CN112158238A - Automatic train driving curve generation method based on target and passenger flow difference - Google Patents
Automatic train driving curve generation method based on target and passenger flow difference Download PDFInfo
- Publication number
- CN112158238A CN112158238A CN202011171867.5A CN202011171867A CN112158238A CN 112158238 A CN112158238 A CN 112158238A CN 202011171867 A CN202011171867 A CN 202011171867A CN 112158238 A CN112158238 A CN 112158238A
- Authority
- CN
- China
- Prior art keywords
- train
- passenger
- weight
- automatic driving
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
- B61L27/04—Automatic systems, e.g. controlled by train; Change-over to manual control
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning, or like safety means along the route or between vehicles or vehicle trains
- B61L23/08—Control, warning, or like safety means along the route or between vehicles or vehicle trains for controlling traffic in one direction only
- B61L23/14—Control, warning, or like safety means along the route or between vehicles or vehicle trains for controlling traffic in one direction only automatically operated
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The invention provides a method for generating a train automatic driving curve based on target and passenger flow difference, which generates different curve sets in advance according to a train operation control target and various passenger weight conditions in different time periods in an off-line manner; in the running process of the train, the current weight of the passenger is obtained according to an image processing technology, then the automatic train driving control system automatically identifies the current time period according to the current time, then the target running time and the current weight of the passenger are matched with the time parameter and the weight parameter in the corresponding curve set, the corresponding automatic driving curve is found, and then the train is controlled to run according to the found automatic driving curve; the beneficial technical effects of the invention are as follows: the scheme considers the control target difference and the passenger weight difference at the same time, so that the automatic driving curve is more consistent with the actual situation.
Description
Technical Field
The invention relates to a train automatic driving curve generation technology, in particular to a train automatic driving curve generation method based on target and passenger flow difference.
Background
The automatic driving curve is used for providing a target speed instruction for the on-line speed tracking control of the train automatic driving control system in the running process of the train, so that the train can finally realize the tracking of the target speed through continuous adjustment according to the line data and the target instruction speed.
The operation time period of the train can be generally divided into a peak time period, a flat time period and a low time period, and the control targets of the train have differences in different operation time periods: the transportation pressure is higher in the peak time period, the train needs to run at a high speed and at a standard point in the interval, the transportation pressure is relatively lower in the peak leveling time period, the riding comfort of passengers can be properly considered at the moment, the transportation pressure is almost not generated in the low peak time period, and the reduction of the operation cost is generally considered as a priority, so that three automatic train driving curves are generally generated aiming at the three operation time periods when the automatic train driving curve is generated in the prior art; there are problems in that:
in the prior art, when three automatic train driving curves are generated, the fixed weight of passengers is taken as a basis, the influence of the change of the weight of the passengers on the operation of the train is not considered, and the resistance of the weight of the passengers on the train is obviously not negligible, so that the automatic train driving curves obtained in the prior art have great difference from actual conditions, particularly, the passenger capacity of the train has randomness, and various passenger capacity conditions (such as no full load at a peak time period or full load at a low peak time period and other various conditions) exist at any operation time period;
therefore, it is necessary to generate an automatic driving curve for a train according to actual situations according to different operation periods and passenger flow differences.
Disclosure of Invention
A train automatic driving curve generation method based on target and passenger flow difference is innovative in that: the method for generating the train automatic driving curve comprises the following steps:
1) designing various scheduled operation times within the operation time adjustment range corresponding to the peak time period; under a single preset operation time condition, taking the weight of the passenger as a variable, designing various weight conditions of the passenger; under the condition of single passenger weight, on the premise of meeting the preset running time according to train parameters and line parameters, taking accurate parking as a single target, and generating a corresponding automatic driving curve in advance in an off-line manner by adopting a single-target optimization algorithm; a plurality of automatic driving curves corresponding to the peak period are marked as a peak period curve set;
(the running time adjustment range can be understood as follows: for example, in the peak time period, the reference running time between two stations is 150 seconds, in the actual running, the actual running time of the train is allowed to make certain positive and negative adjustments on the basis of the reference running time, such as positive and negative 10 seconds, so that the corresponding running time adjustment range is 140-160 seconds; when the preset running time is set, the step length can be 2 seconds, so that 11 preset running times of 140, 142, 144, 146, 148, 150 and 152 … … 160 can be obtained, and if the step length is changed, other kinds of preset running times can also be obtained);
designing various preset operation times within the operation time adjusting range corresponding to the peak-smoothing period; under a single preset operation time condition, taking the weight of the passenger as a variable, designing various weight conditions of the passenger; under the condition of single passenger weight, according to train parameters and line parameters, on the premise of meeting preset running time, taking minimum energy consumption, optimal comfort level and accurate parking as three targets, and adopting a multi-target optimization algorithm to generate a corresponding automatic driving curve in an off-line mode in advance; recording a plurality of automatic driving curves corresponding to the peak-balancing time period as a peak-balancing period curve set;
designing various preset operation times within an operation time adjusting range corresponding to a low peak time period; under a single preset operation time condition, taking the weight of the passenger as a variable, designing various weight conditions of the passenger; under the condition of single passenger weight, according to train parameters and line parameters, on the premise of meeting preset running time, the minimum energy consumption and the accurate parking are taken as two targets (in a low-peak period, the train running speed is relatively low, the riding comfort is good, and therefore the comfort is not needed to be taken as the target), and a multi-objective optimization algorithm is adopted to generate a corresponding automatic driving curve in an off-line mode in advance; marking a plurality of automatic driving curves corresponding to the low peak time period as a low peak time period curve set;
the high peak section curve set, the flat peak section curve set and the low peak section curve set are loaded into an automatic train driving control system in advance;
recording the preset running time and the weight of the passenger corresponding to the single automatic driving curve as a time parameter and a weight parameter respectively;
2) in a carriage of a train, in the running process of the train, when a train door is closed, an automatic train driving control system acquires an image in the carriage through an in-train camera device, processes the image to obtain the number of passengers, and then calculates the current weight of the passengers according to the number of the passengers; meanwhile, a dispatching room issues target running time to the train automatic driving control system;
3) after the target running time and the current weight of the passenger are obtained, the automatic train driving control system automatically identifies which one of the peak time period, the flat time period and the low time period is the current time period according to the current time, then matches the target running time and the current weight of the passenger with the time parameter and the weight parameter concentrated by the corresponding curves, finds out the corresponding automatic driving curves, and then controls the train to run according to the found automatic driving curves; the curve set is one of a peak section curve set, a flat section curve set and a low section curve set.
The principle of the invention is as follows: when the automatic driving curve is generated, the automatic driving control target difference of the train at different time intervals is considered, the target function covers a single target, a double target and three targets, and the weight difference of passengers is also considered, so that the obtained automatic driving curve is more in line with the actual situation; moreover, the invention also designs various preset operation time conditions aiming at the operation time adjustment ranges of different time intervals, and when in actual scheduling, a dispatcher can set specific target operation time in the operation time adjustment range, so that the automatic train driving control system can quickly find out a corresponding automatic driving curve, thereby greatly improving the scheduling flexibility and being beneficial to the macroscopic scheduling of train operation;
certainly, due to more situations, if the automatic driving curve is generated on line, timeliness is difficult to guarantee, so that the automatic driving curve can be generated in advance only by adopting a mode of generating the automatic driving curve in advance in an off-line mode, but the automatic driving curve generation method has certain advantages that the mode of generating the automatic driving curve in advance in an off-line mode is not limited by effectiveness, and the preset operation time condition and the passenger weight change condition can be divided as finely as possible, so that the refinement degree of automatic driving control of the train is improved;
in specific implementation, the existing single-target and multi-target optimization algorithms can be utilized to generate the automatic driving curve, such as evolutionary computation (genetic algorithm, cultural algorithm, differential evolution algorithm, Memetic algorithm and the like), biological computation (artificial immune algorithm, clonal selection algorithm and DNA computation) and cluster intelligent computation (particle swarm algorithm, ant swarm algorithm, artificial bee colony algorithm and artificial fish colony algorithm); based on the prior art, it is known that obtaining information about the number of people in an image through an image is a well-established technology, and therefore, the image processing technology is only a "tool" for obtaining the number of people in the present invention, and in the specific implementation, a person skilled in the art can select a preferred method from the existing image processing technology.
The beneficial technical effects of the invention are as follows: the scheme considers the control target difference and the passenger weight difference at the same time, so that the automatic driving curve is more consistent with the actual situation.
Detailed Description
A train automatic driving curve generation method based on target and passenger flow difference is innovative in that: the method for generating the train automatic driving curve comprises the following steps:
1) designing various scheduled operation times within the operation time adjustment range corresponding to the peak time period; under a single preset operation time condition, taking the weight of the passenger as a variable, designing various weight conditions of the passenger; under the condition of single passenger weight, on the premise of meeting the preset running time according to train parameters and line parameters, taking accurate parking as a single target, and generating a corresponding automatic driving curve in advance in an off-line manner by adopting a single-target optimization algorithm; a plurality of automatic driving curves corresponding to the peak period are marked as a peak period curve set;
designing various preset operation times within the operation time adjusting range corresponding to the peak-smoothing period; under a single preset operation time condition, taking the weight of the passenger as a variable, designing various weight conditions of the passenger; under the condition of single passenger weight, according to train parameters and line parameters, on the premise of meeting preset running time, taking minimum energy consumption, optimal comfort level and accurate parking as three targets, and adopting a multi-target optimization algorithm to generate a corresponding automatic driving curve in an off-line mode in advance; recording a plurality of automatic driving curves corresponding to the peak-balancing time period as a peak-balancing period curve set;
designing various preset operation times within an operation time adjusting range corresponding to a low peak time period; under a single preset operation time condition, taking the weight of the passenger as a variable, designing various weight conditions of the passenger; under the condition of single passenger weight, on the premise of meeting the preset running time according to train parameters and line parameters, taking minimum energy consumption and accurate parking as two targets, and generating a corresponding automatic driving curve in an off-line manner in advance by adopting a multi-objective optimization algorithm; marking a plurality of automatic driving curves corresponding to the low peak time period as a low peak time period curve set;
the high peak section curve set, the flat peak section curve set and the low peak section curve set are loaded into an automatic train driving control system in advance;
recording the preset running time and the weight of the passenger corresponding to the single automatic driving curve as a time parameter and a weight parameter respectively;
2) in a carriage of a train, in the running process of the train, when a train door is closed, an automatic train driving control system acquires an image in the carriage through an in-train camera device, processes the image to obtain the number of passengers, and then calculates the current weight of the passengers according to the number of the passengers; meanwhile, a dispatching room issues target running time to the train automatic driving control system;
3) after the target running time and the current weight of the passenger are obtained, the automatic train driving control system automatically identifies which one of the peak time period, the flat time period and the low time period is the current time period according to the current time, then matches the target running time and the current weight of the passenger with the time parameter and the weight parameter concentrated by the corresponding curves, finds out the corresponding automatic driving curves, and then controls the train to run according to the found automatic driving curves; the curve set is one of a peak section curve set, a flat section curve set and a low section curve set.
Claims (1)
1. A train automatic driving curve generation method based on target and passenger flow difference is characterized in that: the method for generating the train automatic driving curve comprises the following steps:
1) designing various scheduled operation times within the operation time adjustment range corresponding to the peak time period; under a single preset operation time condition, taking the weight of the passenger as a variable, designing various weight conditions of the passenger; under the condition of single passenger weight, on the premise of meeting the preset running time according to train parameters and line parameters, taking accurate parking as a single target, and generating a corresponding automatic driving curve in advance in an off-line manner by adopting a single-target optimization algorithm; a plurality of automatic driving curves corresponding to the peak period are marked as a peak period curve set;
designing various preset operation times within the operation time adjusting range corresponding to the peak-smoothing period; under a single preset operation time condition, taking the weight of the passenger as a variable, designing various weight conditions of the passenger; under the condition of single passenger weight, according to train parameters and line parameters, on the premise of meeting preset running time, taking minimum energy consumption, optimal comfort level and accurate parking as three targets, and adopting a multi-target optimization algorithm to generate a corresponding automatic driving curve in an off-line mode in advance; recording a plurality of automatic driving curves corresponding to the peak-balancing time period as a peak-balancing period curve set;
designing various preset operation times within an operation time adjusting range corresponding to a low peak time period; under a single preset operation time condition, taking the weight of the passenger as a variable, designing various weight conditions of the passenger; under the condition of single passenger weight, on the premise of meeting the preset running time according to train parameters and line parameters, taking minimum energy consumption and accurate parking as two targets, and generating a corresponding automatic driving curve in an off-line manner in advance by adopting a multi-objective optimization algorithm; marking a plurality of automatic driving curves corresponding to the low peak time period as a low peak time period curve set;
the high peak section curve set, the flat peak section curve set and the low peak section curve set are loaded into an automatic train driving control system in advance;
recording the preset running time and the weight of the passenger corresponding to the single automatic driving curve as a time parameter and a weight parameter respectively;
2) in a carriage of a train, in the running process of the train, when a train door is closed, an automatic train driving control system acquires an image in the carriage through an in-train camera device, processes the image to obtain the number of passengers, and then calculates the current weight of the passengers according to the number of the passengers; meanwhile, a dispatching room issues target running time to the train automatic driving control system;
3) after the target running time and the current weight of the passenger are obtained, the automatic train driving control system automatically identifies which one of the peak time period, the flat time period and the low time period is the current time period according to the current time, then matches the target running time and the current weight of the passenger with the time parameter and the weight parameter concentrated by the corresponding curves, finds out the corresponding automatic driving curves, and then controls the train to run according to the found automatic driving curves; the curve set is one of a peak section curve set, a flat section curve set and a low section curve set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011171867.5A CN112158238B (en) | 2020-10-28 | 2020-10-28 | Train automatic driving curve generation method based on target and passenger flow difference |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011171867.5A CN112158238B (en) | 2020-10-28 | 2020-10-28 | Train automatic driving curve generation method based on target and passenger flow difference |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112158238A true CN112158238A (en) | 2021-01-01 |
| CN112158238B CN112158238B (en) | 2022-05-24 |
Family
ID=73864949
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011171867.5A Active CN112158238B (en) | 2020-10-28 | 2020-10-28 | Train automatic driving curve generation method based on target and passenger flow difference |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112158238B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113771918A (en) * | 2021-09-14 | 2021-12-10 | 重庆交通大学 | Automatic driving control method for high-speed train to cope with dynamic passenger flow |
| CN113788045A (en) * | 2021-11-16 | 2021-12-14 | 中国铁道科学研究院集团有限公司通信信号研究所 | Tramcar signal control system and method based on dynamic multi-objective optimization control |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103863364A (en) * | 2014-03-27 | 2014-06-18 | 北京清软英泰信息技术有限公司 | Freight locomotive automatic-control real-time optimization control system based on dispatch signal |
| JP2014144754A (en) * | 2013-01-30 | 2014-08-14 | Mitsubishi Electric Corp | Railway train control system and automatic railway train driving device |
| JP2016010167A (en) * | 2014-06-20 | 2016-01-18 | 株式会社東芝 | Operation curve generation device, operation support device, automatic operation device, operation support system, automatic operation system, operation curve generation method and program |
| CN106379378A (en) * | 2016-09-09 | 2017-02-08 | 北京交通大学 | Method and system for regulating driving curve by combining on-line processing and off-line processing |
| CN106777717A (en) * | 2016-12-23 | 2017-05-31 | 南京理工大学 | A kind of ATO speed command energy conservation optimizing methods for considering handling capacity of passengers change |
| CN108657232A (en) * | 2018-05-18 | 2018-10-16 | 重庆交通大学 | A kind of multi objective control method of municipal rail train |
| RU2683696C1 (en) * | 2018-05-22 | 2019-04-01 | Федеральное государственное бюджетное образовательное учреждение высшего образования "Российский университет транспорта (МИИТ)" РУТ (МИИТ) | Method of controlling train speed |
| CN109858154A (en) * | 2019-01-31 | 2019-06-07 | 广州地铁设计研究院股份有限公司 | A kind of energy-saving train operation method based on multiple-objection optimization |
| CN110239600A (en) * | 2019-06-04 | 2019-09-17 | 北京交通大学 | The operation control method for train and system utilized towards regenerating braking energy |
| CN110901696A (en) * | 2019-10-29 | 2020-03-24 | 北京全路通信信号研究设计院集团有限公司 | Train control method and device based on train weight |
| RU2723572C1 (en) * | 2019-09-20 | 2020-06-16 | Федеральное государственное автономное образовательное учреждение высшего образования "Российский университет транспорта" (ФГАОУ ВО РУТ (МИИТ), РУТ (МИИТ) | Train speed automatic control method at obstacle approach area |
-
2020
- 2020-10-28 CN CN202011171867.5A patent/CN112158238B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014144754A (en) * | 2013-01-30 | 2014-08-14 | Mitsubishi Electric Corp | Railway train control system and automatic railway train driving device |
| CN103863364A (en) * | 2014-03-27 | 2014-06-18 | 北京清软英泰信息技术有限公司 | Freight locomotive automatic-control real-time optimization control system based on dispatch signal |
| JP2016010167A (en) * | 2014-06-20 | 2016-01-18 | 株式会社東芝 | Operation curve generation device, operation support device, automatic operation device, operation support system, automatic operation system, operation curve generation method and program |
| CN106379378A (en) * | 2016-09-09 | 2017-02-08 | 北京交通大学 | Method and system for regulating driving curve by combining on-line processing and off-line processing |
| CN106777717A (en) * | 2016-12-23 | 2017-05-31 | 南京理工大学 | A kind of ATO speed command energy conservation optimizing methods for considering handling capacity of passengers change |
| CN108657232A (en) * | 2018-05-18 | 2018-10-16 | 重庆交通大学 | A kind of multi objective control method of municipal rail train |
| RU2683696C1 (en) * | 2018-05-22 | 2019-04-01 | Федеральное государственное бюджетное образовательное учреждение высшего образования "Российский университет транспорта (МИИТ)" РУТ (МИИТ) | Method of controlling train speed |
| CN109858154A (en) * | 2019-01-31 | 2019-06-07 | 广州地铁设计研究院股份有限公司 | A kind of energy-saving train operation method based on multiple-objection optimization |
| CN110239600A (en) * | 2019-06-04 | 2019-09-17 | 北京交通大学 | The operation control method for train and system utilized towards regenerating braking energy |
| RU2723572C1 (en) * | 2019-09-20 | 2020-06-16 | Федеральное государственное автономное образовательное учреждение высшего образования "Российский университет транспорта" (ФГАОУ ВО РУТ (МИИТ), РУТ (МИИТ) | Train speed automatic control method at obstacle approach area |
| CN110901696A (en) * | 2019-10-29 | 2020-03-24 | 北京全路通信信号研究设计院集团有限公司 | Train control method and device based on train weight |
Non-Patent Citations (3)
| Title |
|---|
| 徐凯: "PSO-ICS算法优化的城轨列车自动驾驶系统", 《铁道科学与工程学报》 * |
| 徐凯: "多种群分层联合优化的城轨列车ATO研究", 《铁道学报》 * |
| 荀径: "列车节能操纵优化求解方法综述", 《铁道学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113771918A (en) * | 2021-09-14 | 2021-12-10 | 重庆交通大学 | Automatic driving control method for high-speed train to cope with dynamic passenger flow |
| CN113771918B (en) * | 2021-09-14 | 2023-10-20 | 重庆交通大学 | Automatic driving control method for high-speed train to cope with dynamic passenger flow |
| CN113788045A (en) * | 2021-11-16 | 2021-12-14 | 中国铁道科学研究院集团有限公司通信信号研究所 | Tramcar signal control system and method based on dynamic multi-objective optimization control |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112158238B (en) | 2022-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112158238B (en) | Train automatic driving curve generation method based on target and passenger flow difference | |
| CN108877256B (en) | Wireless communication-based method for controlling scattered cooperative self-adaptive cruise near intersection | |
| US20180037217A1 (en) | System and method to facilitate satisfying low emission zone requirements | |
| US10635105B2 (en) | Autonomous driving with dynamic skip fire | |
| CN111768616B (en) | Vehicle fleet consistency control method based on vehicle-road cooperation in mixed traffic scene | |
| CN108153145A (en) | A kind of train speed autocontrol method based on PID and filtering algorithm | |
| CN107757620B (en) | Method and device for operating an automated motor vehicle | |
| CN110568758A (en) | Parameter self-adaptive transverse motion LQR control method for automatically driving automobile | |
| CN106707764B (en) | EMU braking process RBF Model Reference Adaptive Control Method based on multistage switching | |
| CN108275142A (en) | A kind of low speed electric vehicle for logistics constant-speed-cruise control method | |
| US20190337399A1 (en) | Method for determining predicted acceleration information in an electric vehicle and such an electric vehicle | |
| US20180290646A1 (en) | Operation Schemes for a Hybrid or Electric Vehicle | |
| WO2013095232A1 (en) | Method and module for controlling a vehicle's speed based on rules and/or costs | |
| CN113156963B (en) | Deep reinforcement learning automatic driving automobile control method based on supervision signal guidance | |
| EP2794378A1 (en) | Method and module for determining of at least one reference value for a vehicle control system | |
| CN104010860A (en) | Module and method pertaining to mode choice when determining reference values | |
| CN108349485A (en) | Method for speed to be adaptively adjusted in the car and the speed adjustment equipment for executing this method | |
| CN111984019A (en) | V2X communication-based hybrid fleet control system and method | |
| CN108394429B (en) | Method for generating automatic driving curve for urban rail train group | |
| TW201819222A (en) | Energy-conservation vehicle driving control system | |
| CN112849137B (en) | Method and device for determining the dynamics of a vehicle train | |
| CN111497842B (en) | Man-machine double-closed-loop layered cooperative longitudinal car following control method | |
| CN104245391A (en) | Speed controller and method for improving the transient state of a speed controller | |
| Miftakhudin et al. | Adaptive cruise control by considering control decision as multistage MPC constraints | |
| CN112849135B (en) | Method and device for tracking and adjusting vehicles in queue and vehicle with device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |