CN112286959A

CN112286959A - Single-class passenger car departure interval optimization modeling method and device

Info

Publication number: CN112286959A
Application number: CN201911002780.2A
Authority: CN
Inventors: 李颖; 安毅生; 杨临涧
Original assignee: Changan University
Current assignee: Changan University
Priority date: 2019-10-21
Filing date: 2019-10-21
Publication date: 2021-01-29

Abstract

The invention discloses a single-class passenger car departure interval optimization modeling method, which comprises the following steps: classifying and extracting source data to obtain operation information of a single line of a single type of passenger car; modeling the operation information to obtain the operation income, the operation total cost and the passenger waiting cost of the single passenger car; and carrying out weighted modeling according to the maximization of the operation profit of the single-class passenger car and the minimization of the passenger waiting cost to obtain a single-class passenger car departure interval optimization model. The invention also discloses a single-class passenger car departure interval optimization modeling device. According to limited passenger car configuration, the profit of a passenger company and the passenger waiting time are comprehensively considered, a passenger car departure interval optimization model is established, and the optimal passenger car departure time is calculated; the passenger flow volume can be dynamically adjusted, so that the defects of the traditional passenger car dispatching mode are overcome, the benefits of a car carrier are improved, and the waiting time of passengers is reduced.

Description

Single-class passenger car departure interval optimization modeling method and device

Technical Field

The invention relates to the technical field of public traffic dispatching optimization, in particular to a method and a device for optimizing and modeling departure intervals of single-class passenger cars.

Background

The passenger transport schedule of the intercity passenger car has great influence on the daily trip of people and the operation condition of passenger transport companies. If the departure interval of the passenger car is too large, the waiting time of the passengers is too long, and therefore the service level of the passenger car is reduced. If the departure interval of the passenger car is too small, the operation cost of the passenger company is increased.

At present, in the operation process of a passenger company, two passenger car dispatching modes are mainly adopted, one mode is a static dispatching mode of dispatching a car according to a passenger transportation schedule, and the other mode is a dynamic dispatching mode of dispatching the car when the passenger is full. The passenger transport schedule is generally established according to factors such as historical passenger flow distribution rules and carrying capacity of passenger transport companies, and once the passenger transport schedule is determined and published, the passenger transport schedule cannot be changed in a short term. The passenger car dispatching mode of passenger car full and instant sending can improve the full load rate of the passenger car to the maximum extent, thereby improving the income of passenger companies.

The two scheduling methods have the following problems: 1. the dispatching interval can not be adjusted in real time according to the passenger flow according to the static dispatching interval mode of the passenger schedule, so that the dispatching interval is often not in accordance with the actual passenger flow rule, and the service level of the passenger car is reduced; 2. the passenger car dispatching mode that the passenger sent out soon after being full can cause passenger's waiting time overlength, is difficult according to the trip plan of departure time arrangement oneself, and passenger's comfort level is lower, causes passenger's loss. The two passenger car dispatching modes often have mismatching of supply and demand, so that the problems of overlong waiting time of passengers or less profit of passenger companies and the like are caused.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a single-class passenger car departure interval optimization modeling method and device, which can improve the profit of a passenger company while reducing the waiting time of passengers.

The technical scheme adopted by the invention is as follows:

a single-class passenger car departure interval optimization modeling method comprises the following steps:

classifying and extracting source data to obtain operation information of a single line of a single type of passenger car;

modeling the operation information to obtain the operation income, the operation total cost and the passenger waiting cost of the single passenger car;

and carrying out weighted modeling according to the maximization of the operation profit of the single-class passenger car and the minimization of the passenger waiting cost to obtain a single-class passenger car departure interval optimization model.

The invention has the further technical scheme that the operation information of a single line of a single type of passenger car is obtained by classifying and extracting the source data; the method specifically comprises the following steps:

importing the source data into an ORACLE database for screening to obtain screening data information;

selecting passenger station information according to the screening data information;

and carrying out time limit screening on the passenger station information to select the operation information of the passenger cars.

The further technical scheme of the invention is that the operation information is modeled to obtain the operation income, the operation total cost and the passenger waiting cost of the single passenger car; the method specifically comprises the following steps:

establishing a single-class passenger car operation income model;

establishing a model of the total operation cost of the single type of passenger car;

and establishing a single passenger car passenger waiting cost model.

According to the further technical scheme, the single-class passenger car departure interval optimization model is obtained by performing weighted modeling according to the maximization of the operation profit of the single-class passenger car and the minimization of the passenger waiting cost; specifically, the method comprises the following steps:

calculating the operation profit of the single type of passenger car, wherein the operation profit of the single type of passenger car is obtained by subtracting the total operation cost of the single type of passenger car from the operation profit of the single type of passenger car;

establishing a single-class passenger car operation profit maximization model;

establishing a passenger waiting cost minimization model;

and carrying out weighting and difference solving on the operation profit maximization model and the passenger waiting cost minimization model of the single-class passenger car to obtain an departure interval optimization model of the single-class passenger car.

The invention also provides a passenger car departure interval optimization modeling device, which comprises:

the source data screening unit is used for classifying and extracting source data to obtain operation information of a single line of a single type of passenger car;

the operation information processing unit is used for modeling the operation information to obtain the operation income, the operation total cost and the passenger waiting cost of the single passenger car;

and the model establishing unit is used for carrying out weighted modeling according to the maximization of the operation profit of the single-class passenger car and the minimization of the passenger waiting cost to obtain a single-class passenger car departure interval optimization model.

The invention has the beneficial effects that:

according to the invention, according to limited passenger car configuration, the profit of a passenger company and the waiting time of passengers are comprehensively considered, a passenger car departure interval optimization model is established, the optimal passenger car departure time is calculated, and the optimized departure interval can be dynamically adjusted according to the passenger flow, so that the defects of the traditional passenger car dispatching mode are overcome, the income of the passenger company is increased, and the waiting time of the passengers is reduced.

Drawings

FIG. 1 is a flow chart of a single-class passenger car departure interval optimization modeling method provided by the invention;

FIG. 2 is a flowchart of a method for classifying and extracting source data to obtain operation information of a single line of a single type of passenger car according to the present invention;

FIG. 3 is a flow chart of a method for establishing a departure interval optimization model of a single-class passenger car according to the present invention;

FIG. 4 is a structural diagram of a single-class passenger car departure interval optimization modeling device provided by the invention;

FIG. 5 is a graph of the full line loading rate of the Kunming station of the present invention;

FIG. 6 is a graph of the Kunming-Luoping line full load rate of the present invention;

FIG. 7 is a diagram of the operation cost of a passenger car with original data, a whole car and a whole bus;

FIG. 8 is a diagram of the departure times of the two models and the raw data of four time periods selected in the present invention;

FIG. 9 is a diagram of waiting time and test revenue for a single-type passenger car model test according to the present invention;

FIG. 10 is a diagram of waiting time and test yield for a model test of a full-large bus and a full-small bus according to the present invention;

FIG. 11 is a diagram of the operation cost of the dual-type passenger car of the present invention;

FIG. 12 is a diagram of departure times for a two-class vehicle model for four time segments selected in the present invention;

FIG. 13 is a graph showing the number of vehicles of a cart and a cart in the present invention;

FIG. 14 is a graph of passenger population and benefit for both the raw data and the dual-type models of the present invention.

