Method and system for evaluating road safety based on multi-dimensional influencing factors

Patent metadata

Field	Value
Publication number	US-11887472-B2
Application number	US-202217925748-A
Country	US
Kind code	B2
Filing date	Jul 4, 2022
Priority date	Dec 3, 2021
Publication date	Jan 30, 2024
Grant date	Jan 30, 2024

Abstract

The present invention discloses a method and system for evaluating road safety based on multi-dimensional influencing factors, and relates to the field of road safety technologies. Based on historical traffic data and corresponding safety influencing factors, safety evaluation models in different dimensions are respectively constructed, and road safety risk exposure is classified flexibly. The safety evaluation models in macro and micro dimensions are linked by using a constraint function, and influence mechanisms of the safety influencing factors are determined respectively. Specifically, a safety evaluation model is constructed and obtained for each sub-region in a limited region range. The safety evaluation model is applied to obtain influencing factors of safety of each traffic road in the sub-region, and safety evaluation is performed on the sub-region. Through the technical solutions of the present invention, an accurate, comprehensive, objective method for evaluating road safety that reflects authentic influence data is provided, which has a wider application scope.

First claim

What is claimed is:

1. A method for evaluating road safety based on multi-dimensional influencing factors, comprising: respectively constructing, for each sub-region in a limited region range, a safety evaluation model through step A to step D, and obtaining, by using the safety evaluation model through step E to step F, influencing factors of safety of each traffic road in the sub-region and performing safety evaluation on the sub-region, the sub-region having a plurality of sub-region characteristics, each traffic road in the sub-region having a plurality of traffic road characteristics:
step A: periodically obtaining, for the sub-region, historical traffic data of the sub-region within a preset duration and historical traffic data of each traffic road in the sub-region within the preset duration, and entering step B;
step B: using motor vehicle daily traffic as safety risk exposure, obtaining safety risk exposure corresponding to the sub-region and safety risk exposure corresponding to each traffic road of the sub-region based on the historical traffic data of the sub-region within the preset duration and the historical traffic data of each traffic road in the sub-region within the preset duration, quantifying the safety risk exposure corresponding to the sub-region and the safety risk exposure corresponding to each traffic road of the sub-region to obtain a categorical variable T corresponding to each of the safety risk exposure corresponding to the sub-region and the safety risk exposure corresponding to each traffic road of the sub-region, and entering step C;
step C: constructing, for each traffic road comprised in the sub-region, a road safety quantification sub-model based on the corresponding historical traffic data and the corresponding categorical variable T obtained in step B, to obtain road safety quantification sub-models respectively corresponding to the traffic roads in the sub-region; and
constructing, based on the road safety quantification sub-models respectively corresponding to the traffic roads in the sub-region and the historical traffic data of the sub-region, a region safety quantification sub-model corresponding to the sub-region, and entering step D;
step D: using, for each sub-region, a model group formed by the region safety quantification sub-model corresponding to the sub-region and the road safety quantification sub-models respectively corresponding to the traffic roads in the sub-region as a safety evaluation model corresponding to the sub-region, wherein an input by each of the region safety quantification sub-models in the model group is the historical traffic data corresponding to the road safety quantification sub-model;
step E: obtaining, according to step A to step C, a region safety quantification sub-model corresponding to the sub-region and each road safety quantification sub-model based on actual traffic data of the sub-region and actual traffic data of each traffic road in the sub-region, and entering step F; and
step F: solving, for the sub-region by using the safety evaluation model according to step D, the region safety quantification sub-model corresponding to the sub-region and the road safety quantification sub-models corresponding to the traffic roads in the sub-region by using a constraint function as a target, obtaining influencing factors for the plurality of sub-region characteristics of the sub-region and the plurality of traffic road characteristics of each traffic road in the sub-region based on the solved region safety quantification sub-model corresponding to the sub-region and road safety quantification sub-models corresponding to the traffic roads in the sub-region, and performing safety evaluation on the sub-region and each traffic road in the sub-region according to the influencing factors.

2. The method for evaluating road safety based on multi-dimensional influencing factors according to claim 1 , comprising: periodically obtaining historical traffic data of each sub-region in the limited region range within the preset duration, wherein the historical traffic data corresponding to each sub-region comprises: population density N of the sub-region, GDP of the sub-region, road network density K of the sub-region, motor vehicle annual average daily traffic AADT1 of the sub-region, a green area ratio L1 of the sub-region, a residential area ratio L2 of the sub-region, a non-residential area ratio L3 of the sub-region, a road area ratio L4 of the sub-region, and an average driving speed V of the sub-region; and
historical traffic data corresponding to each traffic road in each sub-region comprises: a traffic road length D, a traffic road lane quantity J, a traffic road width W, whether the traffic road is provided with an accommodation lane Q, motor vehicle annual average daily traffic AADT2 of the traffic road, A, intersection density A of the traffic road, and a traffic road grade D.

3. The method for evaluating road safety based on multi-dimensional influencing factors according to claim 2 , wherein step B further comprises: based on the historical traffic data of the sub-region within the preset duration and the historical traffic data of each traffic road in the sub-region within the preset duration, obtaining, for each traffic road corresponding to the sub-region according to the following formula:

T
=

{

1
,

AADT
i

>

AADT
i
′

0
,

AADT
i

<

AADT
i
′

the categorical variables T respectively corresponding to the safety risk exposure of the sub-region and the traffic roads, wherein AADT i is AADT1 or AADT2; when AADT i =AADT1, AADT i ′ is a median value of motor vehicle annual average daily traffic of all the sub-regions in the limited region range; and when AADT i =AADT2, AADT i ′ is a median value of motor vehicle annual average daily traffic of all the traffic roads in the sub-region.

4. The method for evaluating road safety based on multi-dimensional influencing factors according to claim 3 , wherein step C further comprises: obtaining, for each traffic road comprised in the sub-region according to the following formula:
lnE 2 n=θ 1 T+θ 2 J n +θ 3 W n +θ 4 Q n +θ 5 T=0 T n +θ 5 T=1 T n +θ 5 T=0 AADT2 n +θ 5 T=1 AADT2 n +θ 6 A n +θ 7 D n +ε n

the road safety quantification sub-model lnE2 n corresponding to each traffic road, wherein E2 is an accident occurrence amount of the traffic road in a preset time period; ε n is an error term of the road safety quantification sub-model; n ranges from 1 to N; N is a total quantity of traffic roads comprised in each sub-

Assignees

• Univ Southeast

Inventors

• Guo Yanyong
• Ding Hongliang
• Wu Yao
• Liu Pan
• Liu Pei

Classifications

• G08G1/0129 Primary

  for creating historical data or processing based on historical data · CPC title

• G08G1/0137

  for specific applications · CPC title

• G06F17/11

  for solving equations {, e.g. nonlinear equations, general mathematical optimization problems (optimization specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G08G1/0141

  for traffic information dissemination · CPC title

• G08G1/0133

  for classifying traffic situation · CPC title