Bus toughness evaluation and weak spot identification method and system under rainstorm event

Abstract

The invention discloses a bus toughness evaluation and weak spot identification method and system under a rainstorm event, and relates to the technical field of bus toughness evaluation and weak spot identification, and the method comprises the steps: collecting the data of a bus route and a road network, and carrying out the preprocessing of a bus GPS track; based on the toughness measurement of the speed, the bus running speed deviation under different weather conditions is calculated, and the duration of medium and low toughness is determined; calculating the adjacent arrival interval of each station of the line; based on the arrival time of each line vehicle under the rainstorm event, the station bus service frequency is obtained, the line service reliability is calculated, lines with low reliability are selected to draw a space-time running diagram, and weak points are identified. According to the method, the problems that based on single bus running track data, the toughness characteristic under the rainstorm event cannot be effectively measured, and effective identification of the bus performance reduction key reason under the condition of comprehensively considering the dependence of bus running on road infrastructures is lacked are solved; decision support is provided for bus toughness improvement in a rainstorm event tending to be normalized.

Description

Technical Field

[0001] The present invention relates to the technical field of public transport resilience assessment and weak point identification, and in particular to a method and system for public transport resilience assessment and weak point identification under heavy rain events. Background Art

[0002] As an important national infrastructure and livelihood project, the urban public transportation system carries important functions of urban residents' travel, material transportation and economic development. However, with the increasing complexity of the operating environment, the operation of the public transportation system is susceptible to disturbances such as natural disasters and traffic accidents, and the public transportation system is vulnerable to certain shocks. In recent years, climate disturbances such as extreme rainstorms have occurred frequently, often causing delays and performance degradation of the public transportation operation system. In addition, due to the dependence of public transportation services on road infrastructure, the interruption of road infrastructure may even cause the interruption of public transportation service areas, seriously affecting residents' travel.

[0003] The concept of resilience provides an effective solution for quantifying the ability of transportation systems to resist, adapt to, and recover from disturbances. Existing studies often simulate the disruption of bus network nodes or links and conduct resilience research based on bus network structure and functional indicators, but the possibility of disruption scenarios is out of touch with reality. Some researchers simulated the disruption of road networks under real rainstorms based on hydrodynamic models. Considering the interdependence between bus networks and road networks, research on the resilience of bus networks under real scenarios such as rainstorms has not yet been involved.

[0004] Therefore, how to quantitatively and multi-facetedly evaluate the resilience of bus operation performance under actual rainstorm scenarios and effectively identify weak points by considering the interdependence between bus operation and road infrastructure has become an urgent problem to be solved. Summary of the invention

[0005] In view of the problems existing in the existing methods for assessing the resilience of public transportation and identifying weak points under heavy rain events, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to quantitatively evaluate the resilience of bus operation performance under actual rainstorm scenarios in multiple aspects, and to identify effective weak points by considering the interdependence between bus operation and road infrastructure.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides a method for assessing the resilience of public transportation and identifying weak points during heavy rain events, which includes collecting static spatial geographic information data of bus routes and road networks, and performing preprocessing based on the collected data; performing speed-based resilience assessment on the selected bus network under heavy rain disturbances based on the preprocessed data, and simultaneously identifying arrival and departure information on the preprocessed data; based on the arrival and departure identification information, calculating the service reliability of the bus routes during medium and low resilience periods, introducing a bus operation diagram, and identifying weak points in the operation of the bus routes.

[0009] As a preferred solution of the method for assessing bus resilience and identifying weak points under rainstorm events described in the present invention, the data collection includes using software to obtain bus GPS trajectory data and local weather station data to obtain rainfall data for the corresponding date period of the bus trajectory data; based on historical rainfall data, working days with rainstorm weather and not affected by other activities are selected as the research period.

[0010] As a preferred solution of the method for assessing bus resilience and identifying weak points under heavy rain events described in the present invention, the preprocessing includes data quality analysis, regional screening and data cleaning, specifically, retaining the vehicle number, sampling time, longitude and latitude, line ID and important fields of the departure station for the GPS trajectory data, retaining the trajectory data within the study area based on Mobmap and kepler.gl visualization, and preliminarily screening the fault drift data of the first and last stations of the line; cleaning up various abnormal values ​​and error values, deleting GPS fault data and multiple redundant trajectory data of vehicles at the same time, matching the trajectory data with the road network data, mapping the trajectory data to the corresponding grid using the grid partitioning method, performing spatial matching based on the grid, and cleaning the trajectory drift data based on the continuous change of the trajectory speed; matching the trajectory data with the bus line and station information, using the project function to find the corresponding position of each GPS point on the bus line, using the interpolate function to generate the nearest position point corresponding to each GPS point on the bus line, calculating the distance between the original point and the matching point, and retaining the GPS points within a certain range according to the kernel density distribution map.

