A spatiotemporal calculation method to quantify the impact of urban built environment on traffic congestion

Abstract

This invention discloses a spatiotemporal calculation method for quantifying the impact of a city's built environment on traffic congestion. The method comprises the following steps: 1. Determine a study area and divide it into multiple equally sized grids, with each grid serving as the study unit; 2. Obtain traffic situation data and built environment data within each grid using an electronic map; 3. Calculate the hourly traffic congestion index within each grid; 4. Develop a spatial and temporal local model of the congestion index; and 5. Use an algorithm to calculate the built environment with the greatest impact on traffic congestion at each moment and within each grid. By studying the relationship between traffic congestion and the built environment within each grid, the method identifies the built environment with the greatest impact on traffic congestion. This method can provide traffic managers with detailed spatiotemporal congestion distribution information, enabling the development of more precise traffic planning and traffic demand management policies.

Description

Technical Field

[0001] The present invention relates to the fields of urban traffic planning and traffic big data research, and specifically to a spatiotemporal calculation method for quantifying the impact of a city's built environment on traffic congestion. Background Art

[0002] With China's rapid socioeconomic development, residents' travel needs have increased. However, existing transportation supply often fails to meet peak-hour demand, leading to a series of problems such as traffic congestion and environmental pollution. The mismatch between transportation supply and demand has become a major factor contributing to urban traffic congestion. Existing research primarily explores traffic congestion management from the perspectives of both transportation supply and demand, but few studies delve into the relationship between transportation demand and land use. Studying this mismatch between transportation supply and demand can help better understand the mechanisms underlying congestion formation and evolution, and fundamentally alleviate the problem. As the world's largest developing country, China has vigorously developed transportation infrastructure over the past few decades to drive urbanization. This massive road and transportation infrastructure has resulted in a vast urban built environment, which is often a significant source of traffic attraction. Therefore, a deep understanding of the role of the built environment in transportation systems is crucial for analyzing congestion characteristics. Traffic congestion is particularly prominent in large Chinese cities. Addressing urban traffic congestion is crucial for building a green, healthy, and sustainable urban environment. It not only alleviates travel problems but also improves residents' well-being.

[0003] Traditional methods for collecting traffic congestion data rely primarily on manual collection and analysis, but this approach has several drawbacks. First, manual data collection requires significant manpower and time, resulting in low efficiency and high costs. Second, manual data collection is susceptible to subjective factors, making it difficult to ensure data accuracy and consistency. Furthermore, due to the complexity of traffic conditions, collecting data from only a limited number of data collection points cannot fully reflect the traffic flow and congestion conditions across the entire road network. Therefore, traditional data collection methods cannot meet the needs of large-scale, real-time traffic congestion assessment. However, with the widespread use of electronic map data, electronic map companies now have access to a large amount of data on daily traffic life and have access to a large amount of traffic flow and congestion data. This data can provide a more comprehensive and accurate reflection of urban traffic congestion conditions and is updated in real time. The widespread use of electronic map data provides a more reliable source of traffic congestion data, enabling us to understand current congestion conditions and better implement traffic management and planning.

[0004] Traditional traffic congestion assessment methods typically only provide an overall picture of congestion, lacking detailed information on its spatial and temporal distribution. However, traffic congestion is not uniformly distributed across a road segment or network, but rather exhibits uneven distribution across time and space. Therefore, simply providing overall congestion information cannot meet the needs of traffic management and planning, nor can it accurately identify and resolve bottlenecks and hotspots of congestion. Summary of the Invention

[0005] The present invention overcomes the shortcomings of the existing technology and proposes a spatiotemporal calculation method to quantify the impact of the urban built environment on traffic congestion, in order to provide traffic managers with detailed spatiotemporal congestion distribution information, so as to formulate more accurate traffic planning and traffic demand management policies to alleviate the problem of urban traffic congestion.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0007] The spatiotemporal calculation method for quantifying the impact of the urban built environment on traffic congestion of the present invention is characterized in that it includes the following steps:

[0008] Step 1: Divide the circumscribed 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; 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] Obtaining the traffic situation in each grid in the t-th time period from the electronic map; the types of traffic situation include: severe congestion state HCS, congestion state CS, slight congestion state SCS, and smooth state SS;

[0010] Count the proportion of HCS in severe congestion state in the grid of row u and column v in the tth time period respectively γ HCS (u,v,t), the proportion of congestion state CS in the grid in row u and column v at time period t, γ CS (u,v,t), the proportion of slightly congested state SCS in the grid of row u and column v in the tth time period γ SCS (u,v,t), the proportion of the smooth state SS in the grid of row u and column v in the tth time period γ SS (u,v,t);

