A bus operation characteristics analysis method based on multi-source data fusion and OD information derivation

Abstract

The present invention provides a method for analyzing bus operation characteristics based on multi-source data fusion and OD information derivation, which belongs to the field of intelligent bus dispatching technology, wherein the method includes determining the total number of actual bus stop sites; determining the critical value of the passenger boarding time difference according to the total number of actual bus stop sites; determining the total number of boarding passengers and the total number of disembarking passengers at each site according to the critical value of the passenger boarding time difference; determining the running time of the bus from the starting station to the terminal station, the total stop time of all sites and the waiting time at the intersection according to the total number of boarding passengers and the total number of disembarking passengers at each site, so as to visualize the delay propagation of the spatial characteristics of bus operation. The present invention comprehensively considers the influence of the basic layer, the interactive layer and the output layer data, performs stratification and fusion according to the data characteristics, ensures the quality and fusion accuracy of the collected data, and can obtain the spatiotemporal distribution characteristics of bus passenger flow and bus OD information.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent bus dispatching, and in particular relates to a bus operation characteristic analysis method based on multi-source data fusion and OD information derivation. Background Art

[0002] The operation characteristics of public transportation involve passenger flow distribution at the temporal level and delay propagation at the spatial level. The spatiotemporal multi-source data fusion and OD (Origin Destination) information derivation are closely related to the operation characteristics of public transportation and are the source of public transportation data mining and visualization analysis. Studying the multi-source data fusion method at the spatiotemporal level of public transportation and conducting OD derivation can improve the data accuracy at the spatiotemporal level, which is the focus of public transportation. Therefore, how to use multi-source data fusion technology to mine complete public transportation information is of great significance.

[0003] In the related technologies, the bus IC card swiping data of passengers is used to determine the passengers' boarding and alighting stations, and the bus passenger flow is inferred, so as to optimize the bus schedule and build a bus driving plan; or the historical and real-time bus GPS data is used to build a bus arrival time prediction model based on the BP neural network, so as to optimize the bus departure frequency; or the multi-source data is used to conduct an in-depth analysis of the bus passenger flow, classify the passengers according to their behavior patterns, and analyze the complex relationship between the passenger type and the influencing factors, and propose a prediction method based on a hybrid decision tree; or based on the bus IC card data, GPS data and bus line station data, a passenger boarding station recognition algorithm based on time matching is proposed, and the alighting station is inferred respectively in combination with the bus travel chain and the deep feedforward network model.

[0004] However, for the data mining and visualization characteristics analysis of public transportation at the spatiotemporal level, the existing research data sources are relatively single, mostly based on IC card data or GPS data, without comprehensive consideration of the impact of public transportation operation statistics, city map data and mobile payment data, and lack comprehensiveness in practical applications. Multi-source data has the problem of missing data and interactive overlap. It is not layered and fused according to data characteristics, and the quality and fusion accuracy of the collected data cannot be guaranteed, so as to establish a complete multi-source data fusion system. The recognition rate and accuracy of the fusion method provided by the existing research are not high, and it is difficult to obtain the spatial distribution characteristics of public transportation passenger flow and public transportation OD information. Summary of the invention

[0005] The present invention provides a bus operation characteristics analysis method based on multi-source data fusion and OD information derivation to solve the problem that the recognition rate and accuracy of the existing fusion method are low and it is difficult to obtain the spatial distribution characteristics of bus passenger flow and bus OD information.

[0006] The present invention provides a method for analyzing bus operation characteristics based on multi-source data fusion and OD information derivation, comprising:

[0007] Determine the total number of actual bus stops;

[0008] Determine the critical value of the passenger travel time difference based on the total number of bus stops;

[0009] Determine the total number of passengers alighted at each stop;

[0010] The total number of boarding passengers at each station is determined according to the critical value of the passenger boarding time difference and the total number of passengers getting off at each station;

[0011] According to the total number of boarding passengers and alighting passengers at each station, the running time of the bus from the starting station to the terminal station, the total stop time at all stations and the waiting time at intersections are determined to visualize the delay propagation of the spatial characteristics of bus operation.

[0012] Optionally, determining the total number of actual bus stop sites includes:

[0013] The total number of actual bus stops N is calculated according to the following formula:

[0014]

[0015] Among them, the three types of information include station departure path information obtained from IC card data, mobile phone payment data and GPS data; i is the weight of the i-th type of information; N i is the total number of boarding stops collected from the i-th type of information.

[0016] Optionally, the weight of the i-th category of information is calculated according to the following formula:

[0017]

[0018] Among them, DI (m i ) is the difference information amount of the i-th category information.

[0019] Optionally, the difference information amount of the i-th type of information is calculated according to the following formula:

[0020] DI(m i )=C d (m i )·ED(m i ,m j );

[0021] Among them, C d (m i ) The credibility of the i-th type of information; ED(m i ,mj ) is the i-th type of information m i With the jth type of information m j The difference between .

[0022] Optionally, the credibility of the i-th type of information is calculated according to the following formula:

[0023]

[0024] Among them, S d (m i ) is the inverse of the average divergence of the i-th type of information; JS(m i ,m j ) is the divergence between the i-th information and the j-th information.

[0025] Optionally, the i-th type of information m is calculated according to the following formula: i With the jth type of information m j Difference between:

[0026]

[0027] Among them, D ij is the reciprocal of the difference between the i-th information and the j-th information; j≠i; e is a natural constant; |·| means taking the absolute value; E dj is the information entropy of the j-th type of information; E di is the information entropy of the i-th type of information; A i Represents an event of the i-th type of information; |A i | indicates included elements; m(·) indicates mass function; R indicates the sample space set.

[0028] Optionally, the step of determining the critical value of the passenger travel time difference according to the total number of bus stops actually made comprises:

[0029] Get the minimum travel time difference as the initial travel time difference critical value t min ;

[0030] The total number of theoretical bus stops P is calculated according to the following formula:

[0031] P=ΣP s +ΣP t ;

[0032] If the difference in travel time between two adjacent passengers is less than t min , then it is determined that the two adjacent passengers boarded the bus at the same station, and the number of stations where the two adjacent passengers boarded the bus at the same station is recorded as P s ; If the difference in travel time between two adjacent passengers is greater than or equal to t min, then it is determined that the two adjacent passengers boarded the bus at different stations; and the number of stations where the two adjacent passengers boarded the bus at different stations is recorded as P t ;P t =[T / 2t min ]; [·] means rounding to the nearest integer;

[0033] Determine whether the total number of theoretical bus stops P is less than the total number of actual bus stops N;

[0034] If it is less than, the initial travel time difference critical value t min As the critical value of the passenger's travel time difference;

[0035] If it is not less than, then the time difference between the adjacent passengers when they board the bus will be divided by t min The minimum value outside the above is taken as the critical value of the passengers' travel time difference.

