A space-time checking method for traffic safety hidden dangers based on regional highway digitization

Abstract

The application discloses a kind of traffic safety hidden danger space-time investigation methods based on regional highway digitization, its steps include:1, determine research area, and the research area is divided into multiple same size grid, and grid is used as research unit;2, obtain the number of accident data and safety hidden danger data in each grid;3, establish the local model of space and time of traffic accident;4, the safety hidden danger that most influences traffic accident in each hour is calculated by algorithm;5, the safety hidden danger that most influences traffic accident in each grid is calculated by algorithm.The application studies the relationship between traffic accident and safety hidden danger in grid, then determines the safety hidden danger that influences traffic accident seriously in each hour and grid, and comprehensively considers the space-time characteristics of safety hidden danger, so that more accurate factors closely related to traffic safety can be found out, to provide more targeted suggestions for traffic management and planning.

Description

Technical Field

[0001] This invention relates to the fields of urban traffic planning and traffic big data research, specifically a method for spatiotemporal investigation of traffic safety hazards based on regional highway digitization. Background Technology

[0002] With the rapid pace of urbanization in China, the explosive growth of urban populations has placed unprecedented pressure on urban sustainability, particularly regarding transportation. While transportation development often fuels urban economic prosperity, it also creates serious safety issues for local residents. Traffic accidents are now one of the leading causes of death worldwide. With the rapid increase in car ownership in China, urban traffic continues to grow. Traffic safety risks are rising and cannot be ignored.

[0003] It is widely believed that the built environment has a significant impact on traffic accidents. As a crucial built environment factor, land use not only influences a city's demographic and socioeconomic characteristics but also determines traffic volume and behavior patterns. Currently, China's land development is shifting from incremental to stock-based growth, with urban renewal and redevelopment primarily focused on intensive land development, characterized by high intensity and high density. This high-intensity urban development in China has led to population concentration and traffic congestion, causing serious road safety problems. Identifying the factors influencing accident frequency can improve road safety. A deeper understanding of how land use characteristics affect accident risk can help policymakers and transportation planners develop effective strategies to improve traffic safety.

[0004] Previous methods for identifying highway safety hazards, while perhaps considering factors related to traffic conditions and road structure, often failed to adequately encompass a comprehensive study of the spatiotemporal aspects of these hazards. Traditional methods typically focus on the condition of traffic facilities and traffic flow data, neglecting the potential impact of safety hazards on traffic safety. Safety hazards encompass various buildings and facilities around highways, intersection layouts, street lighting, pedestrian crossings, and the spatiotemporal distribution of these environmental elements. These factors are closely related to traffic safety; for example, traffic safety hazards in certain areas may be related to building density, street lighting conditions, and intersection design. The characteristics of safety hazards in different time periods and regions also have varying impacts on traffic safety. Therefore, the lack of research on the spatiotemporal perspective of safety hazards may lead to an incomplete and inaccurate identification and analysis of highway safety hazards. Summary of the Invention

[0005] This invention overcomes the shortcomings of existing technologies and proposes a spatiotemporal method for identifying traffic safety hazards based on regional highway digitization, aiming to more accurately identify factors closely related to traffic safety and thus provide more targeted suggestions for traffic management and planning.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] The present invention provides a method for spatiotemporal investigation of traffic safety hazards based on regional highway digitization, characterized by the following steps:

[0008] Step 1: Divide the bounding rectangle of the urban study area into m×n grids with side length l, where m is the total number of rows and n is the total number of columns; where any number of rows is denoted as u and any number of columns is denoted as v, where u∈[1,m] and v∈[1,n].

[0009] Count the number of accidents Y(u,v,t) in the t-th time period for any grid cell in the u-th row and v-th column.

