Method for analyzing dynamic robustness of multi-modal public transport network considering passenger travel behavior, electronic device and storage medium

Abstract

The application discloses a kind of multi-mode public transport network dynamic robustness analysis method considering passenger travel behavior, electronic equipment and storage medium, establish multi-mode public transport topology network and the multi-mode public transport passenger flow network of each period;Based on nonlinear capacity load model, the site load and capacity are calculated;Considering the travel mode selection behavior and path planning behavior of passenger, simulate the cascading failure process after site failure, obtain the multi-mode public transport network after cascading failure;Based on the network maximum connectivity rate after each period each failure site, network operation efficiency and passenger flow loss rate, the comprehensive robustness of each site in each period is calculated, based on comprehensive robustness, the site area prone to trigger cascading failure in each period is evaluated.The application comprehensively considers the passenger flow dynamic time-varying characteristics of site and the travel behavior of passenger, for assisting public transport operator to analyze the dynamic robustness in multi-mode public transport network, provide decision support for emergency plan under emergency.

Description

TECHNICAL FIELD

[0001] The application relates to a multi-modal public transport network dynamic robustness analysis method considering passenger travel behavior, an electronic device and a storage medium, and belongs to the technical field of public transport planning and control. BACKGROUND

[0002] In large and medium-sized cities in China, public transport is the main way to meet the daily travel needs of a large number of residents. At present, many large cities in the world are building a multi-modal public transport network with rail transit as the backbone and regular public transport as the main body. As an extremely complex network system, this multi-modal public transport network is easily affected by various sudden events, such as extreme weather and large passenger flow impact. When a station or line in the multi-modal public transport network is interrupted, the affected passengers are forced to change their travel mode and path, and the re-distributed passenger flow may spread to other stations and trigger cascading failures. This not only seriously affects the passenger travel experience, but also threatens the overall operation reliability of the network.

[0003] Therefore, how to accurately describe the failure propagation mechanism of the multi-modal public transport network and analyze the dynamic robustness of the multi-modal public transport network after cascading failure has important decision support significance for formulating emergency management plans and improving the reliability of the multi-modal public transport network.

[0004] The existing analysis methods mainly have three problems:

[0005] First, most existing researches focus on subway or public transport single network, but the multi-modal public transport network is a typical interdependent network, and each subsystem can operate independently and interact through walking connection and passenger transfer. Therefore, the research on single network cannot reveal the complex coupling characteristics.

[0006] Second, the existing researches do not consider the influence of passenger spatio-temporal heterogeneity on cascading failure. In reality, the passenger flow of different types of stations, different locations and different time periods in the multi-modal public transport network shows significant spatio-temporal heterogeneity. This heterogeneity will affect the ability of stations to handle failure transfer passenger flow, and ultimately lead to different propagation patterns of cascading failure.

[0007] Third, the existing researches assume that when a station fails, passengers will not switch to other transportation modes (such as online car-hailing, taxi, shared bicycle, etc.), and the proportion of transfer to adjacent stations is only related to the node influence (such as capacity, betweenness, degree and clustering coefficient). These assumptions ignore the passenger's travel mode selection and travel path planning behavior, which may exaggerate the size of cascading failure and misestimate its propagation range.

[0008] Therefore, the person skilled in the art urgently needs to take the passenger flow heterogeneity and the passenger flow travel behavior into the propagation process analysis of the cascade failure from the actual passenger flow characteristics and the travel behavior characteristics, and construct a more actual multi-mode public transport dynamic robustness analysis method. SUMMARY

[0009] Objective: In order to overcome the deficiencies in the prior art, the application provides a multi-mode public transport network dynamic robustness analysis method considering passenger travel behavior, electronic equipment and storage medium, which is used for assisting public transport operators to analyze the dynamic robustness of multi-mode public transport network in each period, thereby guiding the line network planning, site layout and capacity configuration of public transport.

[0010] Technical scheme: In order to solve the above technical problems, the technical scheme adopted by the application is:

[0011] In a first aspect, a multi-mode public transport network dynamic robustness evaluation method comprises the following steps:

[0012] Step 1: Obtain the line and station data of subway and public transport and AVL (automated vehicle location) data, and establish a multi-mode public transport topology network.

[0013] Step 2: Obtain the passenger flow data of subway and public transport, count the OD travel volume in each period, and establish a multi-mode public transport passenger flow network in each period.

[0014] Step 3: Calculate the station load in each period, and calculate the station capacity based on a nonlinear capacity load model.

[0015] Step 4: Based on the constructed multi-mode public transport topology network, multi-mode public transport passenger flow network and station capacity, simulate the cascade failure process after the station failure, and establish a passenger flow distribution rule considering passenger travel behavior, to obtain the multi-mode public transport network after cascade failure in each period.

[0016] Step 5: Based on the multi-mode public transport network after cascade failure, calculate the network maximum connectivity rate after each station failure in each period.

