Traffic network bottleneck identification method based on flow cascading

Abstract

The invention discloses a traffic network bottleneck identification method based on flow cascading in the technical field of traffic information control, and the method comprises the steps: obtaining road data and commuting behavior data in a target area, extracting the midpoint of each road in the road data, connecting the origin-destination space of the commuting behavior data to the midpoint of the nearest road, and obtaining the traffic network bottleneck identification result. And recording a corresponding index relationship between the origin-destination point and the midpoint of the road. The incremental traffic distribution algorithm is adopted to distribute the travel matrix of the spatial position information, the congestion effect generated by network flow cascade is simulated by iteratively updating the network edge weight, the network bottleneck under the network flow reconstruction is identified, the cascade conduction mechanism of the traffic network flow under the disturbance condition can be systematically described, and the traffic flow reconstruction efficiency is improved. Network bottlenecks having key influences on system stability are identified, and the response capability and structural toughness of urban risks are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic information control, and in particular to a traffic network bottleneck identification method based on flow cascade. Background Art

[0002] Against the backdrop of intensifying climate change and the frequent occurrence of various risk events, accurately quantifying and enhancing urban resilience has become a core issue in urban governance and planning interventions. As a typical complex system, cities are often abstracted into network structures to help researchers understand their internal dynamics. Network science thus provides a fundamental research paradigm for resilience measurement and assessment. In classic urban network resilience assessment methods, researchers model urban systems by setting rules for nodes and edges, introduce attack strategies to simulate scenarios in which the network structure is damaged, and measure system resilience or robustness by evaluating changes in performance indicators such as network efficiency.

[0003] In many urban systems, transportation networks, as the core spatial carrier of material circulation, have always been the focus of network resilience research. However, existing research often finds it difficult to accurately depict the dynamic response process of transportation networks when responding to external shocks, and it is also difficult to identify key spatial elements to support precise governance needs. Although the transportation field has made significant progress in simulating individual travel behavior and dynamic modeling of traffic flow, existing research usually focuses on the micro-evolution of traffic flow, lacks analysis of the operating status of urban transportation systems from the perspective of network structure and resilience mechanisms, and lacks identification of key bottleneck sections and cascade propagation paths in the network, making it difficult to provide a basis for targeted structural intervention for urban systems. Summary of the Invention

[0004] The purpose of the present invention is to provide a traffic network bottleneck identification method based on traffic cascade. The spatial location information of OD pairs is provided by traffic travel data, and the commuting OD travel matrix is ​​allocated by an incremental traffic allocation algorithm. Through multiple traffic allocations and iterative updates of network edge weights, the congestion effect caused by network traffic cascade is simulated, and the network bottleneck under network traffic reconstruction is identified, which can improve the responsiveness and structural resilience of urban risks.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A traffic network bottleneck identification method based on traffic cascade, comprising:

[0007] S1: Obtain road data and commuting behavior data within the target area. The road data includes the free flow travel time t0, the road capacity C, and the spatial location information of each road. The commuting behavior data includes the spatial location information of the commuting OD and the commuting flow corresponding to the OD.

[0008] S2: Extract the midpoint of each road in the road data, spatially connect the OD pairs of the commuting behavior data to the midpoint of the nearest road, and record the corresponding index relationship between the OD pairs and the midpoint of the road;

[0009] S3: Use the initial modeling method to establish a first road network G1(V,E), where V is the first network node, V is the set of road intersections, E is the first edge, and E is the set of each road. Each edge in E corresponds to the free flow travel time t0 and capacity C of the road, and the free flow travel time t0 is used as the weight of E;

[0010] S4: Use the dual modeling method to establish a second road network G2(V,E), where V is the second network node, V is the set of roads, E is the second edge, and E is the edge set composed of each intersection of the roads;

[0011] S5: Using the incremental traffic assignment algorithm, the commuting flow data corresponding to each OD pair in the commuting behavior data is divided into the first flow part, the second flow part, the third flow part, and the fourth flow part, and the network edge weight t of each network edge of the second road network G2(V,E) is readjusted;

[0012] S6: Output the flow V of each OD pair, use the flow V of the OD pair as the road flow distribution result FM0 of the incremental traffic allocation, remove a certain proportion of road segment sets and a certain proportion of network node sets corresponding to the road segment sets from G2(V,E), represent the network after removal as the third road network G3(V,E), and initialize the node attributes and edge weights of the third road network G3(V,E);

[0013] S7: Based on the index relationship between the OD pairs in the commuting behavior data and the spatial connection of the original road sections, the road sections in the removed network are filtered as the index data of the OD pairs to obtain the index removal data, and the index removal data is eliminated in the incremental traffic distribution. The incremental traffic distribution algorithm is executed on the eliminated commuting behavior data in the third road network G3(V,E), and the road flow distribution result FM1 after the network attack is output. The bottleneck is identified based on the road flow distribution result FM1.

