CN108216311A - A kind of rail traffic dispatching method based on big data and Internet of Things - Google Patents

A kind of rail traffic dispatching method based on big data and Internet of Things Download PDF

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Publication number
CN108216311A
CN108216311A CN201710564309.7A CN201710564309A CN108216311A CN 108216311 A CN108216311 A CN 108216311A CN 201710564309 A CN201710564309 A CN 201710564309A CN 108216311 A CN108216311 A CN 108216311A
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ridership
event
things
internet
track circuit
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CN108216311B (en
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施春峰
代飞
关春子
吴昊
易星
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Shanghai Crrc Shentong Rail Transit Vehicle Co ltd
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Nanjing Communications Institute of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A kind of rail traffic dispatching method based on big data and Internet of Things, it is based under history big data environment, the volume of the flow of passengers of the train of a certain track circuit daily different moments is predicted, and daily different moments headway is determined on this basis, using this headway as basic data, monitor the current volume of the flow of passengers of train in real time by Internet of Things, collect this track circuit current passenger situation, obtain the revised headway of passenger flow forecast amount of subsequent time, it is final to realize that constantly dynamically correcting headway according to the situation of the practical volume of the flow of passengers of train any moment causes train operation more efficiently.

Description

A kind of rail traffic dispatching method based on big data and Internet of Things
Technical field
Patent of the present invention belongs to rail traffic dispatching algorithm, specifically, being related to a kind of based on big data and Internet of Things Rail traffic dispatching algorithm.
Background technology
Existing rail traffic dispatching algorithm is all using prediction algorithm, such as genetic algorithm, population by mathematical modeling Algorithm etc. carries out passenger flow estimation, is then dispatched a car according to prediction, scheduling of stopping；And in the environment for currently thering is big data to support Under, it can be based on history big data completely, carry out more simple and effective passenger flow estimation, while in the present of Internet of Things high speed development My god, each website and train passenger flow data can be obtained in real time, and the realization for dynamic adjustment in real time provides possibility.
Invention content
The purpose of the present invention is establish a kind of based on big data and Internet of Things, open, the track friendship that can continue to optimize Logical dispatching method.
Present invention requirement first has the following conditions：1st, the data warehouse of different moments train passenger number has been established；2、 The device of monitoring train current passenger number in real time has been established, and passes through Internet of Things and synchronizes and be sent to background server and each train, Backstage scheduling control information being capable of each train of delivered in real time simultaneously, it is ensured that Real-Time Scheduling can be realized.
Technical scheme of the present invention includes two parts：1st, using the volume of the flow of passengers of big data prediction daily different moments, and with This is according to the basic data for determining daily different moments headway；2nd, between the data monitored according to Internet of Things are to dispatching a car It is finely adjusted every the time.
Specific embodiment
Above-mentioned part 1 predicts the volume of the flow of passengers of daily different moments using big data, and determines daily on this basis not The basic thought of the basic data of headway is in the same time：Any time volume of the flow of passengers depends on the moment corresponding event Feature, when event includes month belonging to the moment, week, date, hour, festivals or holidays, the new term begins time, school of school have a holiday or vacation Between, various shocking flashes and other dependent events, if obtaining disturbance degree of each event to the volume of the flow of passengers by big data, just More accurately the volume of the flow of passengers of any time can be predicted, specific technical solution includes：
(1) track circuit ridership P is defined sometime to put the sum of all train passenger numbers in current orbit circuit, it will be over the years This track circuit ridership is sampled at regular intervals, is stored in data warehouse；
(2) average value of this track circuit ridership over the years is obtained in the method for arithmetic average, is denoted as respectively The then history average of this track circuit ridershipSimultaneously by the average value of this track circuit ridership over the yearsBy linear regression method, the average value of this track circuit next year ridership is obtained, withIt represents, At this timeNot real data, but to the prediction data of next year average passenger number；
(3) disturbance degree of the different event to the volume of the flow of passengers is obtained, is denoted as the weights of different event, method is：By with different things Part E is index, retrieves all P for meeting event E conditions from data warehouse, the condition for meeting event E equipped with k, the k It is denoted as P respectively1、P2……Pk, then meet the mean value of the track circuit ridership of the eventThen event E is to passenger flow Measure the weights of disturbance degree
(4) according to each event weights, the passenger flow forecast amount at following each moment is obtained, method is：When analysis is following each successively Corresponding event is carved, the weights of its corresponding all event are subjected to arithmetic average, obtain the weights λ at the moment, method is： If a certain moment corresponds to m event, this m event is λ respectively to the weights of volume of the flow of passengers disturbance degree1、λ2……λm, then moment WeightsThe passenger at the track circuit moment predicts that number isWhereinTo be predicted multiplying for year Objective number mean value；
(5) headway at the moment is determined according to the plan carrying number of any moment passenger flow forecast amount, each train, side Method is：If the time of the complete a circuit of train operation, each Train operation plan carrying number was P for TP, then headway t= T·PP/PF, this headway is basic data, in actual moving process, further according to the data of part 2 Internet of Things monitoring Headway is finely adjusted.
Above-mentioned part 2 includes according to the technical solution that the data that Internet of Things monitors are finely adjusted headway：
(1) this track circuit current passenger number P is collected；
(2) by currently practical ridership P and current time prediction ridership PFRatio, current time bias ratio μ=P/ is obtained PF, then the prediction ridership of subsequent time be modified to initial value and be multiplied by bias ratio, i.e. PF=μ PF(equal sign is assignment herein), will repair P after justFValue substitutes into formula t=TPP/PFIn, acquire the headway t after adjustment.
Embodiment
Assuming that the passenger data of known a certain track circuit the first three years,Then History average passenger numberNext year prediction average passenger number is calculated for first three annual data with linear regression It obtains,
Assuming that count in history all January average passenger numbers be 910 people, several 920 people of Monday average passenger, the morning 8:10 ridership, 1080 people, can obtain January, Monday, 8:The disturbance degree of 10 these three events is with λ1、λ2、λ3It represents, respectively：At this time if a certain moment of prediction just only Three above event is triggered, then the ridership that the moment is expected
Assuming that the time of complete one time of this circuit train operation is 100 minutes, i.e. T=100, every time Train operation plan carrying number is 100 people, i.e. PP=100, then headway t=TPP/PF=100100/989.4=10.1 minutes；
Assuming that sampling interval duration is 10 minutes, and all mornings 8 in history:20 average passenger number is 1077 people, and Above-mentioned 8：10 actual passenger numbers are 942 people, then current time bias ratio μ=P/PF=942/989.4=0.952, subsequent time Predicted passenger number isAccording to deviation Rate is adjusted PF=μ PF=0.952988.38=940.94, at this time headway be adjusted to t=TPP/PF= 100100/940.94=10.63, i.e. headway were adjusted to 10.63 minutes from 10.1 minutes.
Compared with prior art, this method has preferable open, accuracy and adaptivity, can be with research Deeply, new event is continuously increased, event is more perfect, and accuracy is higher, while can also be according to the continuous dynamic of actual conditions Amendment headway cause train operation more efficiently.

Claims (3)

1. a kind of rail traffic dispatching method based on big data and Internet of Things, including it is a kind of it is based on big data and Internet of Things, Rail traffic dispatching method that is open, can continuing to optimize, which is characterized in that the method includes two parts：
(1) volume of the flow of passengers of daily different moments is predicted using big data, and between determining that daily different moments dispatch a car on this basis Every the basic data of time；
(2) data monitored according to Internet of Things are finely adjusted headway.
2. a kind of rail traffic dispatching method based on big data and Internet of Things according to claim 1, which is characterized in that The volume of the flow of passengers that daily different moments are predicted using big data, and daily departure interval different moments is determined on this basis The basic data of time, concrete methods of realizing are as follows：
(1) track circuit ridership P is defined sometime to put the sum of all train passenger numbers in current orbit circuit, it will be over the years This track circuit ridership is sampled at regular intervals, is stored in data warehouse；
(2) average value of this track circuit ridership over the years is obtained in the method for arithmetic average, is denoted as respectively The then history average of this track circuit ridershipSimultaneously by the average value of this track circuit ridership over the yearsBy linear regression method, the average value of this track circuit next year ridership is obtained, withIt represents, At this timeNot real data, but to the prediction data of next year average passenger number；
(3) disturbance degree of the different event to the volume of the flow of passengers is obtained, is denoted as the weights of different event, method is：By with different things Part E is index, retrieves all P for meeting event E conditions from data warehouse, the condition for meeting event E equipped with k, the k It is denoted as P respectively1、P2……Pk, then meet the mean value of the track circuit ridership of the eventThen event E is to passenger flow Measure the weights of disturbance degree
(4) according to each event weights, the passenger flow forecast amount at following each moment is obtained, method is：When analysis is following each successively Corresponding event is carved, the weights of its corresponding all event are subjected to arithmetic average, obtain the weights λ at the moment, method is： If a certain moment corresponds to m event, this m event is λ respectively to the weights of volume of the flow of passengers disturbance degree1、λ2……λm, then moment WeightsThe passenger at the track circuit moment predicts that number isWhereinTo be predicted multiplying for year Objective number mean value；
(5) headway at the moment is determined according to the plan carrying number of any moment passenger flow forecast amount, each train, side Method is：If the time of the complete a circuit of train operation, each Train operation plan carrying number was P for TP, then current headway t =TPP/PF。
3. a kind of rail traffic dispatching method based on big data and Internet of Things according to claim 1, which is characterized in that The data monitored according to Internet of Things are finely adjusted headway, and concrete methods of realizing is as follows：
(1) with technology of Internet of things, the currently practical ridership P of this track circuit is collected；
(2) by currently practical ridership P and current time prediction ridership PFRatio, current time bias ratio μ=P/ is obtained PF, then the prediction ridership of subsequent time be modified to initial value and be multiplied by bias ratio, i.e. PF=μ PF(equal sign is assignment herein), will repair P after justFValue substitutes into formula t=TPP/PFIn, acquire the headway t after adjustment.
CN201710564309.7A 2017-07-12 2017-07-12 Rail transit dispatching method based on big data and Internet of things Active CN108216311B (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109017883A (en) * 2018-09-14 2018-12-18 广州达美智能科技有限公司 Rail traffic dispatching method, system and computer readable storage medium
CN109146326A (en) * 2018-09-17 2019-01-04 沈阳工程学院 Dispatching method and system are managed in a kind of campus electric car small station based on data flow
CN111969432A (en) * 2020-07-10 2020-11-20 国网浙江省电力有限公司湖州供电公司 Wind-solar complementary power supply device for ring main unit dehumidification system
CN113276915A (en) * 2021-07-06 2021-08-20 浙江非线数联科技股份有限公司 Subway departure scheduling method and system
EP4071736A4 (en) * 2020-12-25 2024-02-21 Casco Signal Ltd. Intelligent scheduling method and system for rail transit

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CN103198648A (en) * 2013-03-26 2013-07-10 成都希盟科技有限公司 Self-adaption dispatching method used for public traffic system
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CN103810839A (en) * 2012-11-08 2014-05-21 无锡津天阳激光电子有限公司 Internet of Things 3G public transportation intelligent system
CN105575108A (en) * 2016-01-11 2016-05-11 深圳市蓝泰源信息技术股份有限公司 Intelligent bus dispatching operation method
CN106355276A (en) * 2016-08-30 2017-01-25 郑州天迈科技股份有限公司 Departure time-table generation system based on passenger flow simulation analysis

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JP2013242665A (en) * 2012-05-18 2013-12-05 Railway Technical Research Institute Passenger flow estimation system and method at occurrence of railroad transportation failure
CN103810839A (en) * 2012-11-08 2014-05-21 无锡津天阳激光电子有限公司 Internet of Things 3G public transportation intelligent system
CN103198648A (en) * 2013-03-26 2013-07-10 成都希盟科技有限公司 Self-adaption dispatching method used for public traffic system
CN105575108A (en) * 2016-01-11 2016-05-11 深圳市蓝泰源信息技术股份有限公司 Intelligent bus dispatching operation method
CN106355276A (en) * 2016-08-30 2017-01-25 郑州天迈科技股份有限公司 Departure time-table generation system based on passenger flow simulation analysis

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109017883A (en) * 2018-09-14 2018-12-18 广州达美智能科技有限公司 Rail traffic dispatching method, system and computer readable storage medium
CN109146326A (en) * 2018-09-17 2019-01-04 沈阳工程学院 Dispatching method and system are managed in a kind of campus electric car small station based on data flow
CN111969432A (en) * 2020-07-10 2020-11-20 国网浙江省电力有限公司湖州供电公司 Wind-solar complementary power supply device for ring main unit dehumidification system
EP4071736A4 (en) * 2020-12-25 2024-02-21 Casco Signal Ltd. Intelligent scheduling method and system for rail transit
CN113276915A (en) * 2021-07-06 2021-08-20 浙江非线数联科技股份有限公司 Subway departure scheduling method and system
CN113276915B (en) * 2021-07-06 2022-07-15 浙江非线数联科技股份有限公司 Subway departure scheduling method and system

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