evaluating traffic risks at an urban road intersection according to the present invention;
FIG. 2 is a map of a cross road between a guan shan road and a Xin Yu road in Wuhan City;
FIG. 3 is a diagram of the trajectory classification results of the present invention;
FIG. 4 is a diagram of the distribution of the original track points and traffic conflict points of the moving objects in the crossroads between the Guanshan avenues and the New Yu-Lu in Wuhan;
FIG. 5 is a twelve-period traffic risk distribution diagram after risk diffusion at the intersection between the guan-shan avenue and the new jade way in Wuhan City according to the present invention;
FIG. 6 is a graph of the cross road traffic risk spread area ratio of the guan-shan avenue and the Xin Yu-Lu in Wuhan;
FIG. 7 is a risk distribution curve of the crossing traffic trajectory of the guan-shan road and the Xin Yu road in Wuhan City according to the present invention; RL,
Mean and t are respectively a path traffic risk index, a traffic risk integral of each track point of the path, an average traffic risk and transit time of each track point of the path;
FIG. 8 is a traffic risk distribution diagram of each traffic mode at the intersection of the guan-shan road and the new jade road in Wuhan City according to the present invention;
FIG. 9 is a structural diagram of an urban road intersection traffic risk characterization and assessment device according to the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
Referring to fig. 1, the present invention provides a method for characterizing and evaluating traffic risks at an urban road intersection, comprising the following steps:
step S1: classifying the track of the moving object in the intersection by adopting a track classification method of Dynamic Time Warping (DTW), and extracting a passing mode; specifically, firstly, calculating the similarity between each passing track inside the intersection by using Dynamic Time Warping (DTW), setting a proper threshold value to classify the tracks, wherein the classification result meets the effect of distinguishing the passing modes inside the intersection, and extracting the types of the passing modes in the intersection;
step S2: whether two vehicles have traffic conflict or not is measured by adopting a key index, namely, time To Collision (TTC), so as to identify track points with traffic conflict;
the conflict point identification means: and (3) setting a collision time threshold value by calculating collision time (TTC) of all track points in the track, wherein the track points with collision time higher than the threshold value are conflict points, and screening the conflict points.
And step S3: detecting the traffic mode of the conflict point, and classifying the conflict point according to the traffic mode;
the conflict point classification means: and detecting the traffic modes of all the conflict points according to the extracted traffic modes in the intersection, and classifying the conflict points according to the traffic modes.
And step S4: constructing a risk index R by means of the traffic conflict frequency and severity in the traffic conflict technical idea so as to quantify the traffic risk of the conflict point;
quantifying the traffic risk at the conflict point refers to: firstly, a risk index R is constructed by means of the traffic conflict frequency Sum and the severity SI in the technical idea of traffic conflict. And then calculating the traffic risks of all conflict points according to the risk index R. Calculating the traffic risks of all conflict points according to the risk index R, and constructing a formula as follows:
R i ＝0.5×Sum i +0.5×SI i
in the formula, R i Representing a type i conflict point traffic risk value; sum i Representing the conflict point traffic conflict frequency of the type i; SI (Standard interface) i Indicating the severity of the conflict point traffic conflict of type i.
