CN116432956B - Bus allocation method, system, equipment and medium based on passenger flow - Google Patents
Bus allocation method, system, equipment and medium based on passenger flow Download PDFInfo
- Publication number
- CN116432956B CN116432956B CN202310370253.7A CN202310370253A CN116432956B CN 116432956 B CN116432956 B CN 116432956B CN 202310370253 A CN202310370253 A CN 202310370253A CN 116432956 B CN116432956 B CN 116432956B
- Authority
- CN
- China
- Prior art keywords
- passenger flow
- bus
- unit
- predicted
- buses
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000012544 monitoring process Methods 0.000 claims abstract description 38
- 238000001514 detection method Methods 0.000 claims description 34
- 238000012549 training Methods 0.000 claims description 32
- 238000007726 management method Methods 0.000 claims description 22
- 238000013523 data management Methods 0.000 claims description 16
- 238000013528 artificial neural network Methods 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 15
- 238000013179 statistical model Methods 0.000 claims description 14
- 238000002372 labelling Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 238000003062 neural network model Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 230000004927 fusion Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 description 5
- 230000004044 response Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000011897 real-time detection Methods 0.000 description 3
- 206010000117 Abnormal behaviour Diseases 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000007592 spray painting technique Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- G06Q50/40—
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention relates to the technical field of buses, in particular to a bus allocation method, a bus allocation system, bus allocation equipment and a bus allocation medium based on passenger flow, wherein the bus allocation method specifically comprises the following steps: based on the passenger flow statistics unit, determining the passenger flow of a first bus station set according to the first monitoring unit; acquiring the number of vehicles and the passenger flow of all buses reaching each first bus station in the preset arrival time in the first bus station set based on the vehicle positioning unit, the passenger flow counting unit and the second monitoring unit; and according to the passenger flow volume of the first bus station set and the number of vehicles and the passenger flow volume of all buses reaching each first bus station in the preset arrival time in the first bus station set, then allocating the number of vehicles of buses of different bus routes. The invention can effectively monitor busyness of different bus stops in cities, thereby realizing efficient allocation of buses in bus lines.
Description
Technical Field
The invention relates to the technical field of buses, in particular to a bus allocation method, system, equipment and medium based on passenger flow.
Background
In the existing urban bus stations, most of the urban bus stations consist of traditional bus station kiosks and simple stop boards, and informationized equipment is not arranged. Meanwhile, in the area where the bus stop is located, a large number of waiting personnel are easy to gather, and particularly, the phenomenon of crowding is easy to occur in the morning and evening peaks. The bus stop is generally arranged between the motor vehicle lane and the non-motor vehicle lane, so that more buses and non-motor vehicles come and go, and the number of waiting passengers is large, so that great potential safety hazards exist. When platform personnel are crowded, traffic supervision departments cannot acquire the platform crowded information in real time, so that buses are effectively commanded and scheduled, and people are evacuated.
Disclosure of Invention
The invention aims to provide a bus allocation method, system, equipment and medium based on passenger flow, which can effectively monitor busyness of different bus stops in a city by combining a prediction algorithm and a real-time detection technology, thereby realizing efficient allocation of buses in a bus route and solving at least one of the existing problems.
The invention provides a bus allocation method based on passenger flow, which is applied to a digital bus operation management system, wherein the system specifically comprises the following steps: the bus stop module comprises a bus electronic stop board, an advertisement electronic screen and a first monitoring unit; the bus on-board module comprises a vehicle positioning unit and a second monitoring unit; the system comprises a remote management module, a bus arrival prediction unit and a bus arrival prediction unit, wherein the remote management module comprises a passenger flow statistics unit, a data management unit and the bus arrival prediction unit, and the bus arrival prediction unit is used for predicting passengers in a bus which is about to arrive at a station; the method specifically comprises the following steps:
configuring a passenger flow statistical model for the passenger flow statistical unit;
based on the passenger flow statistics unit, determining the passenger flow of a first bus station set according to the first monitoring unit;
acquiring the number of vehicles and the passenger flow of all buses reaching each first bus station in the preset arrival time in the first bus station set based on the vehicle positioning unit, the passenger flow counting unit and the second monitoring unit;
and determining the passenger flow degree of each bus line according to the passenger flow of the first bus stop set and the number of vehicles and the passenger flow of all buses reaching each first bus stop in the preset arrival time in the first bus stop set, and then allocating the number of vehicles of buses of different bus lines.