[0017] As a preferred scheme, it further comprises: Step 6: Based on the multi-mode public transport network after cascade failure, calculate the network operation efficiency after each station failure in each period.

[0018] As a preferred scheme, it further comprises: Step 7: Based on the multi-mode public transport network after cascade failure, calculate the passenger flow loss rate after each station failure in each period.

[0019] As a preferred solution, it further comprises: step 8: based on the network maximum connectivity rate, network operation efficiency and passenger flow loss rate after failure of each station in each period, the comprehensive robustness index of each station in each period is calculated, and the station area prone to triggering cascading failure in each period is evaluated according to the comprehensive robustness index.

[0020] As a preferred solution, the step 1 of establishing a multi-mode public transport topology network comprises the following specific methods:

[0021] Step 1.1: based on the line and station data of subway and public transport, a public transport topology network is established based on Space L method (entity network modeling method) and a subway topology network , wherein, , When two stations are adjacent in subway or bus lines, it is considered that there is a station section edge between the stations. Wherein, represents the subway topology network; represents the set of subway stations; represents the set of station section edges of the subway network; represents the public transport topology network; represents the set of public transport stations; represents the set of station section edges of the public transport network.

[0022] Step 1.2: based on the spatial positions of subway and public transport stations, search for public transport stations within the walking reach of subway stations, when the distance between a subway station and a public transport station is less than R, it is considered that there is a two-way transfer edge between the subway station and the public transport station, and the set of all identified transfer edges is denoted as , wherein R is the distance threshold.

[0023] Step 1.3: based on the AVL data of public transport and subway lines, the corresponding time weights of the set of station section edges of the subway network and the set of station section edges of the public transport network are calculated, and are denoted as and . The time weight represents the average travel time of vehicles between adjacent stations, which can be obtained by the average value of the arrival and departure times of stations in the AVL data.

[0024] Step 1.4: based on the position data of subway stations and public transport stations, the time weight of the transfer edge is calculated, wherein the calculation formula of the transfer edge time weight between stations and is as follows:

[0025]

[0026] wherein,​ Indicates site and Time weights of transfer edges between them; Indicates site and Walking distance between This indicates the passenger's walking speed.

[0027] Step 1.5: Construct a multimodal public transport topology network , .in, V = { V b , V m } = { v i | i ∈ [ 1 , N ]} , Represents a collection of sites. This represents the i-th site in the set of sites. This indicates the number of all stops in a multimodal transit network. , Denotes the set of edges. In the edge set and The edges connecting them; , This represents the set of weights corresponding to the edges. This represents the weight set of the edges. The corresponding weights i , j ∈ [ 1 , N ], i ≠ j .

[0028] As a preferred option, the method for establishing a multi-modal public transport passenger flow network for each time period in step 2 is as follows:

[0029] Step 2.1: Based on passenger subway and bus card swipe data, calculate passenger flow between stations in different time periods. When at station t in time period t... and When there is passenger flow between them, there is a flow edge. The weight of the flow edge is denoted as , Indicates the time period t at the station and The passenger flow between them.

[0030] Step 2.2: For the flow edge Calculating stations based on multi-mode bus topology network and Travel time between Travel time For the site and The travel time of the shortest path between stations includes travel time and transfer time between stations. The shortest path is calculated using the weighted Dijkstra algorithm.

[0031] Step 2.3: Constructing the multimodal public transport OD network of period t , . Wherein, represents the set of stations, which is the same as the multimodal public transport topology network; , represents the set of flow edges of the multimodal public transport OD network of period t; , represents the set of weights corresponding to the flow edges; represents the set of shortest travel times corresponding to the flow edges, i , j ∈ [ 1 , N ], i ≠ j .

[0032] As a preferred solution, the step 3 of calculating the station capacity based on the nonlinear capacity load model, specifically includes:

[0033] Step 3.1: Calculating the station load of each period t, specifically includes:

[0034]

[0035] Wherein, represents the station load of the station in period t.

[0036] Step 3.2: Calculating the maximum load of the station in a day, specifically includes:

[0037]

[0038] Wherein, represents the set of time period divisions in a day; represents the maximum load of the station in a day.

[0039] Step 3.3: Calculating the capacity of the station based on the nonlinear load model, specifically includes:

[0040]

[0041] Wherein, α and β are the capacity control coefficients of the nonlinear load model; represents the capacity of the station .

[0042] As a preferred solution, the step 4, specifically includes:

[0043] Step 4.1: Initializing the multimodal public transport topology network of period t and the multimodal public transport OD network . ​

[0044] Step 4.2: Obtain the time period tsite of the site load and the site capacity .

[0045] Step 4.3: Select an arbitrary site as a failure object, delete the site and its corresponding edges in the multimodal public transport topology .

[0046] Step 4.4: For the passenger flow whose destination is after the site fails, search for all neighbor sites of the site in the multimodal public transport topology , record the set of all neighbor sites as , traverse all neighbor sites in , calculate the total time of the passenger walking to any site in and from to the destination .