[0014] As a further solution of the present invention: the dual modeling method is used to establish a second road network G2(V,E), wherein V is a second network node, V is a set of roads, E is a second edge, and E is an edge set consisting of each intersection of the roads, including:

[0015] S41: Extract each road i and the road set i_link connected to each road from the first road network G1(V,E), use an iterative method to select each connecting road j in the road set i_link, input the road i and the connecting road j as second network nodes in turn into the set second road network G2(V,E), and input the free flow travel time t0 and capacity C of the road i and the connecting road j in the first road network G1(V,E) into the set second road network G2(V,E).

[0016] As a further solution of the present invention: the dual modeling method is used to establish a second road network G2(V,E), wherein V is a second network node, V is a set of roads, E is a second edge, and E is an edge set consisting of each intersection of the roads, further comprising:

[0017] S42: Establish network edge data between road i and connecting road j, and input the network edge data into the set second road network G2(V,E), and use the average value of the free flow travel time t0 of road i and connecting road j as the edge weight t. Repeat step S41 until all roads are input as i into the set second road network G2(V,E), and remove the duplicate nodes and edges of the set second road network G2(V,E) to obtain the dual network G2'(V',E').

[0018] As a further solution of the present invention: the dual modeling method is used to establish a second road network G2(V,E), wherein V is a second network node, V is a set of roads, E is a second edge, and E is an edge set consisting of each intersection of the roads, further comprising:

[0019] S43: Add attribute V to each network node of the dual network G2'(V',E'), and set the initial value of the carrying capacity of the road section of the dual network G2'(V',E') to 0, to obtain the second road network G2(V,E), wherein the network nodes of the second road network G2(V,E) are road sections, and the edges of the second road network G2(V,E) are road intersections.

[0020] As a further solution of the present invention: the incremental traffic assignment algorithm is used to divide the commuting flow data corresponding to each OD pair in the commuting behavior data into a first flow portion, a second flow portion, a third flow portion, and a fourth flow portion, and the network edge weight t of each network edge of the second road network G2(V,E) is readjusted, wherein the first flow portion is 40% of the commuting flow data, the second flow portion is 30% of the commuting flow data, the third flow portion is 20% of the commuting flow data, and the fourth flow portion is 10% of the commuting flow data, including:

[0021] S51: Index the midpoint of the road section corresponding to the OD pair contained in the first part of the traffic, execute the shortest path algorithm between the two points in the second road network G2(V,E), set the edge weight to t, and return the data as a list of nodes passed by the two points, where the list of nodes passed is the road section.

[0022] As a further solution of the present invention: the incremental traffic assignment algorithm is used to divide the commuting flow data corresponding to each OD pair in the commuting behavior data into a first flow portion, a second flow portion, a third flow portion, and a fourth flow portion, and the network edge weight t of each network edge of the second road network G2(V,E) is readjusted, wherein the allocation ratio of the commuting flow data of the first flow portion is 40%, the allocation ratio of the commuting flow data of the second flow portion is 30%, the allocation ratio of the commuting flow data of the third flow portion is 20%, and the allocation ratio of the commuting flow data of the fourth flow portion is 10%, and further comprising:

[0023] S52: For each list of nodes passed by an OD pair, multiply the flow of the OD pair by the corresponding distribution ratio of the commuting flow data to obtain the distributed flow, sum the distributed flow and the original flow to form a new flow, and distribute the new flow to each node in the node list.

[0024] As a further solution of the present invention: the incremental traffic assignment algorithm is used to divide the commuting flow data corresponding to each OD pair in the commuting behavior data into a first flow portion, a second flow portion, a third flow portion, and a fourth flow portion, and the network edge weight t of each network edge of the second road network G2(V,E) is readjusted, wherein the allocation ratio of the commuting flow data of the first flow portion is 40%, the allocation ratio of the commuting flow data of the second flow portion is 30%, the allocation ratio of the commuting flow data of the third flow portion is 20%, and the allocation ratio of the commuting flow data of the fourth flow portion is 10%, and further comprising:

[0025] S53: Using the time and flow resistance function, calculate the current actual travel time t of each road segment of the second road network G2 (V, E) a , the actual travel time t a As the new weight t a , where the time and flow resistance function is:

[0026] Where t0, C and V are the current node attributes, α and β are preset parameters, α is 0.15 and β is 4.

