CN114550445A - Urban area traffic safety state evaluation method and device - Google Patents
Urban area traffic safety state evaluation method and device Download PDFInfo
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Abstract
The invention provides a method and a device for evaluating traffic safety states of urban areas, wherein the method comprises the following steps: acquiring road data, accident data and population data of an area to be evaluated; acquiring a monitoring video and a shooting video of an area to be evaluated, and extracting motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data; constructing a traffic safety accident index and a traffic law-keeping index; and determining the traffic safety state of the area to be evaluated. Compared with the prior art, the method and the system can acquire the traffic safety state index by constructing the static traffic safety accident index and the dynamic traffic law-keeping index, carry out quantitative evaluation on the traffic safety running state of the whole urban area to be evaluated, have higher accuracy and universality, are easy to maintain, and enable travelers and traffic management departments to visually know the traffic safety level of the area.
Description
Technical Field
The invention relates to the field of urban traffic, in particular to a method and a device for evaluating the traffic safety state of an urban area.
Background
With the rapid development of computer technology, how to evaluate traffic safety is getting more and more attention. The traditional traffic safety evaluation mainly focuses on comparatively closed roads such as a certain intersection road section or a high express way, and the evaluation method is based on a statistical analysis method or machine learning, and is used for analyzing historical traffic accident data, casualties of traffic accidents, economic losses and the like, or adding driver data, vehicle performance data, road environment factor data and the like on the basis of the historical traffic accident data to realize the traffic safety level evaluation. However, the method is one-sided, the prior art has few research on the evaluation of traffic safety in large-area urban areas, data such as traffic accidents have hysteresis, and traffic safety conditions cannot be effectively mastered in time, driver data, vehicle performance data, road environmental factor data and the like are added on the basis of historical traffic accident data, information such as people, vehicles and roads needs to be collected, the data volume is large, the maintenance is difficult, and the evaluation of the traffic safety is difficult to effectively support in real time.
Disclosure of Invention
The invention provides a method and a device for evaluating the traffic safety state of an urban area, which realize the quantitative evaluation of the overall traffic safety running state of the urban area and improve the accuracy of the evaluation.
In order to solve the above technical problem, an embodiment of the present invention provides an urban area traffic safety state assessment method, including:
acquiring road data, accident data and population data of an area to be evaluated; acquiring a monitoring video and a shooting video of a region to be evaluated, and extracting motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data from the monitoring video and the shooting video;
constructing a traffic safety accident index according to the road data, the accident data and the population data; according to the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, constructing a traffic law-guarding index of the area to be evaluated;
and determining the traffic safety state of the area to be evaluated according to the traffic safety accident index and the traffic law-keeping index.
By implementing the embodiment of the application, the traffic safety state index can be obtained by constructing the static traffic safety accident index and the dynamic traffic law-keeping index, the quantitative evaluation of the traffic safety operation state is carried out on the whole city area to be evaluated, the accuracy and the universality are higher, the maintenance is easy, and travelers and traffic management departments can visually know the traffic safety level of the area.
As an optimal scheme, the method for constructing the traffic law-guarding index of the area to be assessed according to the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data specifically comprises the following steps:
obtaining the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, and calculating a score S corresponding to the traffic illegal index of the area to be evaluated2Specifically:
wherein S is2The score corresponding to the traffic law-keeping index of the area to be evaluated,
index values of the ith road traffic law-keeping rate evaluation index;
the basic weight of the ith road traffic law-keeping rate evaluation index; n is the number of road traffic law-keeping rate evaluation indexes; the road traffic law-keeping rate evaluation index is determined by the motor vehicle violation data, the bicycle violation data, the electric bicycle violation data and the pedestrian violation data. Implementing the embodiments of the present application, construct a computer program productThe traffic law-keeping indexes of the region are evaluated by considering motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data, the method is comprehensive, the traffic law-keeping evaluation of the region to be evaluated is more accurate, and the traffic law-keeping condition of the region to be evaluated can be effectively obtained.
