A high-speed railway operation network elasticity capability evaluation method based on multi-network fusion

Abstract

The application provides a high-speed railway operation network elasticity capability evaluation method based on multi-network fusion. The method comprises the following steps: constructing a high-speed railway operation network model based on a physical network, a functional network and a demand network; simulating a scenario in which the physical network is subjected to an impact, determining an affected train flow distribution by using the connection between the functional network and the physical network, and determining an affected passenger flow distribution by using the connection between the demand network and the functional network; generating a transfer scheme for the passenger flow affected by the demand network on the functional network, constructing a high-speed railway operation network elasticity capability evaluation model with a passenger travel service rate as an index under the influence of uncertain factors, and optimizing and adjusting the infrastructure construction and train timetable of the high-speed railway network. The method can avoid the occurrence of accidents or restore order in the shortest time after the occurrence of accidents, prevent passenger retention, provide suggestions for the construction of the road network and the adjustment of the train operation state, and provide an efficient and convenient transfer scheme for the affected passengers.

Description

Technical Field

[0001] This invention relates to the field of high-speed railway safety assurance technology, and in particular to a method for assessing the resilience of a high-speed railway operation network based on multi-network integration. Background Technology

[0002] With the gradual improvement of my country's high-speed rail network, rail transit plays a vital role in passenger transport. As of 2020, China's railway operating mileage reached 146,300 kilometers, of which high-speed rail operating mileage was 38,000 kilometers, accounting for 26.6% of the total mileage. Although affected by the pandemic in 2020, with a 39.8% decrease compared to 2019, China's railways still transported 2.2 billion passengers, averaging 6 million passengers per day. This demonstrates the crucial role high-speed rail plays in passenger transport.

[0003] However, train cancellations and passenger delays caused by natural disasters occur frequently. Timely troubleshooting, passenger evacuation, and ensuring the operational efficiency of the high-speed rail network have become the focus of research. To guarantee the operational efficiency of the high-speed rail network in the event of a sudden accident, the main research work is carried out in two aspects. Firstly, identifying key sections of the network and allocating more disaster prevention and mitigation resources to sections with high passenger flow and high risk, minimizing the occurrence of accidents or restoring order as quickly as possible after an accident, and preventing passenger delays. Secondly, improving network and traffic flow construction, and guiding passenger flow when network disruptions occur, encouraging passengers to utilize the network's redundancy to choose other trains for their journeys.

[0004] Current research on the capabilities of high-speed railway networks mainly focuses on vulnerability, robustness, and resilience. This type of research begins with the network's topology, applying complex network theories to analyze the infrastructure construction of the high-speed railway network, thereby identifying key nodes and sections. Furthermore, it establishes relevant measures to conduct attacks on the network from different angles and to varying degrees, thus assessing the network's overall ability to withstand disasters. While research based on network infrastructure topology can analyze network connectivity, it struggles to analyze the uneven distribution of passenger flow within the network.

[0005] In recent years, analyses of high-speed rail networks have incorporated train timetables, using the connections between stations on these timetables to characterize passenger flow distribution and identify key nodes within the network. Such studies effectively reflect passenger travel patterns and provide a detailed description of passenger flow distribution within high-speed rail networks. However, timetable-based research has not yet been integrated with the infrastructure of high-speed rail networks. Therefore, it can only analyze station resilience and cannot determine passenger load conditions on specific sections, lacking crucial traffic allocation mechanisms. Summary of the Invention

[0006] The embodiments of the present invention provide a method for assessing the resilience of a high-speed railway operation network based on multi-network integration, so as to effectively provide efficient and convenient transfer solutions for affected passengers.

[0007] To achieve the above objectives, the present invention adopts the following technical solution.

[0008] A method for assessing the resilience of a high-speed railway operation network based on multi-network integration includes:

[0009] A physical network is constructed based on the connection relationships between different stations and lines of the high-speed railway; a functional network is constructed based on the infrastructure network and train timetables to establish a calculation model of traffic flow in the network; a demand network is constructed based on the high-speed train timetables to establish a passenger travel demand estimation model for different stations; and a high-speed railway operation network model is constructed based on the three-layer coupling of the physical network, the functional network, and the demand network.