Further, the configuring a passenger flow statistical model for the passenger flow statistical unit specifically includes:
acquiring a first training sample set and a second training sample set which are respectively obtained by the first monitoring unit and the second monitoring unit from the data management unit;
determining a pre-trained neural network model, and respectively inputting the first training sample set and the second training sample set into the neural network model for training;
labeling the human head of each image in the first training sample set and the second training sample set by adopting a detection frame to obtain a first target detection model and a second target detection model;
and processing the first target detection model and the second target detection model according to a target tracking algorithm to generate a passenger flow statistical model, and configuring the passenger flow statistical model for the passenger flow statistical unit.
Furthermore, the labeling method of the detection frame satisfies (x, y, w, h, confidence) and the confidence satisfies, wherein x, y, w and h respectively represent two-dimensional coordinates, width and height of the central position of the detection frame, represent whether the detection frame contains a human head or not, and represent an intersection between the prediction frame and the actual frame.
Further, determining the degree of the passenger flow of each bus line according to the passenger flow of the first bus station set and the number of vehicles and the passenger flow of all buses reaching each first bus station in the preset arrival time in the first bus station set, and then allocating the number of vehicles of the buses of each bus line, wherein the method specifically comprises the following steps:
acquiring the passenger flow volume of a first bus station from the data management unit;
acquiring the number of vehicles and the passenger flow of all buses reaching the first bus station in the preset arrival time from the data management unit, and determining a first idle passenger flow according to the number of vehicles and the passenger flow of all buses reaching the first bus station in the preset arrival time;
and determining the predicted boarding and disembarking passenger flows of all buses reaching the first bus stop in the preset arrival time according to the passenger flow prediction model, and allocating the number of buses of each bus line by combining the passenger flow of the first bus stop and the first idle passenger flow.
Further, the method for determining the predicted boarding and alighting passenger flows of all buses arriving at the first bus station in the preset arrival time according to the passenger flow prediction model, and allocating the number of buses of each bus line by combining the passenger flow of the first bus station and the first idle passenger flow specifically comprises the following steps:
determining a first predicted boarding and disembarking passenger flow and a second predicted boarding and disembarking passenger flow according to a passenger flow prediction model, wherein the first predicted boarding and disembarking passenger flow is the predicted passenger flow of each bus on and off the first bus station in different time periods in each working day, and the second predicted boarding and disembarking passenger flow is the predicted passenger flow of each bus on and off the first bus station in different time periods in each holiday;
judging whether all buses arriving at the first bus station in a preset arrival time can process the passenger flow of the first bus station according to the passenger flow of the first bus station, the first idle passenger flow, the first predicted boarding and disembarking passenger flow and the second predicted boarding and disembarking passenger flow, if not, determining the first bus station as a busy bus station;
and according to the number of the busy bus stops of each bus line, real-time allocation is carried out on the number of the buses of each bus line.
Further, the determining the first predicted boarding and disembarking passenger flow and the second predicted boarding and disembarking passenger flow according to the passenger flow prediction model specifically includes:
the method comprises the steps of obtaining historical passenger flow data of a first bus station, dividing the historical passenger flow data according to date factors, and obtaining first historical passenger flow data and second historical passenger flow data, wherein the first historical passenger flow data is the historical passenger flow data of the first bus station in a working day period, and the second historical passenger flow data is the historical passenger flow data of the first bus station in a holiday period;
training the first historical passenger flow data and the second historical passenger flow data based on an LSTM neural network to obtain a passenger flow prediction model;
and determining the first predicted boarding and disembarking passenger flow and the second predicted boarding and disembarking passenger flow according to the passenger flow prediction model.
Further, training the first historical passenger flow data and the second historical passenger flow data based on the LSTM neural network to obtain a passenger flow prediction model specifically includes:
according to the first historical passenger flow data and the second historical passenger flow data, a first time sequence and a second time sequence are obtained by a difference method;
setting LSTM neural network parameters, respectively taking the first time sequence and the second time sequence as input data of the LSTM neural network, and outputting first predicted passenger flow data and second predicted passenger flow data after feature fusion;
and calculating loss according to errors between the first predicted passenger flow data and the second predicted passenger flow data and the real passenger flow data respectively, and obtaining a passenger flow prediction model by adopting an error back propagation algorithm.
The invention also provides a digital bus operation management system, which is applied to the method of any one of claims 1 to 7, and specifically comprises:
the bus stop module comprises a bus electronic stop board, an advertisement electronic screen and a first monitoring unit;
the bus on-board module comprises a vehicle positioning unit and a second monitoring unit;
the system comprises a remote management module, a bus arrival prediction unit and a bus arrival prediction unit, wherein the remote management module comprises a passenger flow statistics unit, a data management unit and the bus arrival prediction unit is used for predicting passengers in a bus which is about to arrive at the station.
The present invention also provides a computer device comprising: memory and processor and computer program stored on the memory, which when executed on the processor, implements a bus allocation method based on passenger traffic as set forth in any one of the above methods.
The invention also provides a computer readable storage medium having stored thereon a computer program which when run by a processor implements a bus allocation method based on passenger traffic as described in any of the above methods.
Compared with the prior art, the invention has at least one of the following technical effects:
1. the number of the waiting persons at each platform can be known in time, so that the line shift can be adjusted in time, and the number of the waiting persons at the waiting platform is reduced.
2. The safety of the supervision platform can actively alarm and prompt the monitoring personnel at the back end for abnormal behaviors, and timely pay attention to and process the abnormal behaviors.
3. The passenger waiting on the platform can acquire real-time and accurate arrival time, distance and other vehicle information, the passenger can plan the trip conveniently, and the trip experience is improved.
4. The electronic screen advertisement putting mode is used for replacing the original advertisement lamp box spray painting advertisement paper, so that the labor cost investment is reduced, only the back end control is needed, and the advertisement does not need to be replaced in a spray painting mode, so that the cost is saved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a bus allocation method based on passenger flow volume provided by an embodiment of the invention;
fig. 2 is a schematic structural diagram of a digital public transportation operation management system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
In the existing urban bus stations, most of the urban bus stations consist of traditional bus station kiosks and simple stop boards, and informationized equipment is not arranged. Meanwhile, in the area where the bus stop is located, a large number of waiting personnel are easy to gather, and particularly, the phenomenon of crowding is easy to occur in the morning and evening peaks. The bus stop is generally arranged between the motor vehicle lane and the non-motor vehicle lane, so that more buses and non-motor vehicles come and go, and the number of waiting passengers is large, so that great potential safety hazards exist. When platform personnel are crowded, traffic supervision departments cannot acquire the platform crowded information in real time, so that buses are effectively commanded and scheduled, and people are evacuated.
Referring to fig. 1, an embodiment of the present invention provides a bus allocation method based on passenger flow, where the method is applied to a digital bus operation management system, and the system specifically includes: the bus stop module comprises a bus electronic stop board, an advertisement electronic screen and a first monitoring unit; the bus on-board module comprises a vehicle positioning unit and a second monitoring unit; the system comprises a remote management module, a bus arrival prediction unit and a bus arrival prediction unit, wherein the remote management module comprises a passenger flow statistics unit, a data management unit and the bus arrival prediction unit, and the bus arrival prediction unit is used for predicting passengers in a bus which is about to arrive at a station; the method specifically comprises the following steps:
s101: and configuring a passenger flow statistical model for the passenger flow statistical unit.
In some embodiments, the configuring a passenger flow statistical model for the passenger flow statistical unit specifically includes:
acquiring a first training sample set and a second training sample set which are respectively obtained by the first monitoring unit and the second monitoring unit from the data management unit;
determining a pre-trained neural network model, and respectively inputting the first training sample set and the second training sample set into the neural network model for training;
labeling the human head of each image in the first training sample set and the second training sample set by adopting a detection frame to obtain a first target detection model and a second target detection model;
and processing the first target detection model and the second target detection model according to a target tracking algorithm to generate a passenger flow statistical model, and configuring the passenger flow statistical model for the passenger flow statistical unit.
Specifically, the labeling method of the detection frame satisfies (x, y, w, h, confidence) that satisfiesWherein x, y, w and h respectively represent two-dimensional coordinates, width and height of the central position of the detection frame, pr (Object) represents whether the detection frame contains a human head, and>representing the intersection between the predicted and actual frames.
In this embodiment, since the first monitoring unit monitors a scene of the bus station and the second monitoring unit monitors a scene in the bus, the monitoring data acquired by the first monitoring unit and the monitoring data acquired by the second monitoring unit are respectively used as a first training sample set and a second training sample set, and the head of the human body is used as a labeling part, in consideration of different scenes. confidence represents the confidence of the detection target (i.e., human head), and a detection frame with low confidence can be eliminated by setting a confidence threshold, when Pr (Object) =1, it indicates that the detection frame contains the human head, and when Pr (Object) =0, it indicates that the detection frame does not contain the human head. Meanwhile, the fact that the detection target has a motion track in a bus stop or a bus is considered, and the motion track of the detection target needs to be tracked by matching with a target tracking algorithm. The passenger flow statistical model is generated after the target detection model and the target tracking algorithm are combined, and the passenger flow statistical unit can detect the passenger flow in the bus stop or the bus in real time according to the passenger flow statistical model.
S102: based on the passenger flow statistics unit, determining the passenger flow of a first bus station set according to the first monitoring unit;
acquiring the number of vehicles and the passenger flow of all buses reaching each first bus station in the preset arrival time in the first bus station set based on the vehicle positioning unit, the passenger flow counting unit and the second monitoring unit;
and determining the passenger flow degree of each bus line according to the passenger flow of the first bus stop set and the number of vehicles and the passenger flow of all buses reaching each first bus stop in the preset arrival time in the first bus stop set, and then allocating the number of vehicles of buses of different bus lines.
In some embodiments, the determining the degree of the passenger flow of each bus line according to the passenger flow of the first bus station set and the number of vehicles and passenger flows of all buses reaching each first bus station in the preset arrival time in the first bus station set, and then allocating the number of vehicles of the buses of each bus line specifically includes:
acquiring the passenger flow volume of a first bus station from the data management unit;
acquiring the number of vehicles and the passenger flow of all buses reaching the first bus station in the preset arrival time from the data management unit, and determining a first idle passenger flow according to the number of vehicles and the passenger flow of all buses reaching the first bus station in the preset arrival time;
and determining the predicted boarding and disembarking passenger flows of all buses reaching the first bus stop in the preset arrival time according to the passenger flow prediction model, and allocating the number of buses of each bus line by combining the passenger flow of the first bus stop and the first idle passenger flow.
In some embodiments, the determining, according to the passenger flow prediction model, the predicted boarding and alighting passenger flows of all buses arriving at the first bus station within a preset arrival time, and allocating the number of vehicles of the buses of each bus line by combining the passenger flow of the first bus station and the first idle passenger flow specifically includes:
determining a first predicted boarding and disembarking passenger flow and a second predicted boarding and disembarking passenger flow according to a passenger flow prediction model, wherein the first predicted boarding and disembarking passenger flow is the predicted passenger flow of each bus on and off the first bus station in different time periods in each working day, and the second predicted boarding and disembarking passenger flow is the predicted passenger flow of each bus on and off the first bus station in different time periods in each holiday;
judging whether all buses arriving at the first bus station in a preset arrival time can process the passenger flow of the first bus station according to the passenger flow of the first bus station, the first idle passenger flow, the first predicted boarding and disembarking passenger flow and the second predicted boarding and disembarking passenger flow, if not, determining the first bus station as a busy bus station;
and according to the number of the busy bus stops of each bus line, real-time allocation is carried out on the number of the buses of each bus line.
In this embodiment, the data acquired by the first monitoring unit, the second monitoring unit and the vehicle positioning unit are all uploaded to the data management unit, the vehicle positioning unit can acquire vehicle position information through satellite technology, the preset arrival time can be set to be 15 minutes, and the number of vehicles and the passenger flow of buses arriving at the station in 15 minutes are calculated through bus speed, traffic jam, weather conditions and the like. The buses in this embodiment do not include those in which bus line information is drawn on the inner and outer walls of the bus in the form of graffiti, but are buses in which bus line information is displayed in the form of electronic screens, because the former can only be used as buses of fixed and unchanged bus lines, and the latter can then change corresponding bus line information in different bus lines. Considering that the first bus station has the condition of being the stations of a plurality of bus lines, the passenger flow of the buses reaching different lines of the first bus station in each time period is needed to be combined, whether the buses reaching different lines of the first bus station in the preset time can process the passenger flow of the first bus station is judged, and the passenger flow of the buses reaching different lines of the first bus station in each time period is unknown, so that the passenger flow of the buses reaching different lines of the first bus station in each time period is needed to be predicted by using a passenger flow prediction model, meanwhile, the factors obtained by real-time detection are combined with the idle passenger flow of the buses, and finally comprehensive analysis is carried out to determine which line is the busiest and the most needed to be temporarily allocated to be new buses, and which line is the most idle and the unnecessary buses are not needed temporarily, so that the resources of the buses between the buses can be efficiently allocated and the buses can be utilized to the maximum extent.
In some embodiments, the determining the first predicted get-on and get-off passenger flow and the second predicted get-on and get-off passenger flow according to the passenger flow prediction model specifically includes:
the method comprises the steps of obtaining historical passenger flow data of a first bus station, dividing the historical passenger flow data according to date factors, and obtaining first historical passenger flow data and second historical passenger flow data, wherein the first historical passenger flow data is the historical passenger flow data of the first bus station in a working day period, and the second historical passenger flow data is the historical passenger flow data of the first bus station in a holiday period;
training the first historical passenger flow data and the second historical passenger flow data based on an LSTM neural network to obtain a passenger flow prediction model;
and determining the first predicted boarding and disembarking passenger flow and the second predicted boarding and disembarking passenger flow according to the passenger flow prediction model.
Specifically, training the first historical passenger flow data and the second historical passenger flow data based on the LSTM neural network to obtain a passenger flow prediction model specifically includes:
according to the first historical passenger flow data and the second historical passenger flow data, a first time sequence and a second time sequence are obtained by a difference method;
setting LSTM neural network parameters, respectively taking the first time sequence and the second time sequence as input data of the LSTM neural network, and outputting first predicted passenger flow data and second predicted passenger flow data after feature fusion;
and calculating loss according to errors between the first predicted passenger flow data and the second predicted passenger flow data and the real passenger flow data respectively, and obtaining a passenger flow prediction model by adopting an error back propagation algorithm.
In this embodiment, the historical traffic data is divided by weekdays and holidays, considering that traffic on weekdays and holidays may differ, wherein holidays include weekend holidays and legal holidays. In the first historical passenger flow data and the second historical passenger flow data, the time between the first bus station arrival of the first shift and the arrival of the last shift at the first bus station is divided into n time slices, the passenger flow of buses of each line on and off the first bus station in each time slice is marked, an original time sequence is obtained, but the original time sequence is a non-stable sequence, the time sequence can be used as a training set for training in the follow-up, the LSTM neural network is required to be used for carrying out stabilizing treatment on the original time sequence by adopting a difference method, a first time sequence and a second time sequence are obtained, the first time sequence is the time sequence obtained after the first historical passenger flow data is subjected to the difference method, and the second time sequence is the time sequence obtained after the second historical passenger flow data is subjected to the difference method. And putting the first time sequence and the second time sequence into an LSTM neural network for training to obtain an initial model capable of predicting the passenger flow. After an initial model capable of predicting the passenger flow is obtained, outputting first predicted passenger flow data and second predicted passenger flow data through the initial model, wherein the first predicted passenger flow data corresponds to a first time sequence serving as input data, the second predicted passenger flow data corresponds to a second time sequence serving as input data, then determining errors between the predicted passenger flow data and the real passenger flow data, and correcting weights of neurons of each layer of the LSTM neural network by adopting an error back propagation algorithm to obtain the passenger flow prediction model. Next, the boarding and disembarking passenger flows of buses of each line reaching the first bus stop within the preset arrival time can be predicted according to the passenger flow prediction model. Based on the predicted passenger flow volume, the passenger flow volume of the first bus station and the free passenger volume on the bus, which are obtained through real-time detection, can be combined, so that buses reaching each line of the first bus station in the preset arrival time can be determined to process the passenger flow volume of the first bus station, whether the bus stations on each line belong to busy bus stations or not is determined, the busy degree of each line is further judged according to the number of the busy bus stations, and bus allocation is carried out.
Referring to fig. 2, an embodiment of the present invention provides a digital bus operation management system 2, where the system 2 is applied to a method according to any one of the above methods, and the system 2 specifically includes: the bus stop module 201 comprises a bus electronic stop board, an advertisement electronic screen and a first monitoring unit; the bus on-board module 202, which includes a vehicle positioning unit and a second monitoring unit; the remote management module 203 comprises a passenger flow statistics unit, a data management unit and a bus arrival forecasting unit, wherein the bus arrival forecasting unit is used for forecasting passengers in a bus which is going to arrive at a station.
In this embodiment, the digital bus operation management system includes a front-end system and a central system, the front-end system includes a bus stop module and a bus vehicle-mounted module, and the central system includes a remote management module. The front-end equipment is used for collecting relevant data information (such as video monitoring, platform passenger flow early warning and the like), the communication network is used for sending data to the central system, the central system integrates, analyzes, processes and stores the front-end data, and meanwhile, relevant information and instructions are issued to the platform, so that intelligent management of the platform is realized. The bus station module comprises products such as video equipment, an electronic station board/intelligent bus shelter, a transparent advertisement screen and the like, so that the safety monitoring of the station, the release of line information and the release of advertisements are realized, and the central system is used for unified management. The network between the front-end system and the central system generally uses optical fibers, when individual stations do not have a network foundation, the network can communicate through mobile communication 4G/5G, and the network is communicated with the existing passenger flow platform through a private network, so that the mutual and mutual communication of data communication and application is realized. The central system has multiple functions of video monitoring, alarm handling, advertisement operation, travel information release and the like, realizes the visibility, controllability and usability of the platform, comprises basic video preview and playback, and upgraded intelligent AI alarm, such as platform personnel density alarm and the like, and helps supervision personnel to promote the supervision capability of the platform; the real-time update and release of the vehicle information are realized, and accurate travel information service is provided for passengers; the remote control of the advertisements is realized, the investment of manpower and material resources is reduced for the advertisement operation of enterprises, and the digital construction of the platform is realized as a whole.
Referring to fig. 3, an embodiment of the present invention further provides a computer device 3, which is characterized by comprising: memory 302 and processor 301 and a computer program 303 stored on memory 302, which computer program 303, when executed on processor 301, implements a bus allocation method based on passenger traffic as described in any of the above methods.
The computer device 3 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The computer device 3 may include, but is not limited to, a processor 301, a memory 302. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the computer device 3 and is not meant to be limiting as the computer device 3, and may include more or fewer components than shown, or may combine certain components, or different components, such as may also include input-output devices, network access devices, etc.
The processor 301 may be a central processing unit (Central Processing Unit, CPU), the processor 301 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 302 may in some embodiments be an internal storage unit of the computer device 3, such as a hard disk or a memory of the computer device 3. The memory 302 may in other embodiments also be an external storage device of the computer device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 3. Further, the memory 302 may also include both an internal storage unit and an external storage device of the computer device 3. The memory 302 is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs, such as program code for the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output.
The embodiment of the invention also provides a computer readable storage medium, which is characterized in that the computer program is stored thereon, and when the computer program is run by a processor, the bus allocation method based on passenger flow according to any one of the above methods is realized.
In this embodiment, the integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Claims (9)
1. A bus allocation method based on passenger flow is applied to a digital bus operation management system, and the system specifically comprises the following steps: the bus stop module comprises a bus electronic stop board, an advertisement electronic screen and a first monitoring unit; the bus-mounted module comprises a vehicle positioning unit and a second monitoring unit; the system comprises a remote management module, a bus arrival prediction unit and a bus arrival prediction unit, wherein the remote management module comprises a passenger flow statistics unit, a data management unit and the bus arrival prediction unit, and the bus arrival prediction unit is used for predicting passengers in a bus which is about to arrive at a station; the method is characterized by comprising the following steps:
configuring a passenger flow statistical model for the passenger flow statistical unit;
based on the passenger flow statistics unit, determining the passenger flow of a first bus station set according to the first monitoring unit; acquiring the number of vehicles and the passenger flow of all buses reaching each first bus station in the preset arrival time in the first bus station set based on the vehicle positioning unit, the passenger flow counting unit and the second monitoring unit;
determining the passenger flow degree of each bus line according to the passenger flow of the first bus stop set and the number of vehicles and the passenger flow of all buses reaching each first bus stop in the preset arrival time in the first bus stop set, and then allocating the number of vehicles of buses of different bus lines, wherein the method specifically comprises the following steps:
acquiring the passenger flow volume of a first bus station from the data management unit;
acquiring the number of vehicles and the passenger flow of all buses reaching the first bus station in the preset arrival time from the data management unit, and determining a first idle passenger flow according to the number of vehicles and the passenger flow of all buses reaching the first bus station in the preset arrival time;
and determining the predicted boarding and disembarking passenger flows of all buses reaching different routes of the first bus station in the preset arrival time according to the passenger flow prediction model, and allocating the number of buses of each bus route by combining the passenger flow of the first bus station and the first idle passenger flow.
2. The method according to claim 1, wherein said configuring a passenger flow statistical model for the passenger flow statistical unit comprises:
acquiring a first training sample set and a second training sample set which are respectively obtained by the first monitoring unit and the second monitoring unit from the data management unit;
determining a pre-trained neural network model, and respectively inputting the first training sample set and the second training sample set into the neural network model for training;
labeling the human head of each image in the first training sample set and the second training sample set by adopting a detection frame to obtain a first target detection model and a second target detection model;
and processing the first target detection model and the second target detection model according to a target tracking algorithm to generate a passenger flow statistical model, and configuring the passenger flow statistical model for the passenger flow statistical unit.
3. The method of claim 2, wherein the labeling method of the detection frame satisfies (x, y, w, h, confidence) that satisfiesWherein x, y, w and h respectively represent two-dimensional coordinates, width and height of the center position of the detection frame, pr (Object) represents whether the detection frame contains a human head or not,representing the intersection between the predicted and actual frames.
4. The system according to claim 1, wherein the determining, according to the passenger flow prediction model, the predicted boarding and alighting passenger flows of all buses arriving at the first bus station within the preset arrival time, and allocating the number of buses of each bus line by combining the passenger flow of the first bus station and the first idle passenger flow specifically includes:
determining a first predicted boarding and disembarking passenger flow and a second predicted boarding and disembarking passenger flow according to a passenger flow prediction model, wherein the first predicted boarding and disembarking passenger flow is the predicted passenger flow of each bus on and off the first bus station in different time periods in each working day, and the second predicted boarding and disembarking passenger flow is the predicted passenger flow of each bus on and off the first bus station in different time periods in each holiday;
judging whether all buses arriving at the first bus station in a preset arrival time can process the passenger flow of the first bus station according to the passenger flow of the first bus station, the first idle passenger flow, the first predicted boarding and disembarking passenger flow and the second predicted boarding and disembarking passenger flow, if not, determining the first bus station as a busy bus station;
and according to the number of the busy bus stops of each bus line, real-time allocation is carried out on the number of the buses of each bus line.
5. The system of claim 4, wherein the determining the first predicted on-off passenger flow and the second predicted on-off passenger flow based on the passenger flow prediction model comprises:
the method comprises the steps of obtaining historical passenger flow data of a first bus station, dividing the historical passenger flow data according to date factors, and obtaining first historical passenger flow data and second historical passenger flow data, wherein the first historical passenger flow data is the historical passenger flow data of the first bus station in a working day period, and the second historical passenger flow data is the historical passenger flow data of the first bus station in a holiday period;
training the first historical passenger flow data and the second historical passenger flow data based on an LSTM neural network to obtain a passenger flow prediction model;
and determining the first predicted boarding and disembarking passenger flow and the second predicted boarding and disembarking passenger flow according to the passenger flow prediction model.
6. The system of claim 5, wherein the training the first historical passenger flow data and the second historical passenger flow data based on the LSTM neural network to obtain a passenger flow prediction model specifically comprises:
according to the first historical passenger flow data and the second historical passenger flow data, a first time sequence and a second time sequence are obtained by a difference method;
setting LSTM neural network parameters, respectively taking the first time sequence and the second time sequence as input data of the LSTM neural network, and outputting first predicted passenger flow data and second predicted passenger flow data after feature fusion;
and calculating loss according to errors between the first predicted passenger flow data and the second predicted passenger flow data and the real passenger flow data respectively, and obtaining a passenger flow prediction model by adopting an error back propagation algorithm.
7. A digital bus operation management system, applied to the method according to any one of claims 1 to 6, characterized in that it comprises in particular:
the bus stop module comprises a bus electronic stop board, an advertisement electronic screen and a first monitoring unit;
the bus on-board module comprises a vehicle positioning unit and a second monitoring unit;
the system comprises a remote management module, a bus arrival prediction unit and a bus arrival prediction unit, wherein the remote management module comprises a passenger flow statistics unit, a data management unit and the bus arrival prediction unit is used for predicting passengers in a bus which is about to arrive at the station.
8. A computer device, comprising: memory and processor and computer program stored on the memory, which when executed on the processor, implements a bus allocation method based on passenger traffic as claimed in any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when run by a processor, implements the bus allocation method based on passenger traffic as claimed in any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310370253.7A CN116432956B (en) | 2023-04-10 | 2023-04-10 | Bus allocation method, system, equipment and medium based on passenger flow |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310370253.7A CN116432956B (en) | 2023-04-10 | 2023-04-10 | Bus allocation method, system, equipment and medium based on passenger flow |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116432956A CN116432956A (en) | 2023-07-14 |
| CN116432956B true CN116432956B (en) | 2024-01-23 |
Family
ID=87088602
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310370253.7A Active CN116432956B (en) | 2023-04-10 | 2023-04-10 | Bus allocation method, system, equipment and medium based on passenger flow |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116432956B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116704778B (en) * | 2023-08-04 | 2023-10-24 | 创意(成都)数字科技有限公司 | Intelligent traffic data processing method, device, equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105427582A (en) * | 2015-11-18 | 2016-03-23 | 中国科学院上海微系统与信息技术研究所 | Intelligent management method for bus lines |
| CN105719476A (en) * | 2016-03-28 | 2016-06-29 | 深圳市佳信捷技术股份有限公司 | Updating method and device for public transport system scheduling information |
| CN110245558A (en) * | 2019-05-07 | 2019-09-17 | 平安科技（深圳）有限公司 | Method for dispatching public transport vehicles, device and computer equipment based on recognition of face |
| CN113221472A (en) * | 2021-07-08 | 2021-08-06 | 北京航空航天大学 | Passenger flow prediction method based on LSTM |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112598182B (en) * | 2020-12-25 | 2022-11-29 | 卡斯柯信号有限公司 | Intelligent scheduling method and system for rail transit |
-
2023
- 2023-04-10 CN CN202310370253.7A patent/CN116432956B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105427582A (en) * | 2015-11-18 | 2016-03-23 | 中国科学院上海微系统与信息技术研究所 | Intelligent management method for bus lines |
| CN105719476A (en) * | 2016-03-28 | 2016-06-29 | 深圳市佳信捷技术股份有限公司 | Updating method and device for public transport system scheduling information |
| CN110245558A (en) * | 2019-05-07 | 2019-09-17 | 平安科技（深圳）有限公司 | Method for dispatching public transport vehicles, device and computer equipment based on recognition of face |
| CN113221472A (en) * | 2021-07-08 | 2021-08-06 | 北京航空航天大学 | Passenger flow prediction method based on LSTM |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116432956A (en) | 2023-07-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110895884B (en) | Traffic signal lamp control method and device | |
| JP2020074179A (en) | Ridesharing management device, ridesharing management method, and program | |
| CN109655107B (en) | Air quality monitoring method and device, vehicle and computer readable storage medium | |
| CN113706737B (en) | Road surface inspection system and method based on automatic driving vehicle | |
| CN109658715A (en) | Statistical method of traffic flow, device, equipment and the storage medium of multilane | |
| CN116432956B (en) | Bus allocation method, system, equipment and medium based on passenger flow | |
| CN103745612A (en) | Vehicle-mounted terminal and intelligent traffic management device capable of realizing real-time interaction with same | |
| CN111932925A (en) | Method, device and system for determining travel passenger flow of public transport station | |
| US11332159B2 (en) | Method for providing transportation service using autonomous vehicle | |
| US20220148428A1 (en) | High innovation distributed system for the management of demarcated areas | |
| CN114743401B (en) | Data visualization bus dispatching management platform based on bus digital transformation | |
| CN111951548B (en) | Vehicle driving risk determination method, device, system and medium | |
| CN112002138A (en) | Wisdom trip system based on mobile unit | |
| Morris et al. | A comprehensive system for assessing truck parking availability | |
| EP3992940A1 (en) | Method and system for enhancing traffic estimation using top view sensor data | |
| CN114662583A (en) | Emergency event prevention and control scheduling method and device, electronic equipment and storage medium | |
| CN115841745A (en) | Vehicle scheduling method and device and electronic equipment | |
| CN111427063B (en) | Mobile device traffic control method, device, equipment, system and medium | |
| CN110046535B (en) | Intelligent travel time prediction system, method and storage medium based on machine learning | |
| CN115759660A (en) | Scheduling method, device, equipment and medium for unmanned vehicle | |
| CN115762131A (en) | Intelligent driving plan compiling method and system applied to public transportation | |
| CN113542364B (en) | Bus and network appointment linkage intelligent traffic system | |
| CN114979204A (en) | Digital management and control platform for highway operation management | |
| CN115359671A (en) | Intersection vehicle cooperative control method and related equipment | |
| CN116416102A (en) | Vehicle service system and interaction method thereof |
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 |