[0047]

[0048] wherein represents the walking distance between the site and ; represents the walking speed of the passenger; represents the shortest travel time from to the destination .

[0049] Step 4.5: Calculate the probability of the passenger transferring to the site based on the multinomial logit model (discrete choice model).

[0050]

[0051] wherein represents the probability of the passenger flow whose origin is the site and whose destination is transferring to the site , is a control parameter for measuring the sensitivity of the passenger to travel time.

[0052] Step 4.6: Based on the transfer probability of each neighbor site, assign the neighbor site to which the passenger transfers, and record the travel time of the passenger flow after being assigned to a specific neighbor site as the updated shortest travel time .

[0053] Step 4.7: When the updated shortest travel time is greater than the shortest travel time of the multi-modal public transit topology network without attack , i.e. / >TT, the passenger flow between the OD pair is lost and is considered to be transferred to other travel modes (such as taxi, online car-hailing, etc.). Conversely, when / <TT, the passenger flow between the OD pair is assigned to the transferred neighbor station.

[0054] Step 4.8: Update the station load of the neighbor station after passenger flow assignment When the station load of the station is greater than the capacity of the station , the station fails, and the station and its corresponding edges are deleted in ;

[0055]

[0056] Step 4.9: Repeat steps 4.4-4.8 until no station fails, and output the multi-modal public transit network in which cascading failures are triggered by station failures in the period.

[0057] As a preferred solution, the method for calculating the network maximum connectivity rate of each station after failure in each period in step 5 is represented as:

[0058]

[0059] wherein: represents the network maximum connectivity rate of the multi-modal public transit network after cascading failure in period t; represents the number of stations in the maximum connected subgraph of the multi-modal public transit topology network after cascading in period t; N represents the number of stations in the multi-modal public transit network without attack. The network maximum connectivity rate is used to measure the impact of cascading failure on the topology structure of the multi-modal public transit network.

[0060] As a preferred solution, the method for calculating the network running efficiency of each station after failure in each period in step 6 is represented as:

[0061]

[0062] wherein: represents the network running efficiency of the multi-modal public transit network after cascading failure in period t; ​​the passenger flow between the stations and at time t; the shortest travel time between the stations and after the cascade failure at time t; N represents the number of stations in the multi-modal public transit network without attack. The network efficiency is used to measure the influence of the cascade failure on the overall travel time efficiency of the multi-modal public transit network.

[0063] As a preferred solution, the passenger flow loss rate calculation method of each station after the failure of each time period in step 7 is represented as:

[0064]

[0065]

[0066] wherein: the passenger flow loss rate of the multi-modal public transit network after the cascade failure at time t; the passenger flow between the stations and at time t; whether the travel time increase between the stations and exceeds the patience threshold of passengers; the shortest travel time between the stations and after the cascade failure at time t; the shortest travel time between the stations and in the multi-modal public transit network without attack at time t; TT represents the patience threshold of passengers for the travel time increase; N represents the number of stations in the multi-modal public transit network without attack. The network efficiency is used to measure the influence of the cascade failure on the overall travel volume of the multi-modal public transit network.

[0067] As a preferred solution, step 8 calculates the comprehensive robustness index of each station in each time period, and evaluates the station area prone to triggering the cascade failure in each time period according to the comprehensive robustness index, and the specific method is:

[0068] Step 8.1: Based on the network maximum connectivity rate, network operation efficiency and passenger flow loss rate after the cascade failure triggered by each station in each time period t, a comprehensive robustness evaluation matrix of the station is established , which can be represented as:

[0069] X t = [ GC 1 ( t ) OE 1 ( t ) LR 1 ( t ) GC 2 ( t ) ⋮ OE 2 ( t ) ⋮ LR 2 ( t ) ⋮ GC N ( t ) OE N ( t ) LR N ( t ) ]

[0070] wherein, The normalized value of the network maximum connectivity rate of the multi-modal public transit network after the cascade failure of the N stations at the time period t, The normalized value of the network operation efficiency of the multi-modal public transit network after the cascade failure of the N stations at the time period t, The normalized value of the passenger flow loss rate of the multi-modal public transit network after the cascade failure of the N stations at the time period t.

[0071] Step 8.2: Normalization of the comprehensive robustness evaluation matrix The absolute values of each index are converted into relative values by normalization processing to obtain the normalized comprehensive evaluation matrix

[0072] X t ¯ = [ GC 1 ( t ) ¯ OE 1 ( t ) ¯ LR 1 ( t ) ¯ GC 2 ( t ) ¯ ⋮ OE 2 ( t ) ¯ ⋮ LR 2 ( t ) ¯ ⋮ GC N ( t ) ¯ OE N ( t ) ¯ LR N ( t ) ¯ ]

[0073] Among them, The normalized value of the network maximum connectivity rate of the multi-modal public transit network after the cascade failure of the N stations at the time period t, The normalized value of the network operation efficiency of the multi-modal public transit network after the cascade failure of the N stations at the time period t, The normalized value of the passenger flow loss rate of the multi-modal public transit network after the cascade failure of the N stations at the time period t.