As a preferred scheme, the constructing a traffic safety accident index according to the road data, the accident data and the population data specifically comprises:
acquiring the total lane mileage L, the total number of the permanent population and the total number of the floating population P of the area to be evaluated, and calculating the equivalent comprehensive mortality K of the area to be evaluatedd：
Wherein, KdTo an equivalent overall mortality, DdIs the equivalent number of deaths; the road data comprises the total lane mileage of the area to be evaluated; the population data comprises the total number of the permanent population and the floating population;
calculating a correction coefficient K of the area to be evaluated;
K＝β1n1+β2n2+β3n3+β4n4；
wherein n is1For a larger number of accidents, n2Number of accidents for special events, n3The number of frequently occurring points of the traffic accident, n4Number of accident risk points, beta1Correction factor for larger traffic accidents, beta2Correction factor for special event accidents, beta3Correction factor, beta, for points of frequent traffic accidents4Correction coefficients of accident risk points; the accident data comprises the number of large accidents, the number of special events, the number of frequent traffic accident points and the number of accident risk points;
according to the correction coefficient K of the area to be evaluated and the equivalent comprehensive mortality K of the area to be evaluateddAnd calculating the traffic safety accident index of the area to be evaluated:
S1＝80-Kd-K；
wherein S is1And the traffic safety accident index of the area to be evaluated is obtained. By implementing the embodiment of the application, the equivalent comprehensive death rate is used as a main parameter, the correction coefficient is used as a correction parameter, and the traffic safety accident index is effectively corrected, so that the acquired traffic safety accident index of the area to be evaluated is accurate and close to the actual application scene.
Preferably, the method for calculating the equivalent number of deaths comprises the following steps:
Dd＝D1+0.33D2+0.11D3；
wherein D isdFor an equivalent number of deaths, D1For the number of deaths in a traffic accident, D2The number of seriously injured people in a traffic accident D3The number of people with traffic accidents; the accident data also includes the equivalent number of deaths, the number of deaths in traffic accidents, the number of serious injuries in traffic accidents, and the number of serious injuries in traffic accidents.
Preferably, the special event accidents include alcohol accidents, cargo accidents, electric accidents, pedestrian accidents and non-motor vehicle accidents.
As a preferred scheme, the motor vehicle illegal data is obtained by identifying motor vehicle illegal behaviors through the monitoring video and the shooting video, wherein the motor vehicle illegal behaviors comprise a first road traffic signal lamp violation behavior, a lane illegal change behavior, a lane driving behavior without guiding, a mobile phone driving behavior, an illegal parking behavior and a pedestrian unlawful behavior;
the bicycle illegal data are obtained by identifying bicycle illegal behaviors through the monitoring video and the shooting video, wherein the bicycle illegal behaviors comprise a second road traffic signal lamp violation behavior, a second illegal man-carrying behavior, a reverse driving behavior, a pushing behavior without getting off when passing through a pedestrian crosswalk and a second motor vehicle lane entering driving behavior;
the electric bicycle illegal data are obtained by identifying electric bicycle illegal behaviors through the monitoring video and the shooting video, wherein the electric bicycle illegal behaviors comprise a third illegal road traffic signal lamp passing behavior, a third illegal man-carrying behavior, a behavior of not wearing a safety helmet, a third behavior of pushing without getting off when passing through a pedestrian crossing and a third behavior of entering a motor vehicle lane;
the pedestrian illegal data are obtained by identifying pedestrian illegal behaviors through the monitoring video and the shooting video, wherein the pedestrian illegal behaviors comprise a fourth traffic signal lamp violation behavior and a motor vehicle lane entering behavior. By implementing the embodiment of the application, the obtained various illegal data are obtained by identifying different illegal behaviors, and the method has real-time performance and universality for different regions.
Correspondingly, the embodiment of the invention also provides a device for evaluating the traffic safety state of the urban area, which comprises an acquisition module, a construction module and an evaluation module, wherein,
the acquisition module is used for acquiring road data, accident data and population data of an area to be evaluated; acquiring a monitoring video and a shooting video of a region to be evaluated, and extracting motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data from the monitoring video and the shooting video;
the construction module is used for constructing a traffic safety accident index according to the road data, the accident data and the population data; according to the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, constructing a traffic law-guarding index of the area to be evaluated;
and the evaluation module is used for determining the traffic safety state of the area to be evaluated according to the traffic safety accident index and the traffic law-keeping index.
As an optimal scheme, the construction module constructs the traffic law-guarding indexes of the area to be assessed according to the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data, and specifically comprises the following steps:
the building module acquires the motor vehicle illegal dataBicycle violation data, electric bicycle violation data and pedestrian violation data, and calculating a score S corresponding to the traffic violation index of the area to be assessed2Specifically:
wherein S is2The score corresponding to the traffic law-keeping index of the area to be evaluated,
index values of the ith road traffic law-keeping rate evaluation index;
the basic weight of the ith road traffic law-keeping rate evaluation index; n is the number of road traffic law-keeping rate evaluation indexes; the road traffic law-keeping rate evaluation index is determined through the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data. The traffic law-keeping indexes of the area to be evaluated are constructed by considering motor vehicle illegal data, electric bicycle illegal data and pedestrian illegal data, the method is comprehensive, the traffic law-keeping evaluation of the area to be evaluated is more accurate, and the traffic law-keeping condition of the area to be evaluated can be effectively obtained.