[0036] Optionally, determining the total number of passengers getting off at each station comprises:

[0037] The total number of passengers getting off at each stop is calculated using the following formula:

[0038] V j' =L j' ·P j' ;

[0039] Among them, P j' is the probability of passengers getting off at station j'; if station j' belongs to a station in a closed travel chain or a micro-closed travel chain, then P j' =1, if station j' does not belong to the station in the closed travel chain and micro-closed travel chain, then P j' =0;

[0040] The station with a boarding frequency greater than a preset threshold in the closed travel chain is regarded as the first station; the station with a passenger flow density greater than a preset passenger flow density threshold in the closed travel chain is regarded as the second station; if station j' is the first station and the second station, then

[0041]

[0042] Among them, M j' is the number of boarding stops at station j'; n0 is the total number of first stations in the closed travel chain; M n' is the number of boarding stops at station n'; n'=1,2,…,n0.

[0043] Optionally, determining the total number of boarding passengers at each station according to the critical value of the passenger boarding time difference and the total number of passengers getting off at each station comprises:

[0044] Determine the travel time difference T between two adjacent passengersj' Is it greater than the passenger's travel time difference t? min The critical value of

[0045] If it is greater, the total number of boarding passengers at each bus stop is calculated according to the following formula:

[0046]

[0047] If it is not greater than, the total number of boarding passengers at each bus stop is calculated according to the following formula:

[0048] N j' =L j'+1 -L j' +V j' ;

[0049] Among them, N j' is the number of passengers boarding at station j'; L j'+1 is the passenger capacity of station j'+1; L j' is the passenger capacity of station j'; V j' is the number of passengers getting off at station j'.

[0050] Optionally, the operation time of the bus from the starting station to the terminal station, the total stop time of all stations and the waiting time at intersections are determined according to the total number of boarding passengers and the total number of alighting passengers at each station, so as to perform a visual analysis on the delay propagation of the spatial characteristics of bus operation, including:

[0051] The running time T1 of the bus from the starting station to the terminal station is calculated according to the following formula:

[0052]

[0053] Where u is the total number of stations on the route from the starting station to the terminal station; L j' is the passenger capacity of station j'; ta j'+1 is the arrival time of the bus at the j'+1th stop; td j' is the departure time of the bus at the j'th station;

[0054] The total stop time T2 of the bus from the starting station to the terminal station is calculated according to the following formula:

[0055]

[0056] Among them, κ j' is the bus’s stop time at station j’; μ is the time it takes for the bus to open and close its doors and accelerate and decelerate when entering and leaving the station; The average boarding or alighting time for each passenger;

[0057] The waiting time T3 of the bus at the intersection on the route from the starting station to the terminal station is calculated according to the following formula:

[0058]

[0059] Among them, q is the total number of intersections on the route from the starting station to the terminal station; is the waiting time of the bus at intersection x.

[0060] The present invention provides a method for analyzing the operation characteristics of a bus based on multi-source data fusion and OD information derivation, wherein the method includes determining the total number of actual bus stop sites; determining the critical value of the passenger boarding time difference according to the total number of actual bus stop sites; determining the total number of passengers getting off at each site; determining the total number of passengers getting on at each site according to the critical value of the passenger boarding time difference and the total number of passengers getting off at each site; determining the running time of the bus from the starting station to the terminal station, the total stop time of all sites and the waiting time at the intersection according to the total number of passengers getting on and the total number of passengers getting off at each site, so as to perform a visual analysis on the delay propagation of the spatial characteristics of the bus operation. The present invention comprehensively considers the influence of the basic layer, the interactive layer and the output layer data, performs stratification and fusion according to the data characteristics, ensures the quality and fusion accuracy of the collected data, thereby establishing a complete multi-source data fusion system, and can obtain the spatial distribution characteristics of bus passenger flow and bus OD information. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 A flowchart of a method for analyzing bus operation characteristics based on multi-source data fusion and OD information derivation provided by an embodiment of the present invention;

[0063] Figure 2 A multi-source data interactive fusion analysis diagram provided by an embodiment of the present invention;

[0064] Figure 3 A schematic diagram of the structure of a bus operation characteristics analysis system based on multi-source data fusion and OD information derivation provided by an embodiment of the present invention;

[0065] Figure 4 A topological structure diagram of Ganzhou map data provided by an embodiment of the present invention;

[0066] Figure 5 A distribution diagram of the bus operation time throughout the day on the K2 route provided by an embodiment of the present invention;

[0067] Figure 6 A distribution diagram of the delay time of each bus trip in a week provided by an embodiment of the present invention;

[0068] Figure 7 A distribution diagram of the running deviations of each bus trip in a week provided by an embodiment of the present invention;

[0069] Figure 8 A scatter plot of the time periods of the bus running time throughout the day provided by an embodiment of the present invention;

[0070] Fig. 9 The inter-station travel time distribution diagram of "Railway Station-Ganjiangyuan Avenue East" and "Ganjiangyuan Avenue East-Wulong Bridge" provided in the embodiment of the present invention;

[0071] Fig.10 The stop time distribution diagram of the "Railway Station" and "Ganjiangyuan Avenue East" provided in the embodiment of the present invention;

[0072] Fig.11 A distribution diagram of the impact of the presence or absence of an intersection on the travel time of a road section provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Example 1

[0075] like Figure 1 As shown, this embodiment provides a bus operation characteristics analysis method based on multi-source data fusion and OD information derivation, including:

[0076] Step 101, determine the total number of actual bus stops.