Step S5: constructing a traffic risk diffusion model according with a traffic motion mechanism by combining a Gaussian plume diffusion model;
in order to simulate the traffic risk diffusion process, the risk diffusion is regarded as pollutants, and the process of diffusing the pollutants to the surrounding environment is simulated; aiming at the directional characteristic of traffic risk diffusion, introducing a smoke plume diffusion model to simulate the propagation of risks along the movement direction of the vehicle; the smoke plume model utilizes the wind direction in the horizontal direction to drive the diffusion of smoke. We use the directional motion speed of objects along different track paths to simulate the risk spread of traffic conflict points to their neighboring areas. Among these paths, the limited risk diffusion path is referred to as a non-risk diffusion path. The smoke plume model initially uses smoke contaminant concentration as a diffusion indicator. The invention takes the traffic risk value as a diffusion index. The diffusion process of the smoke plume model is carried out in a three-dimensional space. In the present invention, diffusion in the vertical direction is not considered, but is limited to a two-dimensional planar space. The spread direction of the traffic risk is set within a 180 degree angle range that is dominated by the speed direction. In the traffic risk diffusion, the traffic risk index R is used as a diffusion index, the main diffusion direction is the movement speed direction, and a 180-degree angle interval of the speed direction is used as a diffusion interval, so that a traffic risk diffusion model is constructed as follows:
where c (x, y) represents a traffic risk value at location (x, y); delta represents the diffusion source risk intensity; μ represents the velocity of the source subject vehicle; sigma y The diffusion coefficient is indicated.
Then, a spatially continuous risk field is simulated by interpolating the risk values of the conflict points and the key risk values obtained after the risk propagates along the path direction using the smoke plume diffusion model. The risk field is constructed by a set of points and typical interpolation algorithms that overcome the problem of uneven density distribution. Therefore, the risk diffusion phenomenon in the intersection is modeled by using a smoke plume model and a spatial interpolation algorithm, risk distribution characteristics are captured, and a representation form of the intersection risk field is obtained.
Step S6: and taking the conflict points as diffusion sources, and performing traffic risk diffusion treatment according to the traffic risk diffusion model to obtain the traffic risk distribution at the intersection.
Step S7: calculating the diffusion area rate of the traffic risks inside the intersection so as to evaluate the traffic risk level of the intersection level;
the diffusion area ratio is: the risk value P is used as a threshold value, an area larger than or equal to P is used as a traffic risk area, an area smaller than P is used as a safety area, the traffic risk areas are accumulated, the area is calculated to obtain the traffic risk diffusion area inside the intersection, and the calculation formula is as follows:
S risk ＝∑s j if s j.risk ≥P
in the formula, S risk Representing the traffic risk diffusion area inside the intersection; s is j Represents the area of region j; s is j.risk A traffic risk value representing region j; p represents a traffic risk threshold.
Step S8: calculating a traffic risk change curve of a traffic track in the intersection by combining traffic risk distribution of the intersection, and evaluating the traffic risk level of the path grade by adopting the integral of the traffic risk change curve;
the traffic risk assessment based on the path hierarchy is based on a local perspective of a single moving object. During the process of passing through the intersection, a single object is not exposed to traffic risks all the time. Calculating a traffic risk change curve of a traffic track in the intersection according to traffic risk distribution of the intersection, and evaluating the traffic risk level of a single object passing through the intersection by adopting traffic risk integrals along a path, wherein the calculation formula is as follows:
in the formula, RL l The traffic risk of the trajectory i is represented,
represents the sum of all track traffic risks in the track l, t represents the transit time of the track l, and alpha is an adjustment coefficient.
Step S9: and calculating the average value of the traffic risk levels of all the routes in the traffic mode by combining the traffic risk levels of the routes, so as to evaluate the traffic risk level of the traffic mode level.
The set of actual travel paths represents the trajectory selection in a specific traffic mode of the intersection, and the mode-level traffic risk assessment aims to quantitatively assess the risk in the specific traffic mode of the intersection. And evaluating the traffic risk level of the driving track set, and reflecting the traffic risk level of the turning mode. In the invention, the traffic risk of the traffic mode or a certain turning mode is evaluated by adopting the average value of the traffic risk levels of the same type traffic mode containing tracks, and the calculation formula is as follows:
in the formula, RM m Representing the traffic risk of the transit mode m,
representing the sum of all the trajectory traffic risks in mode m.
In order to more clearly illustrate the concept of the present invention, the following description will be made by taking the intersection of the guan-shan road and the Xin Yu-Lu in Wuhan City as an example. Referring to fig. 2, fig. 2 is a map of an intersection between a guan-shan road and a new jade road in wuhan city, in which fig. 2 (a) is a real-scene image of a research area; fig. 2 (b) is a satellite map of the study area, and fig. 2 (c) is a street map of the study area.