As a preferred scheme, the construction module constructs a traffic safety accident index according to the road data, the accident data and the population data, specifically:
the construction module obtains the total lane mileage L, the total number of the standing population and the total number of the floating population P of the area to be evaluated, and calculates the equivalent comprehensive mortality K of the area to be evaluatedd：
Wherein, KdTo an equivalent overall mortality, DdIs equivalent deathThe number of people; the road data comprises the total lane mileage of the area to be evaluated; the population data comprises the total number of the permanent population and the floating population;
calculating a correction coefficient K of the area to be evaluated;
K＝β1n1+β2n2+β3n3+β4n4；
wherein n is1For a larger number of accidents, n2Number of accidents for special events, n3The number of frequently occurring points of the traffic accident, n4Number of accident risk points, beta1Correction factor for large traffic accidents, beta2Correction factor for special event accidents, beta3Correction factor, beta, for points of frequent traffic accidents4Correction coefficients of accident risk points;
according to the correction coefficient K of the area to be evaluated and the equivalent comprehensive mortality K of the area to be evaluateddAnd calculating the traffic safety accident index of the area to be evaluated:
S1＝80-Kd-K；
wherein S is1And the traffic safety accident index of the area to be evaluated is obtained. And taking the equivalent comprehensive mortality rate as a main parameter and the correction coefficient as a correction parameter, and effectively correcting the traffic safety accident index, so that the acquired traffic safety accident index of the area to be evaluated is accurate and close to the actual application scene.
Preferably, the method for calculating the equivalent number of deaths comprises the following steps:
Dd＝D1+0.33D2+0.11D3；
wherein D isdFor an equivalent number of deaths, D1For the number of deaths in a traffic accident, D2The number of seriously injured people in a traffic accident D3The number of people with traffic accidents; the accident data also includes the equivalent number of deaths, the number of deaths in traffic accidents, the number of serious injuries in traffic accidents, and the number of serious injuries in traffic accidents.
Drawings
FIG. 1: the invention provides a flow schematic diagram of an embodiment of an urban area traffic safety state evaluation method.
FIG. 2: the invention provides a schematic structural diagram of an embodiment of an urban area traffic safety state evaluation device.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
referring to fig. 1, fig. 1 is a method for evaluating traffic safety status in an urban area according to an embodiment of the present invention, including steps S1 to S3, wherein,
step S1, acquiring road data, accident data and population data of an area to be evaluated; the method comprises the steps of obtaining a monitoring video and a shooting video of an area to be evaluated, and extracting motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data from the monitoring video and the shooting video.
In this embodiment, the acquired road data of the area to be evaluated includes, but is not limited to, an expressway, a main road, a secondary road, a branch road mileage, and an average lane number, and the total lane mileage L in the area is calculated according to the road mileage and the average lane number:
L＝l1×r1+l2×r2+l3×r3+l4×r4+l5×r5；
wherein l1、l2、l3、l4And l5And respectively representing the highway road mileage, the express road mileage, the main road mileage, the secondary main road mileage and the branch road mileage of the area to be evaluated, wherein the unit is km. r is1、r2、r3、r4And r5Respectively representing the average number of lanes on the highway, the average number of lanes on the express way, the average number of lanes on the main road, the average number of lanes on the secondary road and the average number of lanes on the branch road.
The road average lane number calculation method comprises the following steps:
wherein r isiAverage number of lanes for road type i, liCounting the roadway width, w, of road type i for the cityiIs the road lane width of road type i. For the convenience of calculation, the width of a lane of a road is 3.75 meters for an express way, 3.5 meters for a main road and a secondary road and 3.25 meters for a branch.
The acquired population data comprises the total number of the standing population and the floating population of the area to be evaluated. The acquired accident data comprises but is not limited to the number of large accidents, the number of special accidents, the number of frequent traffic accident points and accident risk points, the equivalent number of deaths, the number of deaths of traffic accidents, the number of serious traffic accident injuries and the number of serious traffic accident injuries.
Calculating the equivalent comprehensive mortality K of the area to be evaluated according to the total lane mileage L, the total number of the permanent population and the floating population P of the area to be evaluatedd：
Wherein, KdTo an equivalent overall mortality, DdIs the equivalent number of deaths.
The method for calculating the equivalent number of dead people comprises the following steps:
Dd＝D1+0.33D2+0.11D3；
wherein D isdFor an equivalent number of deaths, D1For the number of deaths in a traffic accident, D2The number of seriously injured people in a traffic accident D3For light injury of traffic accidentThe number of people.
And calculating a correction coefficient K of the area to be evaluated:
K＝β1n1+β2n2+β3n3+β4n4；
wherein n is1Number of major accidents, n2For the number of special event incidents, n3The number of frequently occurring points of the traffic accident, n4Number of accident risk points, beta1Correction factor for large traffic accidents, beta2Correction factor, beta, for special event incidents3Correction factor, beta, for points of frequent traffic accidents4And (4) correction coefficient of accident risk points.
Wherein, the major accidents are major traffic accidents of dying 3 people or more or seriously hurting 10 people once per month; the special event accidents are general procedure accidents of alcohol accidents, cargo accidents, electric accidents, pedestrian accidents and non-motor vehicle accidents (without electric bicycles); the accident frequency points are more than three traffic accidents occurring at the same intersection or the same road section; the accident risk points are more than three dead accidents in the same accident form at the same intersection or the same road section within one year (at least together in the month).
According to the correction coefficient K of the area to be evaluated and the equivalent comprehensive mortality K of the area to be evaluateddAnd calculating the traffic safety accident index of the area to be evaluated:
S1＝80-Kd-K；
wherein S is1And the traffic safety accident index of the area to be evaluated is obtained.
The method comprises the steps of counting intersections with monitoring in an area, sorting and making an intersection list with monitoring equipment in the area, obtaining monitoring videos of the intersections, collecting data in the form of shooting and recording the videos at the intersections aiming at the condition that part of the monitoring equipment is frequently lost, and obtaining the shooting videos of the intersections. The monitoring video and the shooting video comprise contents of three aspects of an entrance/exit lane line, a pedestrian crossing and a signal lamp color of a cross section in a certain direction of the intersection.
Extracting motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data from the monitoring video and the shooting video, specifically:
the method comprises the steps of spot-checking 5 intersections from an intersection list to be used as survey objects, controlling the intersections according to a checked signal, selecting one cross section of an entrance/exit passage in two directions to be used as the survey objects, wherein the survey time of each survey object is two periods (each period is selected to be half an hour), and specifically is the morning peak period (7: 00-9: 00) and the afternoon peak-balancing period (15: 00-17: 00) of a working day. And obtaining motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data according to the investigation result.
Step S2, constructing a traffic safety accident index according to the road data, the accident data and the population data; and constructing a traffic law-guarding index of the area to be evaluated according to the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data.
Specifically, the roads in the area are classified, the main road is used as a first-level road, and the secondary road and the branch road are used as a second-level road. Intersections can be classified into three categories according to different road grades, including: the first-level road is crossed to the first-level road intersection, the first-level road is crossed to the second-level road intersection and the second-level road is crossed to the second-level road intersection.
According to the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, constructing a traffic law-guarding index of the area to be evaluated, which specifically comprises the following steps:
obtaining the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, and calculating a score S corresponding to the traffic illegal index of the area to be evaluated2Specifically:
wherein S is2The score corresponding to the traffic law-keeping index of the area to be evaluated,
index values of the ith road traffic law-keeping rate evaluation index;
the basic weight of the ith road traffic law-keeping rate evaluation index; n is the number of road traffic law-keeping rate evaluation indexes; the road traffic law-keeping rate evaluation index is determined through the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data. The constructed traffic law guarding indexes of the area to be evaluated have real-time performance, the problem of hysteresis in the prior art is solved, and traffic safety conditions can be effectively mastered.
The motor vehicle illegal data are obtained by identifying motor vehicle illegal behaviors through the monitoring video and the shooting video, wherein the motor vehicle illegal behaviors comprise a first road traffic signal lamp violation behavior, a lane illegal change violation behavior, a lane driving behavior without guidance, a mobile phone driving behavior, an illegal parking behavior and a pedestrian unlawful behavior.
As an example of this embodiment, the basic weight of the first violation road traffic signal passing behavior is 0.1, and the corresponding index value is the reference table 1.
TABLE 1 first violation of road traffic Signal passing behavior
| First violation of road trafficTraffic light passing rate | 0～4％ | 5％～9％ | 10％～15％ | 15％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the illegal lane change behavior is 0.03, and the corresponding index value refers to table 2.
TABLE 2 illegal Lane Change behavior
| Illegal lane change rate | 0～7％ | 8％～15％ | 16％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the non-guided lane driving behavior is 0.03, and the index value corresponding to the behavior is shown in table 3.
TABLE 3 non-guided lane driving behavior
| Rate of travel without following a guide lane | 0～7％ | 8％～15％ | 16％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the behavior of using the mobile phone for driving is 0.05, and the corresponding index value refers to table 4.
TABLE 4 Mobile phone behavior during driving
| Probability of using hands in driving | 0～4％ | 5％～9％ | 10％～14％ | 15％～19％ | ＞20％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the illegal parking behavior is 0.03, and the index value corresponding to the basic weight is referred to table 5.
TABLE 5 illegal parking behavior
| Illegal parking rate | 0～8％ | 9％～15％ | 16％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the pedestrian-unfriendly behavior is 0.05, and the corresponding index value refers to table 6.
TABLE 6 behavior of pedestrian without present
| Rate of unlawful pedestrians | 0～5％ | 6％～10％ | 11％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The bicycle illegal data are obtained by identifying bicycle illegal behaviors through the monitoring video and the shooting video, wherein the bicycle illegal behaviors comprise a second road traffic signal lamp violation behavior, a second illegal man-carrying behavior, a reverse driving behavior, a second pushing behavior without getting off when passing through a pedestrian crossing and a second motor vehicle lane driving behavior.
As an example of this embodiment, the basic weight of the second violation road traffic signal passing behavior is 0.04, and the corresponding index value refers to table 7.
TABLE 7 second violation of road traffic Signal passing behavior
| Second violation road traffic signal lamp passing rate | 0～10％ | 11％～16％ | 17％～23％ | 24％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the second illegal manned behavior is 0.03, and the corresponding index value refers to table 8.
TABLE 8 second act of illegal manned
| Second rate of illegal manned | 0～5％ | 6％～10％ | 11％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the pushing behavior without getting off the vehicle when the second passing through the pedestrian crossing is 0.02, and the corresponding index value refers to the table 9.
TABLE 9 second pass through crosswalk without getting off push behavior
| Push rate without getting off when passing through pedestrian crosswalk | 0～5％ | 6％～10％ | 11％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the second entering-lane driving behavior is 0.03, and the corresponding index value refers to the table 10.
TABLE 10 second entry into lane driving behavior
| Second entry lane of travelRate of change | 0～5％ | 6％～10％ | 11％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The illegal electric bicycle data are obtained by identifying illegal electric bicycle behaviors through the monitoring video and the shooting video, wherein the illegal electric bicycle behaviors comprise a third illegal road traffic signal lamp passing behavior, a third illegal man-carrying behavior, a behavior of not wearing a safety helmet, a third behavior of pushing without getting off when passing through a pedestrian crossing and a third behavior of entering a motor vehicle lane.
As an example of this embodiment, the basic weight of the third violation road traffic signal passing behavior is 0.03, and the corresponding index value refers to the table 11.
TABLE 11 third violation of road traffic signal light traffic behavior
| Third violation road traffic signal lamp passing rate | 0～10％ | 11％～16％ | 17％～23％ | 24％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the third illegal manned behavior is 0.02, and the corresponding index value refers to the table 12.
TABLE 12 third illegal manned behavior
| Third illegal manned rate | 0～5％ | 6％～10％ | 11％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the third entering-vehicle-lane running behavior is 0.03, and the corresponding index value refers to table 13.
TABLE 13 third entry Motor vehicle Lane travel behavior
| Third rate of entry into lane | 0～5％ | 6％～10％ | 11％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the pushing behavior without getting off the vehicle when the third passing through the crosswalk is 0.02, and the corresponding index value refers to the table 14.
TABLE 14 third pass through crosswalk push behavior without getting off car
The basic weight of the behavior of not wearing the safety helmet is 0.02, and the corresponding index value refers to table 15.
TABLE 15 behavior without safety helmet
| Rate of not wearing safety helmet | 0～5％ | 6％～10％ | 11％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The pedestrian illegal data are obtained by identifying pedestrian illegal behaviors through the monitoring video and the shooting video, wherein the pedestrian illegal behaviors comprise a fourth traffic signal lamp violation behavior and a motor vehicle lane entering behavior.
As an example of this embodiment, the basic weight of the fourth violation traffic signal passing behavior is 0.1, and the corresponding index value refers to the table 16.
TABLE 16 fourth violation of traffic Signal pass behavior
| Fourth violation traffic signal light passing rate | 0～10％ | 11％～16％ | 17％～23％ | 24％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
The basic weight of the behavior of entering the motor vehicle lane is 0.05, and the corresponding index value refers to the table 17.
TABLE 17 behavior of entering Motor vehicle Lane
| Rate of travel into motor vehicle lane | 0～5％ | 6％～10％ | 11％～20％ | 21％～29％ | ＞30％ |
| Index value | 10 | 7 | 4 | 1 | 0 |
And step S3, determining the traffic safety state of the area to be evaluated according to the traffic safety accident index and the traffic law keeping index.
In the embodiment, a traffic safety state index S of an area to be evaluated is obtained according to the traffic safety accident index and the traffic law keeping index, and S is S1+S2And thus, the traffic safety state of the area to be evaluated is obtained and displayed on an information platform.
Correspondingly referring to fig. 2, an embodiment of the present invention further provides an urban area traffic safety state evaluation apparatus, including an obtaining module 101, a constructing module 102, and an evaluating module 103, wherein,
the acquisition module 101 is used for acquiring road data, accident data and population data of an area to be evaluated; acquiring a monitoring video and a shooting video of a region to be evaluated, and extracting motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data from the monitoring video and the shooting video;
the construction module 102 is configured to construct a traffic safety accident index according to the road data, the accident data and the population data; according to the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, constructing a traffic law-guarding index of the area to be evaluated;
the evaluation module 103 is configured to determine a traffic safety state of the area to be evaluated according to the traffic safety accident indicator and the traffic law keeping indicator.
In this embodiment, the constructing module 102 constructs the traffic law-guarding index of the area to be assessed according to the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data, specifically:
the construction module 102 obtains the motor vehicle violation data, the bicycle violation data, the electric bicycle violation data, and the pedestrian violation data, and calculates a score S corresponding to a traffic violation index of the area to be assessed2Specifically:
wherein S is2The score corresponding to the traffic law keeping index of the area to be evaluated,
index values of the ith road traffic law-keeping rate evaluation index;
the basic weight of the ith road traffic law-keeping rate evaluation index; n is the number of road traffic law-keeping rate evaluation indexes; the road traffic law-keeping rate evaluation index is determined through the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data.
In this embodiment, the building module 102 builds a traffic safety accident index according to the road data, the accident data, and the population data, specifically:
the building module 102 obtains the total lane mileage L, the total number of the standing population and the total number of the floating population P of the area to be evaluated, and calculates the equivalent comprehensive mortality K of the area to be evaluatedd：
Wherein, KdTo an equivalent overall mortality, DdIs the equivalent number of deaths; the road data comprises the total lane mileage of the area to be evaluated; the population data comprises the total number of the permanent population and the floating population;
calculating a correction coefficient K of the area to be evaluated;
K＝β1n1+β2n2+β3n3+β4n4；
wherein n is1For a larger number of accidents, n2Number of accidents for special events, n3The number of frequently occurring points of the traffic accident, n4Number of accident risk points, beta1Correction factor for large traffic accidents, beta2Correction factor for special event accidents, beta3Correction factor, beta, for points of frequent traffic accidents4Correction coefficients of accident risk points;
according to the correction coefficient K of the area to be evaluated and the equivalent comprehensive mortality K of the area to be evaluateddAnd calculating the traffic safety accident index of the area to be evaluated:
S1＝80-Kd-K；
wherein S is1And the traffic safety accident index of the area to be evaluated is obtained.
In this embodiment, the method for calculating the equivalent number of deaths comprises:
Dd＝D1+0.33D2+0.11D3；
wherein D isdFor an equivalent number of deaths, D1For the number of deaths in a traffic accident, D2The number of seriously injured people in a traffic accident D3The number of people with traffic accidents; the accident data also includes the equivalent number of deaths, the number of deaths in traffic accidents, the number of serious injuries in traffic accidents, and the number of serious injuries in traffic accidents.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the invention provides a method and a device for evaluating traffic safety states of urban areas, wherein the method comprises the following steps: acquiring road data, accident data and population data of an area to be evaluated; acquiring a monitoring video and a shooting video of an area to be evaluated, and extracting motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data; constructing a traffic safety accident index and a traffic law-keeping index; and determining the traffic safety state of the area to be evaluated. Compared with the prior art, the method and the system can acquire the traffic safety state index by constructing the static traffic safety accident index and the dynamic traffic law-keeping index, carry out quantitative evaluation on the traffic safety running state of the whole urban area to be evaluated, have higher accuracy and universality, are easy to maintain, and enable travelers and traffic management departments to visually know the traffic safety level of the area.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention.
Claims (10)
1. A method for evaluating traffic safety state in urban area is characterized by comprising the following steps:
acquiring road data, accident data and population data of an area to be evaluated; acquiring a monitoring video and a shooting video of a region to be evaluated, and extracting motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data from the monitoring video and the shooting video;
constructing a traffic safety accident index according to the road data, the accident data and the population data; according to the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, constructing a traffic law-keeping index of the area to be evaluated;
and determining the traffic safety state of the area to be evaluated according to the traffic safety accident index and the traffic law-keeping index.
2. The method for assessing the traffic safety state of the urban area according to claim 1, wherein the traffic law-keeping index of the area to be assessed is constructed according to the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data, and specifically comprises:
obtaining the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, and calculating a score S corresponding to the traffic illegal index of the area to be evaluated2Specifically:
wherein S is2The score corresponding to the traffic law-keeping index of the area to be evaluated,
index values of the ith road traffic law-keeping rate evaluation index;
the basic weight of the ith road traffic law-keeping rate evaluation index; n is the number of road traffic law-keeping rate evaluation indexes; the above-mentionedThe road traffic law-keeping rate evaluation index is determined through the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data.
3. The method according to claim 1, wherein a traffic safety accident indicator is constructed according to the road data, the accident data and the population data, and specifically comprises:
acquiring the total lane mileage L, the total number of the permanent population and the total number of the floating population P of the area to be evaluated, and calculating the equivalent comprehensive mortality K of the area to be evaluatedd：
Wherein, KdTo an equivalent overall mortality, DdIs the equivalent number of deaths; the road data comprises the total lane mileage of the area to be evaluated; the population data comprises the total number of the permanent population and the floating population;
calculating a correction coefficient K of the area to be evaluated;
K＝β1n1+β2n2+β3n3+β4n4；
wherein n is1For a larger number of accidents, n2Number of accidents for special events, n3The number of frequently occurring points of the traffic accident, n4Number of accident risk points, beta1Correction factor for large traffic accidents, beta2Correction factor for special event accidents, beta3Correction factor, beta, for points of frequent traffic accidents4Correction coefficients of accident risk points; the accident data comprises the number of large accidents, the number of special events, the number of frequent traffic accident points and the number of accident risk points;
according to the correction coefficient K of the area to be evaluated and the equivalent comprehensive mortality K of the area to be evaluateddCalculating the traffic of the area to be evaluatedSafety accident indexes are as follows:
S1＝80-Kd-K；
wherein S is1And the traffic safety accident index of the area to be evaluated is obtained.
4. The method for assessing the traffic safety status of an urban area according to claim 3, wherein the method for calculating the equivalent number of dead people comprises:
Dd＝D1+0.33D2+0.11D3；
wherein D isdFor an equivalent number of deaths, D1For the number of deaths in a traffic accident, D2The number of seriously injured people in a traffic accident D3The number of people with traffic accidents; the accident data also includes the equivalent number of deaths, the number of deaths in traffic accidents, the number of serious injuries in traffic accidents, and the number of serious injuries in traffic accidents.
5. The method according to claim 3, wherein the special event accidents comprise alcohol accidents, cargo accidents, electric accidents, pedestrian accidents and non-motor vehicle accidents.
6. The method according to claim 1, wherein the vehicle illegal data is obtained by recognizing vehicle illegal activities through the monitoring video and the shooting video, wherein the vehicle illegal activities include a first road traffic signal violation traffic light passing activity, an illegal lane change activity, a non-guided lane driving activity, a mobile phone driving activity, an illegal parking activity, and a non-courtesy pedestrian activity;
the bicycle illegal data are obtained by identifying bicycle illegal behaviors through the monitoring video and the shooting video, wherein the bicycle illegal behaviors comprise a second road traffic signal lamp violation behavior, a second illegal man-carrying behavior, a reverse driving behavior, a pushing behavior without getting off when passing through a pedestrian crosswalk and a second motor vehicle lane entering driving behavior;
the electric bicycle illegal data are obtained by identifying electric bicycle illegal behaviors through the monitoring video and the shooting video, wherein the electric bicycle illegal behaviors comprise a third illegal road traffic signal lamp passing behavior, a third illegal man-carrying behavior, a behavior of not wearing a safety helmet, a third behavior of pushing without getting off when passing through a pedestrian crossing and a third behavior of entering a motor vehicle lane;
the pedestrian illegal data are obtained by identifying pedestrian illegal behaviors through the monitoring video and the shooting video, wherein the pedestrian illegal behaviors comprise a fourth traffic signal lamp violation behavior and a motor vehicle lane entering behavior.
7. The urban area traffic safety state evaluation device is characterized by comprising an acquisition module, a construction module and an evaluation module, wherein,
the acquisition module is used for acquiring road data, accident data and population data of an area to be evaluated; acquiring a monitoring video and a shooting video of a region to be evaluated, and extracting motor vehicle illegal data, bicycle illegal data, electric bicycle illegal data and pedestrian illegal data from the monitoring video and the shooting video;
the construction module is used for constructing a traffic safety accident index according to the road data, the accident data and the population data; according to the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, constructing a traffic law-guarding index of the area to be evaluated;
and the evaluation module is used for determining the traffic safety state of the area to be evaluated according to the traffic safety accident index and the traffic law-keeping index.
8. The urban area traffic safety state assessment device according to claim 7, wherein the construction module constructs the traffic law-keeping indicator of the area to be assessed according to the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data, specifically:
the construction module acquires the motor vehicle illegal data, the bicycle illegal data, the electric bicycle illegal data and the pedestrian illegal data, and calculates a score S corresponding to the traffic illegal index of the area to be evaluated2Specifically:
wherein S is2The score corresponding to the traffic law-keeping index of the area to be evaluated,
index values of the ith road traffic law-keeping rate evaluation index;
the basic weight of the ith road traffic law-keeping rate evaluation index; n is the number of road traffic law-keeping rate evaluation indexes; the road traffic law-keeping rate evaluation index is determined through the motor vehicle law violation data, the bicycle law violation data, the electric bicycle law violation data and the pedestrian law violation data.