[0074] Step 8.3: Calculation of information entropy of network maximum connectivity rate, network operation efficiency, and passenger flow loss rate 、 、 .

[0075]

[0076]

[0077]

[0078] Step 8.4: Calculation of the weight of network maximum connectivity rate, network operation efficiency, and passenger flow loss rate 、 、 .

[0079]

[0080]

[0081]

[0082] Step 8.5: Calculation of the comprehensive robustness index of each station at the time period t The overall importance of each site was ranked from highest to lowest, with higher-ranked sites being more prone to cascading failures. Among these, the overall robustness index... The expression is as follows:

[0083]

[0084] In a second aspect, a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a dynamic robustness analysis method for a multimodal public transport network that considers passenger travel behavior, as described in any of the first aspects.

[0085] Thirdly, an electronic device includes:

[0086] Memory is used to store instructions.

[0087] A processor is configured to execute the instructions, causing the electronic device to perform operations as described in any of the first aspects of a method for dynamic robustness analysis of multimodal public transport networks that takes into account passenger travel behavior.

[0088] Beneficial Effects: This invention provides a method, electronic device, and storage medium for dynamic robustness analysis of multimodal public transport networks that considers passenger travel behavior. Addressing the shortcomings of current multimodal public transport network dynamic robustness analysis in considering the spatiotemporal heterogeneity of passenger flow and passenger travel behavior, this invention proposes a two-layer network model integrating topological networks and dynamic passenger flow networks. It also incorporates passenger travel mode selection and route planning behavior into a nonlinear capacity load model, constructs a cascading failure propagation mechanism for multimodal public transport networks, and finally proposes dynamic robustness evaluation indicators from three dimensions: network connectivity robustness, travel time robustness, and passenger flow robustness, thus constructing a method for dynamic robustness analysis of multimodal public transport networks.

[0089] This invention can quickly analyze the dynamic robustness of a city's public transportation network at different times based on urban public transportation network data and historical card-swipe travel data, identifying station areas prone to cascading failures. Compared to traditional robustness analysis methods, this method can more accurately characterize passenger travel behavior in multimodal public transportation networks. Furthermore, the dynamic robustness analysis method proposed in this paper comprehensively considers the time-varying characteristics of passenger flow and travel choice characteristics at stations, helping public transportation operators quickly assess station areas in the network prone to cascading failures at different times. Attached Figure Description

[0090] Figure 1 This is a flowchart illustrating a method for dynamic robustness analysis of multimodal public transport networks that considers passenger travel behavior, according to the present invention.

[0091] Figure 2is a passenger flow distribution diagram in a day in an embodiment of the present application.

[0092] Figure 3 is a passenger flow transfer rule diagram of the present application.

[0093] Figure 4 is a top 10% distribution diagram of a comprehensive robust index of a station in an early peak in an embodiment of the present application.

[0094] Figure 5 is a top 10% distribution diagram of a comprehensive robust index of a station in a mid-peak in an embodiment of the present application.

[0095] Figure 6 is a top 10% distribution diagram of a comprehensive robust index of a station in a late peak in an embodiment of the present application. DETAILED DESCRIPTION

[0096] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0097] The present application will be further described below with reference to specific embodiments.

[0098] Embodiment 1

[0099] This embodiment introduces a specific application of a multi-modal public transport network dynamic robustness analysis method considering passenger travel behavior in a certain area. For further detailed description of the method, see the multi-modal public transport network specific embodiments and the accompanying drawings of the specification.

[0100] Step 1: Obtain the line and station data of the subway and public transport and the AVL data, and establish a multi-modal public transport topology network.

[0101] The station data of the subway and public transport includes the following attributes: station serial number, station name, line, station longitude, station latitude, and station type, and the specific data is shown in Table 1.

[0102] Table 1 Subway and public transport station line data

[0103]

[0104] Based on the above data, the public transport topology network and the subway topology network are constructed and On this basis, a buffer zone with a subway station as the center and a radius of 500 meters (R = 500 meters) is searched for bus stations within the walking reach of the subway station, and all identified transfer edge sets are denoted as .

[0105] Then, based on the AVL data as shown in Table 2, the corresponding time weights of the subway station edge set and the bus station edge set are calculated, the time weight representing the average travel time of the vehicle between adjacent stations, in minutes. Denoted as and .

[0106] Table 2 AVL data example

[0107]

[0108] Based on the location data of the subway station and the bus station, the time weight of the transfer edge is calculated, in minutes. An example of the edges and weights of the multi-modal public transport topology network is shown in Table 3.

[0109] Table 3 Multi-modal public transport topology network edges and corresponding weights

[0110]

[0111] Based on the above data method, a multi-modal public transport topology network is established. The network contains a total of 911 stations, of which 869 are bus stations and 42 are subway stations. It contains a total of 2809 edges, of which 2275 are bus network station edges, 86 are subway network station edges, and 448 are transfer edges between the subway network and the bus network.

