CN112598236B - Traffic conflict-based pre-post safety evaluation method - Google Patents

Traffic conflict-based pre-post safety evaluation method Download PDF

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CN112598236B
CN112598236B CN202011470800.1A CN202011470800A CN112598236B CN 112598236 B CN112598236 B CN 112598236B CN 202011470800 A CN202011470800 A CN 202011470800A CN 112598236 B CN112598236 B CN 112598236B
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郭延永
刘攀
吴瑶
丁红亮
欧阳鹏瑛
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Abstract

The invention discloses a traffic conflict-based pre-post safety evaluation method, which comprises the following steps: (1) selecting a facility group and a control group; (2) collecting traffic conflict data; (3) constructing a traffic conflict model; (4) and calculating and judging traffic safety evaluation indexes. The method comprises the steps of firstly selecting a facility group and a contrast group required by traffic safety evaluation, then respectively collecting traffic conflict data of the facility group and the contrast group, then constructing a traffic conflict model, and finally calculating a traffic safety evaluation index R to judge a safety evaluation result. The invention can realize rapid traffic safety evaluation, improves the reliability of traffic safety evaluation and has practical engineering application value in traffic safety management.

Description

Traffic conflict-based pre-post safety evaluation method
Technical Field
The invention relates to the technical field of traffic safety management, in particular to a traffic conflict-based pre-post safety evaluation method.
Background
The traffic safety evaluation is a basic support for road traffic safety audit, a historical data method based on traffic accidents is adopted in the traditional traffic safety evaluation, however, the traffic accident data is difficult to obtain, the collection time is long, the quality is unstable, the traffic safety evaluation result has larger deviation, and especially for some traffic facilities without traffic accident records and newly built traffic facilities, the traditional traffic safety evaluation cannot be carried out. On the other hand, the conventional traffic safety evaluation method based on accidents cannot eliminate the influence of external factors such as traffic volume change on the evaluation result, and the reliability of the obtained result is not high. Compared with a traffic accident, the occurrence of the traffic conflict is more common and easy to observe, so that the traffic safety evaluation based on the traffic conflict has wider application range. The existing safety evaluation research utilizing traffic conflicts is less, and the fresh individual research only uses the number of the traffic conflicts as a safety evaluation index, does not convert the traffic conflicts into traffic accidents, and does not control the influence of external traffic volume factors on the change of the traffic conflicts. Therefore, the existing method cannot obtain scientific, reasonable, accurate and reliable safety evaluation results and can not guide traffic safety reconstruction measures in engineering practice.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a traffic conflict-based pre-post safety evaluation method, which effectively reduces the traffic safety evaluation deviation by adopting a facility group and contrast group combination mode, greatly improves the accuracy and reliability of traffic safety evaluation, and can effectively overcome the problems of difficult traffic accident data acquisition and the like.
The invention adopts the following technical scheme for solving the technical problems: a traffic conflict-based pre-post safety evaluation method comprises the following steps:
step 1, selecting a facility group and a control group: taking the traffic facilities subjected to traffic safety reconstruction as a facility group, taking reconstruction time as a node, and dividing the time into before and after, namely before and after reconstruction; selecting traffic facilities with traffic design and traffic volume consistent with the previous time period of the facility group as a comparison group;
step 2, collecting traffic conflict data: respectively carrying out video recording on the facility group and the contrast group by utilizing an unmanned aerial vehicle carrying a camera, wherein the recording time is respectively within T time before the traffic facility is modified and within T time after the traffic facility is modified; collecting traffic conflicts with a minutes of the videos of the facility group and the control group as time periods and the time TTC of each traffic conflict;
step 3, constructing a traffic conflict model, wherein the formula is as follows:
Figure GDA0003147522490000011
wherein i ═ TB, TA, CB, and CA represent facility group before, facility group after, control group before, control group after, and Fi(y) represents the distribution of the extreme collision time of the traffic collision, y represents the inverse collision time-TTC, alpha corresponding to the traffic collision in the time period of a minutesiPosition parameter, beta, representing the distribution of the time of impact of an extreme value of a traffic conflictiScale parameter, gamma, representing the distribution of the collision time of extreme values of traffic conflictsiRepresenting extreme collision time distributions for traffic conflictsThe shape parameter of (a);
fitting the collision time of the traffic collision extreme value in the time period of a minute by adopting the constructed traffic collision model to obtain a distribution parameter alpha of the collision time of the traffic collision extreme valuei,βi,γi；
And 4, calculating and judging traffic safety evaluation indexes: the traffic conflict extreme value collision time distribution parameter alpha acquired in the step 3i,βi,γiAnd substituting the following formulas to calculate the predicted accident numbers before and after the facility group and the control group respectively:
Figure GDA0003147522490000021
wherein i ═ TB, TA, CB, and CA represent facility group before, facility group after, control group before, control group after, and CiRepresenting the number of predicted accidents in time T, TiRepresenting the duration of the video, a<ti<T; n represents tiThe number of a minutes, N-int (t)iA), int (#) represents an integer;
calculating a traffic safety evaluation index R by using the following formula:
Figure GDA0003147522490000022
when R <1 indicates that the traffic safety improvement measure is effective, and the effect of the safety improvement is 1-R; when R >1, it indicates that the traffic safety improvement measure is invalid.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the traffic conflict-based pre-post safety evaluation method provided by the invention is characterized in that a traffic conflict model is constructed by collecting facility groups and compared traffic conflict data, traffic safety evaluation indexes are calculated, the traffic safety improvement effect is quantitatively evaluated, and the influence of factors except traffic safety improvement on a safety evaluation result is reduced by utilizing a comparison group. The method can effectively reduce the traffic safety evaluation deviation, improve the accuracy and reliability of the traffic safety evaluation result, overcome the problems of difficult acquisition of traffic accident data and the like, and has practical engineering application value.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The invention relates to a traffic rush-based pre-post safety evaluation method, which comprises the following steps of:
step 1, selecting a facility group and a control group: taking traffic facilities (such as signal intersections) subjected to traffic safety reconstruction as a facility group, taking reconstruction time as a node, and dividing the time into a pre-construction period (before reconstruction) and a post-construction period (after reconstruction); traffic facilities whose traffic design and traffic volume are consistent with the facility group's previous time period are selected as the control group.
Step 2, collecting traffic conflict data: the unmanned aerial vehicle carrying the camera is used for respectively carrying out video recording on the facility group and the contrast group, the recording time is respectively in the T time before the traffic facility is transformed and in the T time after the traffic facility is transformed, and the time length T of the video in the previous time period of the facility group can be obtainedTBAnd the video duration t of the post-event time periodtAAnd the comparison group's prior time period video duration tCBAnd the video duration t of the post-event time periodCA. And respectively acquiring traffic conflicts with a minute as a time period and the time to collision TTC of each traffic conflict of the facility group video and the control group video through the recorded videos.
Step 3, constructing a traffic conflict model, wherein the formula is as follows:
Figure GDA0003147522490000031
wherein i ═ TB, TA, CB, and CA represent facility group before, facility group after, control group before, control group after, and Fi(y) represents the distribution of the extreme collision time for a traffic collision, and y represents a minuteTime to collision (TTC, alpha) corresponding to traffic conflict of time intervaliPosition parameter, beta, representing the distribution of the time of impact of an extreme value of a traffic conflictiScale parameter, gamma, representing the distribution of the collision time of extreme values of traffic conflictsiA shape parameter representing a traffic conflict extremum collision time distribution;
fitting the collision time of the traffic collision extreme value in the time period of a minute by adopting the constructed traffic collision model to obtain a distribution parameter alpha of the collision time of the traffic collision extreme valuei,βi,γi。
And 4, calculating and judging traffic safety evaluation indexes: the traffic conflict extreme value collision time distribution parameter alpha acquired in the step 3i,βi,γiAnd substituting the following formulas to calculate the predicted accident numbers before and after the facility group and the control group respectively:
Figure GDA0003147522490000032
wherein i ═ TB, TA, CB, and CA represent facility group before, facility group after, control group before, control group after, and CiRepresenting the number of predicted accidents in time T, TiRepresenting the duration of the video, a<ti<T; n represents tiThe number of a minutes, N-int (t)iA), int (#) represents an integer; in this embodiment, the recording time T is 12 months, and the video duration T isiFor 6 hours, a is 5 minutes.
Calculating a traffic safety evaluation index R by using the following formula:
Figure GDA0003147522490000033
when R <1 indicates that the traffic safety improvement measure is effective, and the effect of the safety improvement is 1-R; when R >1, it indicates that the traffic safety improvement measure is invalid.
The invention is illustrated by the following specific examples.
1) Traffic data collection, as shown in table 1.
TABLE 1 traffic data Collection
Figure GDA0003147522490000034
Figure GDA0003147522490000041
2) Constructing a traffic conflict model:
and respectively constructing traffic conflict models of a facility group and a contrast group based on the traffic conflict model provided by the invention according to the traffic conflict collision time data acquired in the steps 1 and 2.
Figure GDA0003147522490000042
Figure GDA0003147522490000043
Figure GDA0003147522490000044
Figure GDA0003147522490000045
Wherein FTB(y)、FTA(y) and FCB(y)、FCAAnd (y) is respectively expressed as traffic conflict extreme value collision distribution before the facility group, after the facility group, before the comparison group and after the comparison group.
3) Calculation and judgment of traffic safety evaluation index
The number of accidents before and after the facility group and the control group was calculated based on step 1 and step 2 according to the following formula, as shown in table 2.
Figure GDA0003147522490000046
TABLE 2 number of accidents predicted before and after the facility group and the control group
Figure GDA0003147522490000047
The traffic safety evaluation index R is calculated from the number of accident predictions before and after the facility group and the control group, and the present embodiment calculates that R is 0.8, which is less than 1, based on the assumed data, so that it is effective to implement a traffic safety improvement measure, and the safety improvement effect is 0.2.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (1)