[0011] As a preferred solution of the method for evaluating the resilience and identifying weak points of public transportation under heavy rain events described in the present invention, the calculation of the resilience metric in the resilience evaluation includes: preliminarily identifying the work-residence area based on the preprocessed data, and distinguishing the population heat map data on weekdays and weekends to analyze the spatiotemporal mobility characteristics of residents, and finally identifying the work-residence area; based on the spatial distribution of the work-residence area, selecting the dense public transportation network in the corresponding area for research, and calculating the average running speed of the t-line network in normal weather and heavy rain events based on the latitude and longitude and timestamp information of the trajectory data, expressed as:

[0012]

[0013] Among them, V t represents the average speed of the bus network in time period t, v kt represents the running speed of bus line k in time period t, Td k represents the cumulative displacement of the trajectory of vehicle k within a certain period of time, d represents the displacement calculated based on the two trajectory points, Δlat represents the latitude difference between the two trajectory points, Δlon represents the longitude difference between the two trajectory points, lat1 and lat2 represent the latitudes of the two trajectory points, and R is the radius of the earth.

[0014] As a preferred solution of the method for evaluating public transportation resilience and identifying weak points under rainstorm events described in the present invention, the resilience metric is combined with the baseline value of the line network speed in each period under normal weather, and the ratio p of the speed in each period under rainstorm events to the baseline value is used as a performance metric index. Based on the resilience triangle theory, the line network resilience R is calculated, which is expressed as:

[0015]

[0016] Where P(t) represents the performance ratio of the bus network under heavy rain and normal weather at time period t, VW t represents the performance of the bus network during the rainstorm period t, VB t represents the baseline value of bus network performance during period t under normal weather conditions, t s is the disturbance start time, t e is the end time of the disturbance; based on the bus network system performance disturbance curve at a certain time granularity, the duration period of medium and low resilience is further clarified.

[0017] As a preferred solution of the method for assessing public transport resilience and identifying weak points under heavy rain events described in the present invention, the arrival and departure identification information includes identification of arrival and departure behaviors of bus routes in medium and low resilience duration periods in units of bus routes, adding a Buffer buffer to determine the arrival and departure range of each station, using Python to convert the vehicle running trajectory and buffer into linestring and polygon elements in the shapely package, using the intersection function in shapely to extract slices of the intersection area between the two, and obtaining the arrival and departure time information of the intersection point; and correcting the multiple arrival and departure information in a short period of time caused by trajectory drift.

[0018] As a preferred solution of the method for evaluating public transport resilience and identifying weak points under rainstorm events described in the present invention, the quantification of service reliability is based on the arrival time of adjacent trains at the same station, and the frequency of vehicle service at each station within a certain period of time is calculated to quantify the service reliability of the bus line, which is expressed as:

[0019]

[0020] Among them, t inl is the service time interval between adjacent shifts at station j on route l during the study period, q is the number of vehicles passing station j during the study period, and F jl is the average service frequency of station j in line l; N is the number of stations in the line, F l Indicates the service frequency of line l; F El is the service frequency of line l during the study period under heavy rain events, F Bl The baseline service frequency of route l during the study period, and F represents the degree to which bus service frequency is affected by heavy rain.

[0021] In the second aspect, an embodiment of the present invention provides a public transport resilience assessment and weak point identification system under heavy rain events, which includes: a collection module for collecting static spatial geographic information data of bus routes and road networks, and performing preprocessing based on the collected data; an evaluation module for performing speed-based resilience assessment on the selected bus network under heavy rain disturbances based on the preprocessed data, and simultaneously performing arrival and departure information identification on the preprocessed data; an identification module for calculating the service reliability of bus routes during medium and low resilience periods based on the arrival and departure identification information, introducing a bus operation diagram, and identifying weak points in bus route operation.

[0022] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned method for assessing bus resilience and identifying weak points under heavy rain events.

[0023] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for assessing public transport resilience and identifying weak points during rainstorm events is implemented.

[0024] The beneficial effect of the present invention is that the present invention applies historical bus GPS trajectory data, mines bus operation indicators to establish a dynamic resilience assessment model, and evaluates the resilience of regional line networks to bus line services. It reflects the dynamic changes of two key bus operation performances, operating speed and service frequency, during heavy rains, and combines the bus operation map to effectively identify the weak points of the road sections that cause the bus performance to decline. It provides support for the protection of key sections of bus operations in subsequent heavy rain events and the empirical decision-making of emergency rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0026] Figure 1 It is a technical flow chart of the present invention;

[0027] Figure 2 This is a visualization diagram of the preprocessed bus trajectory data;

[0028] Figure 3 It is the effect diagram of the disturbance curve of toughness performance at each time granularity;

[0029] Figure 4 It is the resilience disturbance curve of the regional bus network;

[0030] Figure 5 Identify the schematic diagram for arrival and departure stations;

[0031] Figure 6 This is a time-space operation diagram of buses. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0035] Example 1

[0036] Reference Figure 1 to Figure 6 , which is the first embodiment of the present invention, provides a method for evaluating the resilience of public transportation and identifying weak points under heavy rain events, including:

[0037] S1: Collect static spatial geographic information data of bus routes and road networks, and perform preprocessing based on the collected data.

[0038] Use Python to crawl bus route information and station information on the Yangzhou bus route network and collect bus GPS trajectory data.

[0039] And use python to crawl the Yangzhou weather station data to obtain the rainfall data for the corresponding date period of the bus trajectory data.

[0040] Based on the daily cumulative precipitation (accumulated rainfall in 24 hours exceeded 100 mm), working days with heavy rain weather and not affected by other activities were selected. July 6, 2023 to July 7, 2023 were selected as the heavy rain event as the study period, and October 15, 2022 with normal weather was selected as the control group.

[0041] Preprocess the GPS trajectory data. The specific steps include:

[0042] Excel data was used to delete invalid fields and retain valid fields such as vehicle number, sampling time, longitude and latitude, and line ID. Based on Mobmap and kepler.gl visualization, the trajectory data in the study area was retained, and the fault drift data of the first and last stations of the line were preliminarily screened.

[0043] Clean up all kinds of abnormal values ​​and error values, delete GPS fault data, and redundant multiple trajectory data of vehicles at the same time. The specific steps include:

[0044] Based on the field DATATYPE in the bus trajectory data, whose values ​​are 5 and 55, representing the data collected when the equipment fails, Python is used to eliminate the equipment failure records.

[0045] The relevant estimated data valid fields and related attributes are shown in Table 1:

[0046] Table 1 Estimated data valid fields and related attributes

[0047]

[0048] Redundant data mainly refers to the redundancy of repeated data at the same time. The Python built-in function shift() is used to generate new longitude and latitude fields LONGITUDE-1 and LATITUDE-1, and the repeated data is sorted out by comparing the previous and the next data.

[0049] The trajectory data is matched with the road network data. First, the road network data is read into a "GeoDataFrame" using GeoPandas in Python. Then, the trajectory data is mapped to the corresponding grid using the grid partitioning method, and spatial matching is performed based on the grid. The trajectory drift data is cleaned based on the continuous change of trajectory speed.

[0050] The trajectory data is further matched with the bus routes and station information. The CoordinatesConventer package in Python is used to convert the bus station data and bus GPS trajectory point data from the WGS84 coordinate system to the Beijing 54 coordinate system; the project function is used to find the corresponding position of each GPS point on the bus route, and the interpolate function is used to generate the nearest position point corresponding to each GPS point on the bus route, and the distance between the original point and the matching point is calculated. According to the kernel density distribution map, the GPS points within a certain range are retained.

[0051] The trajectory data is standardized, and the three columns of ROUTEID, PRODUCTID, and ACTDATETIME in the bus GPS trajectory data are sorted in ascending order according to priority using the sort_values(by=['']) function in Python, effectively integrating the trajectory data of each bus in chronological order.

[0052] The final processed bus trajectory visualization result is as follows Figure 2 shown.

[0053] S2: Based on the resilience metric of speed, calculate the deviation of bus speed under normal and rainstorm weather conditions, draw the speed performance disturbance curve under rainstorm events, and determine the duration of medium and low resilience.