[0112] Step 2: Obtain the passenger flow data of the subway and the bus, count the OD travel volume of each period, and establish a multi-modal public transport passenger flow network for each period.

[0113] The bus and subway card data from May 17, 2021 to May 21, 2021 for five working days is selected as the research time range. The bus data includes the card number of the passenger, the boarding time, the boarding station, the boarding line, and the vehicle number. The subway data includes the card number of the passenger, the entry time, the entry station, the exit time, and the exit station. The total data is 3.8 million, and specific data examples are shown in Tables 4 and 5.

[0114] Table 4 Bus card data example

[0115]

[0116] Table 5 Subway card data example

[0117]

[0118] As Figure 2 shown, the OD travel volume of each period is statistically analyzed, and the morning peak (8:00-9:00), the noon peak (12:00-13:00) and the evening peak (17:00-18:00) are selected as the research objects. The multi-modal public transport network of each period is established. The three period flow networks are denoted as , , . The weight of the edge of the flow network represents the passenger flow in this period, and the shortest travel time represents the travel time of the short path calculated by the weighted Dijkstra algorithm, including the travel time and the transfer time between stations. The edge and weight of the three multi-modal public transport flow networks are shown in Tables 6-8.

[0119] Table 6 Edge and corresponding weight of morning peak multi-modal public transport flow network

[0120]

[0121] Table 7 Edge and corresponding weight of noon peak multi-modal public transport flow network

[0122]

[0123] Table 8 Edge and corresponding weight of evening peak multi-modal public transport flow network

[0124]

[0125] Step 3: Calculate the station load of each period, and calculate the station capacity based on the nonlinear capacity load model.

[0126] Based on the nonlinear capacity load model proposed in this paper, the load , , and capacity of each station in the three periods are calculated, and the value of a and β is set to 0.5. The example results are shown in Table 9.

[0127] Table 9 Station load and its capacity in each period

[0128]

[0129] Step 4: Based on the constructed multi-modal public transport topology network, multi-modal public transport flow network and station capacity, the cascading failure process after station failure is simulated, and the passenger flow allocation rules considering passenger travel behavior are established, and the multi-modal public transport network after cascading failure in each period is obtained.

[0130] Take the morning peak of the multi-modal public transit network as an example, select the failed site as Nanjing Station.

[0131] (1) Based on step 1 and step 2, initialize the multi-modal public transit topology network and multi-modal public transit passenger flow network .

[0132] (2) Based on step 3, obtain the load of each station and station capacity .

[0133] (3) Select Nanjing Station as the failure object, delete the corresponding node of Nanjing Station and the corresponding edge connected with it in

[0134] (4) As shown in Figure 3 , for the passenger flow whose destination is (the destination station in this case is Dingshan Hotel) after the failure of Nanjing Station, search for all neighbor stations of in . Traverse all neighbor stations, calculate the total time of passengers walking to any station neighbor station and from the neighbor station to the destination station , the results are shown in Table 10.

[0135] Table 10 Walking time from failed station to neighbor node and total time to destination

[0136]

[0137] (5) Based on the multinomial logit model, calculate the transfer probability of passengers transferring to all neighbor stations, wherein is set to 20. The calculation results are shown in Table 11.

[0138] Table 11 Transfer probability of passengers transferring to all neighbor stations

[0139]

[0140] (6) Based on the transfer probability of each neighbor station, allocate the transfer station of passengers. In this case, the allocated neighbor station is Nanjing Station·Nangang Square West, and the updated shortest travel time is 22.08 minutes.

[0141] ​(7) Determine if the ratio of the updated shortest travel time to the original shortest travel time is greater than the passenger's tolerance threshold TT. In this case, the original shortest travel time is 20.75 minutes and TT is set to 1.5. Since 22.08 / 20.75 < 1.5, the passenger flow from the original origin station Nanjing Station to the destination station Dingshan Hotel is transferred to Nanjing Station Nan Square West.

[0142] (8) Update the load of the neighboring stations after passenger flow allocation. In this case, the passenger flow allocated to Nanjing Station Nan Square West is 488 since Nanjing Station is failed. The load and capacity of Nanjing Station Nan Square West are 344 and 393, respectively.

[0143] Since the load of Nanjing Station Nan Square West plus the allocated passenger flow exceeds its capacity, Nanjing Station Nan Square West is failed, and Nanjing Station Nan Square West and the corresponding edges connected to it are deleted in the network.

[0144] (9) Repeat steps 4-8 until no more stations are failed.

[0145] The result shows that a total of 262 stations are cascaded to fail due to the failure of Nanjing Station.

[0146] Step 5: Calculate the network maximum connectivity rate after the failure of each station in each period based on the multi-modal public transportation network after cascading failure.