1. A traffic conflict-based pre-post safety evaluation method is characterized by comprising the following steps: the method comprises the following steps:
step 1, selecting a facility group and a control group: taking the traffic facilities subjected to traffic safety reconstruction as a facility group, taking reconstruction time as a node, and dividing the time into before and after, namely before and after reconstruction; selecting traffic facilities with traffic design and traffic volume consistent with the previous time period of the facility group as a comparison group;
step 2, collecting traffic conflict data: respectively carrying out video recording on the facility group and the contrast group by utilizing an unmanned aerial vehicle carrying a camera, wherein the recording time is respectively within T time before the traffic facility is modified and within T time after the traffic facility is modified; collecting traffic conflicts with a minutes of the videos of the facility group and the control group as time periods and the time TTC of each traffic conflict;
step 3, constructing a traffic conflict model, wherein the formula is as follows:
Figure FDA0003147522480000011
wherein i ═ TB, TA, CB, and CA represent facility group before, facility group after, control group before, control group after, and Fi(y) represents the distribution of the extreme collision time of the traffic collision, y represents the inverse collision time-TTC, alpha corresponding to the traffic collision in the time period of a minutesiPosition parameter, beta, representing the distribution of the time of impact of an extreme value of a traffic conflictiScale parameter, gamma, representing the distribution of the collision time of extreme values of traffic conflictsiA shape parameter representing a traffic conflict extremum collision time distribution;
fitting the collision time of the traffic collision extreme value in the time period of a minute by adopting the constructed traffic collision model to obtain a distribution parameter alpha of the collision time of the traffic collision extreme valuei,βi,γi；
And 4, calculating and judging traffic safety evaluation indexes: the traffic conflict extreme value collision time distribution parameter alpha acquired in the step 3i,βi,γiAnd substituting the following formulas to calculate the predicted accident numbers before and after the facility group and the control group respectively:
Figure FDA0003147522480000012
wherein i ═ TB, TA, CB, and CA represent facility group before, facility group after, control group before, control group after, and CiRepresenting the number of predicted accidents in time T, TiRepresenting the duration of the video, a<ti<T; n represents tiThe number of a minutes, N-int (t)iA), int (#) represents an integer;
calculating a traffic safety evaluation index R by using the following formula:
Figure FDA0003147522480000013
when R <1 indicates that the traffic safety improvement measure is effective, and the effect of the safety improvement is 1-R; when R >1, it indicates that the traffic safety improvement measure is invalid.
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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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CN109710892A (en) * 2018-12-28 2019-05-03 哈尔滨工业大学 A kind of Evaluation methods of road traffic safety based on conflict extreme value
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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