[0010] Simulate a scenario where the physical network is impacted, determine the affected traffic flow distribution by utilizing the connection between the functional network and the physical network, and determine the affected passenger flow distribution by utilizing the connection between the demand network and the functional network;

[0011] Generate transfer plans for passenger flow affected by the demand network on the functional network;

[0012] Based on the transfer schemes of passenger flow affected by demand network, a high-speed railway operation network resilience assessment model is constructed, with passenger travel service rate as an indicator under the influence of uncertain factors. The high-speed railway operation network resilience assessment model is used to optimize and adjust the infrastructure construction and train timetable of the high-speed railway network.

[0013] Preferably, the construction of the physical network based on the connection relationship between different stations and lines of the high-speed railway includes: establishing the topological connection relationship between stations based on the high-speed railway network map to form a physical network GR = (s, l, d), wherein the physical network GR consists of high-speed railway station nodes s and road segment edges l, and the weight d of the edge represents the length of the road segment.

[0014] Preferably, the step of establishing a traffic flow operation calculation model in the road network based on the infrastructure network and train timetables, and constructing a functional network, includes:

[0015] Initialize the train timetable T based on the high-speed railway timetable. For the i-th and i+1-th stations where train t stops, i ranges from 1 to n-1, and n is the number of stations the train stops at. Initialize the physical network GR. Calculate the shortest path from the i-th station to the i+1-th station using Dijkstra's algorithm. Connect train t to each segment of the shortest path. Infer the travel segment between any two stations of the train. Establish the mapping relationship between train numbers and physical network segments to form the functional network GF = (l, t). The functional network GF consists of the train number node t and the segment name node l. The connection between the train number node and the segment name node indicates that the train passes through that segment.

[0016] Preferably, the method for establishing a passenger travel demand estimation model for different stations based on high-speed train timetables and constructing a demand network includes:

[0017] By using train timetables and physical networks, the number of passengers boarding at each station and the direction of passenger flow are inferred, forming a demand network (s,t,

[0018] v), the demand network (s,t,v) consists of train number node t and station node s. The connection between the train number node and the station node indicates that the train stops at that station to pick up and drop off passengers. The connection weight v represents the number of passengers boarding the train.

[0019] Estimate the passenger flow at the train's origin and destination stations. Assume the proportion of passengers at the origin station to the total train capacity is p, and let T be the passenger flow. s Let H(T) be the set of all trains that stop at station s. s T(T) represents the set of trains originating from a certain station s. s Let M(T) be the set of trains whose final destination is a certain station s. s ) = T s -H(T s )-T(T s () is a collection of train numbers that stop along the way;

[0020] Therefore, for a given train number t, the passenger flow stopping at a certain station s can be represented as:

[0021]

[0022] Where T s Let be the set of all trains passing through station s, where the absolute value represents the number of elements in the set, i.e., the number of trains stopping at the station. Let C be the train capacity, and f(|T) be the train number. s |) is taken as a simple linear function f(|T) s |)=|T s | This allows us to obtain the passenger flow for each train on each section of the railway.

[0023] The passenger flow of train t is expressed as the sum of the number of passengers boarding at each stop, calculated using the following formula: Where S t Let T represent the set of all stations where train t stops. When a section of track l is interrupted by an impact, the set of trains T that pass through that section of track l can be determined using the functional network GF. l Therefore, the passenger flow through this section l is represented as the sum of the passenger flows of all trains.

[0024] Preferably, the scenario of simulating an impact on the physical network, determining the affected traffic flow distribution using the connection between the functional network and the physical network, and determining the affected passenger flow distribution using the connection between the demand network and the functional network, includes:

[0025] Select a road segment l in the physical network GR=(s,l,d), and assume that the road segment l is subjected to an impact;

[0026] Obtain the set T of trains connected to the road segment l in the functional network GF = (l,t). l The train set T l The traffic flow that was interrupted when the road segment l was impacted;

[0027] Obtain the set of train numbers T in the demand network GD = (s,t,v) l The sum of the weights of the connected edges is used to determine the total affected passenger flow, based on the set of train numbers T in the train timetable. l The passenger flow distribution can be obtained from the originating station to the terminal station.