[0147] According to the network state of the multi-modal public transportation network after cascading failure, the network maximum connectivity rate after the failure of each station in each period is calculated, and the results are shown in Table 12.

[0148] Table 12 Network maximum connectivity rate after the failure of each station in each period

[0149]

[0150] Step 6: Calculate the network operation efficiency after the failure of each station in each period based on the multi-modal public transportation network after cascading failure.

[0151] According to the network state of the multi-modal public transportation network after cascading failure, the network operation efficiency after the failure of each station in each period is calculated, and the results are shown in Table 13.

[0152] Table 13 Network operation efficiency after the failure of each station in each period

[0153]

[0154] Step 7: Calculate the passenger flow loss rate after the failure of each station in each period based on the multi-modal public transportation network after cascading failure.

[0155] According to the network state of the multi-modal public transport network after the cascade failure, the passenger flow loss rate of each station after the failure in each period is calculated, and an example is shown in Table 14.

[0156] Table 14 Passenger flow loss rate of each station after failure in each period

[0157]

[0158] Step 8: Based on the network maximum connectivity rate, network operation efficiency and passenger flow loss rate after failure of each station in each period, the comprehensive robustness index of each station in each period is calculated, and the station area prone to trigger cascade failure in each period is evaluated according to the comprehensive robustness index.

[0159] Based on the network maximum connectivity rate, network operation efficiency and passenger flow loss rate after failure of each station in each period, first, the comprehensive robustness evaluation matrix of each station is established. Then, the matrix is normalized to obtain the normalized comprehensive evaluation matrix. Next, the information entropy of the network maximum connectivity rate, network operation efficiency and passenger flow loss rate in each period is calculated, and the calculation results are shown in Table 15. On this basis, the weights of the network maximum connectivity rate, network operation efficiency and passenger flow loss rate in each period are calculated, and the calculation results are shown in Table 16. Finally, the comprehensive robustness index of each station in each period is calculated, and the results are shown in Table 17.

[0160] Table 15 Information entropy of network maximum connectivity rate, network operation efficiency and passenger flow loss rate in each period

[0161]

[0162] Table 16 Weights of network maximum connectivity rate, network operation efficiency and passenger flow loss rate in each period

[0163]

[0164] Table 17 Comprehensive robustness index of each station in each period

[0165]

[0166] The comprehensive robustness index of each station is ranked in order from high to low, and the earlier the ranking, the greater the impact of the station on the cascade failure of the multi-modal public transport network. Figure 4 、 Figure 5 、 Figure 6 The distribution of the comprehensive robustness index of each station in each period is drawn based on the calculation results in Table 17.

[0167] The application discloses a kind of multi-mode public transport network dynamic robustness analysis method considering passenger travel behavior, first, multi-mode public transport topological network and the multi-mode public transport passenger flow network of each period are established;Second, based on nonlinear capacity load model, station load and capacity are calculated;Then, considering the travel mode selection behavior and path planning behavior of passenger, the cascading failure process after station failure is simulated, and the multi-mode public transport network after cascading failure is obtained;Finally, based on the network maximum connectivity rate after each period each failure station, network operation efficiency and passenger flow loss rate, the comprehensive robustness of each station in each period is calculated, and based on comprehensive robustness, the station area prone to trigger cascading failure in each period is evaluated.The application comprehensively considers the passenger flow dynamic time-varying characteristics of station and the travel behavior of passenger, can be used to assist public transport operator to analyze the dynamic robustness in multi-mode public transport network, so as to provide decision support for emergency plan formulation under emergency.

[0168] Embodiment 2:

[0169] This embodiment introduces a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the method for analyzing dynamic robustness of multi-mode public transport network considering passenger travel behavior according to any one of the embodiments 1.

[0170] Embodiment 3:

[0171] This embodiment introduces an electronic device, which includes:

[0172] A memory is configured to store instructions.

[0173] A processor is configured to execute the instructions, so that the electronic device performs operations of the method for analyzing dynamic robustness of multi-mode public transport network considering passenger travel behavior according to any one of the embodiments 1.

[0174] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0175] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks. Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks.

[0176] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks. Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks.

[0177] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks. Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks.