[0077] In this step, if Figure 2 As shown in the figure, multi-source data is divided into three layers: basic layer, interaction layer and output layer, which mainly contain the following information:

[0078] The basic layer information includes: bus stop information data (the bus stop number, station name, longitude and latitude of the station, etc. can be obtained); bus operation statistics (the operating schedule and operating mileage can be obtained, which is convenient for analyzing the driver's schedule and vehicle turnover, and the GPS data of a single route can be extracted from the bus GPS data through the license plate number); city map data (the operation trajectory of the bus on the road can be obtained by matching the bus GPS data and map data; in the process of traffic simulation, the map data can be imported to build the basic road network) and bus "APP" data (the specific information of the travel route can be obtained, including the route, station, license plate number, etc.).

[0079] The interactive layer information includes: IC card data (obtaining the mapping relationship between bus license plate numbers and routes, integrating IC card data with departure and arrival times, and obtaining IC card swiping data for each bus and each station), mobile payment data (obtaining passengers' travel starting point and travel frequency information; obtaining information such as the proportion of bus travel in all modes of travel on the same travel route) and GPS data (through the one-way operating time of different buses and drivers, it is convenient to obtain vehicle one-way delay information, and the station distance can be calculated based on the GPS points on the route).

[0080] The output layer information includes: OD information derivation, simulation and visual analysis. Due to the different granularity and dimensions of data information fusion at each layer, various types of information at the basic layer belong to data-level and feature-level fusion, and the interactive layer information needs to be associated and fused for individual decisions, which is feature-level and decision-level fusion. This time, multi-source data cross-fusion is used to optimize the OD attraction intensity and improve its accuracy, paving the way for OD information derivation.

[0081] On the basis of the basic layer information, the interactive layer information (IC card data, mobile payment data, GPS data) can obtain the passenger OD information. Since the three types of interactive layer information are repeated, missing, and erroneous, in order to improve the accuracy of OD information, this paper, based on the DS theoretical evidence method, combines information entropy and JS divergence matrix to obtain the weight of each information, so as to obtain the weighted average value to perform data fusion on various types of data. This method can efficiently solve the conflict of data information and improve the accuracy of data fusion.

[0082] Exemplarily, the information entropy of the target information among the three types of information is calculated according to the following formula:

[0083]

[0084] Among them, the three types of information include station departure path information obtained from IC card data, mobile phone payment data and GPS data; E di is the information entropy of the i-th type of information; A i Represents an event of the i-th type of information; |A i| indicates included elements; m(·) indicates mass function; R indicates the sample space set.

[0085] The difference between the two types of information is calculated according to the following formula:

[0086]

[0087] Among them, d ij is the difference between the i-th information and the j-th information, j≠i; e is a natural constant; |·| means taking the absolute value; E dj is the information entropy of the j-th type of information.

[0088] The difference between the two types of information is calculated according to the following formula:

[0089]

[0090] Among them, ED(m i ,m j ) is the i-th type of information m i With the jth type of information m j The difference between ij is d ij The reciprocal of .

[0091] The divergence between two types of information is calculated according to the following formula:

[0092]

[0093] Among them, JS(m i ,m j ) is the divergence between the i-th information and the j-th information.

[0094] The average divergence of each type of information is calculated according to the following formula:

[0095]

[0096] in, is the average divergence of the i-th category information.

[0097] The credibility of each type of information is calculated according to the following formula:

[0098]

[0099] Among them, C d (m i ) is the credibility of the i-th type of information; S d (m i )for The reciprocal of .

[0100] The difference information of each type of information is calculated according to the following formula:

[0101] DI(m i )=C d (m i )·ED(m i ,m j ).

[0102] Among them, DI (m i ) is the difference information amount of the i-th category information.

[0103] The weights of various types of information are calculated according to the following formula:

[0104]

[0105] Among them, W i is the weight of the i-th category information.

[0106] The total number of actual bus stops N is calculated according to the following formula:

[0107]

[0108] Among them, N i is the total number of boarding stops collected from the i-th type of information.

[0109] Step 102, determining a critical value of the passenger riding time difference according to the total number of bus stops actually made.

[0110] In this step, the critical value of the boarding time difference is used to distinguish whether the boarding records corresponding to different boarding times are at the same stop, so as to determine whether the boarding stops corresponding to different boarding times are the same. The critical value is defined as follows: when the difference between the two boarding times is greater than the critical value, it indicates that the corresponding boarding stops are different; when the difference between the two boarding times is less than the critical value, it indicates that the corresponding boarding stops are the same. The judgment and selection of the critical value will directly affect the judgment of the passenger's boarding stop.

[0111] The total number of boarding stations P is mainly composed of the number of IC card swipes and the number of mobile phone payment methods. The payment time is different and there are duplications and omissions (currently there are few coin-operated people at each station, which are ignored in the calculation). After differentiating, cross-cutting, decision-making and integration of various types of data, the actual total number of boarding stations is N. Therefore, based on the total number of bus stops that have been investigated, the critical value of the boarding time difference is determined by comparing P and N.

[0112] Exemplarily, this step includes:

[0113] Get the minimum travel time difference as the initial travel time difference critical value t min .

[0114] The total number of theoretical bus stops P is calculated according to the following formula:

[0115] P=ΣP s +ΣP t .

[0116] If the difference in travel time between two adjacent passengers is less than t min , then it is determined that the two adjacent passengers boarded the bus at the same station, and the number of stations where the two adjacent passengers boarded the bus at the same station is recorded as P s ; If the difference in travel time between two adjacent passengers is greater than or equal to t min , then it is determined that the two adjacent passengers boarded the bus at different stations; and the number of stations where the two adjacent passengers boarded the bus at different stations is recorded as P t ;P t =[T / 2t min ]; [·] means rounding to the nearest integer.

[0117] Determine whether the total number of theoretical bus stops P is less than the total number of actual bus stops N.

[0118] If it is less than, the initial travel time difference critical value t min As the critical value of the passengers' travel time difference.

[0119] If it is not less than, then the time difference between the adjacent passengers when they board the bus will be divided by t min The minimum value outside the above is taken as the critical value of the passengers' travel time difference.

[0120] Step 103, determining the total number of passengers getting off at each station.

[0121] Through the derivation of boarding and alighting stations, this embodiment establishes a set through the following four hypothetical scenarios, and the basic assumptions are as follows:

[0122] Assumption 1: When the bus travel chain is closed, the passengers’ boarding and alighting stops overlap.