[0027] As a further scheme of the present application: the incremental traffic assignment algorithm is used to divide the commuting flow data corresponding to each pair of O-D pairs in the commuting behavior data into first part flow, second part flow, third part flow and fourth part flow, and the network edge weight t of each network edge of the second road network G2(V, E) is readjusted, wherein the assignment proportion of the commuting flow data of the first part flow is 40%, the assignment proportion of the commuting flow data of the second part flow is 30%, the assignment proportion of the commuting flow data of the third part flow is 20%, and the assignment proportion of the commuting flow data of the fourth part flow is 10%, and further comprising:

[0028] S54: Based on the new weight t of each node at both ends of the network edge a , the network edge weight t is readjusted, wherein the network edge weight t is the average value of the weight t of the nodes at both ends. a

[0029] As a further scheme of the present application: the index data of the road segments in the network after removal is screened as the index data of the O-D pairs according to the index relationship between the O-D pairs in the commuting behavior data and the original road segments, index removal data is obtained, and the index removal data is excluded in the incremental traffic assignment, the incremental traffic assignment algorithm is executed on the commuting behavior data after exclusion in the third road network G3(V, E), and the road flow distribution result FM1 after network attack is output, and the bottleneck is identified based on the road flow distribution result FM1, comprising:

[0030] S101: The road segment nodes remaining in the network after attack are screened out, and the ratio of flow and capacity before attack V0OC and the ratio of flow and capacity after attack V1 OC .

[0031] As a further scheme of the present application: the index data of the road segments in the network after removal is screened as the index data of the O-D pairs according to the index relationship between the O-D pairs in the commuting behavior data and the original road segments, index removal data is obtained, and the index removal data is excluded in the incremental traffic assignment, the incremental traffic assignment algorithm is executed on the commuting behavior data after exclusion in the third road network G3(V, E), and the road flow distribution result FM1 after network attack is output, and the bottleneck is identified based on the road flow distribution result FM1, further comprising:

[0032] S102: The VOC change of the remaining road segments in the network before and after network attack is calculated.

[0033] As a further scheme of the present application: the VOC change is ΔVOC = V1OC-V0 OC ​, where ΔVOC greater than 0 means that the road traffic volume after the attack is higher than before the attack, V1 OC If it is greater than 1, it means that the traffic volume of the road after the attack is higher than its own capacity. The road that meets both conditions will be identified as a network bottleneck, and V1 OC The higher the value, the higher the traffic volume on that road.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. In the present invention, OD pair information is provided by traffic travel data, and an incremental traffic allocation algorithm is used to allocate the spatial location information travel matrix. Through multiple traffic allocations and iterative updates of network edge weights, the congestion effect caused by the network traffic cascade is simulated, and the network bottleneck under network traffic reconstruction is identified. By integrating the method framework of dynamic traffic evolution modeling and network structure identification, based on the overall traffic network, it is possible to systematically characterize the cascade transmission mechanism of traffic network traffic under disturbance conditions, identify network bottlenecks that have a key impact on system stability, and thus improve the city's risk response capability and structural resilience.

[0036] 2. In the present invention, by simulating the cascading effect of network traffic based on the incremental traffic distribution algorithm, the calculation steps can be simplified without relying on the complex mechanism of microscopic individual simulation or modeling. It can realize traffic distribution of large-scale and complex urban traffic networks and reduce the computational cost of traffic distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flowchart of the method of the present invention;

[0038] Figure 2 in Figure 2 (a) is a diagram showing the road flow distribution results of the incremental traffic allocation according to an embodiment of the present invention. Figure 2 (b) is a diagram showing the road traffic distribution results after the output network attack according to an embodiment of the present invention;

[0039] Figure 3 This is a road network bottleneck diagram based on a 500-year return period rainfall and flood simulation scenario in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Example:

[0042] See also Figure 1 In an embodiment of the present invention, a method for identifying a bottleneck in a traffic network based on traffic cascade includes the following methods:

[0043] S1: Obtain road data and commuting behavior data within the target area. The road data includes the free flow travel time t0, the road capacity C, and the spatial location information of each road. The commuting behavior data includes the spatial location information of the commuting OD and the commuting flow corresponding to the OD.

