CN114495501B - Road safety analysis method based on combined geographic autoregressive matching - Google Patents

Road safety analysis method based on combined geographic autoregressive matching Download PDF

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CN114495501B
CN114495501B CN202210100208.5A CN202210100208A CN114495501B CN 114495501 B CN114495501 B CN 114495501B CN 202210100208 A CN202210100208 A CN 202210100208A CN 114495501 B CN114495501 B CN 114495501B
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郭延永
许轩畅
丁红亮
刘攀
刘佩
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Abstract

The invention discloses a road safety analysis method based on combined geographic autoregressive matching, which relates to the technical field of road traffic safety. By the technical scheme, the safety of the traffic road can be effectively improved.

Description

Road safety analysis method based on combined geographic autoregressive matching
Technical Field
The invention relates to the technical field of road traffic safety, in particular to a road safety analysis method based on combined geographic autoregressive matching.
Background
With the development of social economy, the quantity of automobile reserves in society is continuously increased, and meanwhile, the problems of road safety and the like are also generated. In order to improve road safety, traffic safety management policies are being implemented in many countries and regions. Meanwhile, the effect of the implemented traffic safety management policy becomes a research hotspot in the related field. Among them, the trend score method is an effective technical means for studying causal relationship between road safety management policy and road safety, and the effectiveness of the method is authenticated by many studies. However, the road traffic system is a spatial network structure, and adjacent traffic cells are restricted and influenced by each other, that is, under the influence of no external condition, the attribute of the adjacent cell can also cause the adjacent cell to generate corresponding traffic accidents, which is called geospatial correlation. However, the trend scoring method does not consider the limitation of the problem in the causal analysis, so that certain deviation occurs in the analysis result, and the judgment of the effectiveness of the policy is affected.
Disclosure of Invention
The invention aims to provide a road safety analysis method based on combined geographic autoregressive matching, which aims to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
a road safety analysis method based on combined geographic autoregressive matching is disclosed, wherein road safety analysis is carried out on each target road to be optimized in a target area based on road information of each target road in the target area, which is acquired in a preset time period, so as to obtain safety optimization conditions of each target road to be optimized, and safety optimization is carried out on the target road to be optimized by utilizing the safety optimization conditions, and the road safety analysis method comprises the following steps:
step A, acquiring road information of each target road in a target area and the number of traffic accidents of each target road in a preset time period, and then entering step B;
b, respectively aiming at each target road, extracting the characteristic values of preset various types of characteristics which comprise traffic accident influence factor characteristics and correspond to the target road based on the corresponding road information of the target road, preprocessing the target road based on the extracted traffic accident influence factor characteristics, predicting the predicted number of traffic accidents in a preset time period of the target road corresponding to the current time period towards the future time direction, and then entering the step C;
step C, aiming at each target road, calculating and obtaining the safety factor of each target road based on the road information of each target road, dividing each target road into an optimized target road and a target road to be optimized based on the safety factor corresponding to each target road, sequentially matching the safety factors of each optimized target road and each target road to be optimized in the target area to obtain a safety matching group, namely obtaining each safety matching group corresponding to the target area, and then entering the step D;
and D, respectively aiming at each safety matching group, calculating the safety difference between the target road to be optimized and the optimized target road corresponding to the safety matching group, further acquiring influence factors influencing the safety factor of each target road according to the safety factor, and carrying out safety optimization on the target road to be optimized based on the road information of the corresponding optimized target road in the safety matching group and the influence factors influencing the safety factor corresponding to the optimized target road.
Further, the road information includes a traffic density D of the target road, an economic level GDP of the target road, a daily traffic average traffic Q of the target road, a road network density L of the target road, a bus stop density B of the target road, a track stop density R of the target road, and a traffic node density S of the target road.
Further, in the step B, the target road is preprocessed according to the following formula:
Figure BDA0003492078480000021
calculating and obtaining the predicted number of the traffic accidents, x, of the target road i i Is the traffic accident influencing factor, beta, corresponding to the target road i i A regression coefficient epsilon corresponding to the predicted number of traffic accidents of the target road i i Error terms corresponding to the predicted number of traffic accidents for the target road i,
Figure BDA0003492078480000022
represents the regression coefficient beta i Correcting the obtained traffic accident prediction number based on the traffic accident occurrence number of the target road i to obtain a traffic accident correction number N 'according to normal distribution' i ，N′ i ＝N″ i -N i Wherein N ″) i The number of traffic accidents.
Further, in the aforementioned step C, according to the following formula:
Figure BDA0003492078480000023
calculating safety factor p of target road i in target area n Wherein, in the process,
Figure BDA0003492078480000024
is a constant coefficient, mu 1 -μ 7 And the regression coefficients respectively correspond to the road information of each type.
Further, in the step C, based on the safety factors corresponding to each target road in the target area, safety factor matching is sequentially performed on each optimized target road and each target road to be optimized, that is, when the difference between the safety factor of the optimized target road and the safety factor of the target road to be optimized is minimum, the optimized target road and the target road to be optimized are a safety matching group.
Further, in the aforementioned step D, according to the following formula:
Figure BDA0003492078480000031
calculating and obtaining a safety gap ATT between an optimization target road and an optimization target road in a safety matching group I in the target area, wherein N is the number of target roads contained in the target area, N' iS Optimizing a traffic accident correction coefficient, N ', corresponding to the target road in the safety matching group' iK And modifying the traffic accident correction coefficient of the target road to be optimized in the safety matching group.
Compared with the prior art, the road safety analysis method based on combined geographic autoregressive matching has the following technical effects by adopting the technical scheme: according to the technical scheme of the invention, the number of the traffic accidents is predicted, the prediction result is corrected, the geographic autoregressive model is used for eliminating the geographic space influence, the influence factors and the influence effect of traffic safety on the road safety accidents can be accurately judged, the target road is subjected to safety optimization according to the obtained correction result, the problem that the road safety is influenced by the geographic factors of different roads in the target area is solved, and the safety of the target road is effectively improved.
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Fig. 1 is a schematic flow chart of a road safety analysis method according to an exemplary embodiment of the present invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily defined to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
With reference to fig. 1, the present invention exemplarily provides a road safety analysis method based on combined geographic autoregressive matching, which performs road safety analysis on each target road based on road information corresponding to each target road in a target area acquired within a preset time period, where the road information includes a pedestrian flow density D of the target road, an economic level GDP of the target road, a daily traffic average traffic Q of the target road, a road network density L of the target road, a bus stop density B of the target road, a track stop density R of the target road, and a traffic node density S of the target road, to obtain a safety optimization condition of each target road to be optimized, and performs safety optimization on the target road to be optimized, and includes the following steps:
step A, acquiring road information of each target road corresponding to the target area in a preset time period and the number of traffic accidents of each target road, and then entering step B.
B, respectively aiming at each target road, extracting the characteristic values of preset various types of characteristics corresponding to the target road and containing the traffic accident influence factor characteristics based on the road information corresponding to the target road, preprocessing the target road based on the traffic accident influence factor, and according to the following formula:
Figure BDA0003492078480000041
calculating and obtaining the predicted quantity of the traffic accidents, x, of the target road i in the preset time period corresponding to the current time point to the future time direction i Is the traffic accident influencing factor, beta, corresponding to the target road i i A regression coefficient epsilon corresponding to the predicted number of traffic accidents of the target road i i Error terms corresponding to the predicted number of the traffic accidents of the target road i,
Figure BDA0003492078480000042
following a normal distribution for the geographic regression coefficients
Figure BDA0003492078480000043
The calculation formula is as follows,
Figure BDA0003492078480000044
wherein w ki When the value is 1, the target road k in the target area is adjacent to the target road i, otherwise, w ki Is 0, the obtained predicted number of traffic accidents is corrected based on the number of traffic accidents occurring on the target road i, and a corrected number of traffic accidents N 'is obtained' i ，N′ i ＝N″ i -N i Wherein N ″) i The number of traffic accidents is then entered into step C.
Step C, based on the road information of each target road, according to the following formula:
Figure BDA0003492078480000045
calculating safety factor p of target road i in target area n Wherein, in the step (A),
Figure BDA0003492078480000046
is a constant coefficient, mu 1 -μ 7 Regression coefficients respectively corresponding to various types of road information are obtained, a target road with a safety factor larger than a preset threshold in the target area is divided into an optimized target road, a target road with a safety factor smaller than the preset threshold is divided into a target road to be optimized,
and D, extracting influence factors influencing the safety factor of the target road according to the safety factors respectively corresponding to the optimized target road and the target road to be optimized, matching the safety factors of the optimized target road and the target road to be optimized in the target area, wherein when the difference between the safety factor of the optimized target road and the safety factor of the target road to be optimized is minimum, the optimized target road and the target road to be optimized are safety matching groups, namely, each safety matching group corresponding to the target area is obtained, and then the step D is carried out.
Step D, aiming at each safety matching group, respectively, according to the following formula:
Figure BDA0003492078480000047
calculating and obtaining a safety gap ATT between an optimization target road and an optimization target road in a safety matching group I in a target area, wherein N is the number of target roads contained in the target area, N' iS Optimizing a traffic accident correction coefficient, N ', corresponding to the target road in the safety matching group' iK And performing safety optimization on the target road to be optimized based on the road information of the corresponding optimized target road in the safety matching group and the influence factor of the safety factor for the traffic accident correction coefficient of the target road to be optimized in the safety matching group.
Example (b):
road information of each target road in the target area is collected, as shown in table 1-1:
TABLE 1-1 statistics of sample data collection
Figure BDA0003492078480000051
Based on the process in step B, the following formula is used:
Figure BDA0003492078480000052
Figure BDA0003492078480000053
N′ A1 ＝N″ A1 -N A1
the method comprises the steps of sequentially obtaining the predicted number of traffic accidents and the corrected number of traffic accidents to eliminate the number of accidents under the condition of influence of a geographic space, wherein the influence caused by the geographic space is that an area 1 and an area 2 are adjacent and matched with each other, under the condition that the safety optimization does not exist, the area 2 can enable the area 1 to generate a certain number of accidents per se, and if A1 and C1 are mutually a safety matching group, the condition that p is met A1 -p C1 ＝min(p A1 -p Ci ) The conditions of (1).
Based on the process in the step C, taking A1 as an example, calculating and obtaining a safety factor of the road A1:
Figure BDA0003492078480000061
according to the procedure described in step D,
Figure BDA0003492078480000062
and according to the obtained safety difference, performing safety optimization on the target road to be optimized by using the safety influence factors of the target road to be optimized.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims (1)

1. A road safety analysis method based on combined geographic autoregressive matching is characterized in that road information corresponding to each target road on a target area acquired within a preset time period is based on, the road information comprises pedestrian flow density D of the target road, economic level GDP of the target road, daily traffic average traffic Q of the target road, road network density L of the target road, bus stop density B of the target road, track stop density R of the target road and traffic node density S of the target road, road safety analysis is carried out on each target road, safety optimization conditions of each target road to be optimized are obtained, and safety optimization is carried out on the target road to be optimized, and the road safety analysis method comprises the following steps:
step A, acquiring road information of each target road corresponding to a target area and the number of traffic accidents of each target road in a preset time period, and then entering step B;
b, respectively aiming at each target road, extracting a preset type characteristic value which comprises traffic accident influence factors and corresponds to the target road based on road information corresponding to the target road, preprocessing the target road based on the traffic accident influence factors, and according to the following formula:
Figure FDA0003856314930000011
calculating and obtaining the predicted quantity x of the traffic accidents of the target road i in the preset time period from the current time point to the future time direction i Is the traffic accident influencing factor, beta, corresponding to the target road i i Regression coefficient, epsilon, corresponding to the predicted number of traffic accidents for the target road i i Error terms corresponding to the predicted number of the traffic accidents of the target road i,
Figure FDA0003856314930000012
following a normal distribution for the geographic regression coefficients
Figure FDA0003856314930000013
The calculation formula is as follows,
Figure FDA0003856314930000014
wherein w ki When the value is 1, the target road k in the target area is adjacent to the target road i, otherwise, w ki Is 0, the obtained predicted number of traffic accidents is corrected based on the number of traffic accidents occurring on the target road i, and a corrected number of traffic accidents N 'is obtained' i ，N′ i ＝N″ i -N i Wherein N ″) i C, the number of the traffic accidents is calculated, and then the step C is carried out;
step C, based on the road information of each target road, according to the following formula:
Figure FDA0003856314930000015
calculating safety factor p of target road i in target area n Wherein, in the step (A),
Figure FDA0003856314930000016
is a constant coefficient, mu 1 -μ 7 The regression coefficients corresponding to the road information of each type are respectively, the target road with the safety factor larger than the preset threshold value in the target area is divided into an optimization target road, the target road with the safety factor smaller than the preset threshold value is divided into a target road to be optimized,
according to the safety factors respectively corresponding to the optimized target road and the target road to be optimized, extracting the influence factors influencing the safety factor of the target road, matching the safety factors of the optimized target road and the target road to be optimized in the target area, and when the difference between the safety factor of the optimized target road and the safety factor of the target road to be optimized is minimum, the optimized target road and the target road to be optimized are safety matched groups, namely, each safety matched group corresponding to the target area is obtained, and then the step D is carried out;
and D, aiming at each safety matching group respectively, according to the following formula:
Figure FDA0003856314930000021
calculating and obtaining a safety gap ATT between an optimization target road and an optimization target road in a safety matching group I in a target area, wherein N is the number of target roads contained in the target area, N' iS Optimizing a traffic accident correction coefficient, N ', corresponding to the target road in the safety matching group' iK And performing safety optimization on the target road to be optimized based on the road information of the corresponding optimized target road in the safety matching group and the influence factor of the safety factor for the traffic accident correction coefficient of the target road to be optimized in the safety matching group.
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