[0028] Preferably, the process of generating transfer plans for passenger flow affected by the demand network on the functional network includes:

[0029] Initial transfer passenger flow V transfer =0, for all trains T passing through the impacted section l l The total passenger flow V passing through this section is calculated based on the demand network. l For the train set T passing through section l l For each train t, let its O and D be Ot and Dt respectively. The total passenger flow from Ot to Dt does not exceed the number of passengers C*p at the train's originating station. Initialize the total passenger flow that can be transferred on train t as V. t transfer =0, let T be the set of trains stopping at station Ot. Ot , for T Ot Each train in the t Ot The set of stations that it has not yet stopped at is denoted as S. tOt If Dt∈S tOt Then Vt transfer , :=V t transfer +C*(1-p), that is, train tot can provide transfer services for passengers traveling from the origin station to the destination station on train t, and train tot can carry C*(1-p) transferring passengers;

[0030] Determine the passenger flow that can reach the destination through one intermediate transfer. If train t departing from the origin station Ot of train t ot cannot reach Dt, with t ot the reachable station S tOt as transfer station S transfer , analyze all trains departing from S transfer T(S transfer ) whether they can reach Dt. If they can reach Dt, allocate the remaining capacity of the train to transferring passengers, V t transfer :=V t transfer +C*(1-p), the transfer scheme is t ot -S transfer -t(S transfer );

[0031] The total number of transferring passengers V of train t t transfer is less than the number of passengers from the origin station to the destination station, and also less than the number of passengers boarding at the origin station. If V t transfer <C*p, then V transfer :=V transfer +V t transfer otherwise, V transfer :=V transfer +C*p.

[0032] Preferably, for the construction of transfer schemes for affected passenger flow based on the demand network, a high-speed railway operation network resilience capability assessment model is constructed with the passenger travel service rate under the influence of uncertain factors as an indicator, and the high-speed railway operation network resilience capability assessment model is used to optimize and adjust the infrastructure construction and train timetables of the high-speed railway network, which comprises:

[0033] Construct a high-speed railway network resilience capability assessment model, which is specifically the ratio of the number of passengers that can complete travel after the high-speed railway network is damaged to the number of passengers before the damage, as shown in the following formula:

[0034]

[0035] V l represents the passenger flow passing through section l, V transferThe number of passengers that can be transferred, V transfer V represents the total number of passengers in the road network, and the sum of weights in the demand network GD = (s,t,v);

[0036] Each time, a road segment is selected from the physical network. The number of passengers passing through the segment and the number of passengers transferred after damage are calculated using the functional network and the demand network. The elasticity assessment model of the high-speed railway network is used to conduct elasticity assessment of each road segment and visualize it. Key road segments are selected, and the traffic flow and passenger flow distribution of the key road segments are visualized. The passenger flow transfer scheme of the key road segments is analyzed.

[0037] As can be seen from the technical solutions provided by the embodiments of the present invention described above, the method of the present invention can, on the one hand, identify key road sections in the road network and invest more disaster prevention and mitigation efforts in road sections with high passenger flow and high risk, so as to avoid accidents as much as possible or restore order in the shortest possible time after an accident, and prevent passengers from being stranded; on the other hand, by analyzing passenger flow transfer plans, it can make suggestions for the construction of the road network and the adjustment of train operation status, thereby providing efficient and convenient transfer solutions for affected passengers.

[0038] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the high-speed railway operation network resilience assessment method based on multi-network convergence, as described in an embodiment of the present invention. Detailed Implementation

[0041] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0042] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0043] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0044] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0045] This invention provides a method for analyzing the resilience of high-speed railway network segments based on the high-speed railway infrastructure network and train timetables. Addressing the key issues of limited recovery capacity of high-speed railway networks after impacts, insufficient passenger flow analysis of high-speed railway network segments, and untimely formulation of passenger evacuation plans, this invention designs a method for analyzing the resilience of high-speed railway network segments based on the infrastructure network and train timetables, providing a basis for segment safety assurance and passenger evacuation plan formulation. First, based on high-speed railway line facilities, train timetables, and passenger travel demand, a three-layer high-speed railway operation network model integrating physical network, functional network, and demand network is constructed. Second, a scenario of physical network impact is simulated, using the connection between the functional network and the physical network to determine the distribution of affected train flow, and the connection between the demand network and the functional network to determine the distribution of affected passenger flow. Third, transfer plans are generated for the affected passenger flow in the demand network on the functional network, and the passenger flow transfer volume that the high-speed railway network can bear is analyzed. Finally, a resilience assessment model is established to evaluate the resilience of the high-speed railway network and its segments, providing reference suggestions for improving the resilience of the high-speed railway operation network against external shocks from the perspectives of infrastructure construction and train operation plans.