The method is used for characterizing and evaluating the traffic risk of the urban road intersection, and comprises the following specific steps:
step S1: and classifying the track of the moving object in the intersection by using a track classification method of Dynamic Time Warping (DTW), and extracting a traffic mode. Specifically, firstly, the similarity between the traffic tracks in the intersection is calculated by using Dynamic Time Warping (DTW), a proper threshold value is set to classify the tracks, the classification result meets the effect of distinguishing the traffic modes in the intersection, and the traffic mode types in the intersection are extracted. As shown in FIG. 3, the results (a) - (o) in FIG. 3 correspond to 15 traffic patterns, respectively. Table 1 shows the number of traces in the 15 traffic patterns.
Table 1: number of tracks per traffic pattern
In table 1, model indicates the pass mode, and TrN indicates the number of tracks.
Step S2: whether two vehicles have traffic conflicts is measured by using a key index, namely, time To Collision (TTC), so as to identify track points with traffic conflicts;
conflict point identification refers to: and (3) setting a collision time threshold value to be 1.5s by calculating collision time (TTC) of all track points in the track, and screening out the conflict points, wherein the track points with the collision time higher than the threshold value are conflict points. The result is shown in fig. 4, where fig. 4 (a) corresponds to the original track points and fig. 4 (b) corresponds to the traffic conflict points.
And step S3: detecting the traffic mode of the conflict points, and classifying the conflict points according to the traffic mode;
the conflict point classification means: and detecting the traffic modes of all the conflict points according to the extracted traffic modes in the intersection, and classifying the conflict points according to the traffic modes. The results are shown in Table 2:
TABLE 2 number of traces, number of trace points, and number of conflict points for each mode
And step S4: constructing a risk index R by means of the traffic conflict frequency and severity in the traffic conflict technical idea so as to quantify the traffic risk of the conflict point;
quantifying the traffic risk at the conflict point refers to: firstly, a risk index R is constructed by means of the traffic conflict frequency Sum and the severity SI in the technical idea of traffic conflict. And then calculating the traffic risks of all conflict points according to the risk index R.
Step 5) combining a Gaussian smoke plume diffusion model to construct a traffic risk diffusion model according with a traffic motion mechanism;
the traffic risk diffusion model is as follows: according to the Gaussian smoke plume diffusion model, the risk diffusion ratio is used as a pollutant, and the traffic risk diffusion process is simulated by the process of diffusing the pollutant to the periphery. In the traffic risk diffusion, the traffic risk index R is used as a diffusion index, the main diffusion direction is the movement speed direction, and the 180-degree angle interval of the speed direction is used as a diffusion interval.
And 6) taking the conflict point as a diffusion source, and performing traffic risk diffusion treatment according to the traffic risk diffusion model to obtain intersection traffic risk distribution. The maximum traffic risk results for each time period are shown in fig. 5, and table 3 shows the maximum traffic risk for each time period at the intersection.
Table 3: maximum traffic risk at intersection for 12 time periods
Step 7) calculating the diffusion area of the traffic risk inside the intersection so as to evaluate the traffic risk level of the intersection;
the diffusion area ratio is: and accumulating the traffic risk areas and calculating the area to obtain the traffic risk diffusion area inside the intersection by taking the risk value P as a threshold value, taking the area greater than or equal to P as a traffic risk area and the area smaller than P as a safety area. The results are shown in FIG. 6.
Step 8) calculating a traffic risk change curve of the traffic track in the intersection by combining the traffic risk distribution of the intersection, and evaluating the traffic risk level of the route grade by adopting the integral of the traffic risk change curve;
the route level traffic risk level refers to: and calculating a traffic risk change curve of the traffic track according to the traffic risk distribution of the intersection, and evaluating the traffic risk of a single object passing through the intersection by adopting the traffic risk integral along the path. The results are shown in fig. 7, and table 4 describes the traffic risk condition for each trajectory.
Table 4: traffic risk condition of a trajectory
In table 4, RL represents a traffic risk value of the track,
and the sum of the traffic risk values of all track points in the track is represented, mean represents the Mean value of the traffic risk values of all track points in the track, and t represents the transit time of the track.
And 9) calculating the average value of the traffic risk levels of all the routes in the traffic mode by combining the traffic risk levels of the routes, so as to evaluate the traffic risk level of the traffic mode level.
The mode level traffic risk level refers to: and evaluating the traffic risk of the traffic mode by adopting the traffic risk mean value of the same type traffic mode containing track. As a result, as shown in fig. 8, table 5 describes the traffic risk for each traffic pattern.
Table 5: traffic risk for each traffic pattern
In table 5, model represents the traffic pattern, and RM represents the traffic risk in the traffic pattern.
In addition, in order to implement the traffic risk characterization and evaluation method, the embodiment also provides a traffic risk characterization and evaluation device for the urban road intersection. As shown in fig. 9, the apparatus includes the following modules:
the track classification module 1 is used for classifying the tracks of the moving objects in the intersection by using a track classification method of dynamic time normalization and extracting a passing mode;
the conflict point identification module 2 is used for measuring whether two vehicles have traffic conflicts by using the collision time as a key index so as to identify track points, namely conflict points, where the traffic conflicts occur;
the conflict point classification module 3 is used for detecting the traffic mode to which the conflict point belongs and classifying the conflict point according to the traffic mode to which the conflict point belongs, and different traffic modes apply different initial values of risk diffusion to the risk diffusion simulation of a single object;
the risk index construction module 4 is used for fusing the traffic conflict frequency and the severity and constructing a traffic risk index R so as to quantify the traffic risk of the conflict point;
the risk diffusion model building module 5 is used for building a traffic risk diffusion model according with a traffic motion mechanism by combining the Gaussian smoke plume diffusion model;
the risk diffusion processing module 6 is used for performing traffic risk diffusion processing according to the traffic risk diffusion model by taking the conflict point as a diffusion source to obtain intersection traffic risk distribution;
the intersection level risk evaluation module 7 is used for calculating the diffusion area of the traffic risk inside the intersection so as to evaluate the traffic risk level of the intersection;
the path level risk evaluation module 8 is used for calculating a traffic risk change curve of a traffic track in the intersection by combining the traffic risk distribution of the intersection and evaluating the traffic risk level of the path level by adopting the integral of the traffic risk change curve;
and the mode level risk evaluation module 9 is used for calculating the mean value of the traffic risk levels of all the paths in the traffic mode by combining the traffic risk levels of the path levels so as to evaluate the traffic risk level of the traffic mode level.
In addition, the embodiment also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the method for characterizing and evaluating the traffic risk of the urban road intersection.
The embodiment of the invention provides a method, a device and equipment for characterizing and evaluating traffic risks of urban road intersections. By establishing a multidimensional risk quantification model, crossing space-time dynamic risk distribution is revealed, and quantitative reference is provided for risk decision. Firstly, a traffic risk index is constructed by combining traffic conflict frequency and conflict severity, and traffic risks of traffic conflict points in intersections are quantified and characterized. And then, simulating traffic interactive propagation of the conflict point risks in a spatial neighborhood, introducing a smoke plume diffusion model to simulate the propagation diffusion process of the conflict point traffic risks to the neighborhood, representing the risk distribution of the whole domain of the intersection, and serving the requirements of high concurrency, low delay, whole domain traffic and full-element high-precision real-time digital mapping. And finally, implementing a multi-dimensional hierarchical evaluation system of the intersection fine traffic risk based on the intersection risk distribution, wherein the system mainly comprises the whole risk based on the area, the path traffic risk based on a single motion track and the traffic mode risk based on a plurality of tracks. The intersection global traffic risk quantification model and the multi-dimensional hierarchical evaluation system constructed by the embodiment enable risk assessment under a single-path or group traffic mode through a risk field simulation method, are expected to play an important supporting role for future urban risk analysis, and further provide technical support for development and construction of intelligent automobiles, intelligent traffic, safe trips and novel intelligent cities in China.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third and the like do not denote any order, but rather the words first, second and the like may be interpreted as indicating any order.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A traffic risk characterization and evaluation method for an urban road intersection is characterized by comprising the following steps:
classifying the track of the moving object in the intersection by using a track classification method of dynamic time normalization, and extracting a passing mode;
whether two vehicles have traffic conflict or not is measured by using the collision time as a key index so as to identify track points, namely conflict points, where the traffic conflict occurs;
detecting the traffic mode to which the conflict points belong, classifying the conflict points according to the traffic mode to which the conflict points belong, and enabling different traffic modes to act on the initial values of different risk diffusion on the risk diffusion simulation of a single object;
fusing the traffic conflict frequency and the severity to construct a traffic risk index R so as to quantify the traffic risk of the conflict point;
constructing a traffic risk diffusion model according with a traffic motion mechanism by combining a Gaussian plume diffusion model;
taking the conflict point as a diffusion source, and performing traffic risk diffusion treatment according to a traffic risk diffusion model to obtain intersection traffic risk distribution;
calculating the diffusion area of the traffic risk inside the intersection so as to evaluate the traffic risk level of the intersection level;
calculating a traffic risk change curve of a traffic track in the intersection by combining traffic risk distribution of the intersection, and evaluating the traffic risk level of the path grade by adopting the integral of the traffic risk change curve;
and calculating the average value of the traffic risk levels of all the routes in the traffic mode by combining the traffic risk levels of the route grades, so as to evaluate the traffic risk level of the traffic mode grade.
2. The method for characterizing and evaluating the traffic risk of an urban road intersection according to claim 1, wherein the step of classifying the trajectories of moving objects in the intersection by using a trajectory classification method of dynamic time normalization and extracting a traffic pattern comprises the following steps:
and calculating the similarity between the passing tracks in the intersection by using dynamic time regression, setting a proper threshold value to classify the tracks, wherein the classified result meets the effect of distinguishing the passing modes in the intersection, and extracting the types of the passing modes in the intersection, which are used for endowing different risk diffusion initial values for different conflict points.
3. The method for characterizing and evaluating the traffic risk of an urban road intersection according to claim 1, wherein the step of determining whether two vehicles have a traffic conflict by using the collision time as a key index to identify the track points where the traffic conflict occurs comprises:
and setting a collision time threshold value by calculating collision time of all track points in the track, wherein the track points with the collision time higher than the threshold value are conflict points, and screening the conflict points.
4. The method for characterizing and evaluating the traffic risk of an urban road intersection according to claim 1, wherein the step of fusing the traffic conflict frequency and the severity to construct a traffic risk index R so as to quantify the traffic risk of the conflict point comprises the following steps:
constructing a risk index R by means of the traffic conflict frequency Sum and the severity SI in the technical idea of traffic conflict;
calculating the traffic risk of various conflict points according to the risk index R, and constructing a formula as follows:
R i ＝0.5×Sum i +0.5×SI i
in the formula, R i Representing a type i conflict point traffic risk value; sum i Representing the conflict point traffic conflict frequency of the type i; SI (Standard institute of technology) i Indicating the severity of the conflict point traffic conflict of type i.