Detailed Description

In order to make the technical solutions of the present application better understood, the present application is further described in detail below with reference to the accompanying drawings.

Because the operation of the passenger car is influenced by environmental factors, the modeling method provided by the invention eliminates the influence of uncertain factors in the implementation, and makes the following assumptions on the modeling: (1) in the operation process of the passenger car, scheduling is carried out strictly according to the established departure interval; (2) in the travel of the passenger car line, the road condition is normal, and no traffic accident occurs; (3) the maximum passenger capacity of the passenger car is a fixed value, and no overload behavior exists in the operation process; (4) the passenger flow volume is predicted basically accurately, and the actual passenger flow situation can be reflected; (5) in the optimization process of the departure interval, the fare is constant for a certain period of time.

Referring to fig. 1, the invention provides a single-class passenger car departure interval optimization modeling method, which comprises the following steps:

step 101, classifying and extracting source data to obtain operation information of a single line of a single type of passenger car;

102, modeling operation information to obtain operation income, total operation cost and passenger waiting cost of the single passenger car;

and 103, weighting and modeling according to the maximization of the operation profit of the single-class passenger car and the minimization of the passenger waiting cost to obtain a departure interval optimization model of the single-class passenger car.

The invention establishes a passenger car departure interval optimization model according to limited passenger car configuration and comprehensively considering the profit of passenger companies and the passenger waiting time, and calculates the optimal passenger car departure time by adopting mixed integer linear programming (M I LP). The optimized departure interval is dynamically adjusted according to the passenger flow, so that the defects of the traditional passenger car dispatching mode are overcome, the benefits of a car transportation company are improved, and the waiting time of passengers is reduced.

Referring to fig. 2, the operation information of a single line of a single type of passenger car is obtained by classifying and extracting source data; the method specifically comprises the following steps: step 1011, importing the source data into an ORACLE database for screening to obtain screening data information; step 1012, selecting passenger station information according to the screening data information; and 1013, performing time limit screening on the passenger station information to select the operation information of the passenger cars.

The source data comprises a shift code, a line code, an enterprise code, a shift property, a shift type, an origin code, an end point code, an departure date, departure time, a carrying license plate number, a passenger type, a passenger class, a seat type, a passenger carrying seat number, a real carrying seat number, a maximum carrying seat number, a residual carrying seat number, a ticket sale stop identifier, a data signature, a data abstract, creation time, modification time, an origin passenger station name, an origin station name, an arrival passenger station name, an arrival station name, a vehicle color, running time, ticket price, a line type, an identifier of whether the destination is submitted into a department or not, and information of the rest seat number of the whole vehicle.

The method comprises the steps that original data are collected from a system and then stored in a database file, the database file needs to be imported into an ORACLE database for primary processing of the data, and then source data are screened by using a database language; and observing that all information of the passenger station to be researched can be selected from the database by using the starting station name according to the screened data table information, and increasing the limitation of the departure date and time range on the basis of the information to obtain the relevant information of all passenger car dispatching in a single passenger station in a period of time.

In the embodiment of the invention, the operation information is modeled to obtain the operation income, the operation total cost and the waiting cost of the passenger of the single type of passenger car; the method specifically comprises the following steps: establishing a single-class passenger car operation income model; establishing a model of the total operation cost of the single type of passenger car; and establishing a single passenger car passenger waiting cost model.

The single-class passenger car dispatching interval optimization model can be obtained by establishing a single-class passenger car operation income model, a single-class passenger car operation total cost model and a single-class passenger car passenger waiting cost model as the basis of single-class passenger car dispatching interval optimization and optimizing the models.

The method comprises the steps of establishing a single-class passenger car operation income model; the method specifically comprises the following steps: calculating the operation income of the single line, and summing the operation income of the single line to obtain a single-class passenger car operation income model; the operation income of the single line is the accumulated sum of the product of the ticket price corresponding to each moment and the passenger car passenger carrying total number.

Because the actual conditions of the passenger stations are different at each moment, the corresponding passenger fares change due to the changes of factors such as vehicle types, transportation cost, price comparison relationship, supply and demand relationship, average profit margin of road transportation industry, social bearing capacity and the like; the number of people actually carried by the passenger car at each moment is also changed, and the optional departure moment of each passenger car corresponds to a passenger car fare and the number of people carried by the passenger car. The fare adopted by the invention is a fixed value, and the number of passengers actually carried at each moment is directly obtained from the source data.

The income of the passenger company is an important component of the model, wherein the income of a single line of the passenger company is the accumulated sum of the product of the corresponding fare and the total number of passenger carriers of the passenger train at each moment, and the income of the single line is accumulated on the basis of obtaining the income of the single line, so that the income sum of all lines of the passenger company can be obtained, and the specific formula is as follows:

wherein: l represents a line number; l represents the total number of lines; k represents the time divided according to a fixed time interval in a period of time, namely the optional departure time of the passenger car, and is a discrete variable;

representing the corresponding fare at the time of k in the ith class line of a station;

and the time of the ith shift line k of the station is represented as the corresponding total number of passengers.

In the embodiment of the invention, a model of the total operation cost of the single-class passenger car is established; the method specifically comprises the following steps: calculating the operation cost of a single line, wherein the operation cost is the sum of fixed cost and variable cost; and accumulating and summing the operation cost of the single line to obtain the operation total cost of the single passenger car.

The operation cost of the passenger car refers to expenses such as vehicle depreciation cost, vehicle maintenance cost, fuel oil cost, personnel wage, management cost, financial cost, vehicle road traffic mandatory insurance cost, related tax fees specified by the state and the like generated in the process of a passenger operator engaging in the road class passenger traffic business. The operation cost of the passenger car is generally divided into two parts of fixed cost and variable cost. The fixed cost generally represents a cost that does not change with the change of the operation mileage within a period of time, and the variable cost is generally a cost related to the workload of the passenger car operation (i.e., the driving mileage, the number of receptions, etc.). The operation cost of a single bus operation line is the sum of the fixed cost and the variable cost:

wherein: c_e ^OThe total operating cost for e-type vehicles on a single shift; c_e ^FFixed cost for a single line e-type vehicle of a passenger car; c_e ^VVariable cost for a single-route e-type vehicle for a passenger car.

In the embodiment of the invention, the vehicle depreciation fee averagely distributes the depreciation of the fixed assets into each operation period, and assumes that the depreciation fees in each period are equal, the single-class vehicle depreciation fee on a single line is a residual value obtained by subtracting a residual value ratio from the passenger car purchase fee of a single-class passenger car on the single line, and is divided by the product of the depreciation age limit and the annual utilization rate of the passenger car and then by 365 days to obtain the vehicle depreciation fee in units of days. The operation period is one day, the depreciation age limit is 4 years, and the residual value proportion is less than or equal to 5 percent.

