Robustness calculation method for multimodal transportation network

Abstract

The present invention relates to the field of transportation network planning. Aiming at the needs of coordinated planning of regional multi-transportation network systems, a method for calculating the robustness of a multi-mode transportation network is constructed, which mainly includes: (1) construction of a multi-mode transportation network; (2) calculation of crowd mobility needs based on big data; (3) multiple network simulation attack methods for different disasters / accidents; (4) multiple traffic flow allocation methods for different needs; (5) calculation of the robustness of a multi-mode transportation network based on a multi-level network model. Based on the above steps, the interdependence between different transportation network systems can be taken into account, the comprehensive robustness and vulnerability of a large-area multi-type transportation network system can be effectively determined, and potential vulnerable components can be further excavated, which is expected to guide the coordinated planning of a large-area multi-transportation network system and establish a highly robust and efficient comprehensive transportation network system.

Description

Technical Field

[0001] The present invention relates to the field of traffic network planning, and in particular to a method for calculating the robustness of a multi-mode traffic network. Background Art

[0002] Regarding the robustness and vulnerability of regional or urban public transportation networks, current analysis methods often only focus on a single homogeneous network (such as a road network, water transport network, aviation network, or railway network), while ignoring the interdependence between different types of transportation networks; on the other hand, they often lack real data support to analyze the real needs of personnel or goods flow. As a result, the calculated network robustness is inaccurate when facing regional disasters. In this context, how to construct the dependency between different types of networks, use big data to analyze the real needs of people / goods flow, and analyze the robustness and vulnerability of multimodal transportation networks under random attacks / deliberate attacks / regional attacks are of great guiding significance for regional public transportation planning and disaster emergency response.

[0003] To achieve this goal, on the one hand, it is necessary to use multi-source massive spatiotemporal data to infer real traffic demand and build a sophisticated multimodal transportation network; on the other hand, it is necessary to develop new robustness / vulnerability calculation methods to take into account the dependencies between different modes of transportation networks and analyze the comprehensive robustness / vulnerability of multimodal transportation networks. In summary, the multimodal transportation network robustness calculation method designed by the present invention using multi-source spatiotemporal data and multi-level network models is helpful to analyze the resistance of urban / regional transportation networks in response to large-scale disasters, and is of great significance to the design of transportation networks for large-scale natural disasters. Summary of the invention

[0004] The present invention discloses a method for calculating the robustness of a multi-modal transportation network. Figure 1 The method mainly includes the following steps.

[0005] (1) Construction of multimodal transportation network.

[0006] Collect aviation network information, and build an aviation network with airports as nodes and routes as edges. Collect railway network information, and build a railway network with railway stations as nodes and railway lines as edges. Collect highway network information, and build a highway network with intersections as nodes and highways as edges. Collect ferry network information, and build a water transport network with the starting and ending points of the ferry as nodes and ferry routes as edges. Collect information on sidewalks, overpasses, etc. to build a pedestrian network. Use walking as a connection to build connections between the aviation network and the highway network, between the railway network and the highway network, and between the water transport network and the highway network.

[0007] (2) Calculation of crowd mobility needs based on big data.

[0008] Collect massive amounts of mobile phone signaling big data and determine whether each trajectory belongs to cross-city flow; count the total flow of people between each pair of cities; count the average flow of all people on multiple consecutive dates to construct OD crowd mobility needs.

[0009] (3) Various network simulation attack methods for different disasters / accidents.

[0010] Random attack, deliberate attack and regional attack are used to simulate the attack on nodes in the same network of aviation network, railway network, road network, water transport network or pedestrian network; random attack, deliberate attack and regional attack are used to simulate the attack on nodes in different networks of aviation-water transport-railway-road-pedestrian mixed network.

[0011] (4) Various traffic flow allocation methods for different needs.

[0012] For railway, water transport and aviation networks, a traffic flow allocation method with capacity limitation and unlimited speed is adopted, where the capacity limitation comes from the train / ferry / flight schedule; for pedestrian networks and road networks, under the principles of system optimization and user balance, a traffic flow allocation method with both capacity and speed limitations is adopted to calculate the dynamic passenger flow in each type of transportation network.

[0013] (5) Robustness calculation of multimodal transportation networks based on multi-level network models.