[0123] Hypothesis 2: When the bus travel chain is slightly closed, the passenger's next boarding station appears near the downstream station of the line;

[0124] Hypothesis 3: When Hypothesis 1 and Hypothesis 2 are not met, passengers' alighting stations are generally stations where they boarded the bus many times in recent days, and the attraction rate of passengers alighting at stations where they boarded the bus many times in recent days is positively correlated with the number of boarding times.

[0125] Assumption 4: When the set of recent multiple boarding stations is also unavailable, it is assumed that passengers will get off at downstream stations with high passenger flow, and the probability of getting off is proportional to the passenger flow of the station.

[0126] To facilitate the construction of the model, assume that a passenger gets on the bus at station i' and takes the bus in the downstream direction. Based on this, the following four sets are established.

[0127] Set C1 = {i'+1, i'+2, ..., N'}, which represents the set of bus stops whose alighting stops are located in the downstream direction of stop i'; a closed travel chain is formed.

[0128] Set C2 = {i”+1, i”+2,…, N”}, which means that the alighting station is located in the set of nearby bus stops within 1100 meters of set C, forming a micro-closed travel chain.

[0129] Set C3: Select a period of recent days and organize the boarding stations into multiple boarding station sets. The number of boardings at the stations in set C3 should be greater than the preset number threshold.

[0130] Set C4: A set of downstream sites with large passenger flow. The passenger flow value of the site in the high passenger flow density site set C4 should be greater than the preset passenger flow density threshold.

[0131] In this step, the total number of passengers getting off at each station is calculated illustratively according to the following formula:

[0132] V j' =L j' ·P j' .

[0133] Among them, P j' is the probability of passengers getting off at station j'; if station j' belongs to a station in a closed travel chain or a micro-closed travel chain, then P j' =1, if station j' does not belong to the station in the closed travel chain and micro-closed travel chain, then P j' =0.

[0134] The station with a boarding frequency greater than the preset threshold in the closed travel chain is regarded as the first station; the station with a passenger flow density greater than the preset passenger flow density threshold in the closed travel chain is regarded as the second station; if station j' is the first station and the second station, that is, j'∈{C1∩C3}∪{C4}, then

[0135]

[0136] Among them, M j' is the number of boarding stops at station j'; n0 is the total number of first stations in the closed travel chain; M n' is the number of boarding stops at station n'; n'=1,2,…,n0.

[0137] Step 104, determining the total number of boarding passengers at each station based on the critical value of the passenger boarding time difference and the total number of passengers getting off at each station.

[0138] For example, this step needs to determine the travel time difference T corresponding to two adjacent passengers. j' Is it greater than the passenger's travel time difference t? minThe critical value of .

[0139] If it is greater, the total number of boarding passengers at each bus stop is calculated according to the following formula:

[0140]

[0141] If it is not greater than, the total number of boarding passengers at each bus stop is calculated according to the following formula:

[0142] N j' =L j'+1 -L j' +V j' .

[0143] Among them, N j' is the number of passengers boarding at station j'; L j'+1 is the passenger capacity of station j'+1; L j' is the passenger capacity of station j'; V j' is the number of passengers getting off at station j'.

[0144] Step 105, based on the total number of boarding passengers and the total number of alighting passengers at each station, determine the running time of the bus from the starting station to the terminal station, the total stop time at all stations, and the waiting time at intersections, so as to perform a visual analysis on the delay propagation of the spatial characteristics of bus operation.

[0145] In this step, illustratively, the running time T1 of the bus from the starting station to the terminal station is calculated according to the following formula:

[0146]

[0147] Where u is the total number of stations on the route from the starting station to the terminal station; L j' is the passenger capacity of station j'; ta j'+1 is the arrival time of the bus at the j'+1th stop; td j' is the departure time of the bus at the j'th station.

[0148] The total stop time T2 of the bus from the starting station to the terminal station is calculated according to the following formula:

[0149]

[0150] Among them, κ j' is the bus’s stop time at station j’; μ is the time it takes for the bus to open and close its doors and accelerate and decelerate when entering and leaving the station; The average boarding or alighting time for each passenger.

[0151] The waiting time T3 of the bus at the intersection on the route from the starting station to the terminal station is calculated according to the following formula:

[0152]

[0153] Among them, q is the total number of intersections on the route from the starting station to the terminal station; is the waiting time of the bus at intersection x.

[0154] In order to make the solution of the present invention clearer, specific examples are further disclosed in the embodiments of the present invention.

[0155] The basic data for this study comes from the regular bus route K2 operation data of Ganzhou City in June 2022. The number of stops u is 12; the number of intersections q is 4; the average remaining waiting time for vehicles to arrive at intersection x is 5s; the average boarding and alighting time t for each passenger is 2s; the average time μ for bus door opening and closing and acceleration and deceleration in and out of the station is 30s. The random sampling size in K-means cluster analysis is 100; the departure and arrival times of vehicles are obtained from operational statistical data.

[0156] The main sources of information are as follows:

[0157] 1. City Card System Data

[0158] The Ganzhou City Card System includes IC card ride data (including ordinary cards, student cards, discount cards, etc.); mobile payment receipts (including UnionPay code, Ganzhou Tong code, Meituan payment and Alipay payment and other forms of data). Tables 1 and 2 are part of the daily report data of IC card and mobile payment vehicle settlement on June 6, 2022 on the K2 line.

[0159] The Ganzhou City Bus IC data used this time is stored as a CSV file. After screening, the main fields retained are as follows:

[0160] CZJBH: vehicle-mounted device number; SJ: driver number; XLBH: line number; YYSJ: operating hours; FXKH: issued card number; MKLX: mother card type; ZKLX: child card type; DJ: unit price; YE: balance; JYLX: transaction type; WLKH: physical card number.