[0054] Measuring the resilience of regional bus networks and identifying periods of low and medium resilience can be done as follows:

[0055] Based on POI data, we preliminarily identify work-residence areas, and further analyze residents’ spatiotemporal mobility characteristics based on Baidu population heat map data on weekdays and weekends to ultimately identify work-residence areas.

[0056] Based on the spatial distribution of work-residence areas, a relatively dense bus network in the corresponding area was selected for study.

[0057] Based on the latitude and longitude and timestamp information of the trajectory data, the average running speed of the t-line network during normal weather and rainstorm events is calculated, which is expressed as:

[0058]

[0059] Among them, V t represents the average speed of the bus network in time period t, vkt represents the running speed of bus line k in time period t, Td k represents the cumulative displacement of the trajectory of vehicle k within a certain period of time, d represents the displacement calculated based on the two trajectory points, Δlat represents the latitude difference between the two trajectory points, Δlon represents the longitude difference between the two trajectory points, lat1 and lat2 represent the latitudes of the two trajectory points, and R is the radius of the earth.

[0060] Combined with the baseline value of the line network speed in each period under normal weather, the ratio p of the speed in each period under heavy rain events to the baseline value (BV) is used as a performance measurement indicator. Based on the toughness triangle theory, the line network toughness R is as follows:

[0061]

[0062] Where P(t) represents the performance ratio of the bus network under heavy rain and normal weather at time period t, VW t represents the performance of the bus network during the rainstorm period t, VB t represents the baseline value of bus network performance during period t under normal weather conditions, t s is the disturbance start time, t e is the end time of the disturbance.

[0063] Select appropriate time granularity to measure the resilience of public transportation systems, such as Figure 3 The performance curves were drawn at 5-minute, 15-minute and 1-hour granularities, and the final research time granularity was determined to be 15 minutes.

[0064] Based on the bus network system performance disturbance curve at 15-minute intervals, such as Figure 4 As shown, the specific period during which the medium-low resilience continued was from 6:00 p.m. to 11:00 p.m. on July 7.

[0065] S3: Based on the trajectory data after trajectory matching, the arrival and departure information is identified in combination with the station location information, and the adjacent arrival intervals of each station on the line during the medium and low resilience periods are calculated.

[0066] Taking bus lines as units, for the identification of the arrival and departure behaviors of bus lines in the medium and low resilience duration period, a buffer is added to determine the arrival and departure range of each station. Python is used to convert the vehicle running trajectory and buffer into linestring and polygon features in the shapely package, and the intersection(other) function in shapely is used to extract the slices of the intersection area between the two, such as Figure 5 As shown, the arrival and departure time information of the intersection point is obtained.

[0067] Correct the multiple arrival and departure information in a short time due to trajectory drift. Specifically, the arrival and departure time information is sequentially integrated into the time column, with the flag column values ​​0 and 1 representing the arrival and departure, the time column is moved up as a whole to obtain the time1 column, and the arrival flag segment is moved up to obtain the flag_1 field, and the difference between time and time1 is compared with the time threshold mintime. If the difference is less than the time threshold mintime (time threshold mintime = 3min), the flag_1 value is True, otherwise it is False; move the flag_1 field up to obtain the flag_2 field, and if True appears in flag_1 and flag_2 in each row, the flag_3 value is True, and the corresponding time record needs to be deleted.

[0068] S4: Based on the arrival time of vehicles on each line during a rainstorm event, obtain the bus service frequency at the station, obtain the line service reliability, select bus lines with lower service reliability, draw a bus time-space operation diagram, and identify possible waterlogging points on the road.

[0069] Since the medium and low resilience stages can better reflect the degree of impact of heavy rain on public transportation, this paper uses bus service frequency to quantify the reliability of bus routes. Based on the arrival times of adjacent buses at the same station, the following formula is used to calculate the frequency of vehicle services at each station within a certain period of time, thereby quantifying the reliability of bus line services, which is expressed as:

[0070]

[0071] Among them, t inl is the service time interval between adjacent shifts at station j on route l during the study period, q is the number of vehicles passing station j during the study period, and F jl is the average service frequency of station j in line l; N is the number of stations in the line, F l Indicates the service frequency of line l; F El is the service frequency of line l during the study period under heavy rain events, F Bl The baseline service frequency of route l during the study period, and F represents the degree to which bus service frequency is affected by heavy rain.