The following is the depreciation cost for a type e vehicle scheduled on a single class line

The total depreciation fee of the type e vehicles in the single-day and single-line route is N_eProduct of the per-day depreciation costs of scheduled e-type single vehicles:

in the formula (3), the depreciation cost of a certain type of vehicle on a single class line is the passenger car purchase cost TIP of a e type of passenger car for a passenger car company on a certain class line_eMinus the residual ratio R_vAnd calculating the residual value, and then dividing the residual value by the product of the depreciation age limit D and the U annual utilization rate of the passenger car to obtain the annual depreciation cost of the vehicle, and on the basis, dividing the annual depreciation cost by 365 days to obtain the single-day vehicle depreciation cost. Wherein, since China stipulates that the total number of days of a legal holiday of one year is 29 days, the annual utilization rate U can be expressedIs formula (4), the calculated value is 0.92; according to the thirty-first regulation of the file ' the rules for implementing the temporary tax Act of enterprises of the people's republic of China ': before calculating depreciation of the fixed asset, the residual value should be estimated and subtracted from the original price of the fixed asset, the residual value proportion is within 5 percent of the original price and is determined by enterprises, so the value of the residual value coefficient can be assumed to be R_vNot more than 5%; finally, according to the sixteenth article in the document "regulations on tax Law implementation by enterprises in the people's republic of China", the depreciation period of the passenger car is four years, that is, D is 4.

The formula of the fixed cost can be converted into TIP through the constant obtained above_eMultiplied by a constant, the transformed formula is as follows:

the variable costs on a single wire line of a single class passenger car are: the variable cost of a unit kilometer, the total number of passengers served in a single line passenger car operation station in one day, multiplied by the sum of passenger station service fee charged by each passenger served by the passenger car operation station and total passing bridge fee consumed by a charged kilometer; wherein the variable cost per kilometer is: the total of labor cost, fuel consumption cost, and maintenance cost per kilometer.

The variable cost is the cost related to the workload of passenger car operation (i.e. mileage, number of passengers), and mainly includes the cost of passing a bridge, the cost of fuel oil, the cost of vehicle maintenance, the cost of manpower (the cost of driver and office staff), the cost of passenger station work, etc. The labor cost, the consumed fuel charge, the vehicle maintenance charge and the like are all related to mileage, and the passenger station charge is related to the number of people waiting in the station. The passenger station service charge mainly refers to the service charge charged by the passenger station to each passenger when the passenger uses the related facilities and services provided by the passenger station, such as waiting, resting, security, safety inspection and the like. Thus, the total variable cost of e-type vehicles on a certain class of lines of passenger vehicles

Can be calculated from the following formula (6):

wherein:

variable cost per kilometer, unit is yuan/kilometer; p is the total number of passengers served in the first class line in the bus operating station for one day; q is passenger station service fee, which is collected by each passenger in the passenger station service, and the unit is element/person, and the value can be known by looking up the transportation hall file of the relevant province;

the labor cost is unit kilometer, and the unit is yuan/kilometer;

fuel cost per kilometer consumption, unit is yuan/kilometer;

maintenance cost per kilometer, unit is yuan/kilometer;

the unit is element for charging the total road and bridge passing fee consumed by kilometers.

The unit kilometer operation cost of each type of passenger car is composed of expenses such as labor cost, fuel consumption cost of the passenger car, maintenance cost of the passenger car, and passing bridge fee of a single line, and the like, and specifically comprises the following steps:

wherein: way is the sum of the single-month wages of the driver and the related office staff, and the unit is Yuan/person.month; FuelPrice is the actual market price per liter of fuel, unit is Yuan/liter; FuelVol_eThe fuel consumption of a vehicle of the e type of a certain class in the whole process is in liters; mile is the charging distance of a passenger car on a certain class line, i.e. the distance traveled between two passenger stations.

In general, maintenance of a passenger car is performed once every 5000 kilometers of the passenger car, so that the maintenance cost per kilometer is about:

wherein: MTprice_eThe maintenance cost of carrying out maintenance once for each 5000 kilometers of the e-type vehicle, the unit is element; and the passing bridge fee per kilometer

The cost can be obtained by inquiring a related data table, and the cost of passing the road and passing the bridge of the whole shift line can be obtained by the product of the passing cost per kilometer and the actual charging mileage.

In the embodiment of the invention, a single-class passenger car passenger waiting cost model is established; the method specifically comprises the following steps: calculating the accumulated number of waiting passengers, wherein the accumulated number of waiting passengers is the difference between the number of arriving passengers at the passenger station at the time point on all lines and the actual number of passengers carried by the passenger car at the time point; and carrying out approximate estimation on the waiting time of passengers in the passenger station to obtain the accumulated waiting cost of the passengers, wherein the accumulated waiting time of the passengers is the product of the accumulated number of the passengers waiting in the passenger station and the interval length between each optional moment.

The amount of the waiting cost of the passengers can be obtained by the accumulated waiting time of the passengers, the accumulated waiting number of the passengers can be reflected by the size of the accumulated waiting time of the passengers, the more the waiting number is, the longer the waiting time of the passengers is, and the more the trip cost of the passengers is, so the waiting cost of the passengers can be approximately estimated by using the accumulated waiting time of the passengers in the passenger station, and the accumulated waiting time of the passengers is the product of the accumulated waiting number in the passenger station and the time interval length between each optional moment. Therefore, the accumulated waiting time of the passengers waiting at the bus station is as follows:

wherein: TotT^lAccumulating waiting time for the station of the first class line;

the number of passengers arriving at the station at the time ss of the first route;

the actual passenger carrying number of the passenger car at the time of the first class line ss; at is the length of the time interval between each selectable instant k.

The number of passengers arriving at the station at each moment can not be obtained directly from the source data

In order to obtain the number of passengers arriving at the station at each moment, the arrival station of passengers is determined to obey the poisson distribution, and the average arrival rate of the passengers per unit time can be determined when the arrival distribution of the passengers is determined, so that the number of the passengers arriving at the station in a period of time in the source data is respectively divided and distributed into each unit moment, namely the number of the passengers arriving at the station at each moment is determined

The analysis process of the arrival of passengers subjected to the poisson distribution is as follows: analyzing the number of passengers arriving at a bus station within one hour, dividing one hour into 3600s,the number of passengers arriving at the passenger station (the probability of occurrence of an event) in this 1s can be considered to be very small or even less than one; meanwhile, the number of people arriving in each 1s is stable, and the number of people arriving is proportional to the time length; since there is independence between events of whether passengers arrive at the passenger station every 1s (arriving together regardless of passenger engagement), the number of passengers arriving at the passenger station over time is subject to a poisson distribution. The probability formula for the poisson distribution is as follows:

wherein: p_mRepresenting the probability of arriving at m passengers within the time interval at; λ represents an average arrival rate per unit time.

In the following model application, the input data of the model is the random number of passengers arriving from poisson distribution in a passenger station at departure time 1.. K, the random number of the passengers must be obtained by determining lambda of each time period, the parameter lambda of the poisson distribution is the average incidence rate of random events in unit time (or unit area), in the embodiment of the invention, the quotient of the number of passengers entering a certain shift line of the passenger station within a time interval and the time interval of the passengers arriving at the station is the quotient of the actual passenger number of the single departure in the original data and the arrival time of all passengers arriving at the station before each passenger departure, and the lambda can be obtained, so that the number of the passengers arriving at each time station in the formula (11) can be determined through the lambda

The value of (c).