[0014] The multiple attack modes in step (3) and the multiple traffic flow allocation methods in step (4) are combined in pairs. For each pair of attack modes and traffic flow allocation methods, the robustness of the aviation network, railway network, water transport network, highway network, pedestrian network, highway-pedestrian network, railway-highway-pedestrian network, aviation-highway-pedestrian network, and aviation-railway-water transport-highway-pedestrian network is calculated respectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0016] Figure 1 This is the overall technical diagram of the multi-modal transportation network robustness calculation of the present invention;

[0017] Figure 2 Constructing a flow chart for the multimodal transportation network of the present invention;

[0018] Figure 3 This is a flowchart of the multi-modal transportation network robustness calculation of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] The following will be combined with the attached Figure 2 -Attached Figure 3 , a multi-modal transportation network robustness calculation method provided by an embodiment of the present invention is introduced in detail.

[0022] 1. See attached Figure 2 The embodiment of the present invention provides a method for constructing a multi-modal transportation network. The method of the embodiment of the present invention may include the following steps:

[0023] 1) Collect aviation network information, with airports as nodes and routes between two airports i and j as edges e ij Construct an aviation network, where the number of daily flights on each route is the edge weight w ij , the flight schedule can be directly used to calculate the dynamic capacity of the aviation network c ij (t) and travel time t ij .

[0024] 2) Collect railway information, with the railway station as node i and the railway line as edge e ij Construct a railway network, with the number of trains passing through each railway line every day as the edge weight w ij , the train timetable can be directly used to calculate the dynamic capacity of the railway network c ij (t) and travel time t ij .

[0025] 3) Collect road network information, with intersections as nodes i and roads as edges e ij Construct a highway network and calculate the weight w of each road based on the road grade, number of lanes, and length. ij , travel time t ij and capacity c ij .

[0026] 4) Collect water ferry network information, with the starting and ending points of the ferry as node i and the ferry route as edge e ij Construct a water transport network, and the ferry schedule can be directly used to calculate the dynamic capacity of the water transport network ij (t) and travel time t ij .

[0027] 5) Collect information on pedestrian paths such as sidewalks and overpasses, with pedestrian path nodes or intersections between pedestrian paths and other roads as nodes i and pedestrian paths as edges e ij , construct a walking network, and calculate the travel time t of the walking road according to the walking speed, sidewalk length and width ij and the capacity of the pedestrian path c ij (t).

[0028] 6) Use walking edge features to add the nearest neighbor road nodes (j) between aviation station i and its surroundings 1 ,j 2 , …, j k ) to build the connection between the aviation network and the highway network; the pedestrian edge feature is used to add the railway station u and the surrounding multiple nearest highway nodes (v 1 , v 2 , …, v k ) to build the connection between the railway network and the highway network; the pedestrian edge feature is used to add the water transport node h and the surrounding multiple nearest neighbor highway nodes (g 1 , g 2 , …, g k ) to build the connection between the water transport network and the road network.

[0029] 2. An embodiment of the present invention provides a method for calculating crowd mobility demand based on big data. The method of the embodiment of the present invention may include the following steps:

[0030] 1) Collect massive amounts of mobile phone signaling data every day Extract each track data Analyze the city to which each track point belongs according to the geographic coordinates and construct the city roaming track Traversal For all trajectories, if the passenger stays in a city u for more than 4 hours continuously, it is considered that the passenger roamed to city u during this period; if the passenger roams in two cities u and v continuously, it is considered that there is a directed interaction record between cities u and v.

[0031] 2) Traversal The trajectories of all individuals in the city, and the directed interaction volume W between any two cities u and v is counted uv .

[0032] 3) Repeat steps 1 and 2 to calculate the average flow of people on multiple consecutive dates Build the mobile needs of OD people.

[0033] 3. The embodiment of the present invention provides a method for multiple network attacks against different disasters / accidents. The method of the embodiment of the present invention may include the following steps:

[0034] 1) Define random attack: randomly select some nodes from the network and remove them from the network.

[0035] 2) Define deliberate attack: First, calculate the importance of network nodes (such as betweenness), and secondly, intentionally select some relatively important nodes from the network and remove them from the network.

[0036] 3) Define regional attack: given a geographic space area, remove all network nodes within the area.

[0037] 4) Use random attack, deliberate attack and regional attack to simulate attacks on aviation networks.

[0038] 5) Use random attack, deliberate attack and regional attack to simulate attacks on the water transport network.

[0039] 6) Use random attack, deliberate attack and regional attack to simulate attacks on the railway network.

[0040] 7) Use three methods of random attack, deliberate attack and regional attack to simulate the attack on the highway network.

[0041] 8) Use random attack, deliberate attack and regional attack to simulate the attack on the walking network.

[0042] 9) Treat all nodes in the road-pedestrian hybrid network equally, and attack the road-pedestrian hybrid network using three methods: random attack, deliberate attack, and regional attack.