[0161] Table 1 Ganzhou City Public Transportation IC Card Partial Data Table

[0162] JbXj SJ XLB YJ FXKH MKLX ZL DJ YE JLX Wlq 1528 4263 100100310 2022-6-617:03 1036933 22 10 1 1 13 0036E484 1528 4263 100100310 2022-6-68:52 1118434 22 10 1 11 13 B823F082 1528 4263 100100310 2022-6-69:51 1077389 22 10 1 166 13 67B9503D 1528 4263 100100310 2022-6-619:22 1082449 22 10 1 1 13 4CA0A43C 1528 4263 100100310 2022-6-619:22 1082449 22 10 1 0 13 4CA0A43C 1528 4263 100100310 2022-6-619:30 1017924 22 10 1 42 13 B3F11CE6 1528 4263 100100310 2022-6-619:30 1017924 22 10 1 41 13 B3F11CE6 1528 4263 100100310 2022-6-619:30 1017924 22 10 1 40 13 B3F11CE6

[0163] This time, the mapping relationship between the user card number, the vehicle machine number and the payment method in the mobile payment data is mainly used to obtain the riding information. The Ganzhou public transportation mobile payment data used in this case is stored as a CSV file. After screening, the main fields retained are as follows:

[0164] SIM: user card number; CZJBH: vehicle-mounted device number; JYLX: transaction type; TIME: collection time; DJ: deduction; XLBH: line number.

[0165] Table 2 Partial data of mobile phone payment for public transportation in Ganzhou City

[0166] SIM JbXj JLX TIME DJ XLB <![CDATA[ _ ]]> 1528 10 2022-6-617:10 1 100100310 <![CDATA[ _ ]]> 1528 12 2022-6-68:50 1 100100310 <![CDATA[ _ ]]> 1528 12 2022-6-69:55 2 100100310 <![CDATA[ _ ]]> 1528 12 2022-6-619:15 1 100100310 <![CDATA[ _ ]]> 1528 10 2022-6-619:18 1 100100310 <![CDATA[ _ ]]> 1528 10 2022-6-619:27 2 100100310 <![CDATA[ _ ]]> 1528 11 2022-6-619:19 1 100100310 <![CDATA[ _ ]]> 1528 11 2022-6-619:40 1 100100310

[0167] As shown in Table 3, this is the partial GPS data of buses in Ganzhou City on June 6, 2022. The data is saved in CSV format, and the sampling interval varies due to the differences in data collection equipment on the bus. CSV is a comma-separated value file consisting of any number of records, separated by some kind of line break; each record consists of fields, and this GPS data consists of the following data segments:

[0168] VID: vehicle number; RECVTIME: recording time; LONGITUDE: longitude; LATITUDE: latitude; SPEED: driving speed; DIRECTION: running direction angle; VALID: information validity.

[0169] Table 3 GPS data table of public transportation in Ganzhou City

[0170] VID RECVTIME LONGITUDE LATITUDE SPEED DIRECTION VALID 05180D 2022-6-620:34 113.1264 22.9802 5 140 1 05180D 2022-6-620:36 113.126 22.98052 12 60 1 05180D 2022-6-620:39 113.1253 22.9803 12 70 1 05180D 2022-6-620:40 113.1246 22.9803 11 70 1 05180D 2022-6-620:43 113.1241 22.98008 7 80 1

[0171] As shown in Table 4, this is some statistical data of K2 to the departure station. From this table, we can get the departure and arrival time, direction and GPS mileage of different lines and different trains at different stations, which is convenient for analyzing the early arrival and late arrival information of vehicles at each station.

[0172] Table 4K2 to departure station partial statistical data table

[0173] line Vehicle number One-way number time To and from Station Name GPS mileage / km Recording time <![CDATA[K2]]> 05180D 1 2022-6-66:28 Arrival TRAIN STATION 28699.97 2022-6-66:28 <![CDATA[K2]]> 05180D 1 2022-6-66:30 Departure TRAIN STATION 28700.01 2022-6-66:30 <![CDATA[K2]]> 05180D 2 2022-6-66:31 Arrival Ganjiangyuan Avenue 28700.21 2022-6-66:31 <![CDATA[K2]]> 05180D 2 2022-6-66:32 Departure Ganjiangyuan Avenue 28700.32 2022-6-66:32 <![CDATA[K2]]> 05180D 3 2022-6-66:33 Arrival Wulong Bridge 28700.99 2022-6-66:33 <![CDATA[K2]]> 05180D 3 2022-6-66:34 Departure Wulong Bridge 28701.13 2022-6-66:34 <![CDATA[K2]]> 05180D 4 2022-6-66:36 Arrival Ganjiangyuan Avenue 28701.69 2022-6-66:36 <![CDATA[K2]]> 05180D 1 2022-6-66:37 Arrival TRAIN STATION 64644.57 2022-6-66:37

[0174] 2. Bus stop information data

[0175] The bus stop information data of Ganzhou City is shown in Table 5, including the stop number, stop name, LCD display, longitude and latitude of the stop, line name, one-way line nature, platform form and signboard style, etc., totaling 1441 pieces of information, from which detailed information of the stops along the K2 line can be screened.

[0176] Table 5 Ganzhou bus station information partial data table

[0177] Site Number Site Name LCD Display longitude latitude line Platform form Stop sign style 10010012 Ferry Road Ferry Road 114.953142 25.85306 7th Road Harbor Style Standing Pose 10010014 Ferry Road Ferry Road 114.953513 25.852832 Harbor Style Standing Pose 10010022 Jiafu International Community Jiafu International Community 114.960104 25.852638 7th Road Harbor Style Standing Pose 10010022 Jiafu International Community Jiafu International Community 114.959311 25.853073 Harbor Style Standing Pose 10010024 Nanyang Dongsheng Community Nanyang Dongsheng Community 114.955896 25.853021 7th Road Harbor Style Standing Pose 10010032 Nanyang Dongsheng Community Nanyang Dongsheng Community 114.956133 25.852898 Harbor Style Standing Pose 10010034 Dukou Road East Dukou Road East 114.964761 25.851699 7th Road Harbor Style Standing Pose 10010042 Dukou Road East Dukou Road East 114.9648 25.851267 Harbor Style Standing Pose

[0178] 3. Public transport operation statistics

[0179] The bus operation production statistics are shown in Table 6, including line vehicle numbers, regular schedules (planned schedules, departing schedules and completed schedules), normal mileage, passenger mileage, non-operating mileage (refueling mileage, maintenance mileage, return mileage), operating mileage, operating schedules (planned schedules, departing schedules and completed schedules) and chartered schedules.

[0180] Table 6 Partial data table of K2 line operation and production statistics in June 2022

[0181]

[0182] The operation production statistics table can be used to obtain the operation shifts and operation mileage, which is convenient for analyzing the driver's scheduling and vehicle turnover, and extracting the GPS data of a single route from the bus GPS data through the license plate number.