[0010] Obtain the J types of security risks {X} in the u-th row and v-th column of the grid. j (u,v)|j=1,2,…,J}, where X j (u,v) represents the j-th security hazard in the grid at row u and column v;

[0011] Step 2: Establish a local spatial and temporal model of the congestion index based on equation (1):

[0012]

[0013] In equation (1), β0(u,v,t) represents the intercept term in the grid at row u and column v in the t-th time period, β j (u,v,t) represents the safety hazard X within the grid in the u-th row and v-th column during the t-th time period. j The regression coefficient of (u,v), ε(u,v,t) represents the error term of the grid in row u and column v during the t-th time period;

[0014] Step 3: Calculate the safety hazard with the greatest impact on traffic accidents in the t-th time period;

[0015] Step 3.1: Initialize t = 1, define variable Q, and define evaluation variable S;

[0016] Step 3.2: Initialize j = 1, Q = 0, S = 0;

[0017] Step 3.3: Calculate the j-th safety hazard X in the t-th time period according to formula (2). j The average regression coefficient S j (t);

[0018]

[0019] In equation (2), || represents the absolute value symbol;

[0020] Step 3.4, Determine S j If (t) > S, then change S. j (t) Assign the value to S, assign the value to j to Q, and then execute step 3.5; otherwise, execute step 3.5 directly.

[0021] Step 3.5: Determine if j < J is true. If true, assign j + 1 to j and return to step 3.3; otherwise, it means that the safety hazard with the greatest impact on traffic congestion in the t-th time period is the Q-th safety hazard X. Q ;

[0022] Step 3.6: Determine if t < T is true. If true, assign t+1 to t and return to step 3.2; otherwise, it indicates that the safety hazard with the greatest impact on the congestion index under all time periods has been obtained, and step 4 is executed; where T represents the total number of time periods.

[0023] Step 4: Calculate the safety hazard with the greatest impact on traffic accidents within each grid.

[0024] Step 4.1: Initialize u = 1;

[0025] Step 4.2: Initialize v = 1;

[0026] Step 4.3: Define variable H and evaluation variable K;

[0027] Step 4.4: Initialize j = 1, H = 0, K = 0;

[0028] Step 4.5: Calculate the j-th safety hazard X in the u-th row and v-th column of the grid according to equation (3). j The average regression coefficient K j (u,v);

[0029]

[0030] Step 4.6, Determine K j If (u,v)>K holds true, then set K. j After assigning (u,v) to K and j to H, proceed to step 4.7; otherwise, proceed directly to step 4.7.

[0031] Step 4.7: Determine if j < J is true. If true, assign j + 1 to j and return to step 4.5; otherwise, it means that the safety hazard with the greatest impact on traffic congestion in the u-th row and v-th column of the grid is the H-th safety hazard X. H ;

[0032] Step 4.8: Determine if v < n is true. If true, assign v + 1 to v and return to step 4.3; otherwise, execute step 4.9.

[0033] Step 4.9: Determine if u < m is true. If true, assign u+1 to u and return to step 4.2; otherwise, it means that the safety hazard with the greatest impact on traffic congestion in each grid has been obtained.

[0034] The present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the spatiotemporal investigation method for traffic safety hazards, and the processor is configured to execute the program stored in the memory.

[0035] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs the steps of the method for spatiotemporal investigation of traffic safety hazards.

[0036] Compared with existing technologies, the beneficial technical effects of the present invention are reflected in:

[0037] 1. This invention, based on the concept of highway digitization, divides the highway network into grids of equal size and combines them with real-time traffic data to achieve digital spatiotemporal investigation of highway safety hazards. This method enables a comprehensive and accurate grasp of the distribution and spatiotemporal characteristics of safety hazards, providing more accurate data support for traffic safety management.

[0038] 2. By comprehensively considering the spatiotemporal characteristics of safety hazards, this invention can more accurately identify factors closely related to traffic safety. This helps traffic managers formulate more targeted traffic safety management and planning strategies, fundamentally alleviating the problem of road traffic accidents.

[0039] 3. The method of this invention uses a unified grid division, allowing traffic managers to conduct cross-regional comparisons and analyses. This helps to discover differences and patterns in traffic safety between different regions, providing a more holistic perspective for traffic management and planning.

[0040] 4. This invention divides the road network into grids of equal size, studies the relationship between traffic accidents and safety hazards within the grids, and then identifies the safety hazards that have a serious impact on traffic accidents in each hour and the safety hazards that have a serious impact on traffic accidents within each grid. This research method, which comprehensively considers the spatiotemporal characteristics of safety hazards, can more accurately identify factors closely related to traffic safety and provide more targeted suggestions for traffic management and planning. Attached Figure Description

[0041] Figure 1 This is the overall flowchart of the present invention;

[0042] Figure 2 This is a time cycle diagram of the present invention;

[0043] Figure 3 This is a spatial circulation diagram of the present invention;

[0044] Figure 4 A gridded map of the research scope. Detailed Implementation

[0045] In this embodiment, as Figure 1 As shown, a method for spatiotemporal investigation of traffic safety hazards based on regional highway digitization involves dividing the road network into grids of equal size, studying the relationship between traffic accidents and safety hazards within each grid, and then identifying the safety hazards with the most serious impact on traffic accidents each hour. This method comprehensively considers the spatiotemporal characteristics of safety hazards to more accurately identify factors closely related to traffic safety. Specifically, the method includes the following steps:

[0046] Step 1: Divide the bounding rectangle of the urban study area into m×n grids with side length l, where m is the total number of rows and n is the total number of columns; where any number of rows is denoted as u and any number of columns is denoted as v, where u∈[1,m] and v∈[1,n].

[0047] Count the number of accidents Y(u,v,t) in the t-th time period for any grid cell in the u-th row and v-th column.

[0048] Obtain the J types of security risks {X} in the u-th row and v-th column of the grid. j (u,v)|j=1,2,…,J}, where X j (u,v) represents the j-th security hazard in the grid at row u and column v;

[0049] Step 2: Establish a local spatial and temporal model of the congestion index based on equation (1):

[0050]

[0051] In equation (1), β0(u,v,t) represents the intercept term in the grid at row u and column v in the t-th time period, β j (u,v,t) represents the safety hazard X within the grid in the u-th row and v-th column during the t-th time period. j The regression coefficient of (u,v), ε(u,v,t) represents the error term of the grid in row u and column v during the t-th time period;

[0052] like Figure 2 The diagram shown is a cyclic graph illustrating the safety hazards that have the greatest impact on traffic accidents in each time period, as calculated by this invention.

[0053] Step 3, as follows Figure 3 As shown, calculate the safety hazard with the greatest impact on traffic accidents in the t-th time period;

[0054] Step 3.1: Initialize t = 1, define variable Q, and define evaluation variable S;

[0055] Step 3.2: Initialize j = 1, Q = 0, S = 0;

[0056] Step 3.3: Calculate the j-th safety hazard X in the t-th time period according to formula (2). j The average regression coefficient S j (t);

[0057]

[0058] In equation (2), || represents the absolute value symbol;

[0059] Step 3.4, Determine S j If (t) > S, then change S. j (t) Assign the value to S, assign the value to j to Q, and then execute step 3.5; otherwise, execute step 3.5 directly.

[0060] Step 3.5: Determine if j < J is true. If true, assign j + 1 to j and return to step 3.3; otherwise, it means that the safety hazard with the greatest impact on traffic congestion in the t-th time period is the Q-th safety hazard X. Q ;

[0061] Step 3.6: Determine if t < T is true. If true, assign t+1 to t and return to step 3.2; otherwise, it indicates that the safety hazard with the greatest impact on the congestion index under all time periods has been obtained, and step 4 is executed; where T represents the total number of time periods.

[0062] Step 4: Calculate the safety hazard with the greatest impact on traffic accidents within each grid.

[0063] Step 4.1: Initialize u = 1;

[0064] Step 4.2: Initialize v = 1;

[0065] Step 4.3: Define variable H and evaluation variable K;

[0066] Step 4.4: Initialize j = 1, H = 0, K = 0;

[0067] Step 4.5: Calculate the j-th safety hazard X in the u-th row and v-th column of the grid according to equation (3). j The average regression coefficient K j (u,v);

[0068]

[0069] Step 4.6, Determine K j If (u,v)>K holds true, then set K. j After assigning (u,v) to K and j to H, proceed to step 4.7; otherwise, proceed directly to step 4.7.

[0070] Step 4.7: Determine if j < J is true. If true, assign j + 1 to j and return to step 4.5; otherwise, it means that the safety hazard with the greatest impact on traffic congestion in the u-th row and v-th column of the grid is the H-th safety hazard X. H ;

[0071] Step 4.8: Determine if v < n is true. If true, assign v + 1 to v and return to step 4.3; otherwise, execute step 4.9.

[0072] Step 4.9: Determine if u < m is true. If true, assign u+1 to u and return to step 4.2; otherwise, it means that the safety hazard with the greatest impact on traffic congestion in each grid has been obtained.

[0073] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0074] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

[0075] In this example, taking Hefei's road network as an example, such as... Figure 4As shown, the study area was divided into grids of equal size in ArcGIS, with 37 rows and 38 columns, resulting in a total of 1026 500*500 grids. The number of accident trees and various safety hazards in each grid was counted hourly. The time-based calculations identified the safety hazards with the greatest impact on traffic safety at 7:00, 8:00, 11:00, 12:00, 17:00, and 18:00. Spatially, the grids (10,3), (10,4), (10,5), (11,5), (12,5), and (13,5) were used to identify the safety hazards with the greatest impact on traffic safety.

[0076] Obtain various safety hazards within each grid, including: number of restaurants x1, number of parks and squares x2, number of companies x3, number of stations x4, number of bus stops x5, number of subway entrances x6, number of shopping malls x7, number of banks x8, number of schools x9, and number of residential communities x1. 10 Number of hospitals X 11 Number of hotels X 12 Number of parking lots X 13 Number of motorcycle services X 14 Number of entrances and exits on the road section X 15 Road length X 16 Number of intersections X 17 Number of curves X 18 Number of uphill sections X 19 Elevated length X 20 zebra crossing length X 21 Number of ramps X 22 Number of traffic signs X 23 .

[0077] Based on the steps above, this invention calculates the local regression relationship of each grid in each time period;

[0078] In terms of time, this invention calculates that at 7:00, S9(7) = 75.56 is the maximum, indicating that at this time, the school with safety hazards X9 has the greatest impact on traffic safety; it also calculates that at 8:00, S9(8) = 60.80 is the maximum, indicating that at this time, the school with safety hazards X9 has the greatest impact on traffic safety.

[0079] The calculation shows that at 11:00, S2(11) = 29.65 is the maximum, indicating that the safety hazard park square X2 has the greatest impact on traffic safety at this time; the calculation shows that at 12:00, S2(12) = 34.29 is the maximum, indicating that the safety hazard park square X2 has the greatest impact on traffic safety at this time.

[0080] The calculation shows that at 17:00, S7(17) = 67.63 is the maximum, indicating that the safety hazard shopping mall X7 has the greatest impact on traffic safety at this time; the calculation shows that at 18:00, S7(18) = 70.18 is the maximum, indicating that the safety hazard shopping mall X7 has the greatest impact on traffic safety at this time.

[0081] As can be seen from the above cases, schools have the greatest impact on traffic at 7:00 and 8:00, so we focus on controlling traffic in areas with a high concentration of schools. Similarly, we focus on controlling traffic in areas with a high concentration of parks and squares at 11:00 and 12:00, and in areas with a high concentration of shopping malls at 17:00 and 18:00, to reduce congestion through refined management.

[0082] Spatially, this invention calculates that S1(10,3) = 20.38 is the largest in grid (10,3), indicating that the restaurant X1 with safety hazards in grid (10,3) has the greatest impact on traffic safety;

[0083] The maximum value of S3(10,4) = 32.25 is found in grid (10,4), indicating that the safety hazard company X3 in grid (10,4) has the greatest impact on traffic safety.

[0084] The maximum value of S5(10,5) = 13.98 was found in grid (10,5), indicating that bus stop X5, which poses a safety hazard in grid (10,5), has the greatest impact on traffic safety.

[0085] The maximum value of S8(11,5) = 18.02 was found in grid (11,5), indicating that the bank's X8 value in grid (11,5) has the greatest impact on traffic safety.

[0086] Calculate S in grid (12, 5) 10 The maximum value is (12,5) = 42.76, indicating that the residential area X with safety hazards is located in grid (12,5). 10 This has the greatest impact on traffic safety;

[0087] Calculate S in grid (13, 5) 14 The maximum value at (13,5) is 50.49, indicating that the motorcycle service X in grid (13,5) poses a safety hazard. 14 This has the greatest impact on traffic safety.

