CN113570148A - Passenger simulation-based urban rail station stop time optimal setting method - Google Patents
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Abstract
The invention discloses an optimal setting method for stop time of an urban rail station based on passenger simulation; the method comprises the following steps: firstly, carrying out a plurality of simulation experiments according to specific scenes, station types and passenger flow composition input from the outside to obtain the number of passengers staying and riding in different specific scenes and the correlation between the train full load rate and the stop time; then, calculating the results obtained by multiple simulation experiments under the same specific scene, namely the average value of the correlation between the corresponding number of passengers left to ride and the corresponding train full load rate and the corresponding stop time, and outputting the result as a final result; and finally, outputting the final result to a corresponding specific scene, and giving a stop time scheme in the corresponding specific scene according to the corresponding number of passengers left to ride, the corresponding train full load rate and the corresponding stop time. The invention can more fully consider a plurality of uncertain influence factors related to the optimization of the station stop time and better realize the aim of maximizing the benefit of the station stop time.
Description
Technical Field
The invention relates to the technical field of urban rail station management, in particular to an urban rail station stop time optimal setting method based on passenger simulation.
Background
In recent years, with the rapid development of social economy and urban rail transit, the passenger flow volume of rail transit is greatly increased, and the difficulty of transportation organization is obviously improved. The design of optimizing the stop time is the key for improving the service quality and the efficiency, and the reasonable stop time has important research significance for improving the urban rail transit station environment, improving the station service level and improving the operation efficiency of subway trains.
The passenger boarding process can be abstracted into several phases: the passengers arrive, the passengers select waiting positions at the platform, the individual decision of whether the passengers get on the bus or not, the behavior of getting on and off the bus of the passengers and the passengers getting on the bus. The most complicated is the uncertainty of the decision of whether the passenger gets on the bus or not.
In the prior art, more mathematical analysis methods are used for studying the passenger getting-on process, mainly focusing on the relevant research of the number of people getting on and off the train, the getting-on and getting-off speed and the stop time, and the objective of optimizing the stop time problem can be generally expressed as that the optimal design stop time benefit maximization is realized for the station type and the passenger type in a given scene, and the constraints in the optimization problem mainly comprise the aspects of carriage capacity, subway line transportation capacity and passenger flow.
However, in practical terms, at a certain level of train occupancy, the number of passengers left at a station is mainly influenced by the passenger individual decision: taking the starting station as an example, if the travel distance of passengers is long, some passengers can choose to wait under the condition that the regular bus has no seat, and even if the stop time is prolonged, the stop time is not beneficial. When the train full load rate is greater than 1, the number of passengers left is mainly influenced by the train transportation capacity. With the saturation of the capacity of the carriage, the number of passengers getting on the train in unit time is less and less, and if the stop time is prolonged too much, the marginal benefit is continuously reduced. Therefore, under the common influence of various factors, a mathematical model which has universal significance and accords with the actual situation is difficult to abstract by an analytical method, and if an experimental analysis method can be adopted, the increase rate of the benefit of the stop time in the stop time benefit curve under the limited condition is obviously reduced to a minimum point close to 0, so that the stop time which is more practical and reasonable in different scenes can be obtained. At present, based on passenger individual-level boarding decisions, individual attribute characteristics and environment cognition of passengers are organically combined with train stop time in different scenes, and the results of the stop time optimization research by means of passenger individual-level simulation are few.
Therefore, how to acquire the actual and reasonable stop time in different scenes and achieve the goal of maximizing the benefit of the stop time becomes a technical problem which needs to be solved urgently by the technical personnel in the field.
Disclosure of Invention
In view of the defects in the prior art, the invention provides an urban rail station stop time optimization setting method based on passenger simulation, and the method is used for finding out the minimal point that the stop time benefit increase rate in a stop time benefit curve under the limiting condition is remarkably reduced to be close to 0 by using an individual decision simulation method, acquiring the stop time which is practical and reasonable under different scenes, and achieving the goal of maximizing the stop time benefit.
In order to achieve the aim, the invention discloses a method for optimally setting stop time of an urban rail station based on passenger simulation; the method comprises the following steps:
and 3, outputting the final result to the corresponding specific scene, and giving a corresponding stop time scheme in the specific scene according to the corresponding reserved passenger number and the correlation between the corresponding train full load rate and the corresponding stop time.
Preferably, in step 1, the method of the simulation experiment is as follows:
step 1.1, inputting simulation system parameters comprising the specific scene, the station type and the passenger flow;
step 1.2, simulating a system state corresponding to the station-stopping time of 1s-120s in the specific scene by using a simulation model, and selecting a time point with the station-stopping time and the benefit maximized;
and 1.3, simulating for a plurality of times for each given specific scene, and outputting the number of passengers staying and riding in different specific scenes and the correlation between the train full load rate and the stop time.
More preferably, the simulation model comprises a passenger arrival module, a passenger decision selection module and a passenger reservation module;
the passenger arrival module simulates passengers to arrive and selects the information of waiting positions of the platform;
the passenger decision selection module adopts a rule-based decision tree method to simulate the decision behavior of whether an individual passenger gets on the train or not according to individual attribute characteristics of the passenger, characteristics of station facilities and trains, passenger flow and other environmental factors;
the remaining module determines the getting-on and getting-off time according to whether the full load rate of the train is greater than 1, and when the full load rate of the train is greater than 1, the number of passengers getting on the train within a certain time can adopt an improved binomial distribution model, so that the number of passengers getting on the train is calculated in a simulation mode.
More preferably, the passenger arrival module calculates the average arrival rate of passengers according to the weighted average arrival rate of the number of passengers arriving at the entrance and the number of passengers to be transferred under each specific scene;
wherein the number of arriving passengers at the entrance is distributed from Poisson within a certain period of time, and the walking speed of the transfer passenger in the passage follows normal distribution.
More preferably, the passenger decision selection module is a passenger boarding probability decision tree based on passenger individual attribute characteristics, and comprehensively considers passenger individual attribute characteristics, station facility and train characteristics, passenger flow and other environmental factors;
the final profit or loss in the passenger boarding probability decision tree is a probability value, namely, the probability parameters beta and 1-beta are probabilities of getting on or off the vehicle under the selected condition, the values of beta corresponding to different conditions are different, and the value of beta is more than 0.5 under the normal condition;
the selection of beta is calibrated according to the statistical rule method of experience and survey data.
More preferably, the operation process of the reservation module is as follows:
a1, arrival of individual passengers;
a2, selecting a boarding position by a passenger at the platform;
a3, judging whether the train arrives;
a4, if the train arrives to execute the subsequent steps, if the train does not arrive, returning to the step A1;
a5, making a decision of whether to get on the bus or not by the passenger; if the decision is getting on, executing the step A6; otherwise, executing A10;
a6, passengers get on or off the train;
a7, judging whether the train full load rate is greater than 1;
a8, if the train full load rate is more than 1, executing the step A9; otherwise, executing A10;
a9, a part of passengers get on the vehicle;
and A10, outputting the result.
The invention has the beneficial effects that:
compared with a commonly used mathematical analysis method, the method can more fully consider a plurality of uncertain influence factors related to the optimization of the station stopping time, find the minimum point that the benefit increase rate of the station stopping time in the benefit curve of the station stopping time under the limited condition is obviously reduced until the station stopping time is close to 0, obtain the actual and reasonable station stopping time under different scenes, and better realize the goal of maximizing the benefit of the station stopping time.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 shows a flow chart of an embodiment of the present invention.
FIG. 2 shows a flow chart of a simulation experiment in an embodiment of the invention.
FIG. 3 is a flow diagram illustrating the operation of the ride-reserving module in one embodiment of the invention.
Fig. 4 is a schematic diagram of a passenger boarding probability decision tree of the passenger decision selection module in an embodiment of the invention.
Fig. 5 is a functional architecture diagram of a software system to which the present invention is applied in an embodiment of the present invention.
Fig. 6 is a diagram showing a result of the optimization of the station down time according to an embodiment of the present invention.
Detailed Description
Examples
As shown in fig. 1, an urban rail station stop time optimization setting method based on passenger simulation; the method comprises the following steps:
and 3, outputting the final result to a corresponding specific scene, and giving a stop time scheme in the corresponding specific scene according to the corresponding number of passengers left to ride, the corresponding train full load rate and the corresponding stop time.
According to the invention, by using an individual decision simulation method, numerous uncertain influence factors related to the optimization of the station stopping time can be fully considered, a minimum point that the benefit increase rate of the station stopping time in a station stopping time benefit curve is obviously reduced to be close to 0 under the limiting condition is found, the actual and reasonable station stopping time under different scenes is obtained, the general mathematical analysis method is effectively supplemented, and the goal of maximizing the benefit of the station stopping time is realized.
As shown in fig. 2, in some embodiments, in step 1, the method of simulating the experiment is as follows:
step 1.1, inputting simulation system parameters including specific scenes, station types and passenger flow composition;
the input here is an external user input, and different parameter values may be input according to actual conditions. Specifically, as shown in fig. 6, for example, the station types include an origin station, a transfer station, a destination station, and a large-scale junction station, and a series of parameters are different for different station types. Passenger types include business travel, school, work, travel, commute. The scenes comprise a morning and evening peak and a flat peak scene, and a scene of combining different station types and passenger types.
Step 1.2, simulating a system state corresponding to the station-stopping time of 1s-120s in a specific scene by using a simulation model, and selecting a time point with the station-stopping time and the benefit maximized;
and 1.3, simulating for a plurality of times for each given specific scene, and outputting the number of passengers staying and riding in different specific scenes and the correlation between the train full load rate and the stop time.
In some embodiments, the simulation model includes a passenger arrival module, a passenger decision selection module, and a ride-by module;
the passenger arrival module simulates passengers to arrive and selects the information of waiting positions of the platform;
the passenger decision selection module adopts a rule-based decision tree method to simulate the decision behavior of whether an individual passenger gets on the train or not according to individual attribute characteristics of the passenger, characteristics of station facilities and trains, passenger flow and other environmental factors;
the remaining module determines the getting-on and getting-off time according to whether the full load rate of the train is greater than 1, and when the full load rate of the train is greater than 1, the number of passengers getting on the train within a certain time can adopt an improved binomial distribution model, so that the number of passengers getting on the train can be calculated in a simulation mode.
In some embodiments, the passenger arrival module calculates the average arrival rate of passengers according to the weighted average arrival rate of the number of passengers arriving at the entrance and the number of passengers transferring the passengers under each specific scene;
the number of arriving passengers at the station entrance is distributed from Poisson within a certain time period, and the walking speed of the transfer passengers in the passage is distributed according to normal distribution.
As shown in the figures 5 and 6, the system simulates different scenes to maximize the benefit of the stop time and output the reasonably optimized stop time as an index, mainly comprises four modules, parameter setting, result display, data output and different scenes, and can simulate different scenes of the urban rail transit station based on passenger simulation to optimize the stop time and perform graphic display.
As shown in fig. 4, in some embodiments, the passenger decision selection module is a passenger boarding probability decision tree based on passenger individual attribute features, comprehensively considering passenger individual attribute features, station facility and train features, passenger flow volume and other environmental factors;
the final profit or loss in the passenger boarding probability decision tree is a probability value, namely, the probability parameters beta and 1-beta are probabilities of getting on or off the vehicle under the selected condition, the values of beta corresponding to different conditions are different, and the value of beta is more than 0.5 under the normal condition;
the selection of beta is calibrated according to the statistical rule method of experience and survey data.
As shown in FIG. 3, in some embodiments, the operation of the ride-on module is as follows:
a1, arrival of individual passengers;
a2, selecting a boarding position by a passenger at the platform;
a3, judging whether the train arrives;
a4, if the train arrives to execute the subsequent steps, if the train does not arrive, returning to the step A1;
a5, making a decision of whether to get on the bus or not by the passenger; if the decision is getting on, executing the step A6; otherwise, executing A10;
a6, passengers get on or off the bus as a whole;
a7, judging whether the train full load rate is greater than 1;
a8, if the train full load rate is more than 1, executing the step A9; otherwise, executing A10;
a9, a part of passengers get on the vehicle;
and A10, outputting the result.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (6)
1. An urban rail station stop time optimization setting method based on passenger simulation; the method comprises the following steps:
step 1, carrying out multiple simulation experiments according to specific scenes, station types and passenger flow compositions input from the outside, and obtaining the number of passengers left and passengers left in different specific scenes and the correlation between the train full load rate and the stop time;
step 2, calculating results obtained by multiple simulation experiments under the same specific scene, namely the average value of the correlation between the corresponding number of passengers reserved for the train and the corresponding full load rate of the train and the corresponding stop time, and outputting the result as a final result;
and 3, outputting the final result to the corresponding specific scene, and giving a corresponding stop time scheme in the specific scene according to the corresponding reserved passenger number and the correlation between the corresponding train full load rate and the corresponding stop time.
2. The method for optimally setting the stop time of the urban rail station based on passenger simulation as claimed in claim 1, wherein in the step 1, the simulation experiment method comprises the following steps:
step 1.1, inputting simulation system parameters comprising the specific scene, the station type and the passenger flow;
step 1.2, simulating a system state corresponding to the station-stopping time of 1s-120s in the specific scene by using a simulation model, and selecting a time point with the station-stopping time and the benefit maximized;
and 1.3, simulating for a plurality of times for each given specific scene, and outputting the number of passengers staying and riding in different specific scenes and the correlation between the train full load rate and the stop time.
3. The method for optimally setting the stop time of the urban rail station based on passenger simulation as claimed in claim 2, wherein the simulation model comprises a passenger arrival module, a passenger decision selection module and a passenger reservation module;
the passenger arrival module simulates passengers to arrive and selects the information of waiting positions of the platform;
the passenger decision selection module adopts a rule-based decision tree method to simulate the decision behavior of whether an individual passenger gets on the train or not according to individual attribute characteristics of the passenger, characteristics of station facilities and trains, passenger flow and other environmental factors;
the remaining module determines the getting-on and getting-off time according to whether the full load rate of the train is greater than 1, and when the full load rate of the train is greater than 1, the number of passengers getting on the train within a certain time can adopt an improved binomial distribution model, so that the number of passengers getting on the train is calculated in a simulation mode.
4. The method for optimally setting the stop time of the urban rail station based on passenger simulation as claimed in claim 3, wherein the passenger arrival module calculates the average arrival rate of passengers according to the weighted average arrival rate of the number of passengers arriving at the entrance and the number of passengers to be transferred under each specific scene;
wherein the number of arriving passengers at the entrance is distributed from Poisson within a certain period of time, and the walking speed of the transfer passenger in the passage follows normal distribution.
5. The method for optimally setting the stop time of the urban rail station based on passenger simulation as claimed in claim 3, wherein the passenger decision selection module is a passenger boarding probability decision tree based on passenger individual attribute characteristics, and the passenger individual attribute characteristics, station facility and train characteristics, passenger flow and other environmental factors are comprehensively considered;
the final profit or loss in the passenger boarding probability decision tree is a probability value, namely, the probability parameters beta and 1-beta are probabilities of getting on or off the vehicle under the selected condition, the values of beta corresponding to different conditions are different, and the value of beta is more than 0.5 under the normal condition;
the selection of beta is calibrated according to the statistical rule method of experience and survey data.
6. The method for optimally setting the stop time of the urban rail station based on passenger simulation as claimed in claim 3, wherein the operation process of the passenger reserving module is as follows:
a1, arrival of individual passengers;
a2, selecting a boarding position by a passenger at the platform;
a3, judging whether the train arrives;
a4, if the train arrives to execute the subsequent steps, if the train does not arrive, returning to the step A1;
a5, making a decision of whether to get on the bus or not by the passenger; if the decision is getting on, executing the step A6; otherwise, executing A10;
a6, passengers get on or off the bus as a whole;
a7, judging whether the train full load rate is greater than 1;
a8, if the train full load rate is more than 1, executing the step A9; otherwise, executing A10;
a9, a part of passengers get on the vehicle;
and A10, outputting the result.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016045195A1 (en) * | 2014-09-22 | 2016-03-31 | 北京交通大学 | Passenger flow estimation method for urban rail network |
| CN111071305A (en) * | 2019-12-06 | 2020-04-28 | 北京交通运输职业学院 | Intelligent estimation method and device for stop time of urban rail transit train |
| CN112214873A (en) * | 2020-09-10 | 2021-01-12 | 卡斯柯信号有限公司 | Passenger flow distribution simulation evaluation method and system under rail transit fault |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016045195A1 (en) * | 2014-09-22 | 2016-03-31 | 北京交通大学 | Passenger flow estimation method for urban rail network |
| CN111071305A (en) * | 2019-12-06 | 2020-04-28 | 北京交通运输职业学院 | Intelligent estimation method and device for stop time of urban rail transit train |
| CN112214873A (en) * | 2020-09-10 | 2021-01-12 | 卡斯柯信号有限公司 | Passenger flow distribution simulation evaluation method and system under rail transit fault |
Non-Patent Citations (4)
| Title |
|---|
| 李颖 等: "基于前景理论的城市轨道交通停站方案研究", 《公路交通科技》, vol. 35, no. 4, pages 86 - 90 * |
| 王思韬 等: "二次关门对城市轨道交通列车停站时间的影响", 《城市轨道交通研究》, no. 7 * |
| 王智鹏: "基于动态停站时间的城市轨道交通跨站 列车开行方案研究", 《甘肃科学学报》, vol. 31, no. 1 * |
| 许得杰;巩亮;曾俊伟;: "考虑客流时变需求的大小交路列车时刻表优化模型", 交通运输系统工程与信息, no. 02 * |
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