[0183] 4. Ganzhou map data

[0184] The Shape file of Ganzhou map data consists of three sub-files: ".shp", ".shx" and ".dbf", which constitute the complete geographic information data. The topological structure of the Shape file of Ganzhou map is displayed in ArcMap as follows: Figure 4 As shown, you can view specific routes and road names, etc. The bus running track can be obtained by combining GPS data and map data. At the same time, the map data can be imported into the basic road network for traffic simulation.

[0185] First, Matlab is used to filter and process the massive traffic data to remove redundant data. During the study period, the proportion of passengers swiping cards accounted for a large proportion, and the passenger arrival rate followed the Poisson distribution, which can represent the OD travel chain characteristics. Therefore, based on the bus OD identification method, the output layer information station spacing and passenger OD distribution during the study period were obtained. The distance matrix between the 12 stations is shown in Table 7. According to the stage one interactive layer fusion, the number of passengers getting on and off at the same station j' can be obtained. j' and V j' For example, 10 vehicles are randomly selected from the period 8:00-9:00, and the passenger boarding and alighting situations of each vehicle at each station are shown in Table 8.

[0186] Table 7 Example station distance (m)

[0187] Site <![CDATA[S1]]> <![CDATA[S2]]> <![CDATA[S3]]> <![CDATA[S4]]> <![CDATA[S5]]> <![CDATA[S6]]> <![CDATA[S7]]> <![CDATA[S8]]> <![CDATA[S9]]> <![CDATA[S 10 ]]> <![CDATA[S 11 ]]> <![CDATA[S 12 <!-- 11 -->]]> <![CDATA[S1]]> — 199 236 534 864 1062 1319 2180 3020 3452 4426 5520 <![CDATA[S2]]> — — 117 415 745 943 1200 2061 2901 3333 4307 5401 <![CDATA[S3]]> — — — 298 628 826 1083 1944 2784 3216 4190 5284 <![CDATA[S4]]> — — — — 330 528 785 1646 2486 2918 3892 4986 <![CDATA[S5]]> — — — — — 198 455 1316 2156 2488 3562 4656 <![CDATA[S6]]> — — — — — — 257 1118 1958 2390 3364 4458 <![CDATA[S7]]> — — — — — — — 861 1701 2133 3107 4201 <![CDATA[S8]]> — — — — — — — — 840 1272 2246 3340 <![CDATA[S9]]> — — — — — — — — — 432 1406 2500 <![CDATA[S 10 ]]> — — — — — — — — — — 974 2068 <![CDATA[S 11 ]]> — — — — — — — — — — — 1094 <![CDATA[S 12 ]]> — — — — — — — — — — — —

[0188] Table 8 Passenger boarding and alighting situations of 10 vehicles at various stations

[0189]

[0190] 5. Bus operation time division and cluster analysis

[0191] Based on the basic layer departure and arrival information, the regularity of bus travel time periods was explored, and the operating time data of Ganzhou City K2 Line on June 6, 2022 (Monday) was selected. The K2 line is a ring (the starting and ending points are "train stations"), with the horizontal axis representing the departure time (6:30-22:30) and the vertical axis representing the total running time. Trend analysis shows that there are two obvious peaks in the running time of the day, that is, the driving time fluctuates greatly, which are in the two time periods of 7:28-9:25 and 16:34-18:20, which conforms to the objective law of the morning and evening rush hours on urban weekdays. The bus runs relatively smoothly in other operating time periods, such as Figure 5 shown.

[0192] The K2 line has an average of 114 trips per day. Figure 6 As shown in the figure, it can be seen that the delay time of each bus trip from June 2022 to December 2022 is evenly distributed. The delay time of each station on June 2022 (Monday) is selected. Figure 7 The standard deviation analysis shown shows that the delay of each trip is around 1500s.

[0193] This embodiment uses the data of K2 Road in Ganzhou City on Monday, June 6, 2022 and June 10 (Friday) as the sample set, and obtains the sample clustering center through the K-means clustering algorithm, as shown in Table 9.

[0194] Table 9 Final cluster center table

[0195] category 1 2 3 4 5 6 time 7:55:10 10:35:30 13:10:25 15:20:17 18:15:41 20:30:21 Duration 53:30 52:18 43:45 39:45 45:28 54:26

[0196] Based on the clustering results, a scatter plot of the bus running time throughout the day is drawn, where the horizontal axis represents the departure time, the vertical axis represents the actual running time, and the six colors represent the clustering categories. Based on the passenger travel habits, the bus running time throughout the day is divided into six time periods with morning, noon and afternoon as the three main time nodes, such as Figure 8 shown.

[0197] It can be seen from the figure that the scattered points of various colors show a certain clustering effect. Combined with the running time, the bus running time period throughout the day can be divided, as shown in Table 10.

[0198] Category 1 corresponds to the morning peak period, which is from 6:30 to 9:15.

[0199] Category 2 means the off-peak period in the morning after the morning peak, with the specific time range being 9:15 to 11:20.

[0200] Category 3 represents the off-peak period at noon, with a specific time range of 11:20 to 14:28.

[0201] Category 4 represents the afternoon off-peak period, ranging from 14:28 to 16:16.

[0202] Category 5 corresponds to the evening peak period, with a specific time range from 16:16 to 19:20.

[0203] Category 6 indicates the period after the evening peak and into the evening off-peak period, with the specific time range being 19:20 to 22:30.

[0204] Table 10 Time period division results

[0205] category Time period 1 6:30～9:15 2 9:15～11:20 3 11:20～14:28 4 14:28～16:16 5 16:16～19:20 6 19:20～22:30

[0206] The above cluster analysis results show the distribution characteristics of the single-line bus operation time throughout the day, with a focus on the clustering effect of traffic flow on weekdays. It can be seen that traffic flow at different time periods affects the duration of bus operation. Therefore, when implementing targeted bus operation strategies, it is necessary to consider the impact of time period division on bus operation.

[0207] 6. Visualization analysis of delay propagation of bus operation spatial characteristics

[0208] Bus trips have spatial correlations at stops, intersections, and road sections. To explore this influencing feature, this study uses the data of K2 Road in Ganzhou City on June 6, 2022 (Monday) as the research object, and conducts a visual analysis based on the OD information obtained from the interaction layer.