[0043] 10) Treat all nodes in the railway-road-pedestrian hybrid network equally, and attack the railway-road-pedestrian hybrid network using three methods: random attack, deliberate attack, and regional attack.

[0044] 11) Treat all nodes in the aviation-road-pedestrian hybrid network equally, and attack the aviation-road-pedestrian hybrid network using three methods: random attack, deliberate attack, and regional attack.

[0045] 12) Treat all nodes in the aviation-railway-waterway-road-pedestrian hybrid network equally, and attack the aviation-railway-waterway-road-pedestrian hybrid network using three methods: random attack, deliberate attack, and regional attack.

[0046] 4. The embodiment of the present invention provides a plurality of traffic flow allocation methods for different needs. The method of the embodiment of the present invention may include the following steps:

[0047] 1) For the aviation network, a capacity-limited and speed-unlimited traffic flow allocation method is adopted, where the capacity limit comes from the flight schedule;

[0048] 2) For the railway network, a capacity-limited and speed-unlimited traffic flow allocation method is adopted, where the capacity limit comes from the train timetable;

[0049] 3) For the water transport network, a capacity-limited and speed-unlimited traffic flow allocation method is adopted, where the capacity limit comes from the ferry schedule;

[0050] 4) For the highway network, based on the system optimality and user balance principles, a traffic flow allocation method with both capacity and speed restrictions is adopted to calculate the dynamic passenger flow and dynamic travel time;

[0051] 5) For pedestrian networks, based on the principles of system optimization and user balance, a traffic flow allocation method with both capacity and speed constraints is adopted to calculate dynamic passenger flow and travel time.

[0052] 5. See Appendix Figure 3 The embodiment of the present invention provides a method for calculating the robustness of a multi-modal transportation network based on a multi-level network model. The method of the embodiment of the present invention may include the following steps:

[0053] 1) Use the random attack, deliberate attack, and regional attack in step 3 to simulate the attack on the aviation network. Iterate the capacity-limited and speed-unlimited method in step 4 to allocate traffic flow and remove overloaded nodes. When a stable state is reached, calculate the robustness of the aviation network.

[0054] 2) Use the random attack, deliberate attack, and regional attack in step 3 to simulate the attack on the railway network. Iterate the capacity-limited and speed-unlimited method in step 4 to allocate traffic flow and remove overloaded nodes. When a stable state is reached, calculate the robustness of the railway network.

[0055] 3) Use the random attack, deliberate attack, and regional attack in step 3 to simulate the attack on the water transport network. Iteratively use the capacity restriction and speed unlimited method in step 4 to allocate traffic flow and remove overloaded nodes. When a stable state is reached, calculate the robustness of the water transport network.

[0056] 4) Use the random attack, deliberate attack, and regional attack methods in step 3 to simulate the attack on the highway network. Under the guidance of the system optimality and user equilibrium principles, iteratively use the capacity and speed restrictions in step 4 to allocate traffic flow and remove overloaded nodes. When a stable state is reached, calculate the robustness of the highway network.

[0057] 5) Use the random attack, deliberate attack, and regional attack methods in step 3 to simulate the attack on the pedestrian network. Under the guidance of the system optimality and user equilibrium principles, iteratively use the capacity and speed restrictions in step 4 to allocate traffic flow and remove overloaded nodes. When a stable state is reached, calculate the robustness of the pedestrian network.

[0058] 6) Use the random attack, deliberate attack, and regional attack methods in step 3 to simulate the attack on the highway-pedestrian network. Under the guidance of the system optimality and user equilibrium principles, iteratively use the capacity and speed restrictions in step 4 to allocate traffic flow and remove overloaded nodes. When a stable state is reached, calculate the robustness of the highway-pedestrian network.

[0059] 7) Use the random attack, deliberate attack, and regional attack in step 3 to simulate the attack on the aviation-highway-pedestrian network. Iteratively use the capacity-limited and speed-unlimited method in step 4 to allocate the traffic flow on the aviation network. At the same time, under the guidance of the system optimality and user equilibrium principles, use the capacity and speed-limited method in step 4 to allocate the traffic flow on the highway and pedestrian networks, remove the overloaded nodes, and calculate the robustness of the aviation-highway-pedestrian network after reaching a stable state.