Referring to fig. 3, weighting modeling is performed according to the maximization of the operation profit of the single-class passenger car and the minimization of the passenger waiting cost to obtain an optimal model of the departure interval of the single-class passenger car; the method specifically comprises the following steps:

step 1031, calculating the operation profits of the single type passenger cars, wherein the operation profits of the single type passenger cars are obtained by subtracting the operation total cost of the single type passenger cars from the operation profits of the single type passenger cars;

step 1032, establishing a model for maximizing the operation profits of the single-class passenger cars;

step 1033, establishing a passenger waiting cost minimization model;

and 1034, carrying out weighting and difference solving on the operation profit maximization model and the passenger waiting cost minimization model of the single-class passenger car to obtain an departure interval optimization model of the single-class passenger car.

The profit of the passenger car operation company consists of two parts of income and expenditure of the company, and in order to obtain a maximum value model of the overall profit, the profit part of the company needs to be set as a maximization model in the construction of the overall model.

The waiting time of passengers in the station embodies the satisfaction degree of the passengers on the service level and the comfort degree of the passengers, the number of people detained in the station is less, the waiting time of the passengers is shorter, namely the trip cost of the passengers is lower, in order to improve the comfort degree of the passengers as much as possible, the experience degree part of the passengers in the model needs to be set into a minimized model, and the number of the passengers detained in the station is made as small as possible so that the accumulated waiting time of the passengers in the whole station is also made as small as possible.

The two functions constructed above are used for calculating the travel time cost of the passenger, namely the accumulated number of the detained passengers at the passenger station is used as a benefit function of the passenger, and the other function is used for calculating the operation benefit of the bus company, namely the difference between the income and the expenditure of the passenger car is used as a benefit function of the bus operation company, so that the benefits of both parties in the frequency distribution process of the passenger car are taken into consideration from the perspective of the whole bus system. Since the determination of the departure interval of the passenger car in real life is a scheme that two functions are mutually restricted to obtain two balanced parties, the two functions need to be converted into a single objective function. In order to maximize the benefit of the passenger car company and minimize the waiting cost of the passengers, two sub-functions can be finally unified into a maximization model.

In order to obtain the optimal scheduling mode for balancing the benefits of the two parties, the difference value between the net profit of the passenger car operation company and the waiting cost of passengers is calculated. However, since the passenger interests and the interests of the passenger car operating company are not uniform, the balance between the interests of both parties should be considered and the control should be performed by different weight distribution. Introducing a weight w, wherein the final weighted passenger car departure interval model is as follows:

since the model is built

When the standard for judging whether the passenger cars are dispatched to the station is made, the number of the dispatched passenger cars is the sum of the number of all departure times in 1.. K at the departure time, so that the number of all the passenger cars in the departure interval model of a single passenger car type is as follows:

s.t.

the formula (17) is the upper and lower limit constraint of the passenger carrying number of the vehicle, and should be between 0 and the number of the nuclear passengers of the vehicle; the formula (18) represents the upper limit constraint of the actual passenger number of the first departure, and the number of passengers actually carried away by the dispatched passenger car at the first moment is less than or equal to the number of passengers arriving in the passenger station; the formula (19) is an upper limit constraint of the number of accumulated remaining waiting passengers in the station, and the number of passengers actually carried by the passenger car at the moment k is less than or equal to the number of passengers staying in the station at the moment; the formulas (20) and (21) are restriction of departure time 0, 1; 1 represents that the passenger car dispatching exists at the moment, namely the actual number of passengers is more than or equal to 1 at the moment, and 0 represents that the passenger car dispatching does not exist; equation (22) represents the constraint of the upper and lower limits of departure times, which should be less than the maximum number of passenger cars offered to a single line by the passenger station and greater than the quotient of the total number of passengers and the number of passengers loaded on each passenger car.

Example two

If a passenger car company only dispatches a single type of passenger car to carry passengers, since only passenger cars of one type of seating number can be dispatched, there is a possibility that the passenger car dispatching flexibility is not high enough and the profit margin is reduced. Therefore, if a passenger car company uses various types of vehicles for scheduling, the flexibility of passenger car operation is greatly improved, and the company can generate more profits as much as possible.

Because the types of the single-class passenger cars and the types of the multiple-class passenger cars are only different in the dispatching of the passenger cars, the passenger car type range is increased in the embodiment, and the dispatching interval optimization model of the multiple-class passenger cars can be established.

In the embodiment of the invention, a target model of the bus departure frequency is constructed under the constraints of the number of bus passengers, the operation profit of a bus company, the bus operation cost, the departure interval and the number of buses by aiming at the minimum cost of the passenger trip time and the maximum profit of the bus operation company. The construction of the multi-type passenger car model is mainly different from the construction of the single-type passenger car model in the number of types of passenger car scheduling, so that the types of vehicles in the multi-type passenger car model have a certain range, and the construction mode is as follows:

the operation profit model of the passenger car company is a maximization model, and the difference between income and expenditure of the company is obtained in a maximization mode. Where the corporate spending portion is the sum of fixed and variable costs for multiple types of vehicles.

The waiting cost of the passengers is closely related to the accumulated waiting time of the passengers in the passenger station, the accumulated waiting time of the passengers is the product of the accumulated number of waiting people and the interval of each moment, the number of waiting people at each moment in the passenger station is the difference between the accumulated number of arriving people at each moment in the passenger station and the accumulated number of actually carried passengers of the passenger car at each moment, and the minimum waiting cost of the passengers is the minimum accumulated waiting time model.

In life, the profit of the passenger car and the comfort of the passengers are not distributed equally, and are usually weighted, and distribution is needed, that is, weights are introduced for weighting, and a weighted model formula is shown as follows:

wherein N is₁、N₂As follows below, the following description will be given,

indicating whether the passenger car departs at time k, and is 1 if the passenger car departs,

is shown in

1 when a passenger car is started, if

When the value is 0, the passenger car type 1 is dispatched and started, and when the value is 1, the passenger car type 2 is dispatched and started.

s.t.

The formula (28) is the upper and lower limit constraint of the actual load number of the passenger car, the lower limit should be more than 0, and the upper limit should be less than the number of people staying in the station; formula (29) represents the upper limit constraint of the actual passenger number of the first departure, and the actual passenger number of the dispatched passenger car at the first moment is less than or equal to the number of arriving passengers in the passenger station; the formulas (30) and (31) are constraint of departure time 0 and 1, wherein 1 represents that the passenger car dispatching exists at the moment, namely the actual number of passengers is more than or equal to 1 at the moment, and 0 represents that no passenger car dispatching exists; the formula (32) represents the constraint of the upper limit of the number of departure times, and the number of departure times should be less than the maximum value of the number of passenger cars provided for a single line by the passenger station; in the formula

is shown in

1 when a passenger car is started, if

If the number of the passenger cars is 1, the passenger car type 1 is dispatched and started, and if the number of the passenger cars is 0, the passenger car type 2 is dispatched and started; in the formula (34), MM is the maximum value set in the model; equations (35) and (36) represent the actual passenger number constraint of model 1 and the actual passenger number constraint of model 2, respectively.

EXAMPLE III

Referring to fig. 4, the embodiment further provides a passenger car departure interval optimization modeling apparatus, including:

a source data screening menu 201, which is used for classifying and extracting source data to obtain operation information of a single line of a single type of passenger car;

the operation information processing unit 202 is used for modeling the operation information to obtain the operation income, the operation total cost and the passenger waiting cost of the single passenger car;

and the model establishing unit 203 is used for performing weighted modeling according to the maximization of the operation profit of the single-class passenger car and the minimization of the passenger waiting cost to obtain a single-class passenger car departure interval optimization model.

Example four

The data used in the embodiment is related data collected in a passenger transport shift system in Yunnan province, and the data comprises detailed information of a plurality of shift lines among a plurality of stations. The method comprises the steps of collecting original data from a system and storing the original data in a database file, importing the database file into an ORACLE database for primary processing of the data, and screening the original data by using a database language.

Since the present invention is a research on multiple lines at a single station, the present embodiment needs to select information of multiple lines at a single station for an experiment, and screen out useful information corresponding to the lines. The data format to be used after the screening will be represented in the following table. On the basis, because the single-day departure times of some lines in the multiple shift lines of a single station are less, the number of single-day experimental samples is too small and has no experimental property, the single-day departure frequency of some shift lines is too frequent and is not suitable for the experiment, and the experimental effect of the data may generate a less-ideal effect, so that the shift line data of the types are not suitable for the experiment of departure interval optimization. Meanwhile, in order to enhance the contrast and highlight the optimization effect of the passenger car departure interval optimization model, some line information with the fixed departure interval characteristic is preferably selected when experimental data are selected, so that the comparison between the experimental result and the original data by using the class line data is more obvious. When the route information of the departure interval at fixed time is selected, a plurality of shift lines meeting the conditions need to be selected, so that data can be conveniently screened in the experiment process, and an optimization result when a plurality of lines in one station are dispatched at the same time needs to be obtained in the experiment. Therefore, when selecting data, a plurality of groups of lines are selected, the bus dispatching frequency is moderate, and the bus dispatching interval is basically the same. The data after screening are shown in table 1:

TABLE 1

And observing that all information of the passenger station to be researched can be selected from the database by using the starting station name according to the screened data table information, and on the basis, adding the limitation of the departure date and time range to obtain the relevant information of all passenger car dispatching in a single passenger station in a period of time.

Fig. 5 is a diagram of the full line loading rate of the hamming station according to an embodiment of the present invention;

the full line bus operating load rate situation for a Kunming station of 21 days from 22 months 4 to 12 months 5 in 2018 is presented in FIG. 5. The passenger car carrying condition of a passenger car station, namely the full load rate condition of the passenger car, has a direct relation with the overall operation condition of the passenger car, and a plurality of problems existing in the overall passenger car full load rate of the Kunming station are analyzed from the figure in the following. As can be seen from the graph, the median of the loading rates of the Kunming station has variable height and large fluctuation amplitude, for example, the median of 29 days in 4 months is at the lowest position in multiple days; meanwhile, the maximum value and the minimum value of the rectangle of the single-day full-rate box-shaped graph are far away from each other, and the two days of day 4, 28 days and day 4, 29 days are very obvious in display phenomenon; the problem that the number of the passenger stations is large in distribution, the distribution range is wide and the distribution is scattered exists in the extremely small full load rate of almost every day. The problems suggest that the current operation condition of the passenger car still needs to be improved, the overall full-load rate is improved, and the operation income of the passenger car company can be effectively improved.

After the full load rate condition of the Kunming station is obtained, the information in the screened experimental data basic format table is obtained, a line of a certain fixed shift line is screened out according to the starting station name and the arriving station name, whether departure intervals are the same or not is judged according to adjacent time in the same departure date, and the full load rate condition of the passenger car during each passenger car dispatching is obtained according to the information of the passenger number and the real load number.

Shown in fig. 6 is a box plot of the kunming-ropin line month-passenger full rate in six months of 12 months in 2017 and 1 to 5 months in 2018. The information is obtained on the basis of the existing scheduling mode, and it can be seen that the average value of the full load rate in six months is unstable, for example, the average value is in the lowest state in 2018 in month 2, so that it can be seen that the full load rate in the line is not always in the optimal state; meanwhile, the distance difference between the maximum value and the minimum value of the upper and lower sides of the box-shaped diagram rectangle corresponding to each month is also shown to be larger in the interval between 1 month and 4 months in 2018; finally, the figures show that the extremely small full-load rate of each month generally has the phenomena of scattered distribution and wide distribution range. The invention can bring the collected passenger car dispatching data into the model of the invention, and the dispatching mode obtained after model calculation can solve the problems.

In order to determine the basic input part of the model, the number of real-load seats in the actual data is used as an input basis, the number of passengers arriving at a passenger station in a very short time (the probability of occurrence of an event) can be considered to be very small, the number of passengers arriving in each short time is stable, and the number of arriving passengers is in direct proportion to the length of the time; whether passengers arrive at the passenger station in each period of time or not is independent, so that the passengers arrive according to the Poisson distribution, historical information of the number of real passenger-loaded passengers of the passenger car generates random numbers of the number of the passengers arriving at a plurality of moments through a Poisson distribution function, and accurate input values are determined for the model after the data are processed.

The fare of the passenger car is obtained according to the file of 'the development and reform committee of the Yunnan province of the transportation hall of the Yunnan province about the issuance of the notice of the road transportation price management regulation of the Yunnan province'. The parameters used in the model are shown in table 2 below:

TABLE 2

The overall income table of the original data, particularly the passenger waiting cost in the income table, and the ticket income and the passenger car operation cost are calculated by using the parameters in the parameter table through inquiring the parameters in the experiment test basic parameter table. The passenger waiting cost is calculated by distributing the actual passenger carrying number of each passenger in each bus in each day to each possible departure time in the actual original data, then sequentially multiplying the accumulated number of the waiting passengers at each time by the waiting time of each time and the waiting cost of unit time to obtain the waiting cost of each time, and finally accumulating the waiting costs of all the times in the day to obtain the accumulated waiting cost of the passengers. The ticket income is the product of the total number of passengers and the price of a single ticket on the same day. The operation cost of the passenger car is related to the number of vehicles, the operation cost of the passenger car and the total number of passengers. The income is the ticket income of the passenger car operation company minus the passenger waiting cost and the passenger car operation cost. This resulted in the raw data revenue table, shown in table 3.

TABLE 3

The cost and revenue of the passenger car operating company and the cost of passenger waiting before model optimization are shown in table 3. The data of 21 days including 7 days before, 7 days in and 7 days after the festival are selected in the table, so that the selected data fully represent the characteristics and are comprehensive of most data. Obtained by observing the data in the table for 21 days: the income amount of a passenger car company is large in fluctuation and unstable, and the income of individual days is even negative, such as the income value of 4 months and 28 days in 2018; in a few days with less passengers, the income of a passenger car operation company is low, which indicates that the original way of dispatching the passenger car by the passenger car operation company cannot well cope with the situation with less passengers, such as 24 days in 4 months and 28 days in 4 months in 2018; on some days with more passengers, the waiting cost of the passengers is higher, namely the waiting time of the passengers is longer, such as data of 4-month 30-day, 5-month 1-day and 5-month 12-day in 2018.

