CN-121981482-A - High-speed railway ticket allocation optimization method and system based on heterogeneous social distance

CN121981482ACN 121981482 ACN121981482 ACN 121981482ACN-121981482-A

Abstract

The invention discloses a high-speed railway ticket distribution optimizing method and system based on heterogeneous social distances, which comprises the steps of constructing a mapping relation between heterogeneous social distances, seat layout and train capacity, acquiring passenger demand quantity, running train quantity of each OD pair and risk information of areas where stations are located, establishing a ticket distribution scheme mathematical model which aims at maximizing passenger income and meets passenger demand constraint, fairness constraint, heterogeneous social distance constraint and interval capacity constraint based on the acquired data, converting the ticket distribution scheme mathematical model into an equivalent integer linear programming model, and solving to obtain a train capacity and social distance control scheme and an optimal ticket distribution scheme. The invention can greatly improve the utilization rate and the income of railway transportation capacity on the basis of ensuring the safety of passengers.

Inventors

XU GUANGMING
LI CAN
Zhong Linhuan
LIU XINYI
Yan Mengrong

Assignees

中南大学

Dates

Publication Date: 20260505
Application Date: 20260128

Claims (7)

1. A high-speed railway ticket allocation optimization method based on heterogeneous social distances is characterized by comprising the following steps: s1, constructing a mapping relation between heterogeneous social distances, seat layout and train capacity; S2, acquiring the passenger demand quantity, the number of trains running and the risk information of the region where each station is located of each OD pair, and establishing a ticket allocation scheme mathematical model which aims at maximizing the income of the passenger ticket and meets the passenger demand constraint, the fairness constraint, the heterogeneity social distance constraint and the interval capacity constraint based on the acquired data; and S3, converting the mathematical model of the ticket allocation scheme into an equivalent integer linear programming model and solving to obtain a train capacity and social distance control scheme and an optimal ticket allocation scheme.
2. The optimization method for ticket allocation of high-speed railway based on heterogeneous social distance according to claim 1, wherein the step S1 specifically comprises: S11, assume that all regions are divided into Setting social distance measures of corresponding levels according to each risk level; S12, determining seat layout under each level of social distance measures according to the carriage layout and the social distance requirement; s13, calculating the corresponding actual capacity of the train based on the seat layout under different social distances.
3. The method for optimizing ticket allocation for high-speed railways based on heterogeneous social distances according to claim 1, wherein in the step S2, the step of obtaining the passenger demand of each OD pair in a prediction manner by training a traffic demand prediction model comprises: Step a1, constructing a training set The training sample features comprise features of all OD pairs of the existing high-speed railway in a time period, and the labels of the training sample are actual ticketing amounts of the OD pairs of the research railway in the corresponding time period; Step a2, constructing a traffic demand prediction model based on a machine learning algorithm, and utilizing a training set Training a traffic demand prediction model to predict and obtain the passenger demand of each OD pair 。
4. The high-speed railway ticket allocation optimization method based on the heterogeneous social distance according to claim 1, wherein the ticket allocation scheme mathematical model established in the step S2 is characterized in that the objective function is as follows: (1); Wherein, the For integer decision variables, representing trains Pair OD pair Belongs to the ticket allocation scheme to be optimized; Representing OD pairs Is the fare of (1); a set of all OD pairs; the method is a set of all running trains; Respectively OD pairs To the initial and final stations of (a); Passenger demand constraints are specific to each OD pair The total number of passengers served by the train cannot exceed the passenger demand of the OD pair: (2); In the formula, Representing the data from the OD pair Passenger demand of (2); Representing a service OD pair Is a train set; Fairness constraints are that minimum service proportion is specified for passengers per OD : (3); The social distance constraint is that if passengers from the first-class risk area are loaded on a train in a certain interval, first-class social distance measures are adopted, and the actual capacity of the train is realized Is that If the train is loaded with the information from the section Passengers from regions of higher risk and absence of passengers from regions of higher risk Is used by the passenger of (a), , Then take Stage social distance measure, actual capacity of train Is that When there is only the lowest risk level from the train Passenger or unmanned, actual capacity of train Are all ; (4); In the formula, Is a train In the interval The actual capacity of the train to be output belongs to a control scheme of the capacity of the train to be output and the social distance; Representing the passing interval OD pair sets of (a); representing a risk level OD pair sets of (a); represent the first Train capacity under the requirement of level social distance Is a line section between two adjacent stations; a set formed for all line intervals; The interval capacity constraint is that the train In each interval The actual passenger capacity in the section does not exceed the actual capacity of the section ; (5); The non-negative and integer constraints of the decision variables are additionally set as: (6); (7)。
5. The high-speed railway ticket allocation optimization method based on the heterogeneous social distance according to claim 4, wherein the step S3 is to convert a ticket allocation scheme mathematical model into an equivalent integer linear programming model, specifically: S31, carrying out equivalent reconstruction on the nonlinear constraint type (4) by introducing 0-1 variable Representation interval Inner train Whether the service comes from a risk level If serving the passenger of (2) Otherwise, 0: (8); (9); (10); In the formula, Is a sufficiently large positive number; S32, introducing auxiliary 0-1 variable Calculating a train In the interval Is to be operated in the actual capacity of (a) Wherein Take the value by Decision, namely: (11); (12); (13); (14); (15); S33, converting the model into an integer linear programming model shown as follows: (16)。
6. The high-speed railway ticket allocation optimization method based on heterogeneous social distance according to claim 1, wherein a business solver is used for solving an integer linear programming model to obtain a train capacity and social distance control scheme Optimal ticket allocation scheme 。
7. The high-speed railway ticket allocation optimization system based on the heterogeneous social distance according to claim 1, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to implement the method according to any one of claims 1 to 6.

Description

High-speed railway ticket allocation optimization method and system based on heterogeneous social distance Technical Field The invention belongs to the technical field of data processing, and particularly relates to a high-speed railway ticket allocation optimization method and system based on heterogeneous social distances. Background In recent years, the outbreak of serious infectious diseases such as influenza A H1N1, SARS, COVID-19 and the like brings new challenges to the safe and efficient operation of high-speed railways. The high-speed railway is not only the main artery of national economy, but also an important link for controlling the spread of infectious diseases, and the operation safety and epidemic prevention efficiency are directly related to the stable operation of social economy. To ensure safe travel of passengers, maintaining a certain social distance on the train by the passengers becomes an important means for reducing infection risk. However, there is a significant conflict between the social distance of the passenger and the train boarding rate, in which when the social distance is reduced, the train boarding rate is increased, and the train ticket income can be increased but the infection risk is increased, whereas when the social distance is increased, the train boarding rate is decreased, and the train ticket income can be significantly decreased, and the infection risk is reduced. On the other hand, the high-speed railway line often spans a plurality of areas, the risk difference of different areas is obvious, and passengers from different areas have obvious difference in potential propagation risk, and if a single conservative social distance measure is still adopted, the transport capacity waste is easily caused. Disclosure of Invention Aiming at the contradiction between the social distance of the passengers and the boarding rate of the trains in the prior art, the invention provides a high-speed railway ticket allocation optimization method and system based on the heterogeneous social distance, which can greatly improve the utilization rate and income of railway capacity on the basis of ensuring the safety of the passengers. In order to achieve the technical purpose, the invention adopts the following technical scheme: A high-speed railway ticket allocation optimization method based on heterogeneous social distances comprises the following steps: s1, constructing a mapping relation between heterogeneous social distances, seat layout and train capacity; S2, acquiring the passenger demand quantity, the number of trains running and the risk information of the region where each station is located of each OD pair, and establishing a ticket allocation scheme mathematical model which aims at maximizing the income of the passenger ticket and meets the passenger demand constraint, the fairness constraint, the heterogeneity social distance constraint and the interval capacity constraint based on the acquired data; and S3, converting the mathematical model of the ticket allocation scheme into an equivalent integer linear programming model and solving to obtain a train capacity and social distance control scheme and an optimal ticket allocation scheme. Further, the step S1 specifically includes: S11, assume that all regions are divided into Setting social distance measures of corresponding levels according to each risk level; S12, determining seat layout under each level of social distance measures according to the carriage layout and the social distance requirement; s13, calculating the corresponding actual capacity of the train based on the seat layout under different social distances. Further, in the step S2, the step of obtaining the passenger demand of each OD pair by training a traffic demand prediction model includes: Step a1, constructing a training set The training sample features comprise features of all the OD pairs of the existing high-speed railway within the time period, and the labels of the training samples are ticketing data of the OD pairs within the corresponding historical time period. Step a2, constructing a traffic demand prediction model based on a machine learning algorithm, and utilizing a training setTraining a traffic demand prediction model to predict and obtain the passenger demand of each OD pair。 Further, the mathematical model of the ticket allocation scheme established in the step S2 has the objective function of: (1); Wherein, the For integer decision variables, representing trainsPair OD pairPassenger traffic volume of (2); Representing OD pairs Is the fare of (1); a set of all OD pairs; the method is a set of all running trains; Respectively OD pairs To the initial and final stations of (a); Passenger demand constraints are specific to each OD pair The total number of passengers served by the train cannot exceed the passenger demand of the OD pair: (2); In the formula, Representing the data from the OD pairPassenger demand of (2); Representing a ser