[0046] The processing flow of the high-speed railway network resilience assessment method based on multi-network convergence provided by this invention is as follows: Figure 1 As shown, the processing steps include the following:

[0047] Step S01: Construct a physical network based on the connection relationships between different stations and lines of the high-speed railway; establish a calculation model of traffic flow in the network based on the infrastructure network and train timetables to construct a functional network; establish a passenger travel demand estimation model for different stations based on the high-speed train timetables to form a demand network; and construct a high-speed railway operation network model based on the three-layer coupling of the physical network, functional network, and demand network.

[0048] Step S02: Simulate scenarios where the physical network is impacted, such as strong winds blowing floating objects causing power outages in the overhead contact system, foreign objects intruding into the rails and needing to be cleared, heavy rain and snow causing track interruptions, etc. Use the connection between the functional network and the physical network to determine the distribution of affected traffic flow, and use the connection between the demand network and the functional network to determine the distribution of affected passenger flow.

[0049] Step S03: Generate transfer plans for passenger flows affected by the demand network on the functional network. These transfer plans characterize the service redundancy capacity of the road network by estimating the trains and scale of passenger flow transfers. This transfer model can analyze passenger flow transfer plans for large-scale road networks and traffic flows, providing transfer plans between any OD (Origin to Destination) pairs. These transfer plans include the train the passenger first takes, the intermediate transfer station, and the train the passenger takes after the transfer.

[0050] Step S04: Based on the essential mission of the high-speed railway operation system to provide safe, efficient, and high-quality services for passenger travel, construct a high-speed railway operation network resilience assessment model with passenger travel service rate as an indicator under the influence of uncertain factors, and provide decision support for improving infrastructure construction and optimizing train timetables in the high-speed railway network, as well as enhancing disaster response capabilities.

[0051] Specifically, step S01 includes the following steps:

[0052] Step S101: Based on the high-speed railway network map, establish the topological connection relationship between stations to form a physical network GR = (s, l, d) consisting of high-speed railway station nodes s and road segment edges l, where the weight d of the edge represents the length of the road segment.

[0053] Step S102: Based on the high-speed railway timetable and physical network, Dijkstra's algorithm is used to infer the travel segment between any two stations of a train, establishing a mapping relationship between train numbers and physical network segments, forming a functional network GF = (l,t). The functional network GF consists of train number node t and segment name node l. The connection between the train number node and the segment name node indicates that the train passes through that segment. The main steps include: initializing the train timetable T and the network GR; secondly, for the i-th and i+1-th stations where train t stops, i ranges from 1 to n-1, where n is the number of stations the train stops at; thirdly, using Dijkstra's algorithm to calculate the shortest path from the i-th station to the i+1-th station; finally, connecting train t to each segment of the shortest path. When there is an infrastructure failure on a segment, all trains passing through that segment can be obtained through the functional network, laying the foundation for analyzing passenger flow transfers.

[0054] Step S103: Based on the high-speed railway network timetable, infer the number of passengers boarding at each station and the passenger flow direction to form a demand network, providing data support for passenger transfer and evacuation plans. The demand network (s,t,v) is constructed using the train timetable and physical network, consisting of train number nodes t and station nodes s. The connection between the train number node and the station node indicates that the train stops at that station to pick up and drop off passengers, and the connection weight v represents the number of passengers boarding. The main steps include:

[0055] First, estimate the passenger flow at the originating and terminating stations. At the originating station, the high-speed train usually has the most passengers boarding; in this invention, we assume the proportion of passengers at the originating station to the total train capacity is p. At the terminating station, there are no passengers boarding, and its value is 0 when considering trip generation.

[0056] Secondly, estimate the passenger flow at each station along the route. Generally speaking, high-speed trains generate passenger flow at stations they stop at, and the more stations a train stops at, the greater the passenger flow. However, the number of passengers transferring or getting on and off along the way is relatively small. Therefore, for a train stopping at a particular station, there is usually a maximum passenger flow. At the same time, for stations where high-speed trains stop less frequently, there is usually still a small amount of passenger flow; therefore, there is a minimum passenger flow.

[0057] Let T s Let H(T) be the set of all trains that stop at station s. s T(T) represents the set of trains originating from a certain station s. s Let M(T) be the set of trains whose final destination is a certain station s. s ) = T s -H(T s )-T(T s () is a collection of train numbers that stop along the way.

[0058] Therefore, for a certain train t, the passenger flow V stopping at a certain station s s,t It can be represented as

[0059]

[0060] Where T s Let f(|T) be the set of all trains passing through station s, and its absolute value represents the number of elements in the set, i.e., the number of trains stopping at the station. C is the train capacity. In this invention, f(|T) s |) is taken as a simple linear function f(|T) s |)=|T s By approximating the total passenger flow to the overall data published in the National Statistical Bulletin, the parameters in the model can be estimated, thereby obtaining the passenger flow per train and per section of the line.

[0061] The passenger flow of train t can be expressed as the sum of the number of passengers boarding at each stop, calculated using the following formula: Where S t This represents the set of all stations where train t stops. The daily national passenger volume is the sum of the passenger volumes of all trains, calculated using the following formula: Where T represents the set of all trains. By making the total passenger flow V approximate the overall data published in the National Statistical Bulletin, the parameters in this model can be estimated, thereby estimating the passenger flow per train and per route segment.

[0062] When a section of track l is interrupted by an impact, the set of trains T passing through that section of track l can be determined using the functional network GF. l Therefore, the passenger flow through this section l can be expressed as the sum of the passenger flows of all trains.

[0063] Specifically, step S02 includes the following steps:

[0064] Step S201: Select a path segment l in the physical network GR = (s,l,d);

[0065] Step S202: The set of trains T in the functional network GF=(l,t) that are connected to the road segment l. l This refers to the traffic flow that was interrupted when the road section was impacted;

[0066] Step S203: In the demand network GD=(s,t,v), the train number set T l The sum of the weights of the connected edges represents the total affected passenger flow, based on the train timetable and the set of train numbers T. l The originating and terminating stations represent the main distribution of passenger flow.

[0067] Specifically, step S03 includes the following steps:

[0068] Step S301: Initialize V transfer =0, for all trains T passing through the impacted section l l , first calculate the total passenger flow V passing through this section according to the demand network l . Next, calculate the direct transfer passenger flow. For the train set T passing through section l l for each train t in the set, let its origin and destination be Ot and Dt respectively. The total passenger flow from Ot to Dt does not exceed C*p, which is the number of passengers on the train at the departure station. Initialize the total transferable passenger flow on train t as V t transfer =0. Let the set of trains stopping at station Ot be T Ot , for T Ot each train tot in Ot , the set of stations that it has not yet stopped at is recorded as S tOt , if Dt∈S tOt , then V t transfer : = V t transfer +C*(1-p), that is, train tot can provide transfer services for passengers from the origin station to the destination station on train t, and train tot can carry C*(1-p) transfer passengers.

[0069] Step 302: Determine the passenger flow that can reach the destination with one intermediate transfer. If the train tot departing from the origin station Ot of train t ot cannot reach Dt, use the station S that tot ot can reach as tOt transfer station S transfer , analyze whether all trains T(S transfer ) departing from S transfer ) can reach Dt. If Dt can be reached, allocate the remaining capacity of the train to transfer passengers, and V t transfer : = V t transfer +C*(1-p), the transfer scheme is tot ot -S transfer -t(S transfer )

[0070] Step 303: The total number of transfer passengers V of train t t transfer shall be less than the number of passengers from the origin station to the destination station, and must also be less than the number of passengers boarding at the origin station. If V t transfer <C*p, then V transfer : = V transfer +V t transfer otherwise, Vtransfer :=V transfer +C*p.

[0071] Specifically, step S04 includes the following steps:

[0072] Step S401: Construct a high-speed railway network resilience assessment model to analyze and visualize the resilience of each segment of the high-speed railway network. Specifically, the high-speed railway network resilience model is the ratio of the number of passengers who can complete their journeys after the high-speed railway network is damaged to the number of passengers before the damage, as shown in the following formula:

[0073]

[0074] Among them, V l This represents the passenger flow through road segment l. The number of passengers that can be transferred, V, can be determined using the passenger flow transfer model. transfer ,

[0075] V represents the total passenger flow in the road network, which is the sum of weights in the demand network GD = (s,t,v). This elastic assessment model comprehensively considers constraints such as passenger flow transfer demand and train capacity. By analyzing the transfer plans of affected passengers, it establishes elastic assessment indicators based on the degree to which passenger demand is met, enabling an accurate evaluation of the importance of each road segment.