Therefore, the income, the fixed cost and the variable cost of the passenger car operation company can be obtained through the information in the lookup table, and the sum of the cost and the passenger riding cost can be substituted into the model to carry out model solution on the basis of obtaining the data, so that the passenger car dispatching mode after model optimization is obtained. In this chapter, CPLEX software is used for solving an objective function, namely a function for solving the maximum total benefit value finally obtained after combining the minimum passenger trip time cost and the maximum passenger car operation company profit.

Firstly, carrying out experimental test on a single type of passenger car, selecting multi-day passenger car data with the same departure interval of the passenger car in a Kunming-Luo-plain line and double types of dispatching vehicles of a passenger car operation company every day to carry out experiments, and then respectively solving the data with the double types of dispatching vehicles of the original data by using models of a whole car and a whole cart. And finally, respectively operating the full-small car model and the full-large car model to obtain the results of corresponding passenger car operation company and passenger objective functions. After the model result is obtained, the benefit of the passenger car dispatching mode obtained by the model needs to be calculated, and the effect of the model on passenger car dispatching optimization can be observed by comparing the benefit of the model result with the benefit of the original data.

And substituting the data in the experimental parameter table into the result of the model to calculate various data in the model result income table. The income of the ticket is still the product of the total number of passengers and the fare, the operation cost of the passenger car is the product of the number of the passengers and the cost of a single passenger car, and the waiting cost of the passengers is the product of the accumulated waiting time of the passengers and the waiting cost of the passengers in unit time. The following model result revenue tables for the small and large cars are obtained as shown in tables 4 and 5.

TABLE 4

Date	Number of vehicles	Total number of passengers	Waiting cost of passengers	Ticket income	Cost of passenger car operation	Gain of
							2018/4/22	20	574	5301	41328	27245	8781
2018/4/23	20	556	4973	40032	27231	7827
							2018/4/24	17	485	5156	34920	23156	6607
2018/4/25	20	555	4938	39960	27230	7791
							2018/4/26	19	538	5115	38736	25877	7743
2018/4/27	19	528	4738	38016	25869	7408
							2018/4/28	13	345	3688	24840	17687	3464
2018/4/29	18	516	5214	37152	24520	7417
							2018/4/30	21	601	5318	43272	28606	9346
2018/5/01	23	663	5443	47736	31334	10958
							2018/5/02	20	578	5913	41616	27248	8454
2018/5/03	20	558	4872	40176	27232	8071
							2018/5/04	18	517	5269	37224	24521	7433
2018/5/05	20	559	5078	40248	27233	7936
							2018/5/06	20	561	4846	40392	27235	8311
2018/5/07	20	553	4976	39816	27228	7610
							2018/5/08	21	588	4747	42336	28596	8992
2018/5/09	20	557	4959	40104	27231	7913
							2018/5/10	20	552	4680	39744	27227	7835
2018/5/11	20	579	5840	41688	27249	8597
							2018/5/12	23	655	5240	47160	31328	10591

The result income table of the trolley model in the table 4 is compared with the cost income table of the original data to find that the number of the vehicles obtained by the whole trolley model is not equal to the number of the vehicles scheduled by the original data, but the waiting cost of passengers of the trolley in the table 4 is greatly reduced, and meanwhile, the operation cost of the passenger car is also lower than that of the original data, so that the income amount of the income table of the trolley scheduling model is greatly increased compared with the original data.

TABLE 5

Table 5 the big car model result income table and the former data cost income table are compared and are found that the number of vehicles of the whole big car passenger train dispatch of every day generally reduces, and the cost of waiting that the model obtained has the rise to descend with the former data comparison, and in the aspect of passenger train operation cost, the scheduling mode that big car model reachs makes holistic passenger train operation cost reduce to some extent, compares whole income data with former data income on whole income and has improved by a wide margin.

Obtaining passenger car operation cost according to the ticket income in tables 3 to 5, wherein the total cost is the sum of the passenger car operation cost and the passenger waiting cost, the waiting cost is divided by the total number of passengers to obtain per-person data, namely per-person income (the ticket price is 72 yuan), per-person passenger car operation cost, per-person total cost and per-person waiting time, and obtaining the original data, a passenger car operation cost graph of the whole car and the big car according to the data graph. In the figure, a green circle-shaped folding line is the average passenger income, a black star-shaped folding line is the average passenger car operation cost, a red plus sign-shaped folding line is the average passenger car cost, and a blue fork-shaped folding line is the average passenger car waiting cost, as shown in figure 7.

As can be seen from the four broken lines in fig. 7a, in the process of dispatching vehicles by using original data, the fare of each person is 72 yuan, the operation cost of each passenger car is between 50 yuan and 100 yuan, the total cost of each person fluctuates between 50 yuan and 110 yuan, the operation cost and the total cost of each passenger car are both near the peak value of 110 yuan at 28 days of 4 months, so that the fluctuation range of the original data is too large and unstable in terms of visible cost, and meanwhile, the waiting cost of each person is in a relatively stable state and is between 10 yuan and 11 yuan. Therefore, the original data bus operation cost map fully illustrates that the original bus dispatching mode has many imperfect places, and a considerable improvement space is provided in the subsequent optimization process.

In fig. 7b, it can be known that the average passenger income is 72 yuan when all vehicles are dispatched as cars, the operating cost of the average passenger car basically fluctuates between 45 yuan and 50 yuan, the average passenger car basically fluctuates between 55 yuan and 65 yuan, and the average waiting cost ranges between 8 yuan and 10 yuan.

As can be seen from fig. 7c, when all the scheduled vehicles are large vehicles, the per-person income is 72 yuan, the per-person passenger vehicle operation cost fluctuates between 40 yuan and 50 yuan, the per-person total cost fluctuates between 50 yuan and 60 yuan, the per-person waiting cost fluctuates between 10 yuan and 15 yuan, although the fluctuation range is large, other points except three maximum value points are located between 10 yuan and 12 yuan, and the fluctuation phenomenon is generated to be the goal of the final model to achieve the maximum total benefit of both the whole passenger vehicle and the passengers.

The comparison between fig. 7b and fig. 7c shows that the per-passenger operating cost of the passenger car in the full-bus dispatching mode is slightly higher than that of the full-bus, the per-passenger total cost is not much different from that of the full-bus, and the waiting cost of the full-bus is larger than that of the dispatching cars which are all cars, because the number of seats of each bus is larger than that of the cars, the number of allocated buses is naturally smaller than that of the cars under the condition that the total number of passengers is not changed, and therefore the waiting time of the passengers is generally longer than that of the cars when the dispatching cars are all the buses. On the basis, comparing fig. 7b, 7c and 7a, the data of the per-person operation cost and the per-person total cost obtained after model optimization is found to be greatly improved compared with the original data in terms of range size and overall stability, while in terms of the per-person waiting cost, the per-person waiting cost of all the trolleys is basically and integrally smaller than the original data, the fluctuation range of the per-person waiting cost of all the trolleys is larger than the original data, and the per-person waiting cost of points except maximum points is basically close to the original data. Therefore, most data after model optimization are more stable than original data, and the data fluctuation range is smaller than the original data.

In this embodiment, data of 23 days 4 month, 28 days 4 month, 1 day 5 month, and 7 days 5 month in 2018 are used to obtain a graph 8. Wherein FIG. 8 is a single day of raw data and two models of departure time-departure shift sequence number. And obtaining the dispatching sequence and time of each passenger car when dispatching according to the dispatching information of the original data and the results obtained by the large car model and the small car model. In fig. 8, a square broken line represents the departure time broken line when all the scheduled vehicles are large vehicles, a cross broken line represents the departure time broken line when all the scheduled vehicles are small vehicles, a light star line represents the departure time broken line of the original data, points of the star line represent that the scheduled vehicles are small vehicles at the moment, and points of the dark star line represent that the scheduled vehicles are large vehicles.

From fig. 8, the following information can be obtained: 1) the square broken line represents the relationship between the departure times and the departure time of the dispatched buses, and is generally positioned above the line (light star line) of the original data, so that the departure time of the buses is almost later than that of the buses in the original data when the buses are dispatched. 2) The dispatching cars are all car lines which are cross-shaped broken lines, the cross-shaped lines are basically lower than the light-colored star-shaped lines of the original data when the dispatching cars are in the range from 1 to 15 in the dispatching shift, and the car and the original data lines are attached together from the serial number 16, because the original data from the serial number 16 and the car dispatching types of the cars are the cars, so the original data in the range are close to the dispatching time interval of the cars. The dispatching time of the first 15 vehicles in the dispatching of the trolleys is smaller than that of the passenger vehicles in the original data, and the dispatching time of the trolleys from the back is close to the dispatching time of the original data until the dispatching time is close to the dispatching time of the original data. 3) The departure time of the last dispatching passenger car of the three broken lines is basically the same. 4) When all the dispatched passenger cars are large cars, the number of the passenger cars is less than that of the original data and the number of the passenger cars of the small cars, and because the seat number of the large cars is greater than that of the small cars, the number of the vehicles required when all the passenger cars are dispatched to use the large cars is less than that of the vehicles required when the large cars and the small cars are alternately used and the small cars are all used; 5) when all the dispatched passenger cars are the trolleys, the number of the passenger cars is basically close to or even the same as that of the original data. When dispatching vehicles, the condition that all vehicles are small vehicles is mixed with the original data large vehicles, and the total number of the vehicles is close to or the same with the total number of the vehicles under the influence of dispatching interval distribution.

After the dispatching results of the models of the whole trolley and all the calculated parameter results are obtained, as the predicted value of the number of passengers is not completely the same as the true value, in order to carry out sensitivity analysis on the optimization results and test the adaptability of the results obtained by the models to different input data, a plurality of groups of test experiments are required, namely a plurality of groups of random numbers of the number of passengers arriving at a plurality of moments are generated by real historical information through a Poisson distribution function as input, the results of the models are substituted into different input data to calculate all required data, and then whether each parameter is in a better state compared with the original data and the model data or not is observed. In the testing process, firstly, generating a plurality of groups of different inputs by using the function of Poisson distribution; then substituting the scheduling result obtained by the model, and compiling a program to obtain the actual passenger carrying number of the vehicle corresponding to each scheduled vehicle moment; and finally, calculating the product of the accumulated number of the detained people and the time length of each division moment to obtain the total waiting time.

The model test selects the results of the model of the full car and the full cart in 2018, 4, month and 23, and fig. 9a and 9b are box diagrams of waiting time and income obtained by 20 groups of test data of the full car and the full cart. The average of the waiting time obtained from the full car test data obtained in fig. 9a is about 10500min, and the upper limit and the lower limit are about 11000min and 9700min, respectively; the average value of the waiting time of the test data of the whole bus is 13700min, and the upper limit and the lower limit are 15000min and 12500min respectively. The waiting time of the whole trolley model and the whole big car model is 8575min and 11550min, and the waiting time of the original data is 10568 min. The waiting time of the test data of the whole cart or the whole trolley is larger than that obtained by the model. FIG. 9b shows that the average of the yields of the full car test results is about 6800 Yuan, with the upper and lower limits being 7100 Yuan and 6400 Yuan, respectively; the average value of the benefits of the test results of the whole large vehicle is 7700 yuan, and the upper limit and the lower limit are 8400 yuan and 6900 yuan respectively. The profit values originally obtained by the full cart model and the full cart model are 7827 yuan and 9104 yuan, the profit of the original data is 4255 yuan, so the profit values of the full cart and the full cart test result are all smaller than the profit values obtained by the model, and the profit values of the test result are all larger than the profit of the original data. The result of the model test reflects that the effect of each parameter value after the model result is substituted into new input is better than the original effect of the original data in terms of the two aspects of waiting time and income.

In this section, the waiting time and the benefits of the multi-day all-trolley and all-trolley models are tested, and a graph 10 is obtained after the test. FIG. 10a is a diagram of waiting time for a model test of a full bus or a passenger car; FIG. 10b is a diagram of waiting time for a full car model test; FIG. 10c is a graph of the model test yield of a full bus; FIG. 10d full car passenger car model test yield plot.

The waiting time and the income graph of the test of the whole big car and the whole small car select 21 days of data, 50 groups of random input data are used for testing the test data of each day, a box graph in the four graphs indicates the range of the test data, a yellow broken line graph indicates model data, and a green broken line graph indicates original data. The waiting time chart of the full-bus model test shows that the waiting time data of the full-bus model is larger than the original data; the original data is that two vehicles are alternately scheduled, and if only a large vehicle is used for scheduling, the waiting time is inevitably longer than the original data; the waiting time broken line of the test data of the whole cart is higher than the original data broken line and the data broken line of the whole cart model, but the minimum value of the test data is almost close to the model data. The waiting time chart of the full car model test shows that the waiting time data of the full car model is smaller than the original data, because the original data is that two cars are alternately scheduled, and if only the car is used for scheduling, the waiting time is necessarily smaller than the original data; the waiting time of the whole trolley test data is higher than the broken line of the original data, and meanwhile, the whole trolley test data is also positioned on the data of the whole trolley model, but the test data is generally positioned between the whole trolley model data and the original data. The model test income graph of the whole bus and the passenger car shows that the maximum value of the benefit data of the whole bus test is almost fit with the model data of the whole bus, and the test data is basically positioned on the original data. The full-trolley passenger car model test profit graph shows that the maximum value of full-trolley test profit data is almost fitted with full-trolley model data, and the test data is basically located on the original data. Meanwhile, the fluctuation range of the data is stable except the data of 4 months and 28 days. Therefore, waiting time and income data of all-large-vehicle and all-small-vehicle tests are close to model data, adaptability is good, the test effect of the whole model obtained from stable fluctuation of the test data is good, and the repeated application effect of the model is stable.

EXAMPLE five

In order to improve the income and the flexibility of dispatching vehicles, a passenger car operation company generally adopts at least two types of vehicles for dispatching, and a multi-type passenger car model is more fit with the condition of actual passenger car dispatching. The calculation mode of each parameter of the dual-type passenger car model result income table is the same as that of the single-type passenger car model result income table, and the table 6 is a dual-type passenger car model result income table.

TABLE 6

The results cost benefit table of the double-type passenger car model shown in the table 6 is compared with the original data cost benefit table shown in the table 3 to obtain: the number of vehicles scheduled by the double-type passenger car model is less than the total number of vehicles scheduled in the original data, because the number of the trolley vehicles in most days is reduced compared with the number of the trolley vehicles in the original data; except that data of 5 months, 1 day and 8 days have some values which fluctuate near the original data, the waiting cost of the passengers obtained by the double-type passenger car dispatching model is lower than the value of the original data; the optimized result of the model of the double-type passenger car is greatly reduced compared with the original data; the gains except the data of 5 months, 1 day and 8 days are improved compared with the original data. The effect of each parameter of the result of the double-type model is greatly optimized compared with the effect of the original data.

According to the data in the table 6, the data such as the per-person income, the per-person passenger car operation cost, the per-person total cost, the per-person waiting time and the like can be obtained in the same calculation mode as that of the single-type car, and the passenger car operation cost graph with double-type data is obtained by drawing according to the data. In the figure, the green circle-shaped folding line is the per-capita income, the black star-shaped folding line is the per-capita passenger car operation cost, the red plus sign-shaped folding line is the per-capita total cost, the blue Y-shaped folding line is the per-capita waiting cost, and the operation cost of the double-class passenger cars is shown in figure 11.

Referring to fig. 11, when the dual-type vehicles are mixedly scheduled, the per-person income is 72 dollars per fare, the per-person bus operating cost fluctuates between 40 to 45 dollars, the per-person total cost fluctuates between 50 to 60 dollars except for two peaks, and the per-person waiting cost also fluctuates between substantially 10 dollars to 12 dollars except for two peaks. In conclusion, the overall data of the dual-type passenger car model is stable except that the waiting cost data of two passengers exceed the fluctuation range of other data.

By comparing the operation cost diagrams 11 and 7c of the double-type passenger car and the original data passenger car, the data overall numerical values of the operation cost of the passenger-average passenger car and the total cost of the passenger-average passenger car of the optimized model data are reduced to some extent, the fluctuation range is also greatly reduced, the data stability is greatly improved, and the data in the aspect of the waiting cost of the passenger-average passenger car is expanded to some extent compared with the fluctuation range of the original data.

FIG. 12 is a diagram of departure times of the dual-class car models of 2018-4-23, 2018-4-28, 2018-5-1 and 2018-5-7, respectively; in fig. 12, the broken line of the light star indicates the departure time line of the original data, the point of the dark star is a cart, the point of the light star is a cart, the broken line of the square in the figure is a departure time broken line of a dual-type model, the square indicates that the type of the vehicle to be dispatched at this time is a cart, and the square is hollow indicates that the type of the vehicle to be dispatched is a cart. As can be seen by observing the two broken lines of the original data and the dual-type model in the graph, the total number of the vehicles scheduled after the dual-type model is optimized is less than that of the original data; meanwhile, the broken lines of the double-type model are generally positioned above the original data, which shows that the time for dispatching each vehicle by the double-type model is almost later than the original data; the dual-type model is substantially the same as the launch time of the last vehicle scheduled by the original data. FIG. 13 is a diagram showing the number of raw data and two types of model cart and cart vehicles in the present embodiment; FIG. 14 is a graph of raw data and dual-type model passenger population and benefit in this embodiment.

Finally, it should be noted that: the above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and a person of ordinary skill in the art can make modifications or equivalent substitutions to the specific embodiments of the present invention with reference to the above embodiments, and any modifications or equivalent substitutions which do not depart from the spirit and scope of the present invention are within the scope of the claims of the present invention as filed.

Claims

1. A single-class passenger car departure interval optimization modeling method is characterized by comprising the following steps:

2. The modeling method for optimizing departure interval of single-class passenger car according to claim 1, wherein the source data is classified and extracted to obtain operation information of a single line of the single-class passenger car; the method specifically comprises the following steps:

3. The method as claimed in claim 2, wherein the source data comprises shift codes, line codes, enterprise codes, shift properties, shift types, start codes, end codes, departure dates, departure times, license plate numbers, passenger types, passenger classes, seat types, passenger seats, number of seats in real load, number of maximum passengers in the passenger class, number of remaining passengers in the passenger class, ticket stop-sale identifiers, data signatures, data summaries, creation times, modification times, name of passenger station of origin, name of passenger station of arrival, name of station of arrival, vehicle colors, operation times, operation mileage, ticket prices, line types, identifiers submitted to departments, and information of remaining seats of the whole vehicle.

4. The modeling method for optimizing the departure interval of the single-type passenger car according to claim 1, wherein the modeling of the operation information obtains operation income, total operation cost and waiting cost of the single-type passenger car; the method specifically comprises the following steps:

establishing a single-class passenger car operation income model;

and establishing a single passenger car passenger waiting cost model.

5. The modeling method for optimizing departure interval of single-class passenger car according to claim 4, wherein the model for operation income of single-class passenger car is established; the method specifically comprises the following steps:

calculating the operation income of the single line, and summing the operation income of the single line to obtain a single-class passenger car operation income model; the operation income of the single line is the accumulated sum of the product of the ticket price corresponding to each moment and the passenger car passenger carrying total number.

6. The modeling method for optimizing the departure interval of the single-class passenger car according to claim 4, wherein the model for establishing the total operating cost of the single-class passenger car is established; the method specifically comprises the following steps:

calculating the operation cost of the single line, wherein the operation cost is the sum of the fixed cost and the variable cost;

and accumulating and summing the operation cost of the single line to obtain the operation total cost of the single type of passenger car.

7. The modeling method for optimizing departure interval of a single passenger car according to claim 6, wherein the fixed cost is a vehicle depreciation fee generated in the process of a passenger operator engaging in a road class passenger service; the variable cost is the workload cost of passenger car operation, and mainly comprises the cost of passing a bridge, the cost of fuel oil, the cost of vehicle maintenance, the cost of manpower and the cost of passenger station service.

8. The modeling method for optimizing departure interval of a single-class passenger car according to claim 4, wherein the model for the waiting cost of the single-class passenger car is established; the method specifically comprises the following steps:

calculating the accumulated number of waiting passengers, wherein the accumulated number of waiting passengers is the difference between the number of arriving passengers at a passenger station at a certain time point on all lines and the actual number of passengers carried by a passenger car at the time point;

and carrying out approximate estimation on the waiting time of passengers in the passenger station to obtain the accumulated waiting cost of the passengers, wherein the accumulated waiting time of the passengers is the product of the accumulated number of the passengers waiting in the passenger station and the interval length between each optional moment.

9. The method for optimally modeling the departure interval of the single-class passenger car according to claim 1, wherein the optimal model of the departure interval of the single-class passenger car is obtained by performing weighted modeling according to the maximization of the operation profit of the single-class passenger car and the minimization of the waiting cost of passengers; the method specifically comprises the following steps:

establishing a single-class passenger car operation profit maximization model;

establishing a passenger waiting cost minimization model;

10. The method according to any one of claims 1 to 9, providing a passenger car departure interval optimization modeling apparatus, comprising:

the source data screening unit is used for classifying and extracting source data to obtain the operation information of a single line of a single type of passenger car;