[0060] 8) Use the random attack, deliberate attack, and regional attack methods in step 3 to simulate the attack on the railway-road-pedestrian network, and iteratively use the capacity-limited and speed-unlimited method in step 4 to allocate the traffic flow on the railway network. At the same time, under the guidance of the system optimality and user equilibrium principles, use the capacity and speed-limited methods in step 4 to allocate the traffic flow on the highway and pedestrian networks, and remove the overloaded nodes. When a stable state is reached, calculate the robustness of the railway-road-pedestrian network;

[0061] 9) Use the random attack, deliberate attack, and regional attack in step 3 to simulate the attack on the aviation-railway-road-pedestrian network. Iteratively use the capacity-limited and speed-unlimited method in step 4 to allocate the traffic flow on the aviation and railway networks. At the same time, under the guidance of the system optimality and user equilibrium principles, use the capacity and speed-limited method in step 4 to allocate the traffic flow on the road and pedestrian networks, remove the overloaded nodes, and calculate the robustness of the aviation-railway-road-pedestrian network after reaching a stable state.

[0062] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating the robustness of multimodal transportation networks, It is characterized in that The following steps are involved: 1) Construction of multi-modal transportation network; 2) Calculation of crowd mobility needs based on big data; 3) Multiple simulated cyber attacks for different disasters / accidents; 4) Multiple traffic flow allocations for different needs; 5) Robustness calculation of multimodal transportation network based on multi-level network model; The multi-modal transportation network construction in step 1) includes first constructing an aviation network, a railway network, a highway network, a water transport network and a pedestrian network respectively, and then connecting each type of transportation network using pedestrian roads; Collect aviation network information, with airports as nodes and routes between two airports i and j as edges e ij Construct an aviation network, where the number of daily flights on each route is the edge weight w ij , the flight schedule can be directly used to calculate the dynamic capacity of the aviation network c ij (t) and travel time t ij ; Or, collect railway information, with train stations as nodes i and railway lines as edges e ij Construct a railway network, with the number of trains passing through each railway line every day as the edge weight w ij , the train timetable can be directly used to calculate the dynamic capacity of the railway network c ij (t) and travel time t ij ; Alternatively, collect highway network information, with intersections as nodes i and highways as edges e ij Construct a highway network and calculate the weight w of each road according to road grade, number of lanes, length, etc ij , travel time t ij and capacity c ij ; Alternatively, collect water ferry network information, with the ferry’s starting and ending points as nodes i and the ferry routes as edges e ij Construct a water transport network, and the ferry schedule can be directly used to calculate the dynamic capacity of the water transport network ij (t) and travel time t ij ; Alternatively, collect information on pedestrian paths such as sidewalks and overpasses, with pedestrian path nodes or intersections between pedestrian paths and other roads as nodes i and pedestrian paths as edges e. ij , construct a walking network, and calculate the travel time t of the walking road according to the walking speed, sidewalk length and width ij and the capacity of the pedestrian path c ij (t); The walking edge feature is used to add the nearest neighbor road nodes (j 1 , j 2 , ..., j k ) to build the connection between the aviation network and the highway network; the pedestrian edge feature is used to add the railway station u and the surrounding multiple nearest highway nodes (v 1 , v 2 , ..., v k ) to build the connection between the railway network and the highway network; the pedestrian edge feature is used to add the water transport node h and the surrounding multiple nearest neighbor highway nodes (g 1 , g 2 , ..., g k ) to build the connection between the water transport network and the road network; The multiple network simulation attack methods for different disasters / accidents in step 3) include: random attack, deliberate attack and regional attack; Randomly selecting some nodes from the network and removing them from the network is defined as the random attack; After calculating the importance of network nodes, some relatively important nodes are selected from the network and removed from the network, which is defined as the deliberate attack; The removal of all network nodes within a given geospatial region is defined as said regional attack; In the step 4), for the aviation network, railway network and water transport network, a traffic flow allocation method with capacity restriction and no speed restriction is adopted; for the highway network and pedestrian network, a traffic flow allocation method with both capacity and speed restriction is adopted based on the system optimization and user balance principles respectively; the dynamic passenger flow and dynamic travel time are calculated; In the step 5), based on a multi-level network model using multiple simulated network attacks in the step 3) and iteratively using multiple traffic flow distribution methods in the step 4), traffic flow is distributed and overloaded nodes are removed, and after reaching a stable state, the robustness of the multimodal transportation network is calculated; the calculation objects include: aviation network, water transport network, railway network, highway network, pedestrian network, highway-pedestrian network, aviation-road-pedestrian network, railway-road-pedestrian network, and aviation-rail-water transport-road-pedestrian network.

2. The method for calculating the robustness of a multimodal transportation network according to claim 1, It is characterized in that The calculation of crowd mobility demand based on big data in step 2) includes big data collection, cross-city trajectory extraction, and inter-city interaction volume statistics.

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