A subway ticket revenue allocation method considering passenger behavior inertia

Abstract

The present invention discloses a method for clearing subway ticket revenue that takes into account the inertia of passenger behavior, including first constructing an inertia identification process based on actual application data to identify inertial routes and inertial passengers; secondly, obtaining the total revenue of two types of routes and the total number of two types of passengers based on historical data, dividing the two by the total revenue of subway operations and the total number of passengers, respectively, to measure the impact of inertial behavior in daily operations; finally, combining the mileage route revenue distribution ratio to provide a calculation model for the revenue clearing ratio. The technical solution proposed in the present invention characterizes the inertial behavior of passengers during their actual ride, which is more in line with the actual passenger flow revenue distribution needs. At the same time, using historical passenger flow data and according to the actual OD occurrence status, the passenger travel portrait is fully excavated, providing a scientific basis for operation scheduling and safety emergency response.

Description

Technical Field

[0001] The present invention relates to the field of urban rail transit, and in particular to a method for clearing subway ticket revenue taking into account passenger behavior inertia. Background Art

[0002] The existing subway ticket revenue clearing models in major cities mostly adopt a clearing method based on multi-path selection, which combines a primary allocation according to multiple valid paths based on the starting and ending stations of the ticket and a secondary allocation based on the line mileage occupied by each operator on each valid path.

[0003] Valid paths are screened and controlled through the valid path growth threshold, valid path multiplication threshold and maximum number of paths.

[0004] This method assumes that passengers are rational consumers and believes that the route selection process is only affected by objective factors such as travel time, ride comfort, stop time, transfer walking time and transfer waiting time. It ignores the role of passengers' limited rational decision-making psychology in the route selection process.

[0005] In other words, passengers are not completely rational "economic individuals" but rather "social individuals" with bounded rationality. Furthermore, the model's parameter values, such as comfort, the probability distribution of route selection, and the effective route growth and doubling thresholds, are empirically derived, not based on analysis of actual passenger flow samples, and therefore lack a certain degree of computational precision. Summary of the Invention

[0006] The present invention provides a subway ticket revenue settlement method that takes into account passenger behavioral inertia. After verifying the existence and impact of inertia based on actual operating data, a settlement model that takes into account passenger behavioral inertia is proposed to more accurately depict the actual passenger travel route selection process.

[0007] A method for clearing subway ticket revenue taking into account passenger behavior inertia includes the following steps:

[0008] Step 1: Based on the inertial behavior identification process of actual operation data, the inertial routes of passengers and passengers with inertial behavior are obtained;

[0009] Step 2: Analyze the impact of inertial behavior based on the number and proportion of inertial passengers, and measure the impact of inertial behavior on daily operations:

[0010] Step 3: Clearing subway ticket revenue taking into account passenger behavior inertia, specifically including:

[0011] 3.1) Obtain the passenger flow on each route between each OD pair based on the discrete choice logit model;

[0012] 3.2) The revenue distribution ratio for each route is calculated based on the mileage ratio of different subway lines on each route. The revenue of each subway line is calculated based on the traffic volume and the revenue distribution ratio.

[0013] In the present invention, we first construct an inertia recognition process based on actual application data to identify inertial routes and inertial passengers. Secondly, we obtain the total revenue of the two types of routes and the total number of passengers of the two types based on historical data. These two are divided by the total revenue of subway operations and the total number of passengers, respectively, to measure the impact of inertial behavior on daily operations. Finally, we provide a calculation model for the revenue distribution ratio based on the mileage route revenue distribution ratio. The technical solution proposed in the present invention characterizes the inertial behavior of passengers during their actual ride, which is more in line with the distribution needs of actual passenger flow revenue. At the same time, by using historical passenger flow data and according to the actual occurrence of OD, we fully explore the travel portrait of passengers, providing a scientific basis for operation scheduling and safety emergency response.

[0014] In step 1, the inertial behavior identification process based on actual operation data is used to obtain the inertial routes of passengers and passengers with inertial behavior, which specifically includes:

[0015] 1.1) We need the passenger entry and exit data files for each line and each station, as well as the operation timetable files for each line, to obtain the entry and exit gates and entry and exit times of each passenger, so as to obtain the time-dependent OD (English full name: Origin-Destination, Chinese annotation: starting and ending point) pair set Combined with the subway operation schedule, we can get a set of feasible time-dependent routes between each OD pair.

