CN-117496709-B - Traffic safety hidden danger space-time investigation method based on regional highway digitization

CN117496709BCN 117496709 BCN117496709 BCN 117496709BCN-117496709-B

Abstract

The invention discloses a traffic safety hidden danger space-time investigation method based on regional highway digitization, which comprises the steps of 1, determining a research region, dividing the research region into a plurality of grids with the same size, taking the grids as research units, 2, obtaining accident data and safety hidden danger data in each grid, 3, establishing a local model of space and time of traffic accidents, 4, calculating the safety hidden danger with the greatest influence on the traffic accidents in each hour through an algorithm, and 5, calculating the safety hidden danger with the greatest influence on the traffic accidents in each grid through the algorithm. According to the invention, through researching the relationship between the traffic accidents and the potential safety hazards in the grids, then determining the potential safety hazards with serious influence on the traffic accidents in each hour and the grids, comprehensively considering the space-time characteristics of the potential safety hazards, thereby more accurately finding out factors closely related to the traffic safety and providing more targeted suggestions for traffic management and planning.

Inventors

LIU HUIWEN
ZHANG WEIHUA
CHENG ZEYANG
HUANG WENJUAN
XIONG LIJUN
GAO JIAN
WANG SHIGUANG
LI MENGFAN

Assignees

合肥工业大学

Dates

Publication Date: 20260512
Application Date: 20231108

Claims (3)

1. A traffic safety hidden danger space-time investigation method based on regional highway digitization is characterized by comprising the following steps: Dividing the circumscribed rectangle of the urban research area into m multiplied by n grids with the side length of l, wherein m is the total number of rows and n is the total number of columns, wherein any row number is marked as u, and any column number is marked as v, wherein u is [1, m ] and v is [1, n ]; counting the accident number Y (u, v, t) of any grid of the u th row and v th column in the t-th period; Obtaining J potential safety hazards { X j (u, v) |j=1, 2,.. J }, of a grid of a ith row and a ith column, wherein X j (u, v) represents the jth potential safety hazard of the grid of the ith row and the jth column; Step2, establishing a local model of the space and time of the congestion index according to the formula (1): In the formula (1), β 0 (u, v, t) represents an intercept term in the grid of the u th row and v th column in the t th period, β j (u, v, t) represents a regression coefficient of the potential safety hazard X j (u, v) in the grid of the u th row and v th column in the t th period, and ε (u, v, t) represents an error term of the grid of the u th row and v th column in the t th period; Step 3, calculating potential safety hazards with the greatest influence on traffic accidents in the t-th period; Step 3.1, initializing t=1, defining a variable Q, and defining an evaluation variable S; step 3.2, initializing j=1, q= 0,S =0; step 3.3, calculating an average regression coefficient S j (t) of the jth potential safety hazard X j in the t-th period according to the formula (2); In the formula (2), i represents an absolute value symbol; Step 3.4, judging whether S j (t) is satisfied or not, if so, assigning S j (t) to S, assigning j to Q, and then executing step 3.5, otherwise, directly executing step 3.5; step 3.5, judging whether J < J is true, if so, assigning j+1 to J, and returning to step 3.3, otherwise, indicating that the potential safety hazard with the biggest influence on the traffic jam in the t-th period is the Q-th potential safety hazard X Q ; Step 3.6, judging whether T < T is met, if so, assigning t+1 to T, and returning to step 3.2, otherwise, indicating the potential safety hazard with the greatest influence of the congestion index in all time periods, and executing step 4, wherein T represents the total time period number; step 4, calculating potential safety hazards with the greatest influence on traffic accidents in each grid; Step 4.1, initializing u=1; Step 4.2, initializing v=1; step 4.3, defining a variable H and defining an evaluation variable K; step 4.4, initializing j=1, h=0, k=0; Step 4.5, calculating an average regression coefficient K j (u, v) of the jth potential safety hazard X j in the grid of the ith row and the jth column according to the formula (3); Step 4.6, judging whether K j (u, v) is met or not, if yes, assigning K j (u, v) to K, assigning j to H, and then executing step 4.7; Step 4.7, judging whether J < J is true, if so, assigning j+1 to J, and returning to step 4.5, otherwise, indicating that the potential safety hazard with the biggest influence on the traffic jam in the grid showing the ith row and the ith column is the H-th potential safety hazard X H ; Step 4.8, judging whether v < n > is satisfied, if so, assigning v+1 to v, and returning to step 4.3, otherwise, executing step 4.9; and 4.9, judging whether u < m is met, if so, assigning u+1 to u, and returning to the step 4.2, otherwise, indicating that the potential safety hazard with the greatest influence on the traffic jam in each grid is obtained.
2. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that supports the processor to perform the traffic safety hazard space-time investigation method of claim 1, the processor being configured to execute the program stored in the memory.
3. A computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor performs the steps of the traffic safety hazard space-time investigation method according to claim 1.