[0178] The above only is the preferred embodiment of the present application, it should be pointed out that for those skilled in the technical field, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A dynamic robustness analysis method for multimodal public transport networks considering passenger travel behavior, characterized in that: Includes the following steps: Step 1: Obtain subway and bus route and station data, as well as AVL data, and establish a multi-modal bus topology network; Step 2: Obtain passenger flow data for subways and buses, count the origin-destination (OD) travel volume for each time period, and establish a multi-modal public transport passenger flow network for each time period; Step 3: Calculate the site load for each time period and calculate the site capacity based on the nonlinear capacity load model; Step 4: Based on the constructed multimodal bus topology network, multimodal bus passenger flow network and station capacity, simulate the cascading failure process after station failure, and establish passenger flow allocation rules that take into account passenger travel behavior to obtain the multimodal bus network after cascading failure in each time period. Step 5: Based on the multi-modal bus network after cascading failure, calculate the maximum network connectivity after each station fails in each time period; Step 6: Based on the multi-mode bus network after cascading failure, calculate the network operation efficiency after each station fails in each time period; Step 7: Based on the multi-modal bus network after cascading failure, calculate the passenger loss rate after each station fails at each time period; Step 4 specifically includes: Step 4.1: Initialize the multi-mode bus topology network for time period t and multimodal public transport passenger flow network ; Step 4.2: Obtain the site for time period t Site load and site capacity ; Step 4.3: Select any site As a failed object, in a multimodal bus topology network Delete the site and its corresponding edges; Step 4.4: Targeting the site The destination after failure is Traffic, search sites In multimodal public transport topology network All neighboring sites in the set, denoted as . traversal Calculate the passenger's walking distance to all neighboring stations. any of the sites and from To the destination Total time ; ； in, Indicates site and Walking distance between; Indicates the passenger's walking speed; Indicates from To the destination The shortest travel time; Step 4.5: Calculate passenger transfer to stations based on discrete choice model probability ; ； in, Indicates the starting point is a station. Destination is Passenger flow shifted to the station The probability, These are control parameters used to measure passengers' sensitivity to travel time. Step 4.6: Based on the transfer probability of each neighboring station, assign the passenger to a neighboring station for transfer, and record the passenger's travel time after being assigned to a specific neighboring station as the updated shortest travel time. ; Step 4.7: When the updated shortest travel time and the shortest travel time when the multi-modal bus topology network is not attacked The ratio is greater than the passenger's patience threshold TT, that is / > TT, the passenger flow between OD pairs is lost and is considered to be transferred to other travel modes; on the contrary, when / < TT, the passenger flow between this OD pair is assigned to the transferred neighbor stations; Step 4.8: Update neighboring sites after passenger flow allocation Site load When the site Site load Larger than the site capacity At that time, the site Failure, in Deleting a site and its corresponding connecting edges; ； Step 4.9: Repeat steps 4.4-4.8 until no station fails, and output the multi-mode bus network that caused cascading failures due to station failures during this period.

2. The method for dynamic robustness analysis of multimodal public transport networks considering passenger travel behavior as described in claim 1, characterized in that: Also includes: Step 8: Based on the maximum network connectivity, network operating efficiency, and passenger loss rate after each site fails in each time period, calculate the comprehensive robustness index of each site in each time period, and evaluate the site areas that are prone to cascading failures in each time period based on the comprehensive robustness index.

3. A dynamic robustness analysis method for multimodal public transport networks considering passenger travel behavior, as described in claim 1 or 2, is characterized in that: The establishment of the multi-modal public transport topology network specifically includes: Step 1.1: Based on subway and bus route and station data, establish a bus topology network using a physical network modeling method. and subway topology network ,in, , When two stations are adjacent on a subway or bus line, they are considered to have a station segment edge between them; among them, Represents the subway topology network; Indicates a group of subway stations; Represents the set of station segments and edges in a subway network; Represents the public transport topology network; Indicates a set of bus stops; Represents the set of station segments and edges in a public transport network; Step 1.2: Based on the spatial location of subway and bus stations, search for bus stations within walking distance of subway stations. When the distance between a subway station and a bus station is less than R, it is considered that there is a bidirectional transfer edge between the subway station and the bus station. The set of all identified transfer edges is denoted as . Where R is the distance threshold; Step 1.3: Based on the AVL data of bus and subway lines, calculate the set of station edges in the subway network. and the station sections of the public transport network The corresponding time weights are denoted as follows: and Time weighting represents the average travel time of vehicles between adjacent stations. Step 1.4: Calculate the transfer edge based on the location data of subway stations and bus stops. Time weight Among them, the site and The formula for calculating the time weight of the transfer edges between them is as follows: ； in, Indicates site and Time weights of transfer edges between them; Indicates site and Walking distance between Indicates the passenger's walking speed; Step 1.5: Construct a multimodal public transport topology network , ;in, , Represents a collection of sites. This represents the i-th site in the set of sites. This indicates the number of all stops in a multimodal transit network. , Denotes the set of edges. Indicating the set of edges and The edges connecting them; , This represents the set of weights corresponding to the edges. This represents the weight set of the edges. The corresponding weights .

4. A dynamic robustness analysis method for multimodal public transport networks considering passenger travel behavior, as described in claim 1 or 2, characterized in that: The establishment of a multi-modal public transport passenger flow network for different time periods specifically includes: Step 2.1: Based on passenger subway and bus card swipe data, calculate passenger flow between stations in different time periods. When at station t in time period t... and When there is passenger flow between them, there is a flow edge. The weight of the flow edge is denoted as , Station t represents time period t and Passenger flow between; Step 2.2: For the flow edge Calculating stations based on multi-mode bus topology network and Travel time between Travel time For the site and The travel time of the shortest path between stations, including travel time and transfer time between stations; Step 2.3: Construct a multimodal public transport passenger flow network for time period t , ;in, Represents a collection of sites; , Represents the set of flow edges in the multi-modal public transport passenger flow network during time period t; , This represents the set of weights corresponding to the flow edges; This represents the set of shortest travel times corresponding to the flow edges. .

