Seepage-based urban rail transit system line resilience evaluation method
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-10-18
- Publication Date
- 2026-08-11
AI Technical Summary
[0005]上述前人研究所考虑的轨道交通系统性能主要是基于网络结构，或基于客流量、发车频率等系统运营指标来刻画的，很少能综合考虑网络结构和系统运营两个角度提出综合的城市轨道交通性能指标
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Figure CN115952951B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of urban rail transit, and more particularly to a method for assessing the resilience of urban rail transit systems based on seepage flow. Background Technology
[0002] With the vigorous development and construction of urban infrastructure, urban rail transit has become one of the main modes of transportation in people's daily lives. Along with the increasing demand for rail transit, the load on urban rail transit systems is also increasing. During peak passenger flow periods such as weekday morning and evening rush hours, the congestion at some stations and on some lines drops sharply, leading to a decline in the overall performance of the urban rail transit system and its inability to meet people's daily travel needs. Improving rail transit performance through infrastructure construction requires a high timeframe and cost; therefore, many researchers have attempted to improve performance from the management level of urban rail transit systems. Among these efforts, accurately evaluating the performance of urban rail transit systems has become a crucial fundamental issue.
[0003] Currently, numerous studies have evaluated the performance of urban rail transit systems from the perspectives of robustness, vulnerability, and reliability. Furthermore, system resilience is a comprehensive indicator, encompassing characteristics such as robustness, vulnerability, and recoverability, reflecting the complete process of performance degradation followed by recovery under external disturbances. Therefore, resilience assessment of urban rail transit systems can serve as a comprehensive consideration of their performance, and the assessment results can also guide management decisions for urban rail transit systems.
[0004] The concept of resilience has been widely applied in various important fields such as social systems, ecosystems, power systems, computer networks[8], and transportation systems. The concept of resilience was first proposed by Holling in his research on ecosystems. In the field of transportation, Murray-Tuite first proposed the concept and assessment method of transportation resilience in 2006. In 2003, Bruneau proposed the "resilience triangle" as a system resilience evaluation index, which has become a classic method for measuring system resilience. This method was proposed in response to changes in system performance under earthquake disasters. However, because the mathematical form of this method is universal, it can be extended to various practical complex systems.
[0005] The performance of rail transit systems considered in the previous studies were mainly based on network structure or system operation indicators such as passenger flow and departure frequency. They rarely considered both network structure and system operation to propose comprehensive urban rail transit performance indicators.
[0006] To address this deficiency, this invention proposes a permeation-based performance index for rail transit systems. This index comprehensively considers network structure and system operation, enabling line-level resilience assessment of the metro system during morning peak hours. The proposed method has been validated in the Beijing metro system. The identified lines with poor resilience will provide support for urban rail transit management departments in managing system resilience during peak hours, helping to alleviate congestion in large cities' rail transit systems. Summary of the Invention
[0007] To address the aforementioned technical problems, this invention fully considers the organizational structure and system functions at different levels of urban rail transit systems, and proposes a line performance evaluation index based on seepage flow. This index comprehensively considers the influence of different aspects such as network structure and system operation, not only meeting the specific requirements for the resilience assessment and analysis of urban rail transit system lines, but also objectively evaluating the degree to which the performance of different urban rail transit lines is affected by passenger flow during the morning peak hours. Secondly, it proposes a comparative analysis of resilience area and performance recovery speed to gain a deeper understanding of the possible mechanisms behind the varying resilience of different urban rail transit lines. The specific technical solutions adopted by this invention are as follows:
[0008] A method for assessing the resilience of urban rail transit systems based on seepage flow, the method specifically includes the following steps:
[0009] Step 1: Determine the scope of the urban rail transit lines to be analyzed, collect passenger flow information of urban rail transit line segments within the scope for a period of time, and form a time series of traffic state quantities for each segment within the evaluation scope.
[0010] Step 2: Calculate the congestion level of the section using the time series data from Step 1 to obtain the resilience process of a specific line in the urban rail transit system over time.
[0011] Step 3: Calculate the line toughness value using the toughness process in Step 2 and based on the toughness triangle, and compare it with the recovery speed in the line toughness process.
[0012] Preferably, step 1 specifically includes the following steps:
[0013] (1) Determine the scope of the rail transit network to be evaluated: Calculate the latitude and longitude range of the rail transit network to be evaluated, as well as the location of the stations and the relationship between the lines;
[0014] (2) Collect passenger flow information of rail transit network segments within the assessment area for a period of time to form a time series of traffic status quantities for each segment within the area:
[0015] For each route in (1), collect passenger flow information within a certain period of time.
[0016] Preferably, step 2 specifically includes the following steps:
[0017] (1) Calculate the degree of congestion and resilience:
[0018] For a section of a subway line between two adjacent stations A and B, the passenger flow is denoted as F. AB (t), defining its baseline passenger flow F b To determine the passenger flow at the 95th percentile of passenger flow at different times of the day within this interval, the relative congestion index q0 and the corresponding congestion threshold F are used. c To measure the congestion level of different routes under different passenger volumes, F c =F b ×q0, given a relative congestion index q0 and a corresponding congested passenger flow threshold F c Below, when the passenger flow F AB (t) is greater than the congestion passenger flow threshold F c If the line section between stations A and B is considered congested, then the line section is considered to be in normal operation.
[0019] The congestion level C of a subway line is defined as the ratio of the number of stations in the largest congested sub-cluster formed by congested sections at time t to the total number of stations on the line. L (t), where the most congested sub-cluster is the continuous congested area containing the largest number of subway stations, and the congestion level C. L (t) is specifically represented as
[0020] C L (t)=n L-lcc (t) / n L ,
[0021] Where n L-lcc (t) represents the number of stations contained in the largest congested sub-group formed on this line at time t, n. L This represents the total number of stations on the route.
[0022] Preferably, step 3 specifically includes the following steps:
[0023] (1) Calculate the line toughness value:
[0024] In step 2, the line congestion level C L The negative value of (t) is used as a performance evaluation index of the line, and its degradation and recovery process over time is used to define the line's resilience index R. L ,Right now
[0025]
[0026] (2) Recovery speed:
[0027] Recovery rate refers to the speed at which performance recovers from its lowest point to normal levels, defined as V. L =C L min / (t0-t min ), where t min C L min t0 and t0 respectively represent the time when the performance index reaches its lowest point and the degree of line congestion, and t0 represents the time when the line performance just recovers to 0.
[0028] The present invention has the following beneficial effects:
[0029] First, classic definitions and quantifications of resilience in urban rail transit focus on the entire system. This approach often overlooks inconsistencies between different lines, failing to consider the varying temporal and spatial impacts of different sections, lacking strong physical explanation, and neglecting the influence of line planning within the rail transit network. This invention, however, assigns different thresholds based on varying passenger flows across different sections of the rail transit system, fully considering the differences in the impact of passenger flow on different sections. Furthermore, based on seepage theory, it measures line performance using the size of the largest congested sub-cluster within the line, minimizing the impact of short-term fluctuations caused by unforeseen factors in individual sections. Second, this invention is not limited to the resilience value of urban rail transit lines but compares and analyzes the line's resilience value with its performance recovery speed during the resilience process. This effectively identifies different characteristics exhibited by lines with similar resilience, enabling a more accurate understanding of the resilience of urban rail transit lines during peak hours and providing better decision-making support for relevant management departments. Attached Figure Description
[0030] Figure 1 It is a graph showing the performance evolution of each line.
[0031] Figure 2 Relationship between line resilience and line passenger flow
[0032] Figure 3 This is a graph showing the relationship between line resilience and line recovery speed. Detailed Implementation
[0033] This invention comprises the following main steps:
[0034] Step 1: Determine the scope of the urban rail transit lines to be analyzed, collect passenger flow information of urban rail transit line segments within the scope for a period of time, and form a time series of traffic status quantities for each segment within the evaluation scope.
[0035] Its specific contents include:
[0036] (1) Determine the scope of the rail transit network to be evaluated: Calculate the latitude and longitude range of the rail transit network to be evaluated, as well as the location of the stations and the relationship between the lines.
[0037] (2) Collect passenger flow information of rail transit network segments within a certain period of time to form a time series of traffic status quantities for each line section within the range:
[0038] For each section of the line in (1), collect passenger flow information within a certain period of time.
[0039] Step 2: Calculate the congestion level of the section using the time series data from Step 1 to obtain the resilience process of a specific line in the urban rail transit system over time.
[0040] (1) Calculate the degree of congestion and resilience:
[0041] For a section of a subway line in Beijing between two adjacent stations A and B, the passenger flow is denoted as F. AB (t). Define its baseline passenger flow F. b This represents the passenger flow at the 95th percentile (ranked from smallest to largest) of passenger flow on this route section during different times of the day. Baseline passenger flow F b The specific values vary depending on the route, reflecting the passenger flow levels of different sections of the route. To measure the congestion level of different sections under different passenger flows, a relative congestion index q0 and the corresponding congestion passenger flow threshold F are used. c F c =F b ×q0. Given a relative congestion index q0 and a corresponding congested passenger flow threshold F. c Below, when the passenger flow F AB (t) is greater than the congestion passenger flow threshold F c At time t, the section of the line between stations A and B is considered congested; otherwise, the section is considered to be in normal operation. At time t, for a certain rail transit line, different sections in a congested state may be isolated from each other; or some congested sections may be connected, forming a certain continuous congestion range, containing more adjacent stations in the congested section. The continuous congestion range containing the largest number of subway stations is called the largest congestion component (lcc). Further, at time t, the congestion level C of the line is defined as the ratio of the number of stations contained in the largest congestion component formed by the congested sections of the subway line to the total number of stations on the line. L (t), specifically represented as
[0042] C L (t)=n L-lcc (t) / n L ,
[0043] Where n L-lcc (t) represents the number of stations contained in the largest congested sub-group formed on this line at time t, n. L This represents the total number of stations on the line.
[0044] Here, the line congestion level C L The negative value of (t) can be used as a performance evaluation index of the line. The resilience index R of the line is defined by using the area of the resilience triangle of its degradation and recovery process over time. L ,Right now
[0045]
[0046] Step 3: Calculate the line resilience value using the resilience process in Step 2 and based on the resilience triangle, and compare it with the line's recovery speed.
[0047] (1) Calculate the line toughness value:
[0048] In step 2, the performance indicators of the urban rail transit line were defined based on seepage flow, and the line resilience index R during morning and evening peak hours was given using the resilience triangle method. L Here, based on the aforementioned line resilience indicators, the resilience values of all lines during morning and evening peak hours are calculated. By comparing the resilience values of different lines, the specific lines with poor resilience can be identified.
[0049] (2) Comparative analysis of recovery speed:
[0050] To further investigate the characteristics of line resilience, this study also considers another evaluation metric for assessing resilience: system performance recovery speed. Recovery speed refers to the rate at which performance recovers from its lowest point to normal, and can be defined as V. L =C L min / (t0-t min ), where t min C L min These represent the time when the performance index reaches its lowest point and the degree of line congestion, respectively. For each line, the recovery speed during morning and evening peak hours is calculated. By comparing the resilience value and recovery speed of all lines, the possible reasons behind the relationship between the two are identified, and lines with poor resilience that require special attention are identified.
[0051] Specifically, select a section of a subway line in a certain city between two adjacent stations A and B, and denot its passenger flow as F. AB(t). Given a relative congestion index q0 and a corresponding congested passenger flow threshold F. c Below, when the passenger flow F AB (t) is greater than the congestion passenger flow threshold F c At time t, the section of the line between stations A and B is considered congested; otherwise, the section is considered to be in normal operation. At time t, for a given rail transit line, different congested sections may be isolated from each other; alternatively, some congested sections may be connected, forming a continuous congestion zone containing more adjacent stations within the congested section. The continuous congestion zone containing the largest number of subway stations is called the maximum congestion sub-cluster. Furthermore, at time t, the congestion level C of the line is defined as the ratio of the number of stations in the maximum congestion sub-cluster formed by the congested sections of the subway line to the total number of stations on the line. L (t), specifically represented as
[0052] C L (t)=n L-lcc (t) / n L ,
[0053] Where n L-lcc (t) represents the number of rail transit stations contained in the largest congested sub-group formed on this line at time t, n. L This represents the total number of rail transit stations along the line.
[0054] Here, the line congestion level C L The negative value of (t) can be used as a performance evaluation index of the line, and its degradation and recovery process over time can be used to define the line's resilience index R. L ,Right now
[0055]
[0056] Based on the aforementioned resilience evaluation indicators, this study selects different lines in the city's subway system to investigate and compare their congestion levels and changes during the morning rush hour, thus studying the differences in line resilience among different lines. Five subway lines—Lines 1, 2, 13, 5, and 10—were selected. Their line resilience R... L The evolution over time on the same workday (day1), given q0 = 0.6, is as follows: Figure 1As shown in the figure, to compare the results with those from the morning rush hour, we also illustrate the changes in line performance during the evening rush hour. As can be seen from the figure, the congestion levels of Lines 1, 10, 13, and 5 are slightly lower during the evening rush hour than during the morning rush hour, indicating that the line performance of the rail transit system may exhibit different behavioral characteristics during the morning and evening rush hours. However, the congestion levels of Line 2 are similar during both the morning and evening rush hours. This suggests that different rail transit lines may experience different passenger travel patterns and behaviors during the morning and evening rush hours, leading to different patterns and characteristics in line performance and resilience.
[0057] Similar to the station resilience assessment, this section first presents a graph showing the relationship between line resilience and total passenger volume during the morning peak hours, such as... Figure 2 As shown, there is no significant correlation between the resilience of the city's subway lines and the absolute size of their total passenger volume. This is similar to the characteristics of station resilience.
[0058] Finally, the relationship between the resilience of each line and the line recovery speed is as follows: Figure 3 As shown. Here, Figure 3 (a) Figure 3 (b) Shows the results for the first working day and the average of the five working days, respectively. According to Figure 3 (a) For the vast majority of lines (left half of the scatter plot), the relationship between line resilience and recovery speed shows a linear downward trend, with only a few lines outside this linear trend. For most lines exhibiting this linear downward trend, it can be considered that the degree of performance degradation during the morning rush hour (rather than degradation time or recovery time or other factors) primarily determines their resilience area and recovery speed. For example, some lines experience less performance degradation during the morning rush hour, resulting in high resilience and low recovery speed; others, conversely, exhibit low resilience and high recovery speed. Figure 3 The average results for multiple working days shown in (b) are consistent with... Figure 3 (a) The systems are largely similar, except that the Airport Line's point moves outside the straight line, confirming the similar characteristics of the rail transit system on different weekdays. Furthermore, it is noteworthy that Line 2 exhibits significantly different characteristics, showing markedly lower resilience during the morning rush hour compared to other lines, with severe and prolonged congestion. Therefore, reducing congestion on Line 2 during the morning rush hour is a key issue for the city's rail transit management.
Claims
1. A seepage-based urban rail transit system line resilience assessment method, characterized in that, This method specifically includes the following steps: (1) Determine the scope of the rail transit network to be evaluated: Calculate the latitude and longitude range of the rail transit network to be evaluated, as well as the location of the stations and the relationship between the lines; (2) Collect passenger flow information of rail transit network segments within the assessment area for a period of time to form a time series of traffic status quantities for each segment within the area: (3) Calculate the degree of congestion in the interval using the time series from step (2), specifically: For two adjacent stations on a certain line of the subway system , The passenger flow of the route section between them is recorded as follows: Define its benchmark passenger flow The relative congestion index is used to measure the passenger flow at the 95th percentile of passenger flow at different times of the day within this interval. and the corresponding congested passenger flow threshold To measure the congestion level of different routes under different passenger volumes, among which Given a relative congestion index and corresponding congestion passenger flow thresholds When the passenger flow Greater than the congestion passenger flow threshold At that time, it was considered that the site , The section of the line is congested; Otherwise, the section of the line is in normal operation. The ratio of the number of stations in the maximum congestion sub-cluster formed by the congested sections of the subway line at time t to the total number of stations in the line is defined as the congestion degree of the line The maximum congestion sub-cluster is the continuous congestion range containing the largest number of subway stations, and the congestion degree Specifically represented as in Let t be the number of stations contained in the largest congested sub-cluster formed on this line at time t. This represents the total number of stations on the route. (4) Calculate the line toughness value: The line congestion level obtained in step (3) The negative value of the line is used as a performance evaluation index, and its degradation and recovery process over time is used to define the line's resilience index. ,Right now ； (5) Calculate the recovery rate: Recovery speed refers to the speed at which performance recovers from its lowest point to normal levels, defined as... ,in , These represent the time when the performance index reaches its lowest point and the degree of line congestion, respectively. This indicates the moment when the line performance has just recovered to a value of 0. The resilience values and recovery speeds of all lines are compared to conduct a resilience assessment of the lines.