[0016] Among them, t1 and t2 represent the time when passenger i enters the station at r and leaves the station at s respectively. represents the set of OD pairs with time dependence between station r and station s, represents a subway line that covers passenger i’s arrival time at station r and departure time at station s. The arrival time of this line at station r and departure time from station s are t1' and t'2 respectively. Then t1'>t1, t'2<t2, and

[0017] represents the longest walking time of passengers entering the station at r, represents the longest walking time of passengers exiting the station at s.

[0018] 1.2) All data sets of passenger i in this OD pair and Match and determine the route selected by passenger i, and obtain the shortest route between the OD pairs

[0019] The frequency of each passenger choosing the same route and not choosing the shortest route is counted. If the frequency of a passenger choosing the same route between the same time-dependent OD pair exceeds a set threshold f1, then that route is marked as passenger i's inertial route, and the passenger is marked as a passenger with inertial behavior. If the frequency of a passenger not choosing the shortest time-dependent route exceeds a set threshold f2, then that route is marked as a non-optimal inertial time-dependent route, and the passenger is marked as a non-optimal inertial passenger.

[0020] If the frequency with which the same passenger chooses the same route between the same time-dependent OD pair is less than or equal to a set threshold f1, the route is marked as a non-inertial route for passenger i, and the passenger is marked as a passenger with non-inertial behavior. If the frequency with which the same passenger fails to choose the route with the shortest time dependency is less than or equal to a set threshold f2, the route is marked as an optimal inertial time-dependent route, and the passenger is marked as an optimal inertial passenger.

[0021] When the frequency of passengers choosing the same route is greater than f1, the route is recorded as an inertial time-dependent route and the passengers are recorded as passengers with inertia;

[0022] When the frequency of the same passenger not choosing the shortest time-dependent route is greater than f2, the route is recorded as a non-optimal inertia time-dependent route and the passenger is recorded as a non-optimal inertia passenger.

[0023] When the frequency of passengers choosing the same route is ≤ f1, the route is recorded as a non-inertial time-dependent route and the passenger is recorded as a passenger with non-inertia;

[0024] When the frequency of the same passenger not choosing the shortest time-dependent route ≤ f2, the route is recorded as the optimal inertia time-dependent route and the passenger is recorded as the optimal inertia passenger.

[0025] In step 2, the impact of inertial behavior is analyzed, including:

[0026] Let the inertial path set be P inerrtia , the set of passengers with inertial behavior is I inertia , the set of non-inertial routes is The set of passengers with non-inertial behavior is The total revenue of the two types of routes and the total number of passengers of the two types are calculated;

[0027] The proportion of inertial route revenue to the total revenue of the two types of routes, the proportion of non-inertial route revenue to the total revenue of the two types of routes, the proportion of passengers with inertial behavior to the total number of passengers in the two types of passengers, and the proportion of passengers with non-inertial behavior to the total number of passengers in the two types of passengers are used to measure the impact of inertial behavior in daily operations.

[0028] In step 3.1), the passenger flow on each route between each OD pair is obtained based on the discrete choice logit model, specifically including:

[0029] 3.1.1) The logit-based route selection model defines the probability model of route selection. The logit-based route selection model: Passengers' route choices are influenced by both the objective level of service (LOS) and their own subjective psychological factors;

[0030] Among them, LOS variables include: ride time (driving time + stop time), ride fare, ride comfort, transfer walking time, transfer walking distance and transfer waiting time; subjective psychological factors include: inertia psychology, rational preference, attitude, cognitive ability and perceptual error.

[0031] 3.1.2) The calculation formula of the utility function of passenger perceived OD on each route between rs stations is:

[0032]

[0033] in, represents the utility of passenger i between the rs stations of route p (r is the incoming station and s is the outgoing station), λ ika is the coefficient of the kth objective variable of passenger i on road section a, such as the passenger's perceived value of ride time and ride fare;

[0034] is a 0-1 variable, if Then section a is on route p, otherwise

[0035] x ika is the value of the kth objective variable of passenger i on road section a; μ is the influence degree of inertia psychology;

[0036] y i is the inertia psychology of passenger i;

[0037] ε ip Represents passengers' incomplete perception or perception error of the route, and is used to measure the uncertainty of passengers' perception of subway operations. For example, different passengers have errors in their estimates of congestion during peak and off-peak periods. This variable follows a Gumbel random distribution.

[0038] The ride duration and cost, transfer walking time, distance and transfer waiting time are directly obtained based on the station conditions of each line;

[0039] The riding comfort is about the maximum passenger capacity of the subway train. a and the actual passenger flow F a The function is expressed as:

[0040]

[0041] F a Indicates the actual passenger flow of subway trains;

[0042] θ i is the sensitivity of passenger i to crowding, θ i The smaller it is, the higher the sensitivity;

[0043] c a Indicates the maximum designed passenger capacity of a subway train;

[0044] y i It is a linear function of the passenger attribute Z, expressed as:

[0045]

[0046] η i ~N(0,σ 2 )

[0047] Where, β j is the weight of the jth attribute, Z ij is the value of the jth attribute of passenger i, such as the normalized value of age, occupation, income level, etc., η i is the error variable for passenger i;

[0048] N is the normal distribution, σ is the variance of the normal distribution;

[0049] 3.1.3) Based on the logit choice model, the probability that passenger i chooses route p is equal to the probability that the utility of route p is greater than that of other routes, which is:

[0050]

[0051] in, The probability that passenger i chooses route p is equal to the probability that the utility of route p is greater than that of other routes,

[0052] The probability that passenger i chooses route p is equal to the possibility that the utility of route p is greater than that of other routes (p' represents routes other than route p).

[0053] λ ika is the coefficient of the kth objective variable of passenger i on road section a, such as the passenger's perceived value of ride time and ride fare;

[0054] is a 0-1 variable, if Then section a is on route p, otherwise

[0055] x ika is the value of the kth objective variable of passenger i on road section a;

[0056] μ is the influence degree of inertial psychology;

[0057] y i is the inertia psychology of passenger i;

[0058] p' represents other routes except route p,

[0059] μ is the influence degree of inertia psychology.

[0060] is a 0-1 variable, if Then section a is on route p', otherwise

[0061] In step 3.2), the revenue distribution ratio of each route is obtained based on the mileage ratio of different subway lines on each route. The revenue of each subway line is calculated based on the traffic volume and the revenue distribution ratio, which specifically includes:

[0062] 3.2.1) Mileage-based route revenue distribution model: The revenue ratio of an operator on a route is the mileage ratio of each line on the route. The revenue distribution ratio of operator q on route p is:

[0063]

[0064] in, represents the revenue sharing ratio of operator q between stations rs on route p (r is the entry station and s is the exit station);

[0065] is a 0-1 variable, if Then section a is on route p, otherwise D represents the mileage of the subway line, such as D a is the mileage of section a;

[0066] The numerator represents the total mileage of route p operated by operator q;

[0067] represents the total mileage of route p;

[0068] 3.2.2) Revenue Clearance Ratio: The total revenue clearing ratio of operator q in OD pair rs is the selection probability of all routes p between the OD pair multiplied by the revenue distribution ratio of operator q on each route, that is:

[0069]

[0070] in, It represents the total revenue clearing ratio of operator q in OD to rs;

[0071] The probability that passenger i chooses route p is equal to the probability that the utility of route p is greater than that of other routes;

[0072] It represents the revenue sharing ratio of operator q between stations rs on route p (r is the entry station and s is the exit station).

[0073] Compared with the prior art, the present invention has the following advantages:

[0074] The technical solution proposed in the present invention describes the inertial behavior of passengers during their actual ride, which is more in line with the distribution needs of actual passenger flow revenue.

[0075] The present invention utilizes historical passenger flow data and fully explores passenger travel portraits based on the actual occurrence status of OD, providing a scientific basis for operation scheduling and safety emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 Diagram of the inertial route and the passenger identification process with behavioral inertia;

[0077] Figure 2 The profit-sharing calculation process taking into account the passenger's decision-making psychology;

[0078] Figure 3 This is a flow chart of a method for clearing subway ticket revenue that takes into account passenger behavior inertia according to the present invention;

[0079] Figure 4 This is a diagram of a city's rail transit network. DETAILED DESCRIPTION

[0080] like Figure 3 As shown, a method for clearing subway ticket revenue considering the inertia of passenger behavior includes: an inertial behavior identification process based on actual operation data to obtain the inertial routes of passengers and passengers with inertial behavior; an analysis of the impact of inertial behavior; and a method for clearing subway ticket revenue considering the inertia of passenger behavior.

[0081] Example 1

[0082] like Figure 1 As shown, this embodiment proposes a process for identifying an inertial path and a passenger with behavioral inertia, including:

[0083] Step 1: Obtain time-dependent OD pairs based on the data of station i on N lines and historical passenger flow data;

[0084] Determine by calculation the set of time-dependent routes chosen by each passenger;

[0085] Determine the frequency of the same passenger choosing the same route between the same time-dependent OD pairs;

[0086] When frequency > f1, the route is recorded as an inertial time-dependent route and the passenger is recorded as a passenger with inertia.