[0072] The service frequency under heavy rain events and the service frequency under normal weather are both calculated based on the first two formulas. The baseline value is the value calculated on a selected normal weather weekday. The only difference between the service frequency under heavy rain and the service frequency under normal weather is the selected date.

[0073] Considering the dependence of public transportation on road network operation, the main reason for the decline in public transportation performance during heavy rain events is waterlogging on the road section. The time-space operation diagram of public transportation is introduced, such as Figure 6As shown, based on the bus stop time (5 minutes as the threshold), the weak points of the road section in space can be accurately identified.

[0074] Four bus routes were selected to verify the effectiveness of the bus verification weak point method. A total of eight resilient weak points were identified from the operation diagrams of the four bus routes, namely Cuigang Community, Shita Temple, Zhenyuan, Qionghua Temple, Decoration World, Jingjiang Hotel, Hanjiang Middle Road, Wenchang West Road, and Fengzhuang Station. Finally, cross-validation was performed in combination with the statistical table of flood-prone points in Yangzhou City, and all road weak points were identified.

[0075] The present invention uses historical bus GPS trajectory data to mine bus operation indicators to establish a dynamic resilience assessment model, and evaluates the resilience of regional line networks to bus line services. The present invention can reflect the dynamic changes of two key bus operation performances, operating speed and service frequency, during heavy rains, and can effectively identify weak points on sections that cause bus performance degradation in combination with bus operation diagrams. The conclusions of the present invention can provide support for the protection of key sections of bus operations during subsequent heavy rain events and empirical decision-making for emergency rescue.

[0076] In summary, the present invention solves the problem that the resilience characteristics under rainstorm events cannot be effectively measured based on single bus operation trajectory data, and the key reasons for the decline in bus performance under the comprehensive consideration of bus operation's dependence on road infrastructure are effectively identified, providing decision-making support for improving bus resilience under normalized rainstorm events.

[0077] Example 2

[0078] Reference Figure 1 Based on the first embodiment, this embodiment further provides a public transportation resilience assessment and weak point identification system under heavy rain events, including:

[0079] A collection module is used to collect static spatial geographic information data of bus routes and road networks, and perform preprocessing based on the collected data;

[0080] An evaluation module is used to evaluate the speed-based resilience of the selected bus network under heavy rain disturbance based on the preprocessed data, and to identify the departure information of the preprocessed data;

[0081] The identification module is used to calculate the service reliability of bus lines during medium and low resilience periods based on arrival and departure identification information, introduce bus operation diagrams, and identify weak points in bus line operation.

[0082] This embodiment also provides a computer device, which is suitable for the method of assessing the resilience of public transportation and identifying weak points during rainstorm events, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method of assessing the resilience of public transportation and identifying weak points during rainstorm events as proposed in the above embodiment.

[0083] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0084] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for evaluating the resilience of public transportation and identifying weak points during rainstorm events as proposed in the above embodiment is implemented.

[0085] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

Claims

1. A method for assessing the resilience of public transportation and identifying weak points during heavy rain events, characterized by: include, Collect static spatial geographic information data of bus routes and road networks, and perform preprocessing based on the collected data; Based on the preprocessed data, the selected bus network is evaluated for its speed-based resilience under heavy rain disturbance, and the preprocessed data is used to identify the departure information; Based on the arrival and departure identification information, the service reliability of bus lines during medium and low resilience periods is calculated, and the bus operation diagram is introduced to identify the weak points of bus line operation.

2. The method for assessing public transport resilience and identifying weak points during a rainstorm event as claimed in claim 1, characterized in that: The data collection includes using software to obtain bus GPS track data and local weather station data, and obtaining rainfall data for a corresponding date period of the bus track data; Based on historical rainfall data, working days with heavy rain and not affected by other activities were selected as the research period.

3. The method for assessing public transport resilience and identifying weak points during a rainstorm event as claimed in claim 2, characterized in that: The preprocessing includes data quality analysis, regional screening and data cleaning. Specifically, the vehicle number, sampling time, longitude and latitude, line ID and important fields of the arrival and departure stations are retained for the GPS trajectory data. Based on Mobmap and kepler.gl visualization, the trajectory data within the study area is retained, and the fault drift data of the first and last stations of the line are preliminarily screened. Clean up all kinds of abnormal values ​​and error values, delete GPS fault data and redundant multiple trajectory data of vehicles at the same time, match trajectory data with road network data, use grid partitioning method to map trajectory data to corresponding grids, perform spatial matching based on grids, and clean up trajectory drift data based on continuous changes in trajectory speed; The trajectory data is matched with the bus routes and station information. The project function is used to find the corresponding position of each GPS point on the bus route. The interpolate function is used to generate the nearest position point corresponding to each GPS point on the bus route. The distance between the original point and the matching point is calculated, and the GPS points within a certain range are retained according to the kernel density distribution map.

4. The method for assessing public transport resilience and identifying weak points during a rainstorm event as claimed in claim 3, characterized in that: The calculation of the toughness metric in the toughness assessment includes: Based on the preprocessed data, the work-residence area is initially identified, and the population heat map data of working days and weekends are distinguished to analyze the spatiotemporal mobility characteristics of residents, and finally the work-residence area is identified; Based on the spatial distribution of work-residence areas, the dense bus network in the corresponding area is selected for research, and the average running speed of the T-line network in normal weather and rainstorm events is calculated based on the latitude, longitude and timestamp information of the trajectory data, which is expressed as: Among them, V t represents the average speed of the bus network in time period t, v kt represents the running speed of bus line k in time period t, Td k represents the cumulative displacement of the trajectory of vehicle k within a certain period of time, d represents the displacement calculated based on the two trajectory points, Δlat represents the latitude difference between the two trajectory points, Δlon represents the longitude difference between the two trajectory points, lat1 and lat2 represent the latitudes of the two trajectory points, and R is the radius of the earth.

5. The method for assessing public transport resilience and identifying weak points during a rainstorm event as claimed in claim 4, characterized in that: Based on the toughness metric and the baseline value of the line network speed in each period under normal weather conditions, the ratio p of the speed in each period under heavy rain events to the baseline value is used as a performance metric. Based on the toughness triangle theory, the line network toughness R is calculated and expressed as: Where P(t) represents the performance ratio of the bus network under heavy rain and normal weather at time period t, VW t represents the performance of the bus network during the rainstorm period t, VB t represents the baseline value of bus network performance during period t under normal weather conditions, t s is the disturbance start time, t e is the disturbance end time; Based on the bus network system performance disturbance curve at a certain time granularity, the duration periods of medium and low resilience are further clarified.

6. The method for assessing public transport resilience and identifying weak points during a rainstorm event as claimed in claim 5, characterized in that: The arrival and departure identification information includes the identification of the arrival and departure behaviors of bus lines in the medium and low toughness duration period, adding a buffer to determine the arrival and departure range of each station, using Python to convert the vehicle running track and buffer into linestring and polygon elements in the shapely package, and using the intersection function in shapely to extract the slices of the intersection area of ​​the two, and obtain the arrival and departure time information of the intersection point; Corrections will also be made to the multiple arrival and departure information in a short period of time caused by trajectory drift.

7. The method for assessing public transport resilience and identifying weak points during a rainstorm event as claimed in claim 6, characterized in that: The quantification of the service reliability is based on the arrival time of adjacent buses at the same station, and the frequency of vehicles serving each station within a certain period of time is calculated to quantify the service reliability of the bus line, which is expressed as: Among them, t inl is the service time interval between adjacent shifts at station j on route l during the study period, q is the number of vehicles passing station j during the study period, and F jl is the average service frequency of station j in line l; N is the number of stations in the line, F l Indicates the service frequency of line l; F El is the service frequency of line l during the study period under heavy rain events, F Bl The baseline service frequency of route l during the study period, and F represents the degree to which bus service frequency is affected by heavy rain.

8. A system for assessing the resilience of public transportation and identifying weak points during a rainstorm event, based on the method for assessing the resilience of public transportation and identifying weak points during a rainstorm event according to any one of claims 1 to 7, characterized in that: include, A collection module is used to collect static spatial geographic information data of bus routes and road networks, and perform preprocessing based on the collected data; An evaluation module is used to evaluate the speed-based resilience of the selected bus network under heavy rain disturbance based on the preprocessed data, and to identify the departure information of the preprocessed data; The identification module is used to calculate the service reliability of bus lines during medium and low resilience periods based on arrival and departure identification information, introduce bus operation diagrams, and identify weak points in bus line operation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for assessing public transport resilience and identifying weak points during a rainstorm event according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for assessing public transport resilience and identifying weak points during a rainstorm event as described in any one of claims 1 to 7 are implemented.

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