[0011] Obtain the number of J types of built environments of the grid in row u and column v through the electronic map {X j (u, v)|j=1,2,…,J}; where X j (u,v) represents the number of the jth built environment in the grid at row u and column v;

[0012] Step 2: Calculate the traffic congestion index TPI(u,v,t) in the grid at row u and column v in time period t according to formula (1);

[0013] TPI(u,v,t)=α×γ HCS (u,v,t)+β×γ CS (u,v,t)+δ×γ SCS (u,v,t)+τ×γ SS (u,v,t) (1)

[0014] In formula (1), α, β, δ and τ are weighted parameters of different traffic situations;

[0015] Step 3: Establish the spatial and temporal local model of the congestion index according to formula (2);

[0016]

[0017] In formula (2), β0(u,v,t) represents the intercept term in the grid of row u and column v in the tth period, β j (u, v, t) represents the built environment X in the grid at row u and column v in the tth period. j The regression coefficient of (u,v), ε(u,v,t) represents the error term of the grid at row u and column v in the tth period;

[0018] Step 3: Calculate the built environment that has the greatest impact on traffic congestion in the tth time period;

[0019] Step 3.1, initialize t = 1, define variable H, and define evaluation variable K;

[0020] Step 3.2, initialize j=1, H=0, K=0;

[0021] Step 3.3: Calculate the j-th built environment X in the t-th period according to formula (3): j The average regression coefficient S j (t);

[0022]

[0023] In formula (2), || represents the absolute value symbol;

[0024] Step 3.4, determine S j (t)>K is established, if so, then S j (t) is assigned to K, j is assigned to H, and then step 3.5 is executed; otherwise, step 3.5 is executed directly;

[0025] Step 3.5: Determine whether j < J. If so, assign j + 1 to j and return to step 3.3. Otherwise, the built environment with the greatest impact on traffic congestion in the tth time period is the Hth built environment X. H ;

[0026] Step 3.6: Determine whether t < T. If so, assign t + 1 to t and return to step 3.2. Otherwise, determine the built environment with the greatest congestion index impact across all time periods and execute step 4. T represents the total number of time periods.

[0027] Step 4: Calculate the built environment with the greatest congestion index impact within each grid;

[0028] Step 4.1, initialize u=1;

[0029] Step 4.2, initialize v=1;

[0030] Step 4.3: Define the variable Q and the evaluation variable S.

[0031] Step 4.4, initialize j=1, Q=0, S=0;

[0032] Step 4.5: Calculate the jth built environment X in the grid with the uth row and the vth column according to formula (4): j The absolute value of the average regression coefficient S j (u,v);

[0033]

[0034] Step 4.6, determine S j (u,v)>S is true, if so, then S j After assigning (u, v) to S and j to Q, execute step 4.7; otherwise, execute step 4.7 directly;

[0035] Step 4.7: Determine whether j < J. If so, assign j + 1 to j and return to step 4.5. Otherwise, the built environment with the greatest impact on traffic congestion in the grid at row u and column v is the Hth built environment, which is X. Q ;

[0036] Step 4.8: Determine whether v < n. If so, assign v + 1 to v and return to step 4.3. Otherwise, go directly to step 4.9.

[0037] Step 4.9: Determine whether u < m. If so, assign u + 1 to u and return to step 4.2. Otherwise, the built environment with the greatest impact on traffic congestion in each grid is obtained.

[0038] The electronic device of the present invention includes a memory and a processor, and is characterized in that the memory is used to store a program that supports the processor to execute the spatiotemporal calculation method, and the processor is configured to execute the program stored in the memory.

[0039] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps of the spatiotemporal calculation method when executed by a processor.

[0040] Compared with the existing technology, the beneficial technical effects of the present invention are embodied in:

[0041] 1. This invention accurately identifies congestion bottlenecks and the impact of the built environment at each time and within each grid, allowing traffic managers to take targeted measures. By identifying the built environment with the greatest impact in each area and time, this information helps traffic managers better understand the mechanisms that cause traffic congestion, take targeted measures, optimize traffic planning and management, and improve the operational efficiency of the transportation system. For example, traffic signal control can be optimized to divert traffic flow, thereby improving the overall efficiency of the transportation system. This will help reduce traffic delays, shorten commuting times, increase road capacity, and enhance travel convenience for residents.

[0042] 2. This paper employs a refined grid-based research approach, dividing the urban road network into uniformly sized, neatly arranged grids. By studying the relationship between traffic congestion and the built environment within each grid, the paper conducts an in-depth study of the relationship between traffic congestion and the built environment within each grid. This approach provides a more comprehensive understanding of traffic congestion in different regions and at different times, identifies the built environment that most significantly impacts traffic congestion, and provides a basis for traffic managers to formulate more precise plans and policies.

[0043] 3. This invention, through in-depth research into the relationship between the urban built environment and traffic congestion, offers new perspectives and approaches for urban planning and traffic management. By optimizing the built environment, such as increasing public transportation facilities, improving road planning, and building layout, it can promote sustainable urban development, enhance residents' quality of life, and reduce energy consumption and environmental pollution.

[0044] 4. This invention also reduces the interference of external factors on data collection. Traditional data collection methods may be affected by factors such as weather, resulting in reduced data accuracy. However, using electronic map data can collect data without being affected by natural factors such as weather, ensuring data stability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is the overall flow chart of the present invention;

[0046] Figure 2 It is the cycle diagram of the time maximum influencing factor of the present invention;

[0047] Figure 3 It is the spatial maximum influencing factor cycle diagram of the present invention;

[0048] Figure 4 Gridded map of the study area. DETAILED DESCRIPTION

[0049] In this embodiment, Figure 1 As shown, a spatial calculation method for traffic congestion relationship in an urban built environment includes the following steps:

[0050] Step 1: Divide the circumscribed 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; 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].

[0051] Obtain the traffic situation within each grid in the tth time period from the electronic map; the types of traffic situation include: severe congestion state HCS, congestion state CS, slight congestion state SCS, and smooth state SS;

[0052] Count the proportion of HCS in severe congestion state in the grid of row u and column v in the tth time period respectively γ HCS (u,v,t), the proportion of congestion state CS in the grid in row u and column v at time period t, γ CS (u,v,t), the proportion of slightly congested state SCS in the grid of row u and column v in the tth time period γ SCS (u,v,t), the proportion of the smooth state SS in the grid of row u and column v in the tth time period γ SS (u,v,t);

[0053] Obtain the number of J types of built environments of the grid in row u and column v through the electronic map {X j (u, v)|j=1,2,…,J}; where X j (u,v) represents the number of the jth built environment in the grid at row u and column v;

[0054] Step 2: Calculate the traffic congestion index TPI(u,v,t) in the grid at row u and column v in time period t according to formula (1);

[0055] TPI(u,v,t)=α×γ HCS (u,v,t)+β×γ CS (u,v,t)+δ×γ SCS (u,v,t)+τ×γ SS(u,v,t) (1)

[0056] In formula (1), α, β, δ and τ are weighted parameters of different traffic situations;

[0057] Step 3: Establish the spatial and temporal local model of the congestion index according to formula (2);

[0058]

[0059] In formula (2), β0(u,v,t) represents the intercept term in the grid of row u and column v in the tth period, β j (u, v, t) represents the built environment X in the grid at row u and column v in the tth period. j The regression coefficient of (u,v), ε(u,v,t) represents the error term of the grid at row u and column v in the tth period;

[0060] like Figure 2 As shown, it is a cycle diagram of the built environment that has the greatest impact on traffic congestion in each time period calculated by the present invention;

[0061] Step 3: Figure 3 As shown, calculate the built environment with the greatest impact on traffic congestion in the tth period;

[0062] Step 3.1, initialize t = 1, define variable H, and define evaluation variable K;

[0063] Step 3.2, initialize j=1, H=0, K=0;

[0064] Step 3.3: Calculate the j-th built environment X in the t-th period according to formula (3): j The average regression coefficient S j (t);

[0065]

[0066] In formula (2), || represents the absolute value symbol;

[0067] Step 3.4, determine S j (t)>K is established, if so, then S j (t) is assigned to K, j is assigned to H, and then step 3.5 is executed; otherwise, step 3.5 is executed directly;

[0068] Step 3.5: Determine whether j < J. If so, assign j + 1 to j and return to step 3.3. Otherwise, the built environment with the greatest impact on traffic congestion in the tth time period is the Hth built environment X. H ;

[0069] Step 3.6: Determine whether t < T. If so, assign t + 1 to t and return to step 3.2. Otherwise, determine the built environment with the greatest congestion index impact across all time periods and execute step 4. T represents the total number of time periods.

[0070] Step 4: Calculate the built environment with the greatest congestion index impact within each grid;

[0071] Step 4.1, initialize u=1;

[0072] Step 4.2, initialize v=1;

[0073] Step 4.3: Define the variable Q and the evaluation variable S.

[0074] Step 4.4, initialize j=1, Q=0, S=0;

[0075] Step 4.5: Calculate the jth built environment X in the grid with the uth row and the vth column according to formula (4): j The absolute value of the average regression coefficient S j (u,v);

[0076]

[0077] Step 4.6, determine S j (u,v)>S is true, if so, then S j After assigning (u, v) to S and j to Q, execute step 4.7; otherwise, execute step 4.7 directly;

[0078] Step 4.7: Determine whether j < J. If so, assign j + 1 to j and return to step 4.5. Otherwise, the built environment with the greatest impact on traffic congestion in the grid at row u and column v is the Hth built environment, which is X. Q ;

[0079] Step 4.8: Determine whether v < n. If so, assign v + 1 to v and return to step 4.3. Otherwise, go directly to step 4.9.

[0080] Step 4.9: Determine whether u < m. If so, assign u + 1 to u and return to step 4.2. Otherwise, the built environment with the greatest impact on traffic congestion in each grid is obtained.

[0081] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0082] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

[0083] In this example, take the Hefei road network as an example. Figure 4 As shown, in ArcGIS, the study area is divided into grids of the same size, with 37 rows and 38 columns. A total of 1026 500*500 grids are divided. The present invention collects traffic situation data every 20 minutes, 24 hours a day, and a total of 7 days. In terms of time, the built environment with the greatest impact on traffic congestion at 7:00, 8:00, 11:00, 12:00, 17:00, and 18:00 is calculated. In terms of space, the built environment with the greatest impact on traffic congestion at grids (10, 3), (10, 4), (10, 5), (11, 5), (12, 5), and (13, 5) is determined.

[0084] The built environments obtained in this example include: restaurant X1, park square X2, company X3, station X4, bus station X5, subway X6, shopping mall X7, bank X8, school X9, residential area X1, and so on. 10 , Hospital X 11 , Hotel X 12 , parking lot X 13 , Motorcycle Service X 14 .

[0085] According to the above steps, the present invention calculates the local regression relationship of each grid in each time period;

[0086] In terms of time:

[0087] The present invention calculates that at 7:00, S9(7)=75.56 is the maximum, indicating that the built environment school X9 has the greatest impact on traffic congestion at this time; and calculates that at 8:00, S9(8)=60.80 is the maximum, indicating that the built environment school X9 has the greatest impact on traffic congestion at this time;

[0088] It is calculated that at 11:00, S2(11)=29.65 is the maximum, indicating that the construction of the park square X2 at this time has the greatest impact on traffic congestion; it is calculated that at 12:00, S2(12)=34.29 is the maximum, indicating that the construction of the environmental park square X2 at this time has the greatest impact on traffic congestion;

[0089] It is calculated that at 17:00, S7(17)=67.63 is the maximum, indicating that the built environment shopping mall X7 has the greatest impact on traffic congestion at this time; it is calculated that at 18:00, S7(18)=70.18 is the maximum, indicating that the built environment shopping mall X7 has the greatest impact on traffic congestion at this time.

[0090] According to the above case, it can be seen that at 7:00 and 8:00, schools have the greatest impact on traffic. The present invention suggests focusing on controlling areas with more schools. Similarly, at 11:00 and 12:00, focus on controlling areas with more parks and squares, and at 17:00 and 18:00, focus on controlling areas with more shopping malls, so as to carry out refined management and reduce congestion.

[0091] In space:

[0092] The present invention calculates that S1(10,3)=20.38 is the largest in the grid (10,3), indicating that the built environment restaurant X1 in the grid (10,3) has the greatest impact on traffic congestion;

[0093] The calculation shows that S3(10,4)=32.25 is the largest in the grid (10,4), indicating that the built environment company X3 in the grid (10,4) has the greatest impact on traffic congestion;

[0094] It is calculated that S5(10,5)=13.98 is the largest in the grid (10,5), indicating that the built environment bus station X5 in the grid (10,5) has the greatest impact on traffic congestion;

[0095] It is calculated that S8(11,5)=18.02 is the largest in the grid (11,5), indicating that the built environment, bank X8, in the grid (11,5) has the greatest impact on traffic congestion;