5. A dynamic robustness analysis method for multimodal public transport networks considering passenger travel behavior, as described in claim 1 or 2, characterized in that: The calculation of site capacity based on the nonlinear capacity-load model specifically includes: Step 3.1: Calculate the stations for each time period t. The site load is calculated using the following formula: ； in, Indicates site Site load during time period t; Step 3.2: Calculate the site The maximum load for the entire day is calculated using the following formula: ； in, This represents a set of time periods that divide the entire day. Indicates site Maximum daily load; Step 3.3: Calculate the site based on the nonlinear load model The capacity is calculated using the following formula: ； Where α and β are the capacity control coefficients of the nonlinear load model; Indicates site The capacity.

6. A method for dynamic robustness analysis of multimodal public transport networks considering passenger travel behavior, as described in claim 1 or 2, characterized in that: The method for calculating the maximum network connectivity after each site fails in each time period is expressed as follows: ； in: This represents the maximum network connectivity of the multimodal bus network after the cascading failure in time period t. N represents the number of stations in the largest connected subgraph of the multimodal bus topology network after cascading occurs in time period t; N represents the number of stations in the multimodal bus network when it is not under attack. The method for calculating network operating efficiency after each site fails in each time period is expressed as follows: ； in: Network operation efficiency of multi-mode bus network after time period t cascade failure; Station t represents time period t and Passenger flow between; Indicates the site after the cascading fails during time period t. and The shortest travel time between; N represents the number of stops in the multimodal transit network when it is not under attack; The method for calculating the passenger loss rate after each station fails in each time period is expressed as follows: ； In the formula: ; in: Passenger loss rate of multi-modal bus network after time period t cascade failure; Station t represents time period t and Passenger flow between; Indicates from and Does the increase in travel time exceed passengers' tolerance threshold? Indicates the site after the cascading fails during time period t. and The shortest travel time between; This indicates the number of stations in the multi-modal transit network during time period t when they are not under attack. and The shortest travel time between; TT represents the passenger's tolerance threshold for increased travel time; N represents the number of stops in the multimodal transit network when it is not under attack.

7. The method for dynamic robustness analysis of multimodal public transport networks considering passenger travel behavior as described in claim 6, characterized in that: Step 8 is specifically described as follows: Step 8.1: Based on the maximum network connectivity, network operating efficiency, and passenger churn rate of each site after cascading failures in each time period t, establish a comprehensive robustness evaluation matrix for each site. The comprehensive robustness evaluation matrix is ​​expressed as follows: ； in, This represents the maximum network connectivity of the multimodal transit network after the cascading failure at station N during time period t. This represents the network operating efficiency of the multi-mode bus network after the cascading failure of station N during time period t. This represents the passenger loss rate of the multimodal bus network after the cascading failure of station N during time period t. Step 8.2: Evaluation of the comprehensive robustness matrix Normalization is performed to convert the absolute values ​​of each indicator into relative values, resulting in a normalized comprehensive evaluation matrix. ; ； in, This represents the normalized value of the maximum network connectivity of the multimodal transit network after the cascading failure of station N during time period t. This represents the normalized value of the network operating efficiency of the multimodal transit network after the cascading failure of station N during time period t. This represents the normalized value of the passenger loss rate of the multimodal bus network after the failure of the cascading system at station N during time period t. Step 8.3: Calculate the information entropy of three indicators: maximum network connectivity, network operating efficiency, and passenger churn rate. , , ; ； ； ； in, This represents the normalized value of the maximum network connectivity of the multimodal transit network after the cascading failure at station i during time period t. This represents the normalized value of the network operating efficiency of the multimodal bus network after the cascading failure at station i during time period t. This represents the normalized value of the passenger loss rate of the multimodal bus network after the cascading failure of station i in time period t. Step 8.4: Calculate the weights of the three indicators: maximum network connectivity, network operating efficiency, and passenger churn rate. , , ; ； ； ； Step 8.5: Calculate the values ​​for each station Comprehensive robustness index for time period t The overall importance of each site was ranked from highest to lowest, with higher-ranked sites being more prone to cascading failures. Among these, the overall robustness index... The expression is as follows: 。 8. A computer-readable storage medium, characterized in that: It stores a computer program that, when executed by a processor, implements a dynamic robustness analysis method for multimodal public transport networks that considers passenger travel behavior, as described in any of claims 1 to 7.

9. An electronic device, characterized in that: include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the electronic device to perform operations as described in any one of claims 1 to 7, a method for dynamic robustness analysis of multimodal public transport networks that takes into account passenger travel behavior.

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