CN112233451B - Intelligent traveling plan compiling system considering endurance mileage of pure electric bus - Google Patents
Intelligent traveling plan compiling system considering endurance mileage of pure electric bus Download PDFInfo
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Abstract
The invention discloses a driving plan intelligent compilation system considering the endurance mileage of a pure electric bus, which comprises a basic data acquisition module, an operation data analysis module, a passenger flow data analysis module, a weight analysis module and a planning model module, wherein the basic data acquisition module is used for acquiring the basic data of the pure electric bus; the basic data acquisition module is used for acquiring a basic data set; the operation data analysis module is used for analyzing historical operation data; the passenger flow data analysis module is used for analyzing the passenger flow data collected historically; the planning model module is combined with the basic parameters and the constraint conditions to establish a multi-objective optimization model and generate an initial driving schedule mainly considering the vehicle turnover time and the passenger flow demand interval. The invention can improve the endurance mileage utilization rate of the pure electric bus, reduce frequent vehicle changing and other operations caused by insufficient endurance mileage of the pure electric bus in the real-time scheduling process, and improve the matching degree of the operation site and the driving plan compilation.
Description
Technical Field
The invention relates to the field of bus shift scheduling, in particular to an intelligent driving plan compiling system considering the endurance mileage of a pure electric bus.
Background
In the intelligent public transportation system, the core is an intelligent public transportation scheduling system, and the core of the public transportation scheduling system is the compilation of driving plans. The management capacity and the operation efficiency of the public transport enterprise are directly influenced by the level of driving planning level and the quality. At present, many public transport enterprises in China already purchase new energy pure electric bus vehicles, the driving plan and the dispatching mode of the pure electric bus vehicles are greatly different from those of fuel vehicles, and factors needing to be considered are many, such as the endurance mileage of the vehicles, the positions of charging piles and the like. Moreover, many public transportation enterprises still stop at the stage of compiling the driving plans in the traditional manual mode, and the driving plans are not directly linked with data such as passenger flow, road conditions, vehicle driving mileage and the like mainly according to the experience of workers, so that the requirements of increasingly developed intelligent public transportation operation cannot be met. Therefore, the intelligent compilation method for the running plan of the pure electric bus is deeply researched, and has important significance and economic value.
The existing intelligent planning system for the driving plan has the defects of more manual operation, low intelligent level and inconvenience, and brings certain influence on the use of the intelligent planning system for the driving plan, so that the intelligent planning system for the driving plan considering the endurance mileage of the pure electric bus is provided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: how to solve current driving plan intelligence establishment system, manual operation is more, and intelligent level is low, and is not convenient enough, has brought the problem of certain influence for driving plan intelligence establishment system's use, provides a driving plan intelligence establishment system who considers pure electric bus continuation of the journey mileage.
The invention solves the technical problems through the following technical scheme, and the invention comprises a basic data acquisition module, an operation data analysis module, a passenger flow data analysis module, a weight analysis module and a planning model module;
the basic data acquisition module is used for acquiring a basic data set; the operation data analysis module is used for analyzing historical operation data; the passenger flow data analysis module is used for analyzing the passenger flow data collected historically; the planning model module is combined with the basic parameters and the constraint conditions to establish a multi-objective optimization model and generate an initial driving schedule mainly considering the vehicle turnover time and the passenger flow demand interval;
the weight analysis module is used for generating a final driving schedule according to the initial driving schedule and the initial driving range information of each equipped vehicle;
the use method of the intelligent driving plan compiling system comprises the following steps:
s1: acquiring basic data of a bus line, including data of a first station and a last station of the line, up-down length of the line, operation time and stop time of the first station and the last station, and acquiring initial endurance mileage data of a pure electric bus;
s2: analyzing and predicting the average running time and the fluctuation probability interval of each peak section of the line on different dates and different weathers according to historical operating data;
s3: analyzing and predicting the departure frequency and departure interval of each peak section of the line on different dates and different weathers according to historically collected passenger flow data;
s4: the planning model module generates an initial driving schedule of the line according to the multi-source data, wherein the initial driving schedule comprises each vehicle departure time point, driving time, stop time and departure intervals among the vehicle numbers at the first and last sites, the initially generated driving schedule mainly considers the vehicle turnover time and the passenger flow demand intervals to form a vehicle number chain corresponding to each vehicle, namely the required operation vehicle number and the planned operation mileage of each required vehicle are formed, whether the electric quantity of each pure electric vehicle can support the required operation mileage when the corresponding vehicle number chain is actually operated is not considered in the step, if the electric quantity cannot be met, the vehicle needs to be temporarily replaced during real-time scheduling, and the operation site follows the plan;
s5: the driving schedule can be regarded as an m × n matrix, m is the maximum required vehicle number, n is the maximum required single number, F (i, j), i is 1,2, …, m; j is 1,2, …, n, which refers to the data set of the time point, the travel time and the stop time of each vehicle at each departure time of the first and last stations;
wherein: f (i, j) ≠ Φ, which indicates that there is a data set in i rows and j columns, i is 1,2, …, m; j is 1,2, …, n; f (i, j) ═ Φ, which means that there is no data set in row i and column j, i equals 1,2, …, m; when j is equal to 1,2, …, n, F (i, j) is null, it indicates that the vehicle is in a stop operation state, and all the data in the data set are 0;
according to each F (i, j), i ═ 1,2, …, m; j is the actual value of 1,2, …, n, and the required vehicle number c of the required vehicle can be calculatediAnd the planned operating mileage l of the demandiSet, i ═ 1,2, …, m; j is 1,2, …, n
S6: the weight analysis module combines the initial driving schedule of the line and the initial driving mileage information of each vehicle equipped in the line, sets corresponding weight values, and combines the equipped vehicles DiMileage of endurance LiFollowing the plan demand vehicle djPlanned demand operational mileage liMatching is carried out;
the equipped vehicle D is represented by ω (i, j)iMileage L of enduranceiFollowing the plan demand vehicle djPlanned demand operational mileage liThe matched weight result value, i ═ 1,2, …, m; j is 1,2, …, n, and finally forming a matching result weight value matrix of m × n, wherein m is the number of equipped vehicles, n is the number of required vehicles, and m is more than or equal to n;
s7: suppose equipped vehicle DiThe initial endurance mileage standard value is MiUpper limit of
For any matching result ω (i, j), its corresponding weightThe values are defined as:
then each weight value in the matching result weight value matrix can be obtained, then m equipped vehicles and n demand vehicles are arranged and combined, each combination can obtain a combined weight result value, and then
Searching out an optimal combination result from the combination results, and finding out a vehicle matching scheme which is optimal to the required operating mileage according to the combination result;
s8: and the planning modeling module associates the specific equipped vehicles to the vehicle number and the required planned operating mileage in the initial driving schedule according to the weight analysis result, and finally forms a driving schedule which can meet the driving mileage of each equipped vehicle to the maximum extent.
Preferably, the basic data set comprises a bus route basic data set and a mileage data set of each vehicle.
Preferably, the information analyzed by the operation data analysis module includes average travel time of each peak section of the predicted route on different dates and different weather and fluctuation probability intervals thereof.
Preferably, the data analyzed by the passenger flow data analysis module includes the departure frequency and departure interval of each peak section of the forecast route on different dates and different weathers.
Preferably, the initial driving schedule includes the required number of vehicles, the required operation number of each vehicle and the planned required operation mileage.
Preferably, the specific processing procedure of the final driving schedule is as follows: and analyzing to obtain a weight result matrix after matching the endurance mileage of the equipped vehicle with the planned demand operating mileage of the planned demand vehicle, forming an optimal combined weight result on the basis of the weight result matrix, forming an input condition preset by a driving planning model, and then associating the equipped specific vehicle to the vehicle number and the demanded plan operating mileage in the initial driving schedule by a planning model module according to the combined weight result to form a final driving schedule capable of meeting the endurance mileage of each equipped vehicle to the maximum extent.
Compared with the prior art, the invention has the following advantages: this consider driving plan intelligence establishment system of pure electric bus continuation of journey mileage, improve the continuation of journey mileage utilization ratio of pure electric bus, reduce in the real-time scheduling process because of the operation such as frequent car change that pure electric vehicle continuation of journey mileage is not enough to continue the operation and cause, improve the matching degree of operation scene with driving plan establishment, the intelligent establishment through the driving plan organizes the bicycle of dispersion work, carry out effectual dispatch management to pure electric vehicle and continuation of journey mileage thereof, realize balanced operation production in the planned place, guide line and vehicle operation overall process. Finally, a complete intelligent scheduling chain is formed, the intelligent level of the whole scheduling process is improved, and the workload of manual operation is reduced.
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FIG. 1 is a system block diagram of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
As shown in fig. 1, the present embodiment provides a technical solution: a driving plan intelligent compilation system considering the endurance mileage of a pure electric bus comprises a basic data acquisition module, an operation data analysis module, a passenger flow data analysis module, a weight analysis module and a planning model module;
the basic data acquisition module is used for acquiring a basic data set; the operation data analysis module is used for analyzing historical operation data; the passenger flow data analysis module is used for analyzing the passenger flow data collected historically; the planning model module is combined with the basic parameters and the constraint conditions to establish a multi-objective optimization model and generate an initial driving schedule mainly considering the vehicle turnover time and the passenger flow demand interval;
the weight analysis module is used for generating a final driving schedule according to the initial driving schedule and the initial driving range information of each equipped vehicle.
The basic data set comprises a bus route basic data set and a mileage data set of each vehicle.
The information analyzed by the operation data analysis module comprises average running time and fluctuation probability intervals of each peak section of the predicted line on different dates and different weathers.
The data analyzed by the passenger flow data analysis module comprises the departure frequency and the departure interval of each peak section of the forecast line on different dates and different weathers.
The initial driving schedule comprises the number of the required vehicles, the required operation times of each vehicle and the planned required operation mileage.
The specific processing procedure of the final driving schedule is as follows: and analyzing to obtain a weight result matrix after matching the endurance mileage of the equipped vehicle with the planned demand operating mileage of the planned demand vehicle, forming an optimal combined weight result on the basis of the weight result matrix, forming an input condition preset by a driving planning model, and then associating the equipped specific vehicle to the vehicle number and the demanded plan operating mileage in the initial driving schedule by a planning model module according to the combined weight result to form a final driving schedule capable of meeting the endurance mileage of each equipped vehicle to the maximum extent.
The initial driving schedule is as follows:
the use method of the intelligent driving plan compiling system comprises the following steps:
s1: acquiring basic data of a bus line, including data of a first station and a last station of the line, up-down length of the line, operation time and stop time of the first station and the last station, and acquiring initial endurance mileage data of a pure electric bus;
s2: analyzing and predicting the average running time and the fluctuation probability interval of each peak section of the line on different dates and different weathers according to historical operating data;
s3: analyzing and predicting the departure frequency and departure interval of each peak section of the line on different dates and different weathers according to historically collected passenger flow data;
s4: the planning model module generates an initial driving schedule of the line according to the multi-source data, wherein the initial driving schedule comprises each vehicle departure time point, driving time, stop time and departure intervals among the vehicle numbers at the first and last sites, the initially generated driving schedule mainly considers the vehicle turnover time and the passenger flow demand intervals to form a vehicle number chain corresponding to each vehicle, namely the required operation vehicle number and the planned operation mileage of each required vehicle are formed, whether the electric quantity of each pure electric vehicle can support the required operation mileage when the corresponding vehicle number chain is actually operated is not considered in the step, if the electric quantity cannot be met, the vehicle needs to be temporarily replaced during real-time scheduling, and the operation site follows the plan;
s5: the driving schedule can be regarded as an m × n matrix, m is the maximum required vehicle number, n is the maximum required single number, F (i, j), i is 1,2, …, m; j is 1,2, …, n, which refers to the data set of the time point, the travel time and the stop time of each vehicle at each departure time of the first and last stations;
wherein: f (i, j) ≠ Φ, which indicates that there is a data set in i rows and j columns, i is 1,2, …, m; j is 1,2, …, n; f (i, j) ═ Φ, which means that there is no data set in row i and column j, i equals 1,2, …, m; when j is equal to 1,2, …, n, F (i, j) is null, it indicates that the vehicle is in a stop operation state, and all the data in the data set are 0;
according to each F (i, j), i ═ 1,2, …, m; j is the actual value of 1,2, …, n, and the required vehicle number c of the required vehicle can be calculatediAnd the planned operating mileage l of the demandiSet, i ═ 1,2, …, m; j is 1,2, …, n
S6: the weight analysis module combines the initial driving schedule of the line and the initial driving mileage information of each vehicle equipped in the line, and sets corresponding weight values to the equipped vehicles DiContinuation of the journey ofMileage LiFollowing the plan demand vehicle djPlanned demand operational mileage liMatching is carried out;
the equipped vehicle D is represented by ω (i, j)iMileage of endurance LiFollowing plan demand vehicle djPlanned demand operational mileage liThe matched weight result value, i ═ 1,2, …, m; j is 1,2, …, n, and finally forming a matching result weight value matrix of m × n, wherein m is the number of equipped vehicles, n is the number of required vehicles, and m is more than or equal to n;
s7: suppose equipped vehicle DiThe initial driving range standard value of is MiUpper limit of
For any matching result ω (i, j), the corresponding weight value is defined as:
then each weight value in the matching result weight value matrix can be obtained, then m equipped vehicles and n demand vehicles are arranged and combined, each combination can obtain a combined weight result value, and then
Searching out an optimal combination result from the combination results, and finding out a vehicle matching scheme which is optimal to the required operating mileage according to the combination result;
s8: and the planning model module associates the specific equipped vehicles to the vehicle number and the requirement plan operating mileage in the initial driving schedule according to the weight analysis result, and finally forms a driving schedule which can meet the driving mileage of each equipped vehicle to the maximum extent.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (6)
1. A driving plan intelligent compilation system considering the endurance mileage of a pure electric bus is characterized by comprising a basic data acquisition module, an operation data analysis module, a passenger flow data analysis module, a weight analysis module and a planning model module;
the basic data acquisition module is used for acquiring a basic data set; the operation data analysis module is used for analyzing historical operation data; the passenger flow data analysis module is used for analyzing the passenger flow data collected historically; the planning model module is combined with the basic parameters and the constraint conditions to establish a multi-objective optimization model and generate an initial driving schedule taking the vehicle turnover time and the passenger flow demand interval into consideration;
the weight analysis module is used for generating a final driving schedule according to the initial driving schedule and the initial driving range information of each equipped vehicle;
the use method of the system comprises the following steps:
s1: acquiring basic data of a bus line, including data of a first station and a last station of the line, up-down length of the line, operation time and stop time of the first station and the last station, and acquiring initial endurance mileage data of a pure electric bus;
s2: analyzing and predicting the average running time and the fluctuation probability interval of each peak section of the line on different dates and different weathers according to historical operating data;
s3: analyzing and predicting the departure frequency and departure interval of each peak section of the line on different dates and different weathers according to historically collected passenger flow data;
s4: the planning model module generates an initial driving schedule of the line according to the multi-source data, wherein the initial driving schedule comprises each vehicle departure time point, driving time, stop time and departure intervals among the vehicle numbers at the first and last sites, the initially generated driving schedule mainly considers the vehicle turnover time and the passenger flow demand intervals to form a vehicle number chain corresponding to each vehicle, namely the required operation vehicle number and the planned operation mileage of each required vehicle are formed, whether the electric quantity of each pure electric vehicle can support the required operation mileage when the corresponding vehicle number chain is actually operated is not considered in the step, if the electric quantity cannot be met, the vehicle needs to be temporarily replaced during real-time scheduling, and the operation site follows the plan;
s5: the driving schedule is considered as an m × n matrix, m is the maximum required vehicle number, n is the maximum required single number, F (i, j), i is 1,2, …, m; j is 1,2, …, n, which refers to the data set of the time point, the travel time and the stop time of each vehicle at each departure time of the first and last stations;
wherein: f (i, j) ≠ Φ, which indicates that there is a data set in i rows and j columns, i is 1,2, …, m; j is 1,2, …, n; f (i, j) ═ Φ, which means that there is no data set in row i and column j, i equals 1,2, …, m; when j is equal to 1,2, …, n, F (i, j) is null, it indicates that the vehicle is in a stop operation state, and all the data in the data set are 0;
according to each F (i, j), i ═ 1,2, …, m; j is the actual value of 1,2, …, n, and the required vehicle number c of the required vehicle can be calculatediAnd the planned operating mileage l of the demandiSet, i ═ 1,2, …, m; j is 1,2, …, n
S6: the weight analysis module combines the initial driving schedule of the line and the initial driving mileage information of each vehicle equipped in the line, and sets corresponding weight values to the equipped vehicles DiMileage of endurance LiFollowing the plan demand vehicle djPlanned demand operational mileage liMatching is carried out;
the equipped vehicle D is represented by ω (i, j)iMileage of endurance LiFollowing the plan demand vehicle djPlanned demand operational mileage liThe matched weight result value, i ═ 1,2, …, m; j is 1,2, …, n, and finally forming a matching result weight value matrix of m × n, wherein m is the number of equipped vehicles, n is the number of required vehicles, and m is more than or equal to n;
s7: suppose equipped vehicle DiThe initial endurance mileage standard value is MiUpper limit of
For any matching result ω (i, j), the corresponding weight value is defined as:
then each weight value in the matching result weight value matrix can be obtained, then m equipped vehicles and n demand vehicles are arranged and combined, each combination can obtain a combined weight result value, and then
Searching out an optimal combination result from the combination results, and finding out a vehicle matching scheme which is optimal to the required operating mileage according to the combination result;
s8: and the planning model module associates the specific equipped vehicles to the vehicle number and the requirement plan operating mileage in the initial driving schedule according to the weight analysis result, and finally forms a driving schedule which can meet the driving mileage of each equipped vehicle to the maximum extent.
2. The system for intelligently compiling a driving plan considering the driving mileage of the pure electric bus according to claim 1, is characterized in that: the basic data set comprises a bus route basic data set and a mileage data set of each vehicle.
3. The system for intelligently compiling a driving plan considering the driving mileage of the pure electric bus according to claim 1, is characterized in that: the information analyzed by the operation data analysis module comprises average running time and fluctuation probability intervals of each peak section of the predicted line on different dates and different weathers.
4. The system for intelligently compiling a driving plan considering the driving mileage of the pure electric bus according to claim 1, is characterized in that: the data analyzed by the passenger flow data analysis module comprises the departure frequency and the departure interval of each peak section of the forecast line on different dates and different weathers.
5. The system for intelligently compiling a driving plan considering the driving mileage of the pure electric bus according to claim 1, is characterized in that: the initial driving schedule comprises the number of the required vehicles, the required operation times of each vehicle and the planned required operation mileage.
6. The system for intelligently compiling a driving plan considering the driving mileage of the pure electric bus according to claim 1, is characterized in that: the specific processing procedure of the final driving schedule is as follows: and analyzing to obtain a weight result matrix after matching the endurance mileage of the equipped vehicle with the planned demand operating mileage of the planned demand vehicle, forming an optimal combined weight result on the basis of the weight result matrix, forming an input condition preset by a driving planning model, and then associating the equipped specific vehicle to the vehicle number and the demanded plan operating mileage in the initial driving schedule by a planning model module according to the combined weight result to form a final driving schedule capable of meeting the endurance mileage of each equipped vehicle to the maximum extent.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001338396A (en) * | 2000-05-29 | 2001-12-07 | Hitachi Ltd | Operation information processing system for traffic facilities |
| CN106448137A (en) * | 2016-11-04 | 2017-02-22 | 东南大学 | Public transportation service system and method based on electric automobile |
| CN107067710A (en) * | 2017-04-21 | 2017-08-18 | 同济大学 | A kind of city bus running orbit optimization method for considering energy-conservation |
| JP2018072897A (en) * | 2016-10-24 | 2018-05-10 | 株式会社東芝 | Operation plan creation support apparatus, operation plan creation support method, and program |
| CN109934391A (en) * | 2019-02-26 | 2019-06-25 | 北京邮电大学 | A kind of intelligent dispatching method of pure electric bus |
| CN111754039A (en) * | 2020-06-23 | 2020-10-09 | 北京交通大学 | Method for comprehensive integrated optimization design of pure electric bus network |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107220730B (en) * | 2017-05-23 | 2020-06-16 | 西安交通大学 | Dynamic route planning method for pure electric bus capable of prolonging service life of power battery |
| DE102019101094A1 (en) * | 2019-01-16 | 2020-07-16 | Bayerische Motoren Werke Aktiengesellschaft | Method for route planning in a navigation system of a vehicle, navigation system for a vehicle and vehicle with the same |
-
2020
- 2020-10-13 CN CN202011092195.9A patent/CN112233451B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001338396A (en) * | 2000-05-29 | 2001-12-07 | Hitachi Ltd | Operation information processing system for traffic facilities |
| JP2018072897A (en) * | 2016-10-24 | 2018-05-10 | 株式会社東芝 | Operation plan creation support apparatus, operation plan creation support method, and program |
| CN106448137A (en) * | 2016-11-04 | 2017-02-22 | 东南大学 | Public transportation service system and method based on electric automobile |
| CN107067710A (en) * | 2017-04-21 | 2017-08-18 | 同济大学 | A kind of city bus running orbit optimization method for considering energy-conservation |
| CN109934391A (en) * | 2019-02-26 | 2019-06-25 | 北京邮电大学 | A kind of intelligent dispatching method of pure electric bus |
| CN111754039A (en) * | 2020-06-23 | 2020-10-09 | 北京交通大学 | Method for comprehensive integrated optimization design of pure electric bus network |
Non-Patent Citations (2)
| Title |
|---|
| 纯电动公交车区域行车计划编制模型及算法研究;孟越;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20160815;C034-405 * |
| 考虑充电约束的电动公交区域行车计划编制;姚恩建等;《华南理工大学学报(自然科学版)》;20190930;第47卷(第9期);第68-72页 * |
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