[0209] (1) Travel time between stations

[0210] Ganzhou K2 Line has 114 departures throughout the day. The "Railway Station" is the starting and ending station of the loop line. The two consecutive sections of "Railway Station-Ganjiangyuan Avenue East" and "Ganjiangyuan Avenue East-Wuling Bridge" were selected as the research object on June 6, 2022. Fig. 9 shown.

[0211] from Fig. 9 It can be seen that when the trains on Ganzhou City K2 line run continuously on the two sections of "Railway Station-Ganjiangyuan Avenue East" and "Ganjiangyuan Avenue East-Wulong Bridge", the inter-station travel time trends of the upstream section "Railway Station-Ganjiangyuan Avenue East" and the downstream section "Ganjiangyuan Avenue East-Wulong Bridge" are similar, that is, the inter-station travel time of the downstream section "Ganjiangyuan Avenue East-Wulong Bridge" fluctuates significantly with the upstream section "Railway Station-Ganjiangyuan Avenue East".

[0212] (2) Stop time

[0213] The stop times of the two stations "Railway Station" and "Ganjiangyuan Avenue East" on Line K2 in Ganzhou City on June 6, 2022 were selected as the research object. "Ganjiangyuan Avenue East" is the next station of "Railway Station". The basic settings of the two stations are similar. Fig.10 shown.

[0214] from Fig.10 It can be seen that when the K2 line in Ganzhou City passes through the "Railway Station" and "Ganjiangyuan Avenue East", there will be certain fluctuations at the corresponding stations, that is, the stop time will be too long (peak) or too short (trough) at the station, and the downstream station "Ganjiangyuan Avenue East" will be affected by the stop time fluctuations of the upstream station "Railway Station", and the fluctuation trends show a certain regular similarity, indicating that the arrival time of each station is interrelated and coupled.

[0215] (3) Intersection waiting time

[0216] Taking "Telecom Building Bus Station - Dongyangshan Jianqiang Department Store Bus Station" (no intersection between stations) and "Dongyangshan Road Bus Station - Municipal Administrative Service Center Bus Station" (the section between stations contains 1 intersection) as examples, the influence of intersection waiting time on line operation is presented. Fig.11 shown.

[0217] from Fig.11 It can be seen that the section between "Telecom Building Bus Station - Dongyangshan Jianqiang Department Store" has no intersection signal lights, so there is no need to wait for red lights. The travel time of each bus through this section is stable, and the curve shows a gentle trend; while the section between "Dongyangshan Road Bus Station - Municipal Administrative Service Center Bus Station" contains one intersection, and the travel time of each bus is affected by signal lights, which fluctuates greatly, and the curve shows a peak and trough state. It can be seen that the setting of intersection signal lights has a great impact on the travel between stations, which directly affects the accuracy of the vehicle arrival time.

[0218] In summary, this embodiment provides a bus operation characteristics analysis method based on multi-source data fusion and OD information derivation, which stratifies multi-source data (basic layer, interactive layer and output layer information), and uses information entropy to obtain the relative importance of interactive layer IC data, mobile payment data, and GPS data information based on the DS theoretical evidence method, and combines JS divergence to obtain information credibility. The two are combined to obtain information weight, thereby improving interactive layer data fusion and decision-making accuracy.

[0219] Based on the actual number of boarding stops obtained by multi-source data fusion, combined with IC card data and mobile phone payment data, the critical value of the boarding time difference is used to solve the number of boarding stops and the number of passengers boarding at each stop; the travel chain method and attraction method are used to solve the number of alighting stops and the number of passengers alighting at each stop, thereby improving the accuracy of the output layer OD information.

[0220] Based on the OD information obtained by the interaction layer fusion, combined with the station stop time, section operation time and intersection stop time, the delay propagation of the spatial characteristics of bus operation is visualized and analyzed. Through data mining and visualization analysis, the development trend and future information of bus passenger flow can be obtained, providing strong support for bus operation strategies.

[0221] Example 2

[0222] Based on the same inventive concept as Example 1, this embodiment provides a bus operation characteristics analysis system based on multi-source data fusion and OD information derivation. Since the principle of solving the problem by this system is similar to the aforementioned bus operation characteristics analysis method based on multi-source data fusion and OD information derivation, the implementation of this system can refer to the implementation of the bus operation characteristics analysis method based on multi-source data fusion and OD information derivation.

[0223] like Figure 3 As shown in the figure, the bus operation characteristics analysis system based on multi-source data fusion and OD information derivation includes:

[0224] The first determination module 10 is used to determine the total number of actual bus stop sites.

[0225] The second determination module 20 is used to determine the critical value of the passenger riding time difference according to the total number of actual bus stop sites.

[0226] The third determining module 30 is used to determine the total number of passengers getting off at each station.

[0227] The fourth determination module 40 is used to determine the total number of boarding passengers at each station according to the critical value of the passenger riding time difference and the total number of passengers getting off at each station.

[0228] The fifth determination module 50 is used to determine the running time of the bus from the starting station to the terminal station, the total stop time of all stations and the waiting time at intersections according to the total number of boarding passengers and the total number of alighting passengers at each station, so as to perform a visual analysis on the delay propagation of the spatial characteristics of bus operation.

[0229] For more specific working processes of the above modules, please refer to the corresponding contents disclosed in Example 1, which will not be repeated here.

[0230] Example 3

[0231] This embodiment provides a computer device, including a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the steps of the bus operation characteristics analysis method based on multi-source data fusion and OD information derivation described in Example 1 are implemented.

[0232] For more specific details of the above method, please refer to the corresponding contents disclosed in Example 1, which will not be repeated here.

[0233] Example 4

[0234] This embodiment provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the bus operation characteristics analysis method based on multi-source data fusion and OD information derivation described in Example 1 are implemented.

[0235] For more specific details of the above method, please refer to the corresponding contents disclosed in Example 1, which will not be repeated here.

[0236] Example 5

[0237] This embodiment provides a computer program product, including computer executable instructions or a computer program. When the computer executable instructions or the computer program are executed by a processor, the steps of the bus operation characteristics analysis method based on multi-source data fusion and OD information derivation described in Example 1 are implemented.

[0238] For more specific details of the above method, please refer to the corresponding contents disclosed in Example 1, which will not be repeated here.

[0239] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the systems, devices, storage media, and computer program products disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0240] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention or some parts of the embodiments.

[0241] In some embodiments, computer executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0242] As an example, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0243] As an example, computer executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed at multiple sites and interconnected by a communication network.

[0244] The present invention has been described in detail above in conjunction with specific implementations and exemplary examples, but these descriptions cannot be understood as limiting the present invention. Those skilled in the art understand that, without departing from the spirit and scope of the present invention, a variety of equivalent substitutions, modifications or improvements may be made to the technical solution of the present invention and its implementation methods, all of which fall within the scope of the present invention. The scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A bus operation characteristics analysis method based on multi-source data fusion and OD information derivation, characterized in that: include: Determine the total number of actual bus stops; Determine the critical value of the passenger travel time difference based on the total number of bus stops; Determine the total number of passengers alighted at each stop; The total number of boarding passengers at each station is determined according to the critical value of the passenger boarding time difference and the total number of passengers getting off at each station; According to the total number of boarding passengers and alighting passengers at each station, the running time of the bus from the starting station to the terminal station, the total stop time of all stations and the waiting time at intersections are determined to visualize the delay propagation of the spatial characteristics of bus operation; Wherein, the determining of the total number of actual bus stop sites includes: The total number of actual bus stops N is calculated according to the following formula: ； Among them, the three types of information include station departure path information obtained from IC card data, mobile phone payment data and GPS data; i is the weight of the i-th type of information; N i is the total number of boarding stops collected from the i-th type of information; The weight of the i-th category of information is calculated according to the following formula: ； Among them, DI (m i ) is the difference information amount of the i-th type of information; The difference information of the i-th category information is calculated according to the following formula: My husband i )=C d (I i )·ED(m i ,I j ); Among them, C d (m i ) is the credibility of the i-th type of information; ED(m i ,m j ) is the i-th type of information m i With the jth type of information m j The difference between The credibility of the i-th type of information is calculated according to the following formula: ； Among them, S d (m i ) is the inverse of the average divergence of the i-th type of information; JS(m i ,m j ) is the divergence between the i-th information and the j-th information; Calculate the i-th category information m according to the following formula i With the jth type of information m j Difference between: ； Among them, D ij is the reciprocal of the difference between the i-th information and the j-th information; j≠i; e is a natural constant; |·| means taking the absolute value; E dj is the information entropy of the j-th type of information; E di is the information entropy of the i-th type of information; A i Represents an event of the i-th type of information; |A i | indicates included elements; m(·) indicates mass function; R indicates the sample space set.

2. The method for analyzing bus operation characteristics according to claim 1, characterized in that: Determining the critical value of the passenger travel time difference according to the total number of bus stops actually made comprises: Get the minimum travel time difference as the initial travel time difference critical value t min ; The total number of theoretical bus stops P is calculated according to the following formula: ； If the difference in travel time between two adjacent passengers is less than t min , then it is determined that the two adjacent passengers boarded the bus at the same station, and the number of stations where the two adjacent passengers boarded the bus at the same station is recorded as P s ; If the difference in travel time between two adjacent passengers is greater than or equal to t min , then it is determined that the two adjacent passengers boarded the bus at different stations; and the number of stations where the two adjacent passengers boarded the bus at different stations is recorded as P t ;P t =[T / 2t min ]; [·] means rounding to the nearest integer; Determine whether the total number of theoretical bus stops P is less than the total number of actual bus stops N; If it is less than, the initial travel time difference critical value t min As the critical value of the passenger's travel time difference; If it is not less than, then the time difference between the adjacent passengers when they board the bus will be divided by t min The minimum value outside the above is taken as the critical value of the passengers' travel time difference.

3. The method for analyzing bus operation characteristics according to claim 1, characterized in that: Determining the total number of passengers getting off at each station includes: The total number of passengers getting off at each stop is calculated using the following formula: V j' =L j' ·P j' ; Among them, P j' is the probability of passengers getting off at station j'; if station j' belongs to a station in a closed travel chain or a micro-closed travel chain, then P j' =1, if station j' does not belong to the station in the closed travel chain and micro-closed travel chain, then P j' =0; The station with a boarding frequency greater than a preset threshold in the closed travel chain is regarded as the first station; the station with a passenger flow density greater than a preset passenger flow density threshold in the closed travel chain is regarded as the second station; if station j' is the first station and the second station, then ； Among them, M j' is the number of boarding stops at station j'; n0 is the total number of first stations in the closed travel chain; M n' is the number of boarding stops at station n'; n'=1,2,…,n0.

4. The method for analyzing bus operation characteristics according to claim 3, characterized in that: Determining the total number of boarding passengers at each station according to the critical value of the passenger boarding time difference and the total number of passengers getting off at each station includes: Determine the travel time difference T between two adjacent passengers j' Is it greater than the passenger's travel time difference t? min The critical value of If it is greater, the total number of boarding passengers at each bus stop is calculated according to the following formula: ； If it is not greater than, the total number of boarding passengers at each bus stop is calculated according to the following formula: N j' =L j'+1 -L j' +V j' ; Among them, N j' is the number of passengers boarding at station j'; L j'+1 is the passenger capacity of station j'+1; L j' is the passenger capacity of station j'; V j' is the number of passengers getting off at station j'.

5. The method for analyzing bus operation characteristics according to claim 4, characterized in that: According to the total number of boarding passengers and alighting passengers at each station, the running time of the bus from the starting station to the terminal station, the total stop time of all stations and the waiting time at intersections are determined to visualize the delay propagation of the spatial characteristics of bus operation, including: The running time T1 of the bus from the starting station to the terminal station is calculated according to the following formula: ； Where u is the total number of stations on the route from the starting station to the terminal station; L j' is the passenger capacity of station j'; ta j'+1 is the arrival time of the bus at the j'+1th stop; td j' is the departure time of the bus at the j'th station; The total stop time T2 of the bus from the starting station to the terminal station is calculated according to the following formula: ； Among them, κ j' is the bus’s stop time at station j’; μ is the time for the bus to open and close doors and accelerate and decelerate when entering and leaving the station; The average time it takes for each passenger to get on or off the bus; The waiting time T3 of the bus at the intersection on the route from the starting station to the terminal station is calculated according to the following formula: ； Among them, q is the total number of intersections on the route from the starting station to the terminal station; is the waiting time of the bus at intersection x.

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