[0088] Based on the above cases, it can be seen that in grid (10,3), restaurant X1 poses the greatest safety hazard and has the greatest impact on traffic safety; therefore, management around the restaurant should be strengthened. In grid (10,4), company X3 poses the greatest safety hazard and has the greatest impact on traffic safety; therefore, management around the company should be strengthened. In grid (10,5), bus stop X5 poses the greatest safety hazard and has the greatest impact on traffic safety; therefore, management around the bus stop should be strengthened. In grid (11,5), bank X8 poses the greatest safety hazard and has the greatest impact on traffic safety; therefore, management around the bank should be strengthened. In grid (12,5), residential community X... 10 The greatest impact on traffic safety can be addressed by strengthening management around residential areas; in grid (13, 5), motorcycles pose a safety hazard. 14 The impact on traffic safety is greatest, so it is necessary to strengthen and refine the management of the areas surrounding motorcycle services in order to reduce congestion.

Claims

1. A method for spatiotemporal investigation of traffic safety hazards based on regional highway digitization, characterized in that, Includes the following steps: Step 1: Divide the bounding rectangle of the urban study area into m×n grids with side length l, where m is the total number of rows and n is the total number of columns; where any number of rows is denoted as u and any number of columns is denoted as v, where u∈[1,m] and v∈[1,n]. Count the number of accidents Y(u,v,t) in the t-th time period for any grid cell in the u-th row and v-th column. Obtain the J types of security risks {X} in the u-th row and v-th column of the grid. j (u,v)|j=1,2,…,J}, where X j (u,v) represents the j-th security hazard in the grid at row u and column v; Step 2: Establish a local spatial and temporal model of the congestion index based on equation (1): In equation (1), β0(u,v,t) represents the intercept term in the grid at row u and column v in the t-th time period, β j (u,v,t) represents the safety hazard X within the grid in the u-th row and v-th column during the t-th time period. j The regression coefficient of (u,v), ε(u,v,t) represents the error term of the grid in row u and column v during the t-th time period; Step 3: Calculate the safety hazard with the greatest impact on traffic accidents in the t-th time period; Step 3.1: Initialize t = 1, define variable Q, and define evaluation variable S; Step 3.2: Initialize j = 1, Q = 0, S = 0; Step 3.3: Calculate the j-th safety hazard X in the t-th time period according to formula (2). j The average regression coefficient S j (t); In equation (2), || represents the absolute value symbol; Step 3.4, Determine S j If (t) > S, then change S. j (t) Assign the value to S, assign the value to j to Q, and then execute step 3.5; otherwise, execute step 3.5 directly. Step 3.5: Determine if j < J is true. If true, assign j + 1 to j and return to step 3.3; otherwise, it means that the safety hazard with the greatest impact on traffic congestion in the t-th time period is the Q-th safety hazard X. Q ; Step 3.6: Determine if t < T is true. If true, assign t+1 to t and return to step 3.2; otherwise, it indicates that the safety hazard with the greatest impact on the congestion index under all time periods has been obtained, and step 4 is executed; where T represents the total number of time periods. Step 4: Calculate the safety hazard with the greatest impact on traffic accidents within each grid. Step 4.1: Initialize u = 1; Step 4.2: Initialize v = 1; Step 4.3: Define variable H and evaluation variable K; Step 4.4: Initialize j = 1, H = 0, K = 0; Step 4.5: Calculate the j-th safety hazard X in the u-th row and v-th column of the grid according to equation (3). j The average regression coefficient K j (u,v); Step 4.6, Determine K j If (u,v)>K holds true, then set K. j After assigning (u,v) to K and j to H, proceed to step 4.7; otherwise, proceed directly to step 4.

7. Step 4.7: Determine if j < J is true. If true, assign j + 1 to j and return to step 4.5; otherwise, it means that the safety hazard with the greatest impact on traffic congestion in the u-th row and v-th column of the grid is the H-th safety hazard X. H ; Step 4.8: Determine if v < n is true. If true, assign v + 1 to v and return to step 4.3; otherwise, execute step 4.

9. Step 4.9: Determine if u < m is true. If true, assign u+1 to u and return to step 4.2; otherwise, it means that the safety hazard with the greatest impact on traffic congestion in each grid has been obtained.

2. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the spatiotemporal investigation method for traffic safety hazards as described in claim 1, and the processor is configured to execute the program stored in the memory.

3. A computer-readable storage medium storing a computer program, characterized in that, The computer program, when run by the processor, executes the steps of the spatiotemporal investigation method for traffic safety hazards as described in claim 1.

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