[0044] S2: Extract the midpoint of each road in the road data, spatially connect the OD pairs of the commuting behavior data to the midpoint of the nearest road, and record the corresponding index relationship between the OD pairs and the midpoint of the road;

[0045] S3: Use the initial modeling method to establish a first road network G1(V,E), where V is the first network node, V is the set of road intersections, E is the first edge, and E is the set of each road. Each edge in E corresponds to the free flow travel time t0 and capacity C of the road, and the free flow travel time t0 is used as the weight of E;

[0046] S4: Use the dual modeling method to establish a second road network G2(V,E), where V is the second network node, V is the set of roads, E is the second edge, and E is the edge set composed of each intersection of the roads;

[0047] S5: Using the incremental traffic assignment algorithm, the commuting flow data corresponding to each OD pair in the commuting behavior data is divided into the first flow part, the second flow part, the third flow part, and the fourth flow part, and the network edge weight t of each network edge of the second road network G2(V,E) is readjusted;

[0048] S6: Output the flow V of each OD pair, use the flow V of the OD pair as the road flow distribution result FM0 of the incremental traffic allocation, remove a certain proportion of road segment sets and a certain proportion of network node sets corresponding to the road segment sets from G2(V,E), represent the network after removal as the third road network G3(V,E), and initialize the node attributes and edge weights of the third road network G3(V,E);

[0049] S7: Based on the index relationship between the OD pairs in the commuting behavior data and the spatial connection of the original road sections, the road sections in the removed network are filtered as the index data of the OD pairs to obtain the index removal data, and the index removal data is eliminated in the incremental traffic distribution. The incremental traffic distribution algorithm is executed on the eliminated commuting behavior data in the third road network G3(V,E), and the road flow distribution result FM1 after the network attack is output. The bottleneck is identified based on the road flow distribution result FM1.

[0050] In this example, the collected area is road network data within a specific land area. Roads are simplified into single-line segments, called road segments, in a shapefile format. The data must include the free travel time t0, capacity C, and spatial location information for each road segment. The data's coordinate system is converted to EPSG=32651.

[0051] Preferably, step S2 is used to collect commuting behavior data within the land area of ​​a certain region. The format of the commuting behavior data is csv. The commuting behavior data includes the spatial location information of the commuting OD and the commuting flow of the OD.

[0052] Preferably, in step S3, the Geopandas library in the Python language is used to read the road network shapefile data obtained in step S1, the centroid function is used to identify the midpoint of each road section in the road network data, and the sjoin_nearest function in the Geopandas library is used to perform spatial connection, and the road midpoint is matched to the spatial position of the commuting OD pair obtained in step S2, and the road midpoint index corresponding to the starting point and end point of each OD pair is obtained, which is recorded as ODLinkRoad_dict.

[0053] Preferably, step S4 includes:

[0054] S41: Extract each road i and the road set i_link connected to each road from the first road network G1(V,E), select each connecting road j in the road set i_link using an iterative method, input the road i and the connecting road j as second network nodes in sequence into the set second road network G2(V,E), and input the free flow travel time t0 and capacity C of the road i and the connecting road j in the first road network G1(V,E) into the set second road network G2(V,E);

[0055] S42: Establish network edge data between road i and connecting road j, and input the network edge data into the set second road network G2(V,E). The average of the free flow travel time t0 of road i and connecting road j is used as the edge weight t. Repeat step S41 until all roads are input as i into the set second road network G2(V,E). Remove duplicate nodes and edges from the set second road network G2(V,E) to obtain the dual network G2'(V',E');

[0056] S43: Add attribute V to each network node of the dual network G2'(V',E'), and set the initial value of the carrying capacity of the road section of the dual network G2'(V',E') to 0, to obtain the second road network G2(V,E), wherein the network nodes of the second road network G2(V,E) are road sections, and the edges of the second road network G2(V,E) are road intersections.

[0057] Preferably, in step S4, the Networkx library is called to use an initial modeling method to establish a road network G1(V, E), where V is a set of network nodes, each of which corresponds to a road intersection; E is a set of network edges, each of which corresponds to a road segment. The specific method is as follows: traverse the road data row by row, add road segments as network edges, set the free flow travel time t0 of the road segment as the edge weight of the network edge, set the capacity C of the road segment as the edge attribute of the network edge, and set the starting point of the road segment as a network node.