5. The method for characterizing and evaluating the traffic risk of an urban road intersection according to claim 1, wherein the step of constructing the traffic risk diffusion model conforming to the traffic motion mechanism by combining a Gaussian smoke plume diffusion model comprises the following steps:
according to the Gaussian smoke plume diffusion model, the risk diffusion ratio is used as a pollutant, and the traffic risk diffusion process is simulated by the process that the pollutant diffuses to the periphery;
taking the traffic risk index R as a diffusion index, taking the main diffusion direction as the motion speed direction, taking a 180-degree angle interval of the speed direction as a diffusion interval, and constructing a traffic risk diffusion model as follows:
where c (x, y) represents a traffic risk value at location (x, y); delta represents the diffusion source risk intensity; μ represents the velocity of the source subject vehicle; sigma y The diffusion coefficient is indicated.
6. The method for characterizing and evaluating the traffic risk of an urban road intersection according to claim 1, wherein the step of calculating the area rate of spread of traffic risk inside the intersection comprises:
taking the risk value P as a threshold, an area larger than or equal to P as a traffic risk area, an area smaller than P as a safety area, accumulating the traffic risk areas, and calculating the area to obtain the internal traffic risk diffusion area of the intersection, wherein the calculation formula is as follows:
S risk ＝∑s j if s j.risk ≥P
in the formula, S risk Representing the traffic risk diffusion area inside the intersection; s is j Represents the area of region j; s j.risk A traffic risk value representing region j; p represents a traffic risk threshold.
7. The method for characterizing and evaluating the traffic risks at an urban road intersection according to claim 1, wherein the step of calculating a traffic risk variation curve of a traffic trajectory in the intersection in combination with the intersection traffic risk distribution and evaluating the traffic risk level at a route level by using the integral thereof comprises:
calculating a traffic risk change curve of a traffic track in the intersection according to traffic risk distribution of the intersection, and evaluating the traffic risk level of a single object passing through the intersection by adopting traffic risk integration along a path, wherein the calculation formula is as follows:
in the formula, RL l The traffic risk of the trajectory i is represented,
represents the sum of all track traffic risks in the track l, t represents the transit time of the track l, and alpha is an adjustment coefficient.
8. The method according to claim 7, wherein the step of evaluating the traffic risk level at the transit mode level by calculating the mean of the traffic risk levels of all the routes in the transit mode in combination with the traffic risk level at the route level comprises:
the traffic risk of the traffic mode or a certain turning mode is evaluated by adopting the average value of the traffic risk levels of the same type traffic mode containing tracks, and the calculation formula is as follows:
in the formula, RM m Representing the traffic risk of the transit mode m,
representing the sum of all the trajectory traffic risks in mode m.
9. The utility model provides a traffic risk characterization and evaluation device of urban road intersection which characterized in that includes following module:
the track classification module is used for classifying the tracks of the moving objects in the intersection by using a track classification method of dynamic time normalization and extracting a traffic mode;
the conflict point identification module is used for measuring whether two vehicles conflict with each other by using the collision time as a key index so as to identify track points, namely conflict points, where the traffic conflicts occur;
the conflict point classification module is used for detecting the traffic mode to which the conflict point belongs and classifying the conflict point according to the traffic mode to which the conflict point belongs, and different traffic modes apply different initial values of risk diffusion to the risk diffusion simulation of a single object;
the risk index construction module is used for fusing the traffic conflict frequency and the severity degree and constructing a traffic risk index R so as to quantify the traffic risk of the conflict point;
the risk diffusion model building module is used for building a traffic risk diffusion model according with a traffic motion mechanism by combining a Gaussian smoke plume diffusion model;
the risk diffusion processing module is used for performing traffic risk diffusion processing by taking the conflict point as a diffusion source according to the traffic risk diffusion model to obtain intersection traffic risk distribution;
the intersection level risk evaluation module is used for calculating the diffusion area of the traffic risk inside the intersection so as to evaluate the traffic risk level of the intersection;