[0087] Step 2: Obtain a set of feasible time-dependent routes between OD pairs based on the subway line operation diagram and the time-dependent OD pairs;

[0088] Identify the shortest time dependency route between OD pairs;

[0089] Count the number of passengers who did not choose the shortest time dependent route and their frequency;

[0090] When the frequency > f2, the route is recorded as a non-optimal inertia time-dependent route and the passenger is recorded as a non-optimal inertia passenger.

[0091] Calculate the proportion of the two types of passengers in the total passengers and the proportion of the inertial route in the total revenue.

[0092] like Figure 2 As shown in Figure 1, the set of feasible time-dependent routes between OD pairs is obtained based on the subway line operation diagram;

[0093] Construct a Logi-based route selection model: considering that passengers' route selection is affected by the objective service level (LOS) and their own subjective psychological factors.

[0094] Among them, LOS variables include: ride time (driving time + stop time), ride fare, ride comfort, transfer walking time, transfer walking distance and transfer waiting time; subjective psychological factors include: inertia psychology, rational preference, attitude, cognitive ability and perceptual error.

[0095] The calculation formula of the utility function of passenger perceived OD on each route between rs is:

[0096]

[0097] in, represents the utility of passenger i between the rs stations of route p (r is the incoming station and s is the outgoing station), λ ika is the coefficient of the kth objective variable of passenger i on road section a, such as the passenger's perceived value of ride time and ride fare;

[0098] is a 0-1 variable, if Then section a is on route p, otherwise

[0099] xika is the value of the kth objective variable of passenger i on road section a;

[0100] μ is the influence degree of inertial psychology;

[0101] y i is the inertia psychology of passenger i;

[0102] ε ip Represents passengers' incomplete perception or perception error of the route, and is used to measure the uncertainty of passengers' perception of subway operations. For example, different passengers have errors in their estimates of congestion during peak and off-peak periods. This variable follows a Gumbel random distribution.

[0103] The ride duration and cost, transfer walking time, distance and transfer waiting time are directly obtained based on the station conditions of each line;

[0104] The riding comfort is about the maximum passenger capacity of the subway train. a and the actual passenger flow F a The function is expressed as:

[0105]

[0106] F a Indicates the actual passenger flow of subway trains;

[0107] θ i is the sensitivity of passenger i to crowding, θ i The smaller it is, the higher the sensitivity;

[0108] c a Indicates the maximum designed passenger capacity of a subway train;

[0109] y i is the inertial psychology of passenger i, which is a linear function of passenger attribute Z and is expressed as:

[0110]

[0111] η i ~N(0,σ 2 )

[0112] Where, β j is the weight of the jth attribute, Z ij is the value of the jth attribute of passenger i, such as the normalized value of age, occupation, income level, etc., η i is the error variable for passenger i;

[0113] N is the normal distribution, σ is the variance of the normal distribution;

[0114] Based on the logit choice model, the probability that passenger i chooses route p is equal to the probability that the utility of route p is greater than that of other routes, which is:

[0115]

[0116] in, The probability that passenger i chooses route p is equal to the probability that the utility of route p is greater than that of other routes,

[0117] The probability that passenger i chooses route p is equal to the possibility that the utility of route p is greater than that of other routes (p' represents routes other than route p).

[0118] λ ika is the coefficient of the kth objective variable of passenger i on road section a, such as the passenger's perceived value of ride time and ride fare;

[0119] is a 0-1 variable, if Then section a is on route p, otherwise

[0120] x ika is the value of the kth objective variable of passenger i on road section a;

[0121] μ is the influence degree of inertial psychology;

[0122] y i is the inertia psychology of passenger i;

[0123] p' represents other routes except route p,

[0124] μ is the influence degree of inertia psychology.

[0125] is a 0-1 variable, if Then section a is on route p', otherwise

[0126] Mileage-based route revenue distribution model: The revenue ratio of operators on a route is the mileage ratio of each line on the route. The revenue distribution ratio of operators on a route is:

[0127]

[0128] in, represents the revenue sharing ratio of operator q between stations rs on route p (r is the entry station and s is the exit station);

[0129] is a 0-1 variable, if Then section a is on route p, otherwise

[0130] D represents the mileage of the subway line, such as D a is the mileage of section a;

[0131] The numerator represents the total mileage of route p operated by operator q;

[0132] represents the total mileage of route p;

[0133] Revenue clearing ratio: The total revenue clearing ratio of operator q in OD pair rs is the selection probability of all routes p between the OD pair multiplied by the revenue distribution ratio of operator q on each route, that is:

[0134]

[0135] in, It represents the total revenue clearing ratio of operator q in OD to rs;

[0136] The probability that passenger i chooses route p is equal to the probability that the utility of route p is greater than that of other routes;

[0137] It represents the revenue sharing ratio of operator q between stations rs on route p (r is the entry station and s is the exit station).

[0138] The following takes a certain city's rail transit as an example to further illustrate an embodiment of a method for clearing subway ticket revenue that takes into account passenger behavior inertia.

[0139] A city rail transit network diagram is as follows Figure 4 As shown in the figure, assuming that a certain trip of passenger W (entering the station at A3 and exiting the station at A11) is randomly selected within a statistical period, he has three paths to choose from, namely path 1: A3→P1→P2→A4→A5→A6→A7→A8→A9→A10→P3→P4→A11, path 2: A3→P1→P2→B4→B5→B6→B7→B8→B9→B10→P3→P4→A11, and path 3: A3→P1→P2→C4→C5→C6→C7→C8→C9→C10→C11→P4→A11. According to the entry and exit time points of passenger A and the actual train timetable, path 1 and path 2 are selected to meet the matching conditions. Secondly, based on the walking time in each station and the train schedule, it is calculated that passenger A chooses inertial path 2, passenger W is marked as a passenger with inertial behavior, and path 2 is marked as the inertial path.

[0140] All A3→A11 trips of passenger W during the statistical period are calculated in sequence. The results show that 92.5% of the trips choose route 2, and 7.5% of the trips choose route 1. Considering that route A is operated by operator G, and routes B and C are operated by operator D, then since operator G accounts for 20% of the distance on this trip and operator D accounts for 80% of the distance, operator G's clearing profit ratio is 7.5%*20%=0.015, and operator D's clearing profit ratio is 92.5%*80%=0.74.

Claims

1. A method for clearing subway ticket revenue taking into account passenger behavior inertia, characterized in that: The following steps are involved: Step 1: Based on the inertial behavior identification process of actual operation data, the inertial routes of passengers and passengers with inertial behavior are obtained; Step 2: Analyze the impact of inertial behavior based on the number and proportion of inertial passengers, and measure the impact of inertial behavior on daily operations: Step 3: Clearing subway ticket revenue taking into account passenger behavior inertia, specifically including: 3.1) Based on the discrete choice logit model, the passenger flow on each route between each OD pair is obtained. The OD refers to the origin and destination, specifically including: 3.1.1) The logit-based route selection model defines the probability model of route selection. The logit-based route selection model: Passengers' route selection is influenced by objective service levels and subjective psychological factors; 3.1.2) The calculation formula of the utility function of passenger perceived OD on each route between RS stations is: ； in, Indicates passengers On the route The utility between rs stations, r is the incoming station, s is the outgoing station, For passengers On the road Previous The coefficient of an objective variable; is a 0-1 variable; For passengers On the road Previous The value of an objective variable; The degree of influence of inertia psychology; For passengers Inertia psychology; It represents passengers’ incomplete perception or perception error of the route, and is used to measure passengers’ uncertainty perception of subway operations; The ride duration and cost, transfer walking time, distance and transfer waiting time are directly obtained based on the station conditions of each line; Ride comfort is about the maximum passenger capacity of subway trains The actual passenger flow The function is expressed as: ； Indicates the actual passenger flow of subway trains; For passengers sensitivity to crowding; Indicates the maximum passenger capacity of the subway train; Indicates passengers The inertial psychology is a linear function of the passenger attribute Z, which can be expressed as: ； ； Where, For the The weight of the attribute, For passengers No. The value of an attribute Is a passenger The error variable, is a normal distribution, is the variance of the normal distribution; 3.1.3) Based on the logit selection model, passengers Select route The probability of the route is equal to The utility of is greater than the probability of other routes, which is: ； in, Indicates passengers Select route The probability of the route is equal to The utility of the route is greater than the possibility of other routes; Indicates passengers Select route The probability of the route is equal to The utility of the route is greater than the possibility of other routes, Indicates that the route Other lines outside For passengers On the road Previous The coefficient of an objective variable; is a 0-1 variable; For passengers On the road Previous The value of an objective variable; The degree of influence of inertia psychology; Indicates passengers Inertia psychology; Indicates that the route Other lines outside The degree of influence of inertia psychology; is a 0-1 variable; 3.2) The revenue distribution ratio for each route is calculated based on the mileage ratio of different subway lines on each route. The revenue of each subway line is calculated based on the traffic volume and the revenue distribution ratio.

2. The subway ticket revenue clearing method considering passenger behavior inertia according to claim 1 is characterized in that: In step 1, the inertial behavior identification process based on actual operation data is used to obtain the inertial routes of passengers and passengers with inertial behavior, which specifically includes: 1.1) We need the entry and exit data files of passengers at each station on each line and the operation timetable files of each line, obtain the entry and exit gates and entry and exit times of each passenger, and thus obtain the time-dependent OD pair set , combined with the subway operation schedule, obtain the feasible time-dependent route set between each OD pair ; in, Represents passengers exist Pit stop and Time of departure, Indicates Station and The set of OD pairs with time dependence between stations, Indicates that it can cover passengers exist Pit stop and Subway lines with similar departure times; 1.2) Passengers In all datasets of this OD pair and Match and identify passengers The selected route and the shortest bus route between OD pairs are obtained at the same time ; Count the frequency of each passenger choosing the same route and the frequency of not choosing the shortest route. If the frequency of the same passenger choosing the same route between the same time-dependent OD pairs is greater than the set threshold , then mark the route as a passenger The passenger is marked as a passenger with inertial behavior; if the frequency of the same passenger not choosing the shortest time dependent route is greater than the set threshold When , the route is marked as a non-optimal inertia time-dependent route, and the passenger is marked as a non-optimal inertia passenger.

3. The subway ticket revenue clearing method considering passenger behavior inertia according to claim 2 is characterized in that: In step 1.1), the line reaches Stand and leave The station times are , then , ,and , ; Indicates The longest walking time for passengers entering the station, Indicates The longest walking time for passengers exiting the station.

4. The method for clearing subway ticket revenue considering passenger behavior inertia according to claim 2 is characterized in that: In step 1.2), when the frequency of passengers choosing the same route When , the route is recorded as a non-inertial time-dependent route, and the passengers are recorded as passengers with non-inertia; When the same passenger does not choose the route with the shortest time dependency When , the route is recorded as the optimal inertia time-dependent route, and the passenger is recorded as the optimal inertia passenger.

5. The subway ticket revenue clearing method considering passenger behavior inertia according to claim 1 is characterized in that: In step 2, the impact of inertial behavior is analyzed, including: The set of inertial paths is , the set of passengers with inertial behavior is , the set of non-inertial routes is , the set of passengers with non-inertial behavior is , the total revenue of the two types of routes and the total number of the two types of passengers are obtained, and the proportion of inertial route revenue in the total revenue of the two types of routes, the proportion of non-inertial route revenue in the total revenue of the two types of routes, the proportion of passengers with inertial behavior in the total number of the two types of passengers, and the proportion of passengers with non-inertial behavior in the total number of the two types of passengers are calculated.

6. The subway ticket revenue clearing method considering passenger behavior inertia according to claim 1 is characterized in that: In step 3.1.1), the objective service level variables include: ride time, ride fare, ride comfort, transfer walking time, transfer walking distance, and transfer waiting time; Subjective psychological factors include: inertial psychology, rational preference, attitude, cognitive ability and perceptual error.

7. The method for clearing subway ticket revenue considering passenger behavior inertia according to claim 1 is characterized in that: In step 3.2), the revenue distribution ratio for each route is obtained based on the mileage ratio of different subway lines on each route. The revenue of each subway line is calculated based on the traffic volume and the revenue distribution ratio, which includes: 3.2.1) Mileage-based route revenue distribution model: The revenue ratio of the operator on the route is the mileage ratio of each line on the route. Operator The profit distribution ratio is: ； in, Indicates the route On rs inter-station operator The profit distribution ratio, r is the inbound station, s is the outbound station; is a 0-1 variable; Indicates the mileage of the subway line. For road sections Mileage; Molecular representation route Operators Total mileage operated; Indicates the route Total mileage; 3.2.2) Revenue Distribution Ratio: Operator The total revenue clearing ratio of the OD pair rs is the total revenue clearing ratio of all routes between the OD pair The choice probability multiplied by the operator on each route The profit distribution ratio is: ； in, Indicates operator In the total benefit clearing ratio of OD to rs; Indicates passengers Select route The probability of the route is equal to The utility of the route is greater than the possibility of other routes; Indicates the route On rs inter-station operator The profit distribution ratio, r is the entry point and s is the exit point.

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