[0058] Preferably, step S5 includes:

[0059] S51: Index the midpoint of the road segment corresponding to the OD pair included in the first part of the traffic, execute the shortest path algorithm between the two points in the second road network G2(V,E), set the edge weight to t, and return the data as a list of nodes passed by the two points, where the passed node list is the road segment;

[0060] S52: For each node list passed by an OD pair, multiply the flow of the OD pair by the corresponding distribution ratio of the commuting flow data to obtain the distributed flow, sum the distributed flow and the original flow to obtain the new flow, and distribute the new flow to each node in the node list;

[0061] S53: Using the time and flow resistance function, calculate the current actual travel time t of each road segment of the second road network G2 (V, E) a , the actual travel time t a As the new weight t a , where the time and flow resistance function is:

[0062] wherein t0,C and V are the current node attributes, and a and b are preset parameters, a is 0.15 and b is 4;

[0063] S54: based on the new weight t of each network edge at both ends of the network edge a , the network edge weight t is adjusted for each network edge, wherein the network edge weight t is the average value of the weights t of both ends of the network edge. a

[0064] Preferably, in step S5, the igraph library is called to establish the road network G2(V,E) using the dual modeling method, wherein V is a set of network nodes, each network node corresponding to a road intersection; E is a set of network edges, each network edge corresponding to a road section, and the specific implementation steps are as follows:

[0065] S501: extract each network edge in G1(V,E) and mark it as edge i; obtain other network edges connected to the starting point of edge i, and mark the set composed of these network edges as i_link;

[0066] S502: iteratively select a network edge in i_link and mark it as edge j; input edge i and edge j into G2(V,E) as network nodes, and set their free-flow travel time t0 and capacity C in G1(V,E) as node attributes;

[0067] S503: for each edge i and edge j, establish a connection between the two and input it into G2(V,E), and set the average value of the free-flow travel time t0 of the two as the edge weight t of the network G2(V,E);

[0068] S504: repeat the above steps S501-S503 until all network edges of G1(V,E) have been processed as edge i;

[0069] S505: use the simplify function of the igraph library to delete duplicate network nodes and network edges in G2(V,E) to obtain the final G2(V,E);

[0070] S506: add a traffic attribute V to each network node in G2(V,E), and set the initial value as 0, indicating the current road section carrying capacity.

[0071] Preferably, in step S5, the network G2(V,E) is obtained, in which the network nodes are road sections, C and V, and the edges are road intersections, the attributes of the road sections are t0, and the weight t of the road intersections is the average value of the weights t0 of both ends of the network edge.

[0072] ​Execute the incremental traffic assignment algorithm. This algorithm first divides the traffic of each OD pair in the commuting behavior data into four parts: 40%, 30%, 20%, and 10%. For the selected commuting spatial location information data, use ODLinkRoad_dict to index the corresponding road segment midpoint and find the corresponding network node pair in G2(V,E). Use get_shortest_paths to execute the shortest path algorithm between the two nodes, with an edge weight of t. The returned data is a list of nodes (i.e., road segments) passed by the two points, recorded as vpaths.

[0073] For each node list vpaths that an OD pair passes through, multiply the traffic of the OD pair by the current allocation ratio (i.e., 40%, 30%, 20% or 10%) and add it to each node in the list. The sum of the traffic and the original traffic attribute V is the new traffic V, which replaces the original attribute.

[0074] Execute the “time-flow” road resistance function for each network node of G2(V,E) and update the actual travel time t of the road segment as the node a . This function can be expressed as Where t0, C and V are the current node attributes, α and β are preset parameters, which are 0.15 and 4;

[0075] For each network edge, use source and target to find the two end nodes, and re-calculate the two end nodes based on the new weights t a Adjust the network edge weight t, calculated as the two-node weight t a The average value of .

[0076] like Figure 2 As shown in (a), after completing the above steps, the flow V of each node in the final network is output, which represents the road flow distribution result FM0 of incremental traffic allocation.

[0077] Preferably, step S10 includes:

[0078] S101: Filter out the remaining road segment nodes in the network after the attack, and calculate the ratio of the flow and capacity of the remaining road segment nodes before and after the attack, where the ratio of the flow and capacity before the attack is V0OC and the ratio of the flow and capacity after the attack is V1 OC .

