CN111311950A - Intelligent dispatching method and system for bus in peak period - Google Patents
Intelligent dispatching method and system for bus in peak period Download PDFInfo
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- CN111311950A CN111311950A CN202010087319.8A CN202010087319A CN111311950A CN 111311950 A CN111311950 A CN 111311950A CN 202010087319 A CN202010087319 A CN 202010087319A CN 111311950 A CN111311950 A CN 111311950A
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Abstract
The invention discloses a method and a system for intelligently scheduling buses in a peak period, wherein the method comprises the following steps: s1, obtaining the riding information registered by the rider; s2, acquiring current bus waiting data according to the bus taking information; s3, establishing a transport capacity scheduling quantification model according to historical bus waiting data; and S4, acquiring the bus demand based on the capacity scheduling quantification model, and scheduling the bus. The intelligent scheduling method and system for the buses in the rush hour can arrange and increase line bus scheduling by combining with the actual bus capacity condition, solve the problem of insufficient capacity scheduling in the rush hour and improve the citizen travel efficiency.
Description
Technical Field
The invention relates to the technical field of public transportation, in particular to an intelligent dispatching method and system for a bus in a peak period.
Background
The public transport vehicle has the characteristics of fixed driving route, fixed stop, stop when meeting the stop and stop and stop report, and the public transport scheduling systems in the market at present comprise an artificial public transport scheduling system, a public transport scheduling system by means of a radio station, a public transport scheduling system by means of a GPS (global positioning system) and the like.
The existing bus dispatching system cannot sense the bus station queuing waiting situation and the bus line situation of the bus station queuing and the like in real time, generally distributes the bus departure times according to time period intervals, for example, vehicles can be increased when the vehicles are on the morning peak of work and the vehicles are off the evening peak of work, and the vehicles can be reduced to run at other times, so that the transportation efficiency of the vehicles can be improved, but the dispatching error is easy to waste, and the peak period of each route is different. The dispatching time of each route is the approximate riding intensity counted by a common bus driver or a dispatcher, and the real demand of each route cannot be really reflected.
Therefore, in order to solve the above technical problems, it is necessary to provide an intelligent bus peak scheduling method and system.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for intelligent scheduling of a bus during peak hours.
In order to achieve the above object, an embodiment of the present invention provides the following technical solutions:
an intelligent bus peak scheduling method comprises the following steps:
s1, obtaining the riding information registered by the rider;
s2, acquiring current bus waiting data according to the bus taking information;
s3, establishing a transport capacity scheduling quantification model according to historical bus waiting data;
and S4, acquiring the bus demand based on the capacity scheduling quantification model, and scheduling the bus.
As a further improvement of the present invention, the riding information includes information of a bus route, an boarding platform and a disembarking platform.
As a further improvement of the present invention, the step S2 specifically includes:
and acquiring the number of bus waiting persons passing through all the stations on a certain bus route at the current time according to the bus taking information.
As a further improvement of the present invention, the step S3 specifically includes:
and dividing the historical bus waiting number into a test set and a training set, and training by a deep learning method to obtain a model of the bus waiting data to a transport capacity quantitative value and the bus demand.
As a further improvement of the invention, the quantified value of the capacity ranges from 0 to 100.
As a further improvement of the present invention, the step S4 further includes:
acquiring the real-time bus volume running at the current moment;
and dispatching the buses according to the difference value of the bus demand and the real-time bus quantity.
As a further improvement of the present invention, the step S1 further includes:
identifying a riding certificate registered by a rider;
and if the riding voucher is an effective voucher, judging the riding information to be effective, and if the riding voucher is an ineffective voucher or the riding voucher is not obtained, judging the riding information to be ineffective.
The technical scheme provided by another embodiment of the invention is as follows:
an intelligent bus peak scheduling system, the system comprising:
the information registration unit is used for acquiring the riding information registered by the rider;
the data processing unit is used for acquiring current bus waiting data according to the bus taking information;
the model establishing unit is used for establishing a transport capacity scheduling quantification model according to historical bus waiting data;
and the public traffic scheduling unit is used for acquiring the public traffic demand based on the capacity scheduling quantification model and scheduling the public traffic.
As a further improvement of the present invention, the information registration unit includes:
the line registration terminal is used for acquiring the path of the line registration module;
the line registration module is used for acquiring the riding information registered by the passengers;
and the riding voucher verification module is used for identifying a riding voucher registered by a rider, judging that the riding information is valid if the riding voucher is a valid voucher, and judging that the riding information is invalid if the riding voucher is an invalid voucher or the riding voucher is not acquired.
As a further improvement of the present invention, the bus dispatching unit includes:
the bus dispatching decision module is used for acquiring a difference value between the bus demand and the real-time bus quantity;
and the bus scheduling module is used for scheduling buses according to the difference value of the bus demand and the real-time bus quantity.
The invention has the beneficial effects that:
the intelligent scheduling method and system for the buses in the rush hour can arrange and increase line bus scheduling by combining with the actual bus capacity condition, solve the problem of insufficient capacity scheduling in the rush hour and improve the citizen travel efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the intelligent bus peak scheduling method of the present invention;
FIG. 2 is a schematic block diagram of the intelligent bus peak scheduling system of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the invention discloses an intelligent dispatching method for a bus in a peak period, which comprises the following steps:
s1, obtaining the riding information registered by the rider;
s2, acquiring current bus waiting data according to the bus taking information;
s3, establishing a transport capacity scheduling quantification model according to historical bus waiting data;
and S4, acquiring the bus demand based on the capacity scheduling quantification model, and scheduling the bus.
Referring to fig. 2, the invention also discloses an intelligent dispatching system for the bus in the peak period, comprising:
an information registration unit 10 for acquiring riding information registered by a rider;
the data processing unit 20 is used for acquiring current bus waiting data according to the bus taking information;
the model establishing unit 30 is used for establishing a transport capacity scheduling quantification model according to historical bus waiting data;
and the bus scheduling unit 40 is used for acquiring the bus demand based on the capacity scheduling quantification model and scheduling buses.
The method and system for intelligent bus peak-time scheduling according to the present invention will be described in detail with reference to the following embodiments.
Referring to fig. 2, the intelligent bus scheduling system in the embodiment includes an information registration unit 10, a data processing unit 20, a model building unit 30, and a bus scheduling unit 40.
Wherein, the information registering unit 10 includes:
the line registration terminal is installed at a bus station, such as a two-dimensional code display terminal, and is used for scanning and using a mobile phone of a passenger;
the bus station line display system comprises a line registration module, a bus stop and a bus display terminal, wherein a bus passenger scans a two-dimensional code of the bus stop two-dimensional code display terminal and then jumps to a page of the line registration module, the bus passenger can see bus line data of a displayed current station on the page, selects a bus class needing to be picked up and fills in relevant information and uploads the data;
and the passenger voucher verification module is used for scanning and paying the vehicle fee by a passenger with own mobile phone and uploading the passenger vouchers (the payment information, the passenger information, the time information and other passenger information) to the background data processing unit.
And the data processing unit 20 is used for acquiring current bus waiting data according to the bus taking information, and comprises data for analyzing platform registration and verification of bus taking certificates (bus taking information such as payment information, bus passenger information and time information).
And the model establishing unit 30 is used for establishing a transportation capacity scheduling quantification model according to the historical bus waiting data, and providing prediction of advance scheduling according to the historical registered data and the real-time bus scheduling times.
The bus scheduling unit 40 includes a bus scheduling decision module and a bus scheduling module, wherein:
the bus scheduling decision module is used for analyzing data by receipts and making decisions on the analyzed data;
the bus scheduling module is used for scheduling buses and solving the problem of insufficient vehicles in rush hours.
For example, the method for intelligently scheduling the bus in the peak period in the embodiment includes the following specific steps.
Scanning a two-dimensional code of a line registration terminal at a bus station by a passenger, enabling the passenger to check a line registration module, jumping to a line registration module page, enabling the passenger to see bus line data of the displayed current station on the page, selecting the number of cars needing to be picked up, filling related riding information, and uploading the data to a background data analysis module, wherein the riding information comprises bus lines, getting-on station information, getting-off station information and the like;
the passenger pays the fare through the riding voucher verification module and uploads riding information to the data processing unit;
the data processing unit can calculate the number of the bus waiting people of all the stations passing through a certain bus route at the current time according to the bus taking information of the bus stations and the bus taking information of the passengers on the route, and uploads the calculated number of the bus waiting people of the certain route of the stations to the model establishing unit and the bus scheduling decision module;
the model building unit stores the calculated data, analyzes and predicts by combining with the previous historical data, gives a transportation capacity scheduling quantitative suggestion, and transmits the data to the bus scheduling decision module;
the bus dispatching decision module carries out data grade classification on the number of real-time bus waiting people and determines whether to increase the line number or not by combining the transportation dispatching quantification suggestion analyzed by the historical data analysis model establishing unit;
after receiving the instruction from the bus scheduling decision module, the bus scheduling module combines the actual bus capacity situation to arrange and increase the line bus scheduling so as to solve the problem of insufficient capacity scheduling in the peak period and improve the citizen trip efficiency.
The model establishing unit 30 establishes a model specifically as follows:
and dividing the historical bus waiting number into a test set and a training set, and training by a deep learning method to obtain a model of the bus waiting data to a transport capacity quantitative value and the bus demand. For example, the quantified value of the capacity ranges from 0 to 100.
The model building unit collects a large number of people counting data, manually marks the corresponding transport capacity quantitative value range (1-100) of the counting data and marks the bus demand, divides historical data into a test set and a training set, and then trains a model of the number of waiting people in the bus to the transport capacity quantitative value and the bus demand by a deep learning method.
After the real-time data are transmitted into the trained model, a transport capacity quantitative value and the bus demand can be automatically analyzed and submitted to a bus scheduling decision module.
After the real-time bus volume running at the current moment is obtained, the bus dispatching module can dispatch buses according to the difference value of the bus demand volume and the real-time bus volume. If the difference is negative, the current transport capacity is sufficient, and the number of buses can be properly reduced.
In addition, the riding voucher verification module further comprises:
identifying a riding certificate registered by a rider;
and if the riding voucher is an effective voucher, judging the riding information to be effective, and if the riding voucher is an ineffective voucher or the riding voucher is not obtained, judging the riding information to be ineffective.
According to the technical scheme, the invention has the following beneficial effects:
the intelligent scheduling method and system for the buses in the rush hour can arrange and increase line bus scheduling by combining with the actual bus capacity condition, solve the problem of insufficient capacity scheduling in the rush hour and improve the citizen travel efficiency.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the modules may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, C D-ROM, optical storage, and the like) having computer-usable program code embodied therein.
One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. An intelligent bus peak period scheduling method is characterized by comprising the following steps:
s1, obtaining the riding information registered by the rider;
s2, acquiring current bus waiting data according to the bus taking information;
s3, establishing a transport capacity scheduling quantification model according to historical bus waiting data;
and S4, acquiring the bus demand based on the capacity scheduling quantification model, and scheduling the bus.
2. The intelligent bus peak period scheduling method according to claim 1, wherein the riding information includes information of bus lines, boarding platforms and disembarking platforms.
3. The intelligent bus peak period scheduling method according to claim 2, wherein the step S2 specifically comprises:
and acquiring the number of bus waiting persons passing through all the stations on a certain bus route at the current time according to the bus taking information.
4. The intelligent bus peak period scheduling method according to claim 1, wherein the step S3 specifically comprises:
and dividing the historical bus waiting number into a test set and a training set, and training by a deep learning method to obtain a model of the bus waiting data to a transport capacity quantitative value and the bus demand.
5. The intelligent bus peak period scheduling method according to claim 4, wherein the range of the capacity quantization value is 0-100.
6. The intelligent bus peak-hour scheduling method according to claim 4, wherein the step S4 further comprises:
acquiring the real-time bus volume running at the current moment;
and dispatching the buses according to the difference value of the bus demand and the real-time bus quantity.
7. The intelligent bus peak-hour scheduling method according to claim 1, wherein the step S1 further comprises:
identifying a riding certificate registered by a rider;
and if the riding voucher is an effective voucher, judging the riding information to be effective, and if the riding voucher is an ineffective voucher or the riding voucher is not obtained, judging the riding information to be ineffective.
8. An intelligent bus peak scheduling system, comprising:
the information registration unit is used for acquiring the riding information registered by the rider;
the data processing unit is used for acquiring current bus waiting data according to the bus taking information;
the model establishing unit is used for establishing a transport capacity scheduling quantification model according to historical bus waiting data;
and the public traffic scheduling unit is used for acquiring the public traffic demand based on the capacity scheduling quantification model and scheduling the public traffic.
9. The intelligent bus peak-hour scheduling system according to claim 8, wherein the information registering unit includes:
the line registration terminal is used for acquiring the path of the line registration module;
the line registration module is used for acquiring the riding information registered by the passengers;
and the riding voucher verification module is used for identifying a riding voucher registered by a rider, judging that the riding information is valid if the riding voucher is a valid voucher, and judging that the riding information is invalid if the riding voucher is an invalid voucher or the riding voucher is not acquired.
10. The intelligent bus peak scheduling system according to claim 8, wherein the bus scheduling unit comprises:
the bus dispatching decision module is used for acquiring a difference value between the bus demand and the real-time bus quantity;
and the bus scheduling module is used for scheduling buses according to the difference value of the bus demand and the real-time bus quantity.
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Cited By (4)
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| CN112669603A (en) * | 2020-12-17 | 2021-04-16 | 广东南方通信建设有限公司 | Urban traffic cooperation method and device based on big data |
| CN113936494A (en) * | 2021-06-30 | 2022-01-14 | 深圳市巴滴科技有限公司 | Public transport scheduling method and device based on time-sharing riding demand |
| CN113971486A (en) * | 2021-10-21 | 2022-01-25 | 国网山东省电力公司寿光市供电公司 | Power inspection vehicle scheduling method and system based on artificial intelligence algorithm |
| CN113971486B (en) * | 2021-10-21 | 2024-09-06 | 国网山东省电力公司寿光市供电公司 | Electric power inspection vehicle dispatching method and system based on artificial intelligence algorithm |
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| US20170270791A1 (en) * | 2013-02-28 | 2017-09-21 | Here Global B.V. | Method and apparatus for automated service schedule derivation and updating |
| CN109903549A (en) * | 2017-12-11 | 2019-06-18 | 郑州宇通客车股份有限公司 | A kind of bus intelligent dispatching system |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112669603A (en) * | 2020-12-17 | 2021-04-16 | 广东南方通信建设有限公司 | Urban traffic cooperation method and device based on big data |
| CN113936494A (en) * | 2021-06-30 | 2022-01-14 | 深圳市巴滴科技有限公司 | Public transport scheduling method and device based on time-sharing riding demand |
| CN113936494B (en) * | 2021-06-30 | 2022-08-05 | 深圳市巴滴科技有限公司 | Public transport scheduling method and device based on time-sharing riding demand |
| CN113971486A (en) * | 2021-10-21 | 2022-01-25 | 国网山东省电力公司寿光市供电公司 | Power inspection vehicle scheduling method and system based on artificial intelligence algorithm |
| CN113971486B (en) * | 2021-10-21 | 2024-09-06 | 国网山东省电力公司寿光市供电公司 | Electric power inspection vehicle dispatching method and system based on artificial intelligence algorithm |
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