Description

Traffic safety hidden danger space-time investigation method based on regional highway digitization Technical Field The invention relates to the field of urban traffic planning and traffic big data research, in particular to a traffic safety hidden danger space-time investigation method based on regional highway digitization. Background With the acceleration of urban steps in China, the rapid increase of urban population brings unprecedented pressure to urban sustainability, especially in traffic problems. Traffic development often promotes the prosperity of urban economy, but the development of such traffic also causes safety problems that seriously affect local residents. Traffic accidents are now one of the leading causes of death worldwide. With the rapid increase of the quantity of Chinese automobiles, urban traffic continues to increase. The traffic safety risk continues to rise and cannot be ignored. It is widely recognized that the construction environment has a considerable impact on the occurrence of traffic accidents. As an important building environment factor, land utilization not only affects the population and socioeconomic characteristics of cities, but also determines traffic volume and traffic behavior patterns. Currently, the development of Chinese lands is undergoing the transition from increment to stock, and the update and transformation of old urban areas are mainly performed by intensive development of lands, and the method has the characteristics of high strength and high density development. The high-intensity development of the China urban land causes population concentration and traffic jam, and causes serious road safety problems. By determining factors that affect the frequency of occurrence of accidents, road safety may be improved. Deep knowledge of how land use features affect accident risk can help decision makers and traffic planners to formulate effective strategies to improve traffic safety. In the prior method for checking the potential safety hazard of the highway, although some factors related to traffic conditions and road structures may be considered, intensive researches are often not conducted in a space-time angle which fully covers the potential safety hazard. Traditional investigation methods are usually focused on the condition of the traffic facilities and the traffic flow data, and the potential influence of potential safety hazards on traffic safety is often ignored. The potential safety hazards are various building facilities around the road, intersection layouts, road lighting, pedestrian crossing facilities, etc., and the temporal and spatial distribution of these environmental elements. These factors are closely related to traffic safety, for example, traffic safety concerns in certain areas may be related to building concentration, street lamp lighting, intersection design, and other safety factors. The safety hidden trouble features of different time periods and areas can also have differential influence on traffic safety. Therefore, lack of research on space-time angles of potential safety hazards may result in insufficient comprehensive and accurate investigation and analysis of the potential safety hazards of roads. Disclosure of Invention The invention overcomes the defects of the prior art, and provides a traffic safety hidden danger space-time investigation method based on regional highway digitization, so that factors closely related to traffic safety can be more accurately found out, thereby providing more targeted suggestions for traffic management and planning. In order to achieve the aim of the invention, the invention adopts the following technical scheme: The invention discloses a traffic safety hidden danger space-time investigation method based on regional highway digitization, which is characterized by comprising the following steps: Dividing the circumscribed rectangle of the urban research area into m multiplied by n grids with the side length of l, wherein m is the total number of rows and n is the total number of columns, wherein any row number is marked as u, and any column number is marked as v, wherein u is [1, m ] and v is [1, n ]; counting the accident number Y (u, v, t) of any grid of the u th row and v th column in the t-th period; Obtaining J potential safety hazards { X j (u, v) |j=1, 2,.. J }, of a grid of a ith row and a ith column, wherein X j (u, v) represents the jth potential safety hazard of the grid of the ith row and the jth column; Step2, establishing a local model of the space and time of the congestion index according to the formula (1): In the formula (1), β 0 (u, v, t) represents an intercept term in the grid of the u th row and v th column in the t th period, β j (u, v, t) represents a regression coefficient of the potential safety hazard X j (u, v) in the grid of the u th row and v th column in the t th period, and ε (u, v, t) represents an error term of the grid of the u th row and v th column in the t th