[0079] Preferably, the method further comprises: screening the road segments in the network after the removal as index data of the OD pairs based on the index relationship between the OD pairs in the commuting behavior data and the spatial connection of the original road segments, obtaining index removal data, removing the index removal data from the incremental traffic distribution, executing the incremental traffic distribution algorithm on the removed commuting behavior data in the third road network G3(V,E), outputting the road flow distribution result FM1 after the network attack, and identifying bottlenecks based on the road flow distribution result FM1.

[0080] S102: Calculate the VOC changes of the remaining road segments in the network before and after the network attack.

[0081] Preferably, the VOC change is ΔVOC=V1 OC -V0 OC , where ΔVOC greater than 0 means that the road traffic volume after the attack is higher than before the attack, V1 OC If it is greater than 1, it means that the traffic volume of the road after the attack is higher than its own capacity. The road that meets both conditions will be identified as a network bottleneck, and V1 OC The higher the value, the higher the traffic volume on that road.

[0082] In this embodiment, the network attack scenario is a 500-year return period rainfall flood scenario. The specific implementation method is as follows:

[0083] Obtain a Digital Elevation Model (DEM) data for the study area with an accuracy of 30 meters, using each grid cell as a watershed unit. Convert the data's coordinate system to EPSG=32651 and the data type to TIF.

[0084] Obtain the land use type data within the study area, convert the coordinate system of the data to EPSG=32651, connect it with the DEM data space, and find the land use type of each catchment unit;

[0085] According to the calculation formula of the "Rainstorm Intensity Formula and Design Rainfall Type Standard" in the study area, the rainfall q is calculated with a 500-year return period and a rainfall duration of 60 minutes;

[0086] The SCS-CN method is used to calculate rainfall runoff. First, the corresponding Curve number (CN) value is found according to the land use type. The saturated water storage capacity S is obtained according to the formula 25400 / (CN-254). Then, the saturated water storage capacity S is obtained according to the formula (q-0.05S). 2 / (q+0.95S) calculates the runoff Q;

[0087] According to the drainage data of the study area, the drainage capacity of the rainwater drainage pump in the area is about 4996.85 cubic meters per second. Assuming that the drainage network is evenly distributed, the total drainage capacity is evenly distributed to each catchment unit. The final waterlogging volume is calculated as the difference between the runoff volume Q and the drainage volume.

[0088] The total flood volume is allocated using the equal volume method. This is achieved by selecting the lowest-lying unit among all catchment units and gradually allocating the flood volume. This process is repeated until all flood volumes are allocated. The submerged volume of each catchment is obtained, and its submerged depth is calculated based on the catchment area.

[0089] The watershed units with a flooding depth of not less than 30.5 cm are set as flooded areas, and the data are converted into spatial vector data in the shapefile format;

[0090] The Geopandas library in Python was used to read the inundation area data and perform a spatial join with the road network using the sjoin_nearest function. The overlap length between each road segment and the inundation area was calculated. If the overlap length was greater than 7% of the total length (a parameter obtained from robustness testing), the road segment was considered inundated.

[0091] Filter all flooded road sections and use the remove function to remove their corresponding network node sets in G2(V,E). Re-express the network after removal as G3(V,E), and initialize the node attributes (t0, C and V) and edge weights (t) to their original values.

[0092] According to the index relationship ODLinkRoad_dict between the OD pairs in the commuting behavior data and the spatial connection between the original road segments, the removed road segments are selected as the OD pair index data and are removed from the subsequent incremental traffic allocation.

[0093] The commuting behavior data after elimination is re-executed in the incremental traffic assignment algorithm in G3(V,E). The specific steps are the same as S7. Output the road traffic distribution results after the network attack like Figure 2 (b)

[0094] Identify bottlenecks based on the network traffic reconstruction results caused by the attack scenario. Filter out the remaining nodes (i.e. road sections) in the network after the attack, and and Calculate the ratio of the traffic and capacity of the nodes before and after the attack, that is, the S5-3 resistance function Indicated as V0 OC and V1 OC ;

[0095] likeFigure 3 As shown in the figure, the VOC change of this road section before and after the network attack is calculated as ΔVOC=V1OC-V0 OC Among them, ΔVOC greater than 0 means that the road traffic after the attack is higher than before the attack, and V1OC greater than 1 means that the road traffic after the attack is higher than its own capacity. The road that meets both conditions is identified as a network bottleneck, and V1OC OC The higher the value, the higher the traffic volume on that road.

[0096] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A traffic network bottleneck identification method based on traffic cascade, characterized in that: include: S1: Obtain road data and commuting behavior data within the target area. The road data includes the free travel time t0 of each road, the road capacity C, and the spatial location information of the road. The commuting behavior data includes the spatial location information of the commuting OD and the commuting flow corresponding to the OD. S2: Extract the midpoint of each road in the road data, spatially connect the OD pairs of the commuting behavior data to the midpoint of the nearest road, and record the corresponding index relationship between the OD pairs and the midpoint of the road; S3: Use the initial modeling method to establish a first road network G1(V,E), where V is the first network node, V is the set of road intersections, E is the first edge, and E is the set of each road. Each edge in E corresponds to the free flow travel time t0 and capacity C of the road, and the free flow travel time t0 is used as the weight of E; S4: Use the dual modeling method to establish a second road network G2(V,E), where V is the second network node, V is the set of roads, E is the second edge, and E is the edge set composed of each intersection of the roads; S5: Using the incremental traffic assignment algorithm, the commuting flow data corresponding to each OD pair in the commuting behavior data is divided into the first flow part, the second flow part, the third flow part, and the fourth flow part, and the network edge weight t of each network edge of the second road network G2(V,E) is readjusted; S6: Output the flow V of each OD pair, use the flow V of the OD pair as the road flow distribution result FM0 of the incremental traffic allocation, remove a certain proportion of road segment sets and a certain proportion of network node sets corresponding to the road segment sets from G2(V,E), represent the network after removal as the third road network G3(V,E), and initialize the node attributes and edge weights of the third road network G3(V,E); S7: Based on the index relationship between the OD pairs in the commuting behavior data and the spatial connection of the original road sections, the road sections in the removed network are filtered as the index data of the OD pairs to obtain the index removal data, and the index removal data is eliminated in the incremental traffic distribution. The incremental traffic distribution algorithm is executed on the eliminated commuting behavior data in the third road network G3(V,E), and the road flow distribution result FM1 after the network attack is output. The bottleneck is identified based on the road flow distribution result FM1.

2. The traffic network bottleneck identification method based on traffic cascade according to claim 1 is characterized by: The dual modeling method is used to establish a second road network G2(V,E), where V is a second network node, V is a set of roads, E is a second edge, and E is an edge set consisting of each intersection of the roads, including: S41: Extract each road i and the road set i_link connected to each road from the first road network G1(V,E), use an iterative method to select each connecting road j in the road set i_link, input the road i and the connecting road j as second network nodes in turn into the set second road network G2(V,E), and input the free flow travel time t0 and capacity C of the road i and the connecting road j in the first road network G1(V,E) into the set second road network G2(V,E).

3. The traffic network bottleneck identification method based on traffic cascade according to claim 2 is characterized by: The dual modeling method is used to establish a second road network G2(V,E), wherein V is a second network node, V is a set of roads, E is a second edge, and E is an edge set composed of each intersection of the roads, and further includes: S42: Establish network edge data between road i and connecting road j, and input the network edge data into the set second road network G2(V,E), and use the average value of the free flow travel time t0 of road i and connecting road j as the edge weight t. Repeat step S41 until all roads are input as i into the set second road network G2(V,E), and remove the duplicate nodes and edges of the set second road network G2(V,E) to obtain the dual network G2'(V',E').

4. The traffic network bottleneck identification method based on traffic cascade according to claim 3 is characterized by: The dual modeling method is used to establish a second road network G2(V,E), wherein V is a second network node, V is a set of roads, E is a second edge, and E is an edge set composed of each intersection of the roads, and further includes: S43: Add attribute V to each network node of the dual network G2'(V',E'), and set the initial value of the carrying capacity of the road section of the dual network G2'(V',E') to 0, to obtain the second road network G2(V,E), wherein the network nodes of the second road network G2(V,E) are road sections, and the edges of the second road network G2(V,E) are road intersections.

5. The traffic network bottleneck identification method based on traffic cascade according to claim 4 is characterized in that: The incremental traffic assignment algorithm is used to divide the commuting flow data corresponding to each OD pair in the commuting behavior data into a first flow portion, a second flow portion, a third flow portion, and a fourth flow portion, and readjust the network edge weight t of each network edge of the second road network G2(V,E), wherein the first flow portion is 40% of the commuting flow data, the second flow portion is 30% of the commuting flow data, the third flow portion is 20% of the commuting flow data, and the fourth flow portion is 10% of the commuting flow data, including: S51: Index the midpoint of the road section corresponding to the OD pair contained in the first part of the traffic, execute the shortest path algorithm between the two points in the second road network G2(V,E), set the edge weight to t, and return the data as a list of nodes passed by the two points, where the list of nodes passed is the road section.