[0096] Calculate S in the grid (12, 5) 10 (12,5)=42.76 is the largest, indicating that the built environment residential area X in the grid (12,5) 10 , which has the greatest impact on traffic congestion;

[0097] Calculate S in the grid (13, 5) 14 (13,5)=50.49 is the largest, indicating that the built environment motorcycle service X in the grid (13,5) 14 , which has the greatest impact on traffic congestion;

[0098] According to the above case, we can see that the built environment restaurant X1 in grid (10, 3) has the greatest impact on traffic congestion, and management around the restaurant can be strengthened; the built environment company X3 in grid (10, 4) has the greatest impact on traffic congestion, and management around the company can be strengthened; the built environment bus station X5 in grid (10, 5) has the greatest impact on traffic congestion, and management around the bus station can be strengthened; the built environment bank X8 in grid (11, 5) has the greatest impact on traffic congestion, and management around the bank can be strengthened; the built environment residential area X 10, which has the greatest impact on traffic congestion, and can strengthen management around residential areas; in grid (13, 5), the built environment motorcycle service X 14 , which has the greatest impact on traffic congestion. We can strengthen management around motorcycle services and carry out refined management to reduce congestion.

Claims

1. A spatiotemporal calculation method for quantifying the impact of urban built environment on traffic congestion, characterized by: The following steps are involved: Step 1: Divide the circumscribed rectangle of the urban study area into The side length is A grid, where is the total number of rows, is the total number of columns; any number of rows is recorded as , for any number of columns, ,in, , ; Obtaining the traffic situation in each grid in the t-th time period from the electronic map; the types of traffic situation include: severe congestion state HCS, congestion state CS, slight congestion state SCS, and smooth state SS; Statistics are respectively The next period Rank The proportion of severe congestion status HCS within the column grid , in The next period Rank The proportion of congestion status CS within the grid of the column , in The next period Rank The proportion of slightly congested SCS in the column grid , in The next period Rank The proportion of smooth SS in the column grid ; Get the first Rank The number of J built environments in the grid column ;in, Indicates the Rank The number of the j-th built environment in the column grid; Step 2: Calculate the The next period Rank Traffic congestion index within the grid column ; (1) In formula (1), , , and are the weighted parameters for different traffic situations; According to formula (2), a local model of congestion index in space and time is established; (2) In formula (2), Indicates in The next period Rank The intercept term within the grid of columns, Indicates the The next period Rank Built environment within a grid of columns The regression coefficient of Indicates in In the period Rank The error term for the grid of columns; Step 3: Calculate the The built environment that has the greatest impact on traffic congestion at each time period; Step 3.1, Initialization , define the variable , define the evaluation variables ; Step 3.2, Initialization , , ; Step 3.3: Calculate the The jth built environment in the period The average regression coefficient ; (3) In formula (2), Indicates the absolute value symbol; Step 3.4, judgment Is it established? If so, Assign to ,Will Assign to If yes, go to step 3.5; otherwise, go to step 3.5 directly; Step 3.5, judgment Is it established? If so, Assign to Then, return to step 3.3; otherwise, it means that the The built environment with the greatest impact on traffic congestion during the period is Built Environment ; Step 3.6, judgment Is it established? If so, Assign to , and return to step 3.2; otherwise, it means that the built environment with the greatest impact on the congestion index in all time periods is obtained, and step 4 is executed; where, Indicates the total number of time periods; Step 4: Calculate the built environment with the greatest congestion index impact within each grid; Step 4.1, Initialization ; Step 4.2, Initialization ; Step 4.3, define variables , define the evaluation variables ; Step 4.4, Initialization , , ; Step 4.5: Calculate the Rank The jth built environment in the grid of the column The absolute value of the average regression coefficient ; (4) Step 4.6, judgment Is it established? If so, Assign to ,Will Assign to If yes, go to step 4.7; otherwise, go to step 4.7 directly; Step 4.7, judgment Is it established? If so, Assign to Then, return to step 4.5; otherwise, it means that the Rank The built environment with the greatest impact on traffic congestion in the grid of column The built environment is ; Step 4.8, judgment Is it established? If so, Assign to If yes, return to step 4.3; otherwise, go directly to step 4.9; Step 4.9, judgment Is it established? If so, Assign to After that, return to step 4.2; otherwise, it means that the built environment with the greatest impact on traffic congestion in each grid is 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 to execute the spatiotemporal computing method according to claim 1, and the processor is configured to execute the program stored in the memory.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the spatiotemporal computing method according to claim 1 are executed.

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