9. The urban area traffic safety state evaluation device according to claim 7, wherein the construction module constructs a traffic safety accident indicator according to the road data, the accident data and the population data, specifically:
the construction module obtains the total lane mileage L, the total number of the standing population and the total number of the floating population P of the area to be evaluated, and calculates the equivalent comprehensive mortality K of the area to be evaluatedd：
Wherein, KdTo an equivalent overall mortality, DdIs the equivalent number of deaths; the road data comprises the total lane mileage of the area to be evaluated; the population data comprises the total number of the permanent population and the floating population;
calculating a correction coefficient K of the area to be evaluated;
K＝β1n1+β2n2+β3n3+β4n4；
wherein n is1For a larger number of accidents, n2Number of accidents for special events, n3The number of frequently occurring points of the traffic accident, n4Number of accident risk points, beta1Correction factor for large traffic accidents, beta2Correction factor for special event accidents, beta3Correction factor, beta, for points of frequent traffic accidents4Correction coefficients of accident risk points; the accident data comprises the number of large accidents, the number of special events, the number of frequent traffic accident points and the number of accident risk points;
according to the correction coefficient K of the area to be evaluated and the equivalent comprehensive mortality K of the area to be evaluateddAnd calculating the traffic safety accident index of the area to be evaluated:
S1＝80-Kd-K；
wherein S is1And the traffic safety accident index of the area to be evaluated is obtained.
10. The apparatus for evaluating traffic safety status in urban area according to claim 9, wherein the method for calculating the equivalent number of dead people comprises:
Dd＝D1+0.33D2+0.11D3；
wherein D isdEquivalent number of dead people, D1For the number of deaths in a traffic accident, D2The number of seriously injured people in a traffic accident D3The number of people with traffic accidents; the accident data also includes the equivalent number of deaths, the number of deaths in traffic accidents, the number of serious injuries in traffic accidents, and the number of serious injuries in traffic accidents.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115148029A (en) * | 2022-06-29 | 2022-10-04 | 交通运输部公路科学研究所 | A method for predicting pedestrian traffic accidents Medium and electronic device |
| CN115966079A (en) * | 2022-11-28 | 2023-04-14 | 武汉珩链云信息科技有限公司 | Traffic road safety operation monitoring management platform based on data analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108922170A (en) * | 2018-06-13 | 2018-11-30 | 同济大学 | A kind of Intersection Safety Evaluation Method based on electric police grasp shoot data |
| CN108961748A (en) * | 2018-07-09 | 2018-12-07 | 同济大学 | A kind of intersection operational safety state arrangement method based on the illegal rate of equivalent |
| CN109584565A (en) * | 2018-12-25 | 2019-04-05 | 天津易华录信息技术有限公司 | A kind of Evaluation of Traffic Safety system and its evaluation number calculation method |
| CN110766258A (en) * | 2018-07-25 | 2020-02-07 | 高德软件有限公司 | Road risk assessment method and device |
| CN112037513A (en) * | 2020-09-01 | 2020-12-04 | 清华大学 | Real-time traffic safety index dynamic comprehensive evaluation system and construction method thereof |
-
2022
- 2022-01-24 CN CN202210083352.2A patent/CN114550445B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108922170A (en) * | 2018-06-13 | 2018-11-30 | 同济大学 | A kind of Intersection Safety Evaluation Method based on electric police grasp shoot data |
| CN108961748A (en) * | 2018-07-09 | 2018-12-07 | 同济大学 | A kind of intersection operational safety state arrangement method based on the illegal rate of equivalent |
| CN110766258A (en) * | 2018-07-25 | 2020-02-07 | 高德软件有限公司 | Road risk assessment method and device |
| CN109584565A (en) * | 2018-12-25 | 2019-04-05 | 天津易华录信息技术有限公司 | A kind of Evaluation of Traffic Safety system and its evaluation number calculation method |
| CN112037513A (en) * | 2020-09-01 | 2020-12-04 | 清华大学 | Real-time traffic safety index dynamic comprehensive evaluation system and construction method thereof |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115148029A (en) * | 2022-06-29 | 2022-10-04 | 交通运输部公路科学研究所 | A method for predicting pedestrian traffic accidents Medium and electronic device |
| CN115148029B (en) * | 2022-06-29 | 2023-09-19 | 交通运输部公路科学研究所 | Method, medium and electronic equipment for predicting pedestrian traffic accident |
| CN115966079A (en) * | 2022-11-28 | 2023-04-14 | 武汉珩链云信息科技有限公司 | Traffic road safety operation monitoring management platform based on data analysis |
| CN115966079B (en) * | 2022-11-28 | 2023-11-17 | 安徽光谷智能科技股份有限公司 | Traffic road safety operation monitoring management platform based on data analysis |
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