6. The traffic network bottleneck identification method based on traffic cascade according to claim 5, characterized in that: The incremental traffic assignment algorithm is used to divide the commuting flow data corresponding to each OD pair in the commuting behavior data into a first flow portion, a second flow portion, a third flow portion, and a fourth flow portion, and the network edge weight t of each network edge of the second road network G2(V,E) is readjusted, wherein the allocation ratio of the commuting flow data of the first flow portion is 40%, the allocation ratio of the commuting flow data of the second flow portion is 30%, the allocation ratio of the commuting flow data of the third flow portion is 20%, and the allocation ratio of the commuting flow data of the fourth flow portion is 10%, and further includes: S52: For each list of nodes passed by an OD pair, multiply the flow of the OD pair by the corresponding distribution ratio of the commuting flow data to obtain the distributed flow, sum the distributed flow and the original flow to form a new flow, and distribute the new flow to each node in the node list.

7. The method for identifying bottlenecks in a traffic network based on traffic cascade according to claim 1, characterized in that: The incremental traffic assignment algorithm is used to divide the commuting flow data corresponding to each OD pair in the commuting behavior data into a first flow portion, a second flow portion, a third flow portion, and a fourth flow portion, and the network edge weight t of each network edge of the second road network G2(V,E) is readjusted, wherein the allocation ratio of the commuting flow data of the first flow portion is 40%, the allocation ratio of the commuting flow data of the second flow portion is 30%, the allocation ratio of the commuting flow data of the third flow portion is 20%, and the allocation ratio of the commuting flow data of the fourth flow portion is 10%, and further includes: S53: Using the time and flow resistance function, calculate the current actual travel time t of each road segment of the second road network G2 (V, E) a , the actual travel time t a As the new weight t a , where the time and flow resistance function is: Where t0, C and V are the current node attributes, α and β are preset parameters, α is 0.15 and β is 4.

8. The traffic network bottleneck identification method based on traffic cascade according to claim 7, characterized in that: The incremental traffic assignment algorithm is used to divide the commuting flow data corresponding to each OD pair in the commuting behavior data into a first flow portion, a second flow portion, a third flow portion, and a fourth flow portion, and the network edge weight t of each network edge of the second road network G2(V,E) is readjusted, wherein the allocation ratio of the commuting flow data of the first flow portion is 40%, the allocation ratio of the commuting flow data of the second flow portion is 30%, the allocation ratio of the commuting flow data of the third flow portion is 20%, and the allocation ratio of the commuting flow data of the fourth flow portion is 10%, and further includes: S54: Based on the new weight t of the nodes at both ends of each network edge a , readjust the network edge weight t for each network edge, where the network edge weight t is the weight t of the nodes at both ends a The average value of .

9. The traffic network bottleneck identification method based on traffic cascade according to claim 8, characterized in that: The method comprises the following steps: selecting the removed road segments in the network as index data of the OD pairs based on the index relationship between the OD pairs in the commuting behavior data and the spatial connection between the original road segments, obtaining index-removed data, removing the index-removed data from the incremental traffic distribution, executing the incremental traffic distribution algorithm on the removed commuting behavior data in the third road network G3(V,E), outputting a road flow distribution result FM1 after the network attack, and identifying bottlenecks based on the road flow distribution result FM1, including: S101: Filter out the remaining road segment nodes in the network after the attack, and calculate the ratio of the flow and capacity of the remaining road segment nodes before and after the attack, where the ratio of the flow and capacity before the attack is V0OC and the ratio of the flow and capacity after the attack is V1OC.

10. The traffic network bottleneck identification method based on traffic cascade according to claim 9, characterized in that: The method further comprises: screening the road segments in the network after the removal as index data of the OD pairs based on the index relationship between the OD pairs in the commuting behavior data and the spatial connection between the original road segments, obtaining index removal data, removing the index removal data from the incremental traffic distribution, executing the incremental traffic distribution algorithm on the removed commuting behavior data in the third road network G3(V,E), outputting a road flow distribution result FM1 after the network attack, and identifying bottlenecks based on the road flow distribution result FM1. S102: Calculate the VOC changes of the remaining road segments in the network before and after the network attack.

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