[0076] Step S402: First, select one road segment from the physical network each time, and use the functional network and demand network to calculate the number of passengers passing through the segment and the number of passengers transferred after the disruption. Use the high-speed railway network resilience assessment model to conduct resilience assessment on each road segment and visualize it. Second, select key road segments and visualize the traffic flow and passenger flow distribution of the segments. Finally, analyze the passenger flow transfer schemes for the segments to provide decision support for improving the network structure and train operation schemes.

[0077] The road network connections and station locations in this embodiment are derived from Baidu Maps, and the train timetables are from China Railway 12306. The road network involves 1012 high-speed rail stations and 6708 trains nationwide. Since the stops in the train timetable are a subset of the stations the train actually passes through, and any disruption to a small segment of the complete path will prevent the train from reaching its destination, the path estimation algorithm proposed in step S102 is used to determine the train numbers affected by the road segment interruption and establish a mapping relationship between the road segment and the trains passing through that segment.

[0078]

[0079] Based on the complete train route, a segment-train number mapping relationship is established to determine all trains affected after a segment is interrupted.

[0080]

[0081]

[0082] To analyze the number of passengers that may be affected, the number of passengers boarding at the train stops is estimated based on step S103. This determines the passenger capacity of each train.

[0083] G89 642 G659 934 …… ……

[0084] By establishing a mapping relationship between train segments and train numbers, as well as a mapping relationship between train numbers and passengers, a comprehensive understanding of the traffic flow and passenger load on any segment of the high-speed railway network can be obtained. This allows for analysis of the impact on passenger flow transfer after a segment is interrupted. The passenger flow transfer algorithm proposed in step S04 comprehensively considers train capacity, passenger demand, and a complete passenger transfer plan. Taking the Zhengzhou-Gongyi segment as an example, its passenger flow distribution and transfer plan are shown in the table below, where the transfer station is between two trains.

[0085] G89 Beijing, Chengdu D705, Nanjing, D2254 G659 Beijing, Xi'an G601, Taiyuan, D1901 …… …… ……

[0086] Based on the above analysis, a one-day disruption on the Zhengzhou-Gongyi section will affect the normal operation of train 207, impacting the travel of over 200,000 passengers, primarily affecting passenger flow between Beijing and Xi'an, Nanjing and Xi'an, and Guangzhou and Xi'an. Based on passenger transfer algorithms, 218 transfer options were generated, allowing approximately 20,000 passengers to reach their destinations through the redundant transfer capacity of the high-speed rail network.

[0087] There are approximately seven major urban clusters across the country based on high-speed rail, as shown in Table 1. Among them, the Beijing-Shanghai line has the most trains.

[0088] Table 1 Major urban clusters in China based on high-speed rail connections.

[0089]

[0090]

[0091] The busiest high-speed rail line in my country is the Beijing-Shanghai line, with an average of over 250 trains running in both directions daily. Secondly, the "four horizontal and four vertical" railway trunk lines form the backbone of my country's high-speed rail network, with an average daily train flow of over 100 trains. Among these, the Xi'an-Zhengzhou section, the Changchun-Shenyang section, the Beijing-Guangzhou line, and the Beijing-Shanghai line have the highest number of trains.

[0092] Most sections of my country's high-speed railway network have a flexibility greater than 99%. Considering the daily passenger flow of approximately 5 million, the total number of passengers affected by damage to these sections is less than 50,000. Vertically, the Beijing-Shanghai line, as the busiest line in the country, has a flexibility of less than 95%, affecting more than 250,000 passengers per day. Horizontally, the Zhengzhou-Xi'an section is the busiest, with some sections having a flexibility of less than 96%, affecting more than 200,000 passengers per day.

[0093] The Zhengzhou-Xi'an section is an important corridor connecting eastern my country (Beijing, Shanghai, Qingdao, Guangzhou) and western China (Xi'an, Lanzhou, Chengdu, Chongqing). Among these, the Beijing-Xi'an, Shanghai-Xi'an, and Guangzhou-Xi'an routes have the highest passenger volumes.

[0094] When the Zhengzhou-Xi'an section is interrupted, passengers traveling from Beijing to Xi'an can transfer via the Beijing-Shijiazhuang-Xi'an route; passengers traveling from Guangzhou to Xi'an can transfer via the Guangzhou-Guiyang-Chengdu-Xi'an route. Passengers traveling from Shanghai to Xi'an will need to transfer via Shanghai-Nanjing-Wuhan-Chongqing-Chengdu-Xi'an, significantly increasing travel time and costs. From the perspective of improving network structure, opening up the Wuhan-Xi'an high-speed rail line and connecting the Shiyan-Shangnan section could greatly improve the flexibility of the high-speed rail network.

[0095] In summary, the method of this invention fully considers the traffic flow situation under the condition of a road segment interruption, and then comprehensively analyzes the distribution of affected passenger flow based on the traffic flow situation, and designs corresponding passenger flow transfer algorithms to provide transfer solutions for affected passengers. The functional network based on the traffic flow allocation algorithm constructs the connection between traffic flow and the infrastructure network. When the infrastructure network is damaged, the functional network can determine the affected traffic flow. The demand network estimates the distribution of passenger flow and the number of passengers, making the estimated total passenger flow close to the total passenger flow in the national statistical bulletin, and analyzes the transfer solutions for passengers after trains are affected based on the passenger flow transfer algorithm, fully considering the network's redundancy capabilities.

[0096] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0097] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0098] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0099] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing the resilience of a high-speed railway operation network based on multi-network integration, characterized in that, include: A physical network is constructed based on the connection relationships between different stations and lines of high-speed railways; A calculation model for traffic flow in the road network is established based on the infrastructure network and train timetables, and a functional network is constructed; a passenger travel demand estimation model for different stations is established based on the high-speed train timetables, and a demand network is constructed; a high-speed railway operation network model based on the three-layer coupling of the physical network, the functional network and the demand network is constructed. Simulate a scenario where the physical network is impacted, determine the affected traffic flow distribution by utilizing the connection between the functional network and the physical network, and determine the affected passenger flow distribution by utilizing the connection between the demand network and the functional network; Generate transfer plans for passenger flow affected by the demand network on the functional network; Based on the transfer schemes of passenger flow affected by demand network, a high-speed railway operation network resilience assessment model is constructed with passenger travel service rate as an indicator under the influence of uncertain factors. The high-speed railway operation network resilience assessment model is used to optimize and adjust the infrastructure construction and train timetable of the high-speed railway network. The construction of a physical network based on the connection relationship between different stations and lines of high-speed railway includes: establishing the topological connection relationship between stations based on the high-speed railway network map to form a physical network GR = (s, l, d), where the physical network GR consists of high-speed railway station nodes s and road segment edges l, and the weight d of the edge represents the length of the road segment; The aforementioned model for calculating traffic flow within the road network based on the infrastructure network and train timetables, and the construction of a functional network, includes: Initialize the train timetable T based on the high-speed railway timetable. For the i-th and i+1-th stations where train t stops, i ranges from 1 to n-1, and n is the number of stations the train stops at. Initialize the physical network GR. Calculate the shortest path from the i-th station to the i+1-th station using Dijkstra's algorithm. Connect train t to each segment of the shortest path. Infer the travel segment between any two stations of the train. Establish the mapping relationship between train numbers and physical network segments to form the functional network GF = (l, t). The functional network GF consists of the train number node t and the segment name node l. The connection between the train number node and the segment name node indicates that the train passes through the segment. The aforementioned model for estimating passenger travel demand at different stations based on high-speed train timetables, and the construction of a demand network, includes: By using train timetables and physical networks, the number of passengers boarding at each station and the direction of passenger flow are inferred, forming a demand network (s, t, v). The demand network (s, t, v) consists of train number nodes t and station nodes s. The connection between the train number node and the station node indicates that the train stops at that station to pick up and drop off passengers, and the connection weight v indicates the number of passengers boarding. Estimate the passenger flow at the train's origin and destination stations. Assume the proportion of passengers at the origin station to the total train capacity is p, and let T be the passenger flow. s Let H(T) be the set of all trains that stop at station s. s T(T) represents the set of trains originating from a certain station s. s Let M(T) be the set of trains whose final destination is a certain station s. s ) = T s - H(T s ) - T(T s () is a collection of train numbers that stop along the way; Therefore, for a given train number t, the passenger flow stopping at a certain station s can be represented as: Where T s Let S be the set of all trains that pass through station S, where the absolute value of S represents the number of elements in the set, i.e., the number of trains stopping at the station. Let C be the train capacity. Take it as a simple linear function = This allows us to obtain the passenger flow for each train on each section of the railway. The passenger flow of train t is expressed as the sum of the number of passengers boarding at each stop, calculated using the following formula: S t Let T represent the set of all stations where train t stops. When a section of track l is interrupted by an impact, the set of trains T that pass through that section of track l can be determined using the functional network GF. l Therefore, the passenger flow through this section l is represented as the sum of the passenger flows of all trains. ; The aforementioned transfer scheme based on passenger flow affected by demand network construction uses passenger travel service rate as an indicator to assess the resilience of high-speed railway operation network. This model is then used to optimize and adjust the infrastructure construction and train timetables of the high-speed railway network, including: A high-speed railway network resilience assessment model is constructed, specifically the ratio of the number of passengers who can complete their journeys after the high-speed railway network is damaged to the number of passengers before the damage, as shown in the following formula: V represents the passenger flow through road segment l. transfer The number of passengers that can be transferred, V transfer V represents the total number of passengers in the road network, and the sum of weights in the demand network GD = (s, t, v). Each time, a road segment is selected from the physical network. The number of passengers passing through the segment and the number of passengers transferred after damage are calculated using the functional network and the demand network. The elasticity assessment model of the high-speed railway network is used to conduct elasticity assessment of each road segment and visualize it. Key road segments are selected, and the traffic flow and passenger flow distribution of the key road segments are visualized. The passenger flow transfer scheme of the key road segments is analyzed.

2. The method according to claim 1, characterized in that, The scenario simulating an impact on the physical network, determining the affected vehicle flow distribution using the connection between the functional network and the physical network, and determining the affected passenger flow distribution using the connection between the demand network and the functional network, includes: Select a road segment l in the physical network GR = (s, l, d), and assume that the road segment l is subjected to an impact; Obtain the set T of trains connected to the road segment l in the functional network GF = (l,t). l The train set T l The traffic flow that was interrupted when the road segment l was impacted; Obtain the set of train numbers T from the demand network GD = (s, t, v). l The sum of the weights of the connected edges is used to determine the total affected passenger flow, based on the set of train numbers T in the train timetable. l The passenger flow distribution can be obtained from the originating station to the terminal station.

3. The method according to claim 2, characterized in that, The method of generating transfer plans for passenger flow affected by the demand network on the functional network includes: Initial transfer passenger flow V transfer =0, for all trains T passing through the impacted section l l The total passenger flow V passing through this section is calculated based on the demand network. l For the train set T passing through section l l For each train t, let its O and D be Ot and Dt respectively. The total passenger flow from Ot to Dt does not exceed the number of passengers C*p at the train's originating station. Initialize the total passenger flow that can be transferred on train t as V. t transfer =0, let T be the set of trains stopping at station Ot. Ot , for T Ot Each train in the t Ot The set of stations that it has not yet stopped at is denoted as S. tOt ,if Then V t transfer , :=V t transfer + C*(1-p), which represents train t Ot It can provide transfer services for passengers on train t from the originating station to the destination station, and train t Ot It can accommodate C*(1-p) passengers for transfer; Determine the passenger flow that can reach its destination in one trip by transferring trains. If train t departs from station Ot of train t... Ot Unable to reach Dt, with t Ot Station S that can be reached tOt For transfer station S transfer Analysis by S transfer All departing trains T(S) transfer If the train can reach Dt, then the remaining capacity will be allocated to connecting passengers. t transfer := V t transfer + C*(1-p), the transfer scheme is t Ot -S transfer -t(S transfer ); Total number of transfer passengers V for train t t transfer is less than the number of passengers from the origin station to the destination station, and also less than the number of passengers boarding at the origin station, if V t transfer < C*p, then V transfer := V transfer +V t transfer otherwise, V transfer := V transfer +C*p.

Images