CN-115689054-B - Modularized bus operation scheduling optimization method, modularized bus operation scheduling optimization device and storage medium

CN115689054BCN 115689054 BCN115689054 BCN 115689054BCN-115689054-B

Abstract

The invention discloses a modularized public transportation operation scheduling optimization method, a modularized public transportation operation scheduling optimization device and a storage medium, wherein the method comprises the steps of determining public transportation lines providing modularized public transportation service and a range of stations involved, collecting passenger flow demand data of different stations, determining model parameter values according to operation characteristics and geographic environment factors of the public transportation lines, building a model, solving the model according to the passenger flow demand data and the public transportation operation characteristics to obtain an operation scheduling scheme under the condition of lowest total cost of the system, and optimizing the operation scheduling scheme of the modularized public transportation system to realize optimal balance in the aspects of economy, energy and environmental benefit. The invention considers the uncertainty and random delay of the arrival of the passenger flow, is more in line with the actual situation of the passenger when taking the bus, can provide a more practical scheme for the dispatching of the modularized bus, and improves the service efficiency and the social benefit of the bus system. The invention can be widely applied to the field of public transportation operation scheduling.

Inventors

PEI MINGYANG
LIN PEIQUN
Hong Yuanbo
ZHONG LINGSHU

Assignees

华南理工大学

Dates

Publication Date: 20260512
Application Date: 20221118

Claims (6)

1. The modularized bus operation scheduling optimization method is characterized by comprising the following steps of: s1, determining bus lines and related station ranges for providing modularized bus service, and collecting passenger flow demand data of different stations; s2, determining a model parameter value according to the operation characteristics of the bus line and geographic environment factors, and building a model; s3, solving a model according to passenger flow demand data and bus operation characteristics, and acquiring an operation scheduling scheme under the condition of lowest system total cost, wherein the system total cost comprises operation cost and passenger waiting cost; s4, optimizing an operation scheduling scheme of the modularized public transportation system to realize the optimal balance in the aspects of economy, energy and environmental benefit; The model is built by the following means: a1, determining an assumption condition to be met by the model according to passenger demands and modularized bus operation characteristics; a2, determining constraint conditions to be met by the model, wherein the constraint conditions comprise vehicle operation constraint, minimum headway constraint and constraint for meeting passenger demand service; A3, determining an objective function of the model as the minimum total cost of the system; a4, linearizing the established nonlinear programming model by using equivalent mathematical transformation; the expression of the vehicle operation constraint is: Wherein, the As binary variables, when the system is in Time slave station Scheduling The carriage saving time value is 1, otherwise, 0; Representing a collection of subway stations, ; A set of time points is represented and, ; Representing a set of the number of cars per station, ; The expression of the minimum headway constraint is: Wherein, the As binary variable, when a car is in Starting from a station at the moment, wherein the time value is 1, otherwise, the time value is 0; Representing a designed minimum headway between carriages; The departure time of a carriage on the station is represented; the expression of the constraint meeting the passenger demand service is: Wherein, the Is an integer variable, expressed in time Internally arriving station And go to the station Waiting for riding The number of passengers of the vehicle starting at the moment; is an integer variable, expressed in time Internal boarding station And want to go to the station Waiting for riding The number of passengers of the vehicle starting at the moment; Is shown in Departure station on departure vehicle The number of passengers; Representing at any given time Internally arriving station And go to the site The number of passengers of (1) satisfies Is a random variable subject to poisson distribution; The expression of the objective function is: Wherein, the Representing the average operating cost per car, Represents the energy consumption cost of each carriage for overcoming wind resistance, Environmental costs caused by greenhouse gas emissions from each car, Representing a site To site , Indicating the cost of the waiting time for the passenger, Representing from a first site to a site Is used for the time period of (a), Representing a time interval; The linearizing the established nonlinear programming model by using equivalent mathematical transformation comprises: converting the demand service constraint (1) to be satisfied by the model into the following linear constraint: Wherein the method comprises the steps of Representing a given larger positive number; Introducing auxiliary variables The demand service constraints (5) that the model needs to satisfy are converted into the following linear constraints: linearization is performed by the following formula: random variables that obey poisson distribution are represented in demand service constraints (2) and (3), expressed as the following formulas: Wherein, the Expressed in time Internal boarding station And want to go to the site Waiting for riding Average number of passengers of the vehicle starting at the moment; introducing auxiliary variables meeting opportunity constraints To linearize the demand service constraints (2) and (3): Wherein, the Indicating acceptable errors when And When known, the following formula is solved for Is the minimum value of (2): When (when) When the value is given to be given, The value is also fixed, thus When solving the objective function, can be seen as a constant, the demand service constraints (2) and (3) translate into: the objective function is then converted into the following linear problem: 。
2. The method for optimizing modular bus operation scheduling according to claim 1, wherein the step S1 specifically comprises: And determining bus lines and stations of the modularized bus service covered by the scheduling scheme, determining the number of stations, the number of carriages of each station and the distance between stations, and collecting passenger flow demand data from and to different stations, wherein the passenger flow demand data comprises the number and time of passengers from and to each station.
3. The modular bus operation scheduling optimization method according to claim 1, wherein the determining the model parameter values comprises: according to the running characteristics of the modularized buses, determining the number of passengers in the carriages, the average running cost of each carriage, the average waiting time cost of passengers, the average running speed of the vehicles and the minimum design headway; The average operation cost of each carriage comprises depreciation cost, energy consumption cost for overcoming air resistance and environmental pollution cost caused by carbon dioxide emission.
4. The modular bus operation scheduling optimization method according to claim 1, wherein the assumption condition in the step A1 includes: a) Each station is not allowed to oversaturate, and passengers can climb onto the first carriage after arriving at the station; b) The residence time of each carriage at the stations is constant, and the running speed among the stations is also constant; c) All stations have enough cars and the overall capacity of the system is not limited.
5. A modular bus operation scheduling optimization device, comprising: At least one processor; at least one memory for storing at least one program; The at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of any one of claims 1-4.
6. A computer readable storage medium, in which a processor executable program is stored, characterized in that the processor executable program is for performing the method according to any of claims 1-4 when being executed by a processor.

Description

Modularized bus operation scheduling optimization method, modularized bus operation scheduling optimization device and storage medium Technical Field The invention relates to the field of public transportation operation scheduling, in particular to a modularized public transportation operation scheduling optimization method, a modularized public transportation operation scheduling optimization device and a storage medium. Background In extra large city bus systems, there is a long-term conflict between the uncertain passenger demands and the fixed transport capacity, which can result in significant passenger waiting time costs and wasted vehicle capacity. The proposal of modularized buses provides the possibility for solving the problem, and dynamic modification of the vehicle capacity is realized by dynamically disassembling and reassembling bus queues in the same carriage on the station. At present, a better scheduling scheme of modularized buses is not available. Disclosure of Invention In order to solve at least one of the technical problems existing in the prior art to a certain extent, the invention aims to provide a modularized bus operation scheduling optimization method, a modularized bus operation scheduling optimization device and a storage medium. The technical scheme adopted by the invention is as follows: A modularized bus operation scheduling optimization method comprises the following steps: s1, determining bus lines and related station ranges for providing modularized bus service, and collecting passenger flow demand data of different stations; s2, determining a model parameter value according to the operation characteristics of the bus line and geographic environment factors, and building a model; s3, solving a model according to passenger flow demand data and bus operation characteristics, and acquiring an operation scheduling scheme under the condition of lowest system total cost, wherein the system total cost comprises operation cost and passenger waiting cost; And S4, optimizing an operation scheduling scheme of the modularized public transportation system, and realizing the optimal balance in the aspects of economy, energy and environmental benefit. Further, the step S1 specifically includes: And determining bus lines and stations of the modularized bus service covered by the scheduling scheme, determining the number of stations, the number of carriages of each station and the distance between stations, and collecting passenger flow demand data from and to different stations, wherein the passenger flow demand data comprises the number and time of passengers from and to each station. Further, the determining the model parameter values includes: according to the running characteristics of the modularized buses, determining the number of passengers in the carriages, the average running cost of each carriage, the average waiting time cost of passengers, the average running speed of the vehicles and the minimum design headway; The average operation cost of each carriage comprises depreciation cost, energy consumption cost for overcoming air resistance and environmental pollution cost caused by carbon dioxide emission. Further, the model is built by: a1, determining an assumption condition to be met by the model according to passenger demands and modularized bus operation characteristics; a2, determining constraint conditions to be met by the model, wherein the constraint conditions comprise vehicle operation constraint, minimum headway constraint and constraint for meeting passenger demand service; A3, determining that an objective function of the model is minimum in total system cost, wherein the total system cost comprises operation cost and passenger waiting cost, and the operation cost comprises vehicle operation cost, energy consumption cost generated by overcoming wind resistance and environmental cost caused by greenhouse gas emission; and A4, linearizing the established nonlinear programming model by using equivalent mathematical transformation. Further, the assumption conditions in step A1 include: ① Each station is not allowed to oversaturate, and passengers can climb onto the first carriage after arriving at the station; ② The residence time of each carriage at the stations is constant, and the running speed among the stations is also constant; ③ All stations have enough cars and the overall capacity of the system is not limited. Further, the expression of the vehicle operation constraint is: Wherein x tis is a binary variable, when the system schedules s carriages from station i at time t, the time value of each carriage is 1, otherwise, the time value of each carriage is 0; Representing a collection of subway stations, A set of time points is represented and,Representing a set of the number of cars per station, The expression of the minimum headway constraint is: Wherein y t is a binary variable, when a car starts from a station at time t, the time value is 1, otherwise 0;h i