CN105930937B - A kind of train operation scheduling Integrated Optimization considering subway speed line - Google Patents
A kind of train operation scheduling Integrated Optimization considering subway speed line Download PDFInfo
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Abstract
The invention discloses a kind of train operations for considering subway speed line to dispatch Integrated Optimization.By analyzing fixed subway line information, the handover scheme of Train Control mode between each platform of optimization, input of its result as traffic control Integrated Model, establish the train operation scheduling Integrated optimization model for considering subway speed line, wherein by utilizing to get up the operation of train with dispatch contact to regenerating braking energy, Study on Integration；And is solved by the operation curve and timetable for obtaining train, emulation platform is inputted to and is emulated, and carry out later period closed-loop control for model solution using particle swarm algorithm.The invention considers the real background of subway speed line on the basis of traffic control Study on Integration, has far-reaching practical significance to research urban track traffic.
Description
Technical field
The invention belongs to traffic controls to optimize field, and in particular to a kind of train operation scheduling one for considering subway speed line
Body optimization method
Background technique
With the fast development of urban public transport, subway is increasingly becoming each metropolitan major public transportation mode, ground
Railway network scale constantly expands, while China's energy and relative scarcity, therefore the operational efficiency of subway and problems of energy consumption are just
Start to be particularly important.Brake energy recovery technology on the basis of not changing train original operational efficiency to the greatest extent
The utilization rate of the energy is improved, energy crisis at this stage can be largely alleviated.The speed line system of subway exists
The city operations such as Beijing and Guangzhou, it also meets other than not influencing original Metro Passenger to the demand that stops of each website
The demand for the passenger that a part has the emergency to need quickly to arrive at the destination.Therefore energy-efficient subway circulation scheduling one
Body method needs while considering the speed line system and the recycling of braking energy of subway.
At present the research of more vehicle energy-saving drivings is concentrated on saving rear car in the case where extending rear car runing time substantially
Operation energy consumption and the recycling of regenerating braking energy etc..In off-peak period, moving block system can be by between reduction station
Train average speed come reach energy conservation.In this case, when can reduce the dwell time as needed to guarantee that train is completely run
Between.
Regeneration energy brake is also the important method of energy source dispensing.Regeneration energy brake be mainly utilized in same website or
The energetic interaction process between two trains that person's adjacent sites have one section of overlapping time to draw and brake respectively, it belongs to
Regeneration energy can present formula Land use systems, and principle is when train braking, and braking energy returns to electricity by inverter
Net, if other trains for being located at same power supply section with it this moment are in traction energy-absorbing state, regeneration energy passes through contact net
It is directly passed to tractor-trailer train use.Therefore the train energy consumption of total line just will be greatly reduced.
During traffic control presented above, the dynamic that introducing regenerating braking energy recycling but has ignored adjacent train is chased after
Track process has ignored influence of the rear car to front truck, and both defaults and to have identical rate curve, due to depositing for speed car system
Actual conditions are really not so, in peak phase, influence especially pronounced.Therefore optimization will not only consider the recycling benefit of braking energy
With it is also contemplated that the dynamic tracing of adjacent speed car.The present invention introduces according to above-mentioned analysis in traditional utilizing fast on model
The idea of slow train proposes a kind of traffic control Integrated optimization for more comprehensively considering subway speed line and Brake energy recovery
Method.
Summary of the invention
The present invention is directed to lack for the prior art while the considerations of to the dynamic tracing and braking energy of subway speed car,
A kind of traffic control Integrated Optimization is provided.Consider the influence of subway speed car, the first fixed line information of acquisition subway
With the attribute of the speed car of fixed subway line road traveling, the optimization of the sequence of operation between being stood by these attributes is established
Consider that Integrated optimization model is dispatched in the train operation of subway speed line.
The purpose of the present invention is achieved through the following technical solutions: a kind of train operation tune considering subway speed line
Integrated Optimization is spent, this method realizes that the system comprises the following modules in subway circulation scheduling Integrated optimization system:
Database module, manipulation sequence optimisation module, Integrated optimization module, speed calculation module, tracking interval computing module and
Emulation platform；Database module train car line information, train data itself, original train operation curve, timetable letter
Operation curve, table information after breath and optimization；Manipulation sequence optimisation module is fallen out according to the optimization of the line information of input
Vehicle respectively stand between control model sequence；Train, line information and the behaviour that Integrated optimization module is inputted according to database module
The train that vertical sequence optimisation module institute's optimization is come control model sequence optimal between each station establishes subway circulation scheduling one
Body Optimized model, and model is solved, obtain train speed curve and running schedule；Speed calculation module according to
Duration of control model carries out speed calculating between the train station of Integrated optimization module optimization；Tracking interval calculates mould
The speed that root tuber is transmitted according to speed calculation module calculates the tracking interval of adjacent train in real time, guarantees that adjacent train is spaced in
Within secure threshold；Emulation platform receives the train speed curve and running schedule of Integrated optimization module institute optimization,
Train carries out simulation run according to optimum results, according to the deviation real-time perfoming closed-loop control of simulation result and optimum results；It should
Method the following steps are included:
(1) the fixation subway line information that train is travelled, including the specific ramp of distance between sites, route, bend number are obtained
According to, road section speed limit information, speed car stop site information, overtaking site information and Zhan Jian power supply station data.
(2) it obtains the attribute information for the speed train that fixed subway line road is travelled, including train model, weight, leads
Draw characteristic, braking characteristic, watt current and tensionvelocity curve and the original departure time-table of train, train is original to dispatch a car
The dwell time of runing time and each website between timetable includes departure interval, number of dispatching a car, stands.
(3) sequence optimisation is manipulated between standing: the manipulation sequence of the train i.e. switch sequence of Train Control mode is arranged using four kinds
Vehicle energy-saving driving mode, respectively maximum traction, part traction (cruise), coasting and maximum braking, according to the ground between each station
Railway road Advance data quality goes out the optimal manipulation sequence of train.
(4) it establishes and considers that Integrated optimization model is dispatched in the train operation of subway speed line, Model Background assumes M station
Point, I train is issued in speed car 1:1 ratio, with energy consumption of the train on sliver road at least for target, the speed limit of road,
Tracking interval, runing time and the waiting time of train are constraint condition, and model is as follows:
Min E=E1+E2-W
Objective function indicates total energy consumption of all trains on sliver road, and regeneration is subtracted out for total operation energy consumption of train
The energy utilized is braked, traffic control Integrated Model is established by the utilization of regenerating braking energy；Due to express and slow train
Pulling figure, braking characteristic it is different, therefore total operation energy consumption of train is divided into two parts E1And E2, they are all by four
Part forms: highest traction energy consumption, cruise time section energy consumption, coasting energy consumption and the braking energy consumption of train, it can be found that this four
Divide and all introduce 0-1 variable, this represents that manipulation sequence optimisation module institute's optimization is come as a result, E2 is again by two parts group
At it represents two kinds of operating statuses of train: overtaking and non-overtaking.The part of deduction is that regenerative braking utilizes energy W, and W is by three
Part composition, first part are slow train regenerating braking energy using value, and rear two parts are that express regenerating braking energy utilizes value, by
In train, there are overtaking and non-overtaking state, therefore are divided into two parts.
In constraint condition, first, second and third speed limit condition for train converts speed limit to the constraint of runing time；The
Four, five, six reach constraint condition for platform, have not arrived the case where platform just stops to train and constrain；Seven, the eight
Indicate that train is guaranteed efficiency, total run time cannot be too long in the constraint of whole route total run time；Section 9 describes
The constraint of tracking interval between train, guarantees the safety traffic of train；Ten, the 11 indicate departure intervals and dwell time
Range constraint.The meaning of another each variable is defined as follows:
Decision variable:
Slow train is in the m platform highest pull-in time；
Slow train is in the m platform cruise time；
Slow train is in the m platform coasting time；
Slow train is in m platform braking time；
Express is in the m platform highest pull-in time；
Express is in the m platform cruise time；
Express is in the m platform coasting time；
Express is in m platform braking time；
In the m platform highest pull-in time when express generation overtaking；
In the m platform cruise time when express generation overtaking；
In the m platform coasting time when express generation overtaking；
In m platform braking time when express generation overtaking；
h1: slow-fast departure interval；
h2: fast-slow departure interval；
xm: train is in the m platform dwell time；
Intermediate variable:
I:I train；
M:M platform；
Uw: slow train is by voltage at bow；
U’w: express is by voltage at bow；
Pull (m): 0-1 variable indicates whether that using highest traction control, 0 indicates not use, and 1 indicates to use；
Cru (m): 0-1 variable indicates whether that using cruise control, 0 indicates not use, and 1 indicates to use；
None (m): 0-1 variable indicates whether to control using coasting, and 0 indicates not use, and 1 indicates to use；
De (m): 0-1 variable indicates whether that using control for brake, 0 indicates not use, and 1 indicates to use；
C: overtaking station number on route；
Ip1: slow train maximum load is averaged watt current；
λy: tractive force of train coefficient of utilization, value are taken as 0.9；
Ip0: slow train draws personal watt current；
Ip2: draw average watt current in slow train part；
Ip'0: personal watt current when slow train coasting, braking；
I'p1: express maximum load is averaged watt current；
I’p0: express draws personal watt current；
I'p2: draw average watt current in express part；
I’p'0: personal watt current when express coasting, braking；
λ (m-1, m): 0-1 variable judges that the station m-1 and the station m indicate not exist whether in same power-feed section, 0, and 1 indicates
Same power-feed section；
Minimum runing time between each station；
Overtaking occurs for express, i.e., across runing time minimum between standing behind station；
sm: the distance between the station m and the station m+1；
vi,m: i-th slow train is from the speed behind the station m；
v’i,m: overtaking does not occur for i-th express from the speed behind the station m；
v”i,m: from the speed behind the station m after i-th express generation overtaking；
T: whole route maximum total run time；
Si: the real-time displacement of i-th train；
Li,m: the tracking interval of i-th train and forward direction train；
Lmin: adjacent train minimum tracking interval；
Tmin、Un: the lower limit and the upper limit of departure interval；
Umin、Umax: the lower limit and the upper limit of each platform dwell time；
Over (m): 0-1 variable is 0 if being overtaking station if m platform, is otherwise 1；
Stop: express stops summation on route is indicated；
(5) the traffic control Integrated optimization model in solution procedure (4), obtains train speed curve and the time of running
Table.
(6) result that (5) optimization order goes out is inputed into emulation platform, carries out environment Visualization.Train is according to optimization
As a result simulation run is carried out, the later period can carry out closed-loop control according to the deviation of simulation result and optimum results.
Further, in the step 5, traffic control Integrated optimization model is solved by particle swarm algorithm.
Further, in the step 5, when solving optimization model, situation is divided to calculate different time by the distribution of overtaking station
The adjacent train tracking interval of section.
Beneficial effects of the present invention are as follows: the operation of train and scheduling are carried out Study on Integration by energy consumption by the present invention,
And the idea for introducing speed car tracking and overtaking on this basis, meets the development trend of current urban track traffic, has
It is of practical significance.Based on the established above traffic control Integrated optimization mould for considering subway speed line and Brake energy recovery
Type optimizes the raising of train energy-saving and efficiency, obtains the operation of each train on the line using PSO Algorithm
Curve and timetable.
Detailed description of the invention
Fig. 1 is subway circulation scheduling Integrated optimization system；
Fig. 2 is that subway regenerating braking energy utilizes schematic diagram；
Fig. 3 is subway minimum tracking interval schematic diagram；
Fig. 4 is model solution step.
Specific embodiment
With reference to the accompanying drawing and specific example invention is further described in detail.
The present invention is a kind of train operation scheduling Integrated Optimization for considering subway speed line, and this method is transported in subway
It is realized in row scheduling Integrated optimization system, such as Fig. 1.
The system comprises the following modules: database module, manipulation sequence optimisation module, Integrated optimization module, speedometer
Calculate module, tracking interval computing module and emulation platform；Database module train car line information, train data itself,
Operation curve, table information after original train operation curve, table information and optimization；Manipulate sequence optimisation module
Control model sequence between respectively being stood herein according to the line information optimization train of input；Integrated optimization module is according to database
The train that train, line information and the manipulation sequence optimisation module institute's optimization of module input are come control optimal between each station
Mode sequences processed establish subway circulation scheduling Integrated optimization model, and solve to model, obtain train speed curve with
And the operation curve and timetable of running schedule speed car on the line；Speed calculation module is excellent according to Integrated optimization module
The duration of control model carries out speed calculating between the train station dissolved；Tracking interval computing module calculates mould according to speed
The speed that block transmits calculates the tracking interval of adjacent train in real time, guarantees that adjacent train is spaced within secure threshold；It is imitative
True platform receives the train speed curve and running schedule of Integrated optimization module institute optimization, and train is according to optimum results
Simulation run is carried out, according to the deviation real-time perfoming closed-loop control of simulation result and optimum results.
Method includes the following steps:
(1) fixation subway line information, including the specific ramp of distance between sites, route, bend etc. that train is travelled are obtained
Data, road section speed limit information, speed car stop site information and overtaking site information；
(2) attribute information for the speed train that fixed subway line road is travelled is obtained, including train model, weight, each
From the original departure time-table of pulling figure, braking characteristic, watt current and tensionvelocity curve and train, including dispatch a car
Be spaced, number of dispatching a car, stand between the dwell time of runing time and each website；
(3) sequence optimisation is manipulated between standing: the step for according to the optimal of the subway road information optimization train between each station
Sequence is manipulated, such as train can sequentially enter inertia mode and maximum braking mode before entering corresponding stop a little to realize
It stops.
(4) it establishes and considers that Integrated optimization model is dispatched in the train operation of subway speed line, model is as follows: min E
=E1+E2-W
Regenerating braking energy used in the present invention, which belongs to regenerating braking energy using method, can present formula Land use systems, former
Reason is that when train braking, braking energy returns to power grid by inverter, if being located at same service area with it this moment
Between other trains be in traction energy-absorbing state, regeneration energy then pass through contact net be directly passed to tractor-trailer train use.It is adjacent
Regenerating braking energy utilization twice: forward direction train destinating station platform can occur over time and space for train, and the dwell time ties
Beam will be drawn, backward train will reach same platform, start to brake, and the two can use；Forward direction train will reach down
One platform prepares braking, and backward train was already expired in the upper platform dwell time, prepares traction, if this two platform belongs to same confession
Electric section, then the two can use；Certainly express is not that station station is stopped in the present invention, and in overtaking point, express will not stop, because
This introduces 0-1 variable over (m), otherwise it is 1 that over (m), which is 0, if m is overtaking station, is indicated to regenerate in objective function
Before braking energy formula.Situation, such as Fig. 2, when having write out its overlapping are transmitted according to described two kinds of regenerating braking energies before
Between expression formula:
Symbol description:
ti,m: at the time of train i leaves the station m；
Train i is drawn from the station m highest at the time of end,
Train i is cruised from the station m at the time of end,
At the time of train i is braked before reaching the station m+1,
Train i reaches m+1 and stands the moment,
tm: train m stand runing time,
Due toH=ti+1,m-ti,m
So the expression formula of overlapping time is just only related to decision variable, then according to formulaThen regenerating braking energy expression formula are as follows:
The real-time speed that train can be used is further noted that in constraint condition, and the expression formula of real-time speed is as follows:
Acceleration of the train in highest traction, coasting, braking, M are respectively representedg+ ∑ n is described
The total weight of train and passenger.In solution procedure, time step is set to solve the real-time speed of train, previous time step
Required speed is substituting in the Acceleration Formula in current time step, and then finds out present speed.
It is related to minimum tracking interval in tracking interval constraint condition, present invention employs based on the exhausted of movable block mode
Expression to the minimum tracking interval under the conditions of standard, it is seen that shown in attached drawing 3, expression formula is as follows:
(5) the traffic control Integrated optimization model in solution procedure (4), since train operation scheduling problem is more multiple
Miscellaneous, many scholars can use intelligent algorithm in such issues that research, comprising: genetic algorithm, ant group algorithm, simulation are moved back
Fire and particle swarm algorithm etc., so the present embodiment uses particle swarm algorithm, solution procedure is as follows:
(5.1) population initializes: updating including population scale, the initial velocity of each particle, initial position, particle
Maximum speed, maximum number of iterations and inertia weight, the initial bit of particle is by operation curve and timetable original in database
Definition；Since decision variable is all the time, and about their constraint in constraint condition, when initialization, define between each station
Runing time, route total run time, the range of departure interval and each station dwell time, the region that trapped particle updates；
(5.2) fitness function calculates: fitness function just uses the objective function of model:
Min E=E1+E2-W
(5.3) individual extreme value and group's extreme value update.
(5.4) particle rapidity updates and location updating checks whether it meets platform and arrive at a station after the completion of particle position updates
And tracking interval constraint condition；When carrying out constraint condition judgement, need to calculate all trains speed on the line, away from
From curve and timetable, then judge whether train meets constraint of arriving at a station；In addition information trunk is needed during calculating tracking interval
Then the distribution situation and number of road overtaking station are distributed again by overtaking situation and discuss and calculate adjacent train in different time periods
Tracking interval, judge whether tracking interval meets minimal separation constraint according to calculated result.
(5.5) termination condition judges: if reaching maximum number of iterations, stopping optimizing.Solution procedure is as shown in Figure 4.
The foregoing is merely the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto.Any
The technical staff in field in technical scope disclosed by the invention, it can be carried out it is appropriate be altered or varied, and this change
Become or variation should be covered by the protection scope of the present invention.
Claims (3)
1. Integrated Optimization is dispatched in a kind of train operation for considering subway speed line, which is characterized in that this method is in subway
Realized in traffic control Integrated optimization system, which comprises the following modules: database module, manipulation sequence optimisation module,
Integrated optimization module, speed calculation module, tracking interval computing module and emulation platform；Database module train car line
Operation curve, timetable after road information, train itself data, original train operation curve, table information and optimization
Information；Manipulation sequence optimisation module according to the line information optimization train of input respectively stand between control model sequence；Integration
Train that optimization module is inputted according to database module, line information and manipulation sequence optimisation module institute's optimization come train
Optimal control model sequence establishes subway circulation scheduling Integrated optimization model between each station, and solves to model,
Obtain train speed curve and running schedule；Between train station of the speed calculation module according to Integrated optimization module optimization
The duration of control model carries out speed calculating；The speed that tracking interval computing module is transmitted according to speed calculation module
Degree calculates the tracking interval of adjacent train in real time, guarantees that adjacent train is spaced within secure threshold；Emulation platform receives one
Change the train speed curve and running schedule of optimization module institute optimization, train carries out simulation run according to optimum results,
According to the deviation real-time perfoming closed-loop control of simulation result and optimum results；Method includes the following steps:
(1) the fixation subway line information that train is travelled, including the specific ramp of distance between sites, route, curve data, road are obtained
Section speed-limiting messages, speed car stop site information, overtaking site information and Zhan Jian power supply station data；
(2) attribute information for the speed train that fixed subway line road is travelled, including train model, weight, traction spy are obtained
Property, braking characteristic, watt current and tensionvelocity curve and the original departure time-table of train, the original frequency of train
The dwell time of runing time and each website between table includes departure interval, number of dispatching a car, stands；
(3) sequence optimisation is manipulated between standing: the manipulation sequence of the train i.e. switch sequence of Train Control mode, using four kinds of train sections
Energy driving mode, respectively maximum are drawn, part traction is cruised, coasting and maximum are braked, according to the subway road between each station
Advance data quality goes out the optimal manipulation sequence of train；
(4) the train operation scheduling Integrated optimization model of foundation consideration subway speed line, Model Background M website of hypothesis, I
Train issues in speed car 1:1 ratio, with energy consumption of the train on sliver road at least for target, the speed limit of road, train
Tracking interval, runing time and waiting time are constraint condition, and model is as follows:
Min E=E1+E2-W
Objective function indicates total energy consumption of all trains on sliver road, and regenerative braking is subtracted out for total operation energy consumption of train
The energy utilized establishes traffic control Integrated Model by the utilization of regenerating braking energy；Due to leading for express and slow train
Draw that characteristic, braking characteristic are different, therefore total operation energy consumption of train is divided into two parts E1And E2, they are all by four parts
Composition: highest traction energy consumption, cruise time section energy consumption, coasting energy consumption and the braking energy consumption of train, this four part all introduces 0-1
Variable, this represent manipulation sequence optimisation module institute's optimization come as a result, E2 consists of two parts again, it represents train
Two kinds of operating statuses: overtaking and non-overtaking；The part of deduction is that regenerative braking utilizes energy W, W to consist of three parts, first
It is divided into slow train regenerating braking energy and utilizes value, rear two parts is express regenerating braking energy using value, since there are overtaking for train
With non-overtaking state, therefore it is divided into two parts；
In constraint condition, first, second and third speed limit condition for train converts speed limit to the constraint of runing time；4th,
Five, six reach constraint condition for platform, have not arrived the case where platform just stops to train and constrain；Seven, the eight tables
Show that train is guaranteed efficiency, total run time cannot be too long in the constraint of whole route total run time；Section 9 describes column
The constraint of tracking interval between vehicle, guarantees the safety traffic of train；Ten, the 11 indicate the model of departure interval and dwell time
Enclose constraint；The meaning of another each variable is defined as follows:
Decision variable:
Slow train is in the m platform highest pull-in time；
Slow train is in the m platform cruise time；
Slow train is in the m platform coasting time；
Slow train is in m platform braking time；
Express is in the m platform highest pull-in time；
Express is in the m platform cruise time；
Express is in the m platform coasting time；
Express is in m platform braking time；
In the m platform highest pull-in time when express generation overtaking；
In the m platform cruise time when express generation overtaking；
In the m platform coasting time when express generation overtaking；
In m platform braking time when express generation overtaking；
h1: slow-fast departure interval；
h2: fast-slow departure interval；
xm: train is in the m platform dwell time；
Intermediate variable:
I:I train；
M:M platform；
Uw: slow train is by voltage at bow；
U’w: express is by voltage at bow；
Pull (m): 0-1 variable indicates whether that using highest traction control, 0 indicates not use, and 1 indicates to use；
Cru (m): 0-1 variable indicates whether that using cruise control, 0 indicates not use, and 1 indicates to use；
None (m): 0-1 variable indicates whether to control using coasting, and 0 indicates not use, and 1 indicates to use；
De (m): 0-1 variable indicates whether that using control for brake, 0 indicates not use, and 1 indicates to use；
C: overtaking station number on route；
Ip1: slow train maximum load is averaged watt current；
λy: tractive force of train coefficient of utilization, value are taken as 0.9；
Ip0: slow train draws personal watt current；
Ip2: draw average watt current in slow train part；
Ip'0: personal watt current when slow train coasting, braking；
I'p1: express maximum load is averaged watt current；
I’p0: express draws personal watt current；
I'p2: draw average watt current in express part；
I’p'0: personal watt current when express coasting, braking；
λ (m-1, m): 0-1 variable judges that the station m-1 and the station m indicate not exist whether in same power-feed section, 0, and 1 indicates same
Power-feed section；
Minimum runing time between each station；
Overtaking occurs for express, i.e., across runing time minimum between standing behind station；
sm: the distance between the station m and the station m+1；
vi,m: i-th slow train is from the speed behind the station m；
v’i,m: overtaking does not occur for i-th express from the speed behind the station m；
v”i,m: from the speed behind the station m after i-th express generation overtaking；
T: whole route maximum total run time；
Si: the real-time displacement of i-th train；
Li,m: the tracking interval of i-th train and forward direction train；
Lmin: adjacent train minimum tracking interval；
Tmin、Un: the lower limit and the upper limit of departure interval；
Umin、Umax: the lower limit and the upper limit of each platform dwell time；
Over (m): 0-1 variable is 0 if being overtaking station if m platform, is otherwise 1；
Stop: express stops summation on route is indicated；
(5) the traffic control Integrated optimization model in solution procedure (4), obtains train speed curve and running schedule；
(6) result of step (5) optimization is inputed into emulation platform, carries out environment Visualization；Train according to optimum results into
Row simulation run, later period carry out closed-loop control according to the deviation of simulation result and optimum results.
2. Integrated Optimization is dispatched in a kind of train operation for considering subway speed line according to claim 1, special
Sign is, in the step (5), is solved by particle swarm algorithm to traffic control Integrated optimization model.
3. Integrated Optimization is dispatched in a kind of train operation for considering subway speed line according to claim 1, special
Sign is, in the step (5), when solving optimization model, divides situation to calculate by the distribution of overtaking station in different time periods adjacent
Train operation organization.
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| CN106494469B (en) * | 2016-10-27 | 2018-06-19 | 上海电气泰雷兹交通自动化系统有限公司 | A kind of speed car crosses the method for avoidance automatically |
| CN108001487B (en) * | 2016-10-27 | 2020-04-21 | 湖南中车时代通信信号有限公司 | Method and device for calculating running interval of train signal system |
| CN106842901B (en) * | 2017-01-19 | 2019-11-08 | 重庆交通大学 | For the method for train automated driving system formation speed control command |
| CN107180282B (en) * | 2017-07-01 | 2020-08-25 | 南京理工大学 | Energy-saving optimization method for multiple trains in high peak time period |
| CN108564209A (en) * | 2018-03-30 | 2018-09-21 | 广州地铁设计研究院有限公司 | A kind of urban track traffic speed car service chart optimization method and system |
| CN109017880A (en) * | 2018-06-28 | 2018-12-18 | 武汉钢铁有限公司 | A kind of rail vehicle management system |
| CN109815536B (en) * | 2018-12-19 | 2020-10-30 | 西南交通大学 | Urban rail transit energy-saving timetable and operation curve optimization method |
| CN109711752B (en) * | 2019-01-16 | 2020-08-18 | 北京交通大学 | Subway transport capacity calculation method for different vehicle types |
| CN110877617B (en) * | 2019-11-27 | 2020-11-10 | 浙江大学 | Freight electric locomotive departure point reference speed automatic generation device and method based on two-stage orthogonal configuration |
| CN111791923B (en) * | 2020-05-21 | 2021-06-29 | 北京交通大学 | Energy-saving-oriented single-track railway train scheduling method and system |
| CN113147841A (en) * | 2021-05-13 | 2021-07-23 | 中车长春轨道客车股份有限公司 | Rail vehicle capacity management and energy-saving auxiliary driving method and related device |
| CN113592419B (en) * | 2021-05-31 | 2024-05-14 | 南京理工大学 | Rail transit speed and time table optimization method considering passenger flow and energy conservation |
| CN116523267B (en) * | 2023-06-28 | 2024-01-12 | 深圳市海成智联科技有限公司 | Vehicle dispatching optimization method, system and storage medium suitable for rail transit |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200286A (en) * | 2014-09-10 | 2014-12-10 | 东南大学 | Urban railway transit timetable optimizing technology application framework |
| CN104192176A (en) * | 2014-05-19 | 2014-12-10 | 广州市地下铁道总公司 | Method for reducing metro traction energy consumption |
| CN104680327A (en) * | 2015-03-12 | 2015-06-03 | 北京全路通信信号研究设计院有限公司 | Method and device for automatically adjusting railway operation plan |
| CN104866925A (en) * | 2015-05-27 | 2015-08-26 | 上海工程技术大学 | Train timetable optimization method based on ATS adjusting function |
-
2016
- 2016-05-07 CN CN201610301839.8A patent/CN105930937B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104192176A (en) * | 2014-05-19 | 2014-12-10 | 广州市地下铁道总公司 | Method for reducing metro traction energy consumption |
| CN104200286A (en) * | 2014-09-10 | 2014-12-10 | 东南大学 | Urban railway transit timetable optimizing technology application framework |
| CN104680327A (en) * | 2015-03-12 | 2015-06-03 | 北京全路通信信号研究设计院有限公司 | Method and device for automatically adjusting railway operation plan |
| CN104866925A (en) * | 2015-05-27 | 2015-08-26 | 上海工程技术大学 | Train timetable optimization method based on ATS adjusting function |
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