the path level risk evaluation module is used for calculating a traffic risk change curve of a traffic track in the intersection by combining intersection traffic risk distribution and evaluating the traffic risk level of the path level by adopting the integral of the traffic risk change curve;
and the mode level risk evaluation module is used for calculating the mean value of the traffic risk levels of all the paths in the traffic mode by combining the traffic risk levels of the path levels so as to evaluate the traffic risk level of the traffic mode level.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements the steps of the method of traffic risk characterization and assessment at urban road intersections according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211266983.4A CN115691123A (en) | 2022-10-17 | 2022-10-17 | Method, device and equipment for characterizing and evaluating traffic risks of urban road intersections |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211266983.4A CN115691123A (en) | 2022-10-17 | 2022-10-17 | Method, device and equipment for characterizing and evaluating traffic risks of urban road intersections |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115691123A true CN115691123A (en) | 2023-02-03 |
Family
ID=85066345
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211266983.4A Pending CN115691123A (en) | 2022-10-17 | 2022-10-17 | Method, device and equipment for characterizing and evaluating traffic risks of urban road intersections |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115691123A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116485284A (en) * | 2023-06-21 | 2023-07-25 | 南京地铁运营咨询科技发展有限公司 | Rail transit comprehensive joint debugging evaluation system and method |
-
2022
- 2022-10-17 CN CN202211266983.4A patent/CN115691123A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116485284A (en) * | 2023-06-21 | 2023-07-25 | 南京地铁运营咨询科技发展有限公司 | Rail transit comprehensive joint debugging evaluation system and method |
| CN116485284B (en) * | 2023-06-21 | 2023-09-15 | 南京地铁运营咨询科技发展有限公司 | Rail transit comprehensive joint debugging evaluation system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shi et al. | Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory | |
| Forehead et al. | Review of modelling air pollution from traffic at street-level-The state of the science | |
| Hou et al. | Situation assessment and decision making for lane change assistance using ensemble learning methods | |
| Charly et al. | Estimation of traffic conflicts using precise lateral position and width of vehicles for safety assessment | |
| Killi et al. | Proactive evaluation of traffic safety at an unsignalized intersection using micro-simulation | |
| Chen et al. | Analysis of factors affecting the severity of automated vehicle crashes using XGBoost model combining POI data | |
| Farah | Age and gender differences in overtaking maneuvers on two-lane rural highways | |
| Alhajyaseen | The development of conflict index for the safety assessment of intersections considering crash probability and severity | |
| Hu et al. | Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models | |
| Ma et al. | Predicting traffic conflicts for expressway diverging areas using vehicle trajectory data | |
| Kanagaraj et al. | Study of unique merging behavior under mixed traffic conditions | |
| Mathew et al. | Roundabout capacity in heterogeneous traffic condition: Modification of HCM equation and calibration | |
| CN115691123A (en) | Method, device and equipment for characterizing and evaluating traffic risks of urban road intersections | |
| Yang et al. | A novel approach for fine-grained traffic risk characterization and evaluation of urban road intersections | |
| Mamidipalli et al. | A review of analysis techniques and data collection methods for modeling pedestrian crossing behaviors | |
| Chen et al. | Prediction of user perceptions of signalized intersection level of service based on fuzzy neural networks | |
| Joo et al. | A generalized driving risk assessment on high-speed highways using field theory | |
| CN109284889B (en) | Unmanned vehicle reaction agility evaluation method and device | |
| Dolatalizadeh et al. | Analysis of speed profiles at an unsignalised intersection for left turning vehicles | |
| Wang et al. | Wide-area vehicle trajectory data based on advanced tracking and trajectory splicing technologies: Potentials in transportation research | |
| Wei | Characterize dynamic dilemma zone and minimize its effect at signalized intersections. | |
| Tang et al. | Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression | |
| Yadav et al. | Determination of passenger car noise equivalent for mid-sized cities in India | |
| Li et al. | The Types Division and Severity Threshold of Traffic Conflict on Freeway Interchange | |
| Wang et al. | Behavior-based safety evaluation model of vehicles turning left at intersections with a permitted left-turn phase |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |