CN113570148B - Urban rail station stop time optimization setting method based on passenger simulation - Google Patents
Urban rail station stop time optimization setting method based on passenger simulation Download PDFInfo
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Abstract
The invention discloses a passenger simulation-based urban rail station stop time optimization setting method; the method comprises the following steps: firstly, carrying out multiple simulation experiments according to specific scenes, station types and passenger flow compositions which are input from the outside, and obtaining the number of passengers reserved in different specific scenes and the correlation between the full rate of the train and the stop time; then, calculating the results obtained by multiple simulation experiments under the same specific scene, namely the corresponding number of passengers and the average value of the correlations between the corresponding train full load rate and the corresponding stop time, and outputting the results as final results; 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 and the correlation between the corresponding train full rate and the corresponding stop time. The invention can more fully consider a plurality of uncertainty influencing factors related to the stop time optimization, and better realize the goal of maximizing the stop time benefit.
Description
Technical Field
The invention relates to the technical field of urban rail station management, in particular to a passenger simulation-based urban rail station stop time optimization setting method.
Background
In recent years, along with the rapid development of social economy and urban rail transit, the passenger flow of the rail transit is greatly increased, and the difficulty of transportation organization is remarkably improved. The optimization of the stop time design is a 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 running efficiency of the subway train.
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 or off the bus is made, the passengers get on or off the bus, and the passengers get on the bus. The uncertainty of the decision of whether the passenger gets on or off the vehicle is the most complex.
In the prior art, a plurality of mathematical analysis methods for researching the passenger boarding process are mainly focused on the related researches of the number of passengers boarding and alighting and the speed of boarding and alighting and the stop time, and the aim of optimizing the stop time problem can be generally expressed as 'optimizing the design of the stop time benefit maximization for the station type and the passenger type of a given scene', wherein the constraint in the optimizing problem mainly comprises the aspects of carriage capacity, subway line transportation capacity and passenger flow.
However, in practical problems, at a certain train full load rate level, the number of passengers remaining in the station is mainly affected by individual decisions of passengers: taking the starting station as an example, if the distance of travel of the passengers is long, in the case of a passenger without seats in the bus, some passengers will choose to wait, and even if the stop time is prolonged, it is not beneficial. When the train full load rate is greater than 1, the number of passengers left is mainly affected by the train transportation capacity. With the saturation of the capacity of the carriage, the number of passengers capable of getting on in a unit time is smaller and smaller, and if the stop time is prolonged too much, the marginal benefit is continuously reduced. Therefore, under the common influence of a plurality of factors, a mathematical model which has universal meaning and accords with the actual situation is difficult to abstract by an analytic method, if an experimental analysis method can be adopted, the stop time benefit increase rate in the stop time benefit curve under the limiting condition is found to be obviously reduced until the minimum point which is close to 0, and the more practical and reasonable stop time under different scenes can be obtained. At present, based on individual boarding decisions of passengers, individual attribute characteristics and environmental awareness of the passengers are organically combined with the stop time of the train in different scenes, and the result of the stop time optimization research by individual simulation means of the passengers is relatively low.
Therefore, how to obtain the actual and reasonable stop time of the joint in different scenes and achieve the aim of maximizing the benefit of the stop time becomes a technical problem which needs to be solved by the technicians in the field.
Disclosure of Invention
In view of the above-mentioned defects of the prior art, the invention provides a city rail station stop time optimizing setting method based on passenger simulation, and the aim of realizing the stop time benefit is to find out the minimum point that the stop time benefit increasing rate is obviously reduced to be close to 0 in the stop time benefit curve under the limiting condition by using an individual level decision simulation method, obtain the actual and reasonable stop time in different scenes and realize the aim of maximizing the stop time benefit.
In order to achieve the above purpose, the invention discloses a city rail station stop time optimizing setting method based on passenger simulation; the method comprises the following steps:
step 1, performing multiple simulation experiments according to specific scenes, station types and passenger flow constitution input from the outside to obtain correlations between the number of passengers reserved and the stop time when the full load rate of the train is greater than 1 and less than or equal to 1 in different specific scenes;
step 2, calculating the results obtained by multiple simulation experiments under the same specific scene, namely, the average value of the correlations between the corresponding number of passengers and the corresponding stop time when the full load rate of the train is greater than 1 and less than or equal to 1, and outputting the average value as a final result;
and step 3, outputting the final result to the corresponding specific scene, and giving a stop time scheme under the corresponding specific scene according to the correlation between the corresponding number of passengers and the corresponding stop time when the train full load rate is greater than 1 and less than or equal to 1.
Preferably, in the step 1, the method of the simulation experiment is as follows:
step 1.1, inputting simulation system parameters including the specific scene, the station type and the passenger flow composition;
step 1.2, simulating a system state corresponding to the stop time of 1s-120s in the specific scene by using a simulation model, and selecting a time point with maximum stop time benefit;
and 1.3, carrying out simulation for each given specific scene for a plurality of times, and outputting correlations between the number of passengers remaining and the stop time when the train full load rate is greater than 1 and less than or equal to 1 under different specific scenes.
More preferably, the simulation model comprises a passenger arrival module, a passenger decision selection module and a ride-leave module;
the passenger arrival module simulates the arrival of passengers and selects the waiting position information of a platform;
the passenger decision selection module adopts a rule-based decision tree method to simulate decision behaviors of whether individual passengers get on or not according to individual attribute characteristics of the passengers, station facilities and train characteristics, passenger flow and other environmental factors;
the boarding and alighting time is determined by the boarding and alighting module according to whether the full rate of the train is greater than 1, and when the full rate of the train is greater than 1, the number of passengers crowded with the train in a certain time can adopt an improved binomial distribution model, so that the number of passengers boarding the train is calculated in a simulation mode.
More preferably, the passenger arrival module calculates the average arrival rate of the passengers according to the weighted average arrival rate of the two parts of the arrival passenger number and the transfer passenger number of the entrance 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 speeds of the transfer passengers in the channel are subjected to 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 facilities and train characteristics, passenger flow and other environmental factors;
the final damage in the decision tree of the passenger getting-on probability is a probability value, namely, regarding probability parameters beta and 1-beta as the probability of getting on and off under the selected condition, the values of beta corresponding to different conditions are different, and the beta is usually larger than 0.5;
and (5) calibrating the selection of beta according to an experience and investigation data statistical rule method.
More preferably, the simulation works as follows:
a1, passenger individual arrival;
a2, passengers select boarding positions at the platform;
a3, judging whether the train arrives or not;
a4, if the train arrives to execute the subsequent steps, returning to the step A1 if the train does not arrive;
a5, the individual passenger decides whether to get on the bus or not; if the decision is that the vehicle gets on, executing the step A6; otherwise, executing A10;
a6, passengers get on or off the vehicle;
a7, judging whether the full load rate of the train is greater than 1;
a8, if the full load rate of the train is greater than 1, executing the step A9; otherwise, executing A10;
a9, squeezing partial passengers on the vehicle;
a10, outputting a result.
The invention has the beneficial effects that:
compared with a commonly used mathematical analysis method, the invention can more fully consider a plurality of uncertainty influence factors related to the stop time optimization, find out the minimum point that the stop time benefit increase rate in the stop time benefit curve is obviously reduced to be close to 0 under the limiting condition, acquire the actual and reasonable stop time which is fit in different scenes, and better realize the aim of maximizing the stop time benefit.
The conception, specific structure, and technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, features, and effects of the present invention.
Drawings
Fig. 1 shows a flow chart of an embodiment of the invention.
FIG. 2 is a flow chart of a simulation experiment in an embodiment of the invention.
FIG. 3 is a flow chart illustrating the operation of the ride-on module in one embodiment of the invention.
FIG. 4 is a schematic diagram of a passenger boarding probability decision tree of a passenger decision selection module in an embodiment of the invention.
FIG. 5 shows a functional architecture diagram of a software system to which the present invention is applied in an embodiment of the present invention.
FIG. 6 shows a plot of the results of downtime optimization using the present invention in one embodiment of the present invention.
Detailed Description
Examples
As shown in fig. 1, a city rail station stop time optimizing setting method based on passenger simulation; the method comprises the following steps:
step 1, performing multiple simulation experiments according to specific scenes, station types and passenger flow constitution which are input from outside to obtain correlations between the number of passengers reserved and the stop time when the full load rate of the train is greater than 1 and less than or equal to 1 in different specific scenes;
step 2, calculating the results obtained by multiple simulation experiments under the same specific scene, namely, the average value of the correlations between the corresponding number of passengers and the corresponding stop time when the full load rate of the train is more than 1 and less than or equal to 1, and outputting the average value as a final result;
and step 3, outputting the final result to a corresponding specific scene, and providing a stop time scheme under the corresponding specific scene according to the correlation between the corresponding number of passengers and the corresponding stop time when the full rate of the train is greater than 1 and less than or equal to 1.
The invention uses the simulation method of individual level decision, can fully consider a plurality of uncertainty influence factors related to the stop time optimization, find out the minimum point that the stop time benefit increase rate in the stop time benefit curve is obviously reduced to be close to 0 under the limiting condition, acquire the actual and reasonable stop time which is jointed in different scenes, effectively supplement the general mathematical analysis method and realize the goal of maximizing the stop time benefit.
As shown in fig. 2, in some embodiments, in step 1, the method of simulation experiment is as follows:
step 1.1, inputting simulation system parameters including specific scenes, station types and passenger flows;
the input here is an external user input, and different parameter values are input according to actual conditions. As shown in fig. 6, for example, the station type includes an originating station, a transfer station, a destination station, and a large-scale junction station, and a series of parameters are different from each other in different station types. Passenger types include business travel, school, attendant, travel, commute. The scenes comprise peak and flat peak scenes in the morning and evening, and combined scenes of different stations and passenger types.
Step 1.2, simulating a system state corresponding to the stop time of 1s-120s in a specific scene by using a simulation model, and selecting a time point with maximum stop time benefit;
and 1.3, carrying out simulation for a plurality of times for each given specific scene, and outputting correlations between the number of passengers remaining and the stop time when the full load rate of the train is greater than 1 and less than or equal to 1 in different specific scenes.
In some embodiments, the simulation model includes a passenger arrival module, a passenger decision selection module, and a ride module;
the passenger arrival module simulates the arrival of passengers and selects the waiting position information of a platform;
the passenger decision selection module adopts a rule-based decision tree method to simulate decision behaviors of whether individual passengers get on or not according to individual attribute characteristics of the passengers, station facilities and train characteristics, passenger flow and other environmental factors;
the boarding and disembarking time is determined by the boarding and disembarking module according to whether the full rate of the train is greater than 1, and when the full rate of the train is greater than 1, the number of passengers getting on the train can be calculated by adopting an improved binomial distribution model in a simulation manner.
In some embodiments, the passenger arrival module calculates an average arrival rate of passengers based on a weighted average arrival rate of two parts, the number of arrival passengers at the entrance and the number of transfer passengers, for each particular scene;
the number of arriving passengers at the entrance is distributed from poisson in a certain period of time, and the walking speeds of the transfer passengers in the channel are subjected to normal distribution.
As shown in fig. 5 and 6, the system for simulating different scenes to maximize the stop time benefit and reasonably optimizing the stop time according to the index output mainly comprises four modules, parameter setting, result display, data output and different scenes.
As shown in fig. 4, in some embodiments, the passenger decision selection module is a passenger boarding probability decision tree based on individual passenger attribute features, comprehensively considering individual passenger attribute features, station facility and train features, passenger flow and other environmental factors;
the final damage in the decision tree of the passenger getting-on probability is a probability value, namely, regarding probability parameters beta and 1-beta as the probability of getting on and off under the selected condition, the values of beta corresponding to different conditions are different, and the beta is usually larger than 0.5;
and (5) calibrating the selection of beta according to an experience and investigation data statistical rule method.
As shown in fig. 3, in some embodiments, the simulation works as follows:
a1, passenger individual arrival;
a2, passengers select boarding positions at the platform;
a3, judging whether the train arrives or not;
a4, if the train arrives to execute the subsequent steps, returning to the step A1 if the train does not arrive;
a5, the individual passenger decides whether to get on the bus or not; if the decision is that the vehicle gets on, executing the step A6; otherwise, executing A10;
a6, passengers get on and off the vehicle as a whole;
a7, judging whether the full load rate of the train is greater than 1;
a8, if the full load rate of the train is greater than 1, executing the step A9; otherwise, executing A10;
a9, squeezing partial passengers on the vehicle;
a10, outputting a result.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (3)
1. The urban rail station stop time optimizing and setting method based on passenger simulation; the method comprises the following steps:
step 1, performing multiple simulation experiments according to specific scenes, station types and passenger flow constitution input from the outside to obtain correlations between the number of passengers reserved and the stop time when the full load rate of the train is greater than 1 and less than or equal to 1 in different specific scenes;
the simulation experiment method comprises the following steps:
step 1.1, inputting simulation system parameters including the specific scene, the station type and the passenger flow composition;
step 1.2, simulating a system state corresponding to the stop time of 1s-120s in the specific scene by using a simulation model, and selecting a time point with maximum stop time benefit;
step 1.3, carrying out simulation for a plurality of times for each given specific scene, and outputting correlations between the number of passengers remaining and the stop time when the full load rate of the train is greater than 1 and less than or equal to 1 under different specific scenes;
step 2, calculating the results obtained by multiple simulation experiments under the same specific scene, namely, when the full load rate of the train is greater than 1 and less than or equal to 1, the average value of the correlations between the corresponding number of passengers and the corresponding stop time is output as a final result;
step 3, outputting the final result to the corresponding specific scene, and giving a stop time scheme under the corresponding specific scene according to the correlation between the corresponding number of passengers and the corresponding stop time when the full rate of the train is greater than 1 and less than or equal to 1;
the simulation model comprises a passenger arrival module, a passenger decision selection module and a ride-on module;
the passenger arrival module simulates the arrival of passengers and selects the waiting position information of a platform;
the passenger decision selection module adopts a rule-based decision tree method to simulate decision behaviors of whether individual passengers get on or not according to individual attribute characteristics of the passengers, station facilities and train characteristics, passenger flow and other environmental factors;
the boarding and disembarking time is determined by the boarding and disembarking module according to whether the full rate of the train is greater than 1, and when the full rate of the train is greater than 1, the number of passengers crowded on the train in a certain time adopts an improved binomial distribution model, so that the number of passengers boarding is calculated in a simulation mode;
the working process of the simulation is as follows:
a1, passenger individual arrival;
a2, passengers select boarding positions at the platform;
a3, judging whether the train arrives or not;
a4, if the train arrives to execute the subsequent steps, returning to the step A1 if the train does not arrive;
a5, the individual passenger decides whether to get on the bus or not; if the decision is that the vehicle gets on, executing the step A6; otherwise, executing A10;
a6, passengers get on and off the vehicle as a whole;
a7, judging whether the full load rate of the train is greater than 1;
a8, if the full load rate of the train is greater than 1, executing the step A9; otherwise, executing A10;
a9, squeezing partial passengers on the vehicle;
a10, outputting a result.
2. The urban rail station stop time optimization setting method based on the passenger simulation according to claim 1, wherein the passenger arrival module calculates the average arrival rate of the passengers according to the weighted average arrival rate of the two parts of the arrival passenger number of the entrance and the transfer passenger number in 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 speeds of the transfer passengers in the channel are subjected to normal distribution.
3. The urban rail station stop time optimization setting method based on passenger simulation according to claim 1, wherein 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 facilities and train characteristics, passenger flow and other environmental factors;
the last benefit in the passenger getting on probability decision tree is the probability value.
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| 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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| 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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