Method for identifying key elements that affect emergence of global efficiency of rail transit system and simulation system for implementing the same

Patent metadata

Field	Value
Publication number	US-11915252-B2
Application number	US-202016942662-A
Country	US
Kind code	B2
Filing date	Jul 29, 2020
Priority date	Jul 30, 2019
Publication date	Feb 27, 2024
Grant date	Feb 27, 2024

Abstract

A method for identifying key elements that affect emergence of global efficiency of a rail transit system and a simulation system based on evolution of intelligent group behaviors for implementing the method. The global efficiency of the rail transit system is determined in form of an index vector. Around the index vector of the global efficiency, the agent models of the micro-subjects in the rail transit system and a simulation system of the emergence of the global efficiency based on the evolution of intelligent group behaviors are established. An algorithm implemented in the simulation system is established to identify key elements of the emergence of the global efficiency of the rail transit system. The method and the simulation system of the present disclosure provide a systematic solution for the improvement of the global efficiency of the rail transit system.

First claim

What is claimed is:

1. A computer-implemented method of simulating a rail transit system, comprising:
a) determining, at a computer simulation system executed by a processor, a global efficiency of the rail transit system in form of an index vector;
b) around the index vector, establishing, at the computer simulation system executed by the processor, agent models of micro-subjects in the rail transit system to simulate an emergence of the global efficiency based on evolution of intelligent group behaviors by:
establishing a general agent model of the micro-subjects with intelligent perception, communication, behavior autonomy and collaboration abilities, to simulate actual behaviors of the micro-subjects comprising a control center, a plurality of control sub-centers, a plurality of lines, a plurality of stations and a plurality of trains in the rail transit system;
establishing a specific mathematical model of the behaviors of the micro-subjects according to duties, attributes, functions and performances of the micro-subjects; and
generating a control center agent, a plurality of control sub-center agents, a plurality of line agents, a plurality of station agents and train agents by using the established general agent model, and then generating specific virtual entities to simulate the emergence of the global efficiency;
c) transmitting, via network communication, signals in real time among a computer system for the control center, a plurality of computer systems for the control sub-centers, and a plurality of computer systems for the lines, the stations and the trains in the computer simulation system executed by the processor, such that a master-slave cooperation and a parallel cooperation between different computer systems and between different virtual entities are established;
d) establishing an algorithm implemented in the computer simulation system executed by the processor to identify key elements of the emergence of the global efficiency;
e) identifying the key elements by the computer simulation system executed by the processor through a mapping relationship between an optimal global efficiency and adjustable input parameters of the rail transit system;
f) according to the adjustable input parameters of the rail transit system including a dynamic spatio-temporal distribution of passenger or cargo flows and a dynamic road network environment, automatically updating, at the computer simulation system executed by the processor, the global efficiency in real time through autonomous behaviors and mutual cooperation of the micro-subjects until the optimal global efficiency is generated;
g) automatically generating, at the computer simulation system executed by the processor, a systemic solution of the rail transit system based on identification of the key elements affecting the emergence of the optimal global efficiency;
h) determining a systematic solution of the rail transit system comprising train behaviors in a peak season or in an off season according to a transportation demand, and sending, by the control center and the sub-control centers, the systematic solution of the rail transit system to the trains; and
i) controlling, based on the systemic solution of the rail transit system obtained in step g), the trains by automation to take off cars from the trains during the off season or add on cars to the trains during the peak season and to operate according to the train behaviors.

2. The method of claim 1 , wherein the index e Efficiency is defined in form of an index vector and calculated according to equation (1):

e
Efficiency

=

[

e
EnergyEfficiency

⁢

R
_

⁢

N
Actual

N

×
100
⁢
%
⁢

t
_

New

t
_

Old

×
100
⁢
%

]

T

;

(
l
)

wherein R is an average load ratio of passenger/cargo trains; N Actual is an actual number of the passenger/cargo trains running in a day in the rail transit system; N is the number of trains allowed to run in a day; t New is an average running time of the passenger/cargo trains after equipment update, technological transformation, system upgrade or improvement of organization and management of the rail transit system; t Old is an average running time of the passenger/cargo trains of a previous rail transit system; and e EnergyEfficiency is a weight of cargo or the number of people carried by the passenger/cargo trains and the travel distance of the passenger/cargo trains in the rail transit system per unit of energy consumption within a preset time period.

3. The method of claim 2 , wherein e EnergyEfficiency is calculated according to equation (2):

e
EnergyEfficiency

=

N
A

×

D
A

E

;

(
2
)

wherein E is a total energy consumption of the rail transit system to complete passenger/cargo transportation tasks; N A is an actual total number of passengers or the total weight of cargo that have been transported; and D A is an actual total travel distance of the passe

Assignees

• Univ Tongji

Inventors

• Pan Deng
• Chen Zejun

Classifications

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06Q10/04

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G06Q10/06315

  Needs-based resource requirements planning or analysis · CPC title

• G06Q10/06393 Primary

  Score-carding, benchmarking or key performance indicator [KPI] analysis · CPC title

• G06Q50/30

  Physics · mapped topic