Wheel ferry vehicle and passenger intelligent dredging method and system
By improving the column generation algorithm and the spatiotemporal synchronous clustering algorithm, the batch and time nodes of ferry vehicles and passengers are intelligently planned, and identification access codes are generated. This solves the problem of the coupling complexity between vehicle flow and passenger flow in the capacity scheduling of ferry port area, and improves operational efficiency and passenger experience.
Patent Information
- Application Number
- CN202511547814.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-28
- Publication Date
- 2026-02-03
AI Technical Summary
Modern ferry port capacity scheduling lacks comprehensive consideration of dynamic factors such as ticket purchase information, actual arrival time, and gate congestion, resulting in complex coupling between vehicle flow and passenger flow. This leads to problems such as misaligned queues of vehicles and passengers in the same batch, fluctuating waiting times, congestion at boarding gates, and decreased resource utilization.
By employing an improved column generation algorithm and a spatiotemporal synchronous clustering algorithm, the system intelligently plans the boarding and disembarking batches and time nodes for vehicle drivers and passengers, generates identification access codes, and optimizes batch intervals and congestion risk control through real-time scanning and rearrangement mechanisms.
It has achieved a higher level of operational efficiency, resource utilization and passenger experience, reduced traffic congestion and improved the efficiency of boarding and disembarking of ferry vehicles and passengers.
Smart Images
Figure CN121459577A_ABST
Abstract
Description
[0001] A method and system for intelligent traffic management of vehicles and passengers on ferries. Technical Field This invention relates to the field of intelligent traffic planning technology, and in particular to an intelligent method and system for managing the movement of vehicles and passengers on ferries. Background Technology
[0002] Modern ferry port capacity scheduling faces numerous challenges, especially during peak hours when the coupling between vehicle and passenger flows and ship berthing operations becomes increasingly complex. Traditional boarding batches typically rely on manual experience and static rules, lacking comprehensive consideration of dynamic factors such as ticketing information, actual arrival times, gate congestion, and traffic conditions. This can easily lead to problems such as misaligned queues of vehicles and passengers within the same batch, fluctuating waiting times, congestion at boarding gates, and decreased resource utilization. With the advancement of smart port and integrated urban transportation concepts, there is an urgent need for intelligent management methods for ferry vehicles and passengers that can dynamically allocate boarding and disembarking batches from a global perspective, balancing passenger experience, operational efficiency, and road network congestion control. Summary of the Invention
[0003] In view of the above-mentioned prior art, the present invention provides an intelligent management method and system for ferry vehicles and passengers, which mainly solves the technical problems existing in the background art.
[0004] To achieve the above objectives, the technical solution of this invention is implemented as follows: In a first aspect, the present invention provides an intelligent method for managing the movement of vehicles and passengers on ferries, the method comprising the following steps: Step S1: Obtain the ticketing information for this ferry and classify the drivers and passengers of vehicles on the same trip; In this embodiment, the ferry ticketing information obtained includes: passenger identity information, order number, and ticket purchase time.
[0005] Step S2: Based on the ticket purchase time and the set batch interval time, the improved column generation algorithm is used to intelligently plan the boarding batches and time nodes of vehicle drivers and passengers, and generate identification access codes based on the boarding batches and time nodes and send them to the user terminal. Step S3: Scan the identification pass code at the boarding gate in real time and perform statistics. If there are any cases of failure to board within the corresponding boarding batch time window, the boarding will be automatically reassigned to the next boarding batch. Step S4: Calculate the predicted departure time of the vehicle and its passengers, and use a spatiotemporal synchronous clustering algorithm to intelligently plan the departure batches; push the departure batch information to the user terminal.
[0006] As a preferred embodiment of the present invention, step S1 specifically includes: passengers under the same order are bound into one boarding batch, and vehicle drivers and accompanying passengers are bound into one boarding batch.
[0007] As a preferred embodiment of the present invention, step S2, which involves planning the boarding batches and time nodes for vehicle drivers and passengers using an improved column generation algorithm based on the ticket purchase time and a set batch interval time, specifically includes: Define the objective function for the production batches to be shipped:
[0008] in, This indicates the total number of passenger entities that need to board the ship on this flight. Indicates the priority score. Indicates the total number of boarding batches. , , , All represent weighting coefficients. This indicates the number of people allocated to the current boarding batch k. This represents the number of people who purchased or reserved tickets within time t. and Both are binary decision variables, representing passenger i or passenger i. Should it be bound to the boarding batch k? This indicates the start time of the current boarding batch k. This represents the average ticket purchase time for all passengers in the current boarding batch k. The set of edges in the graph relating people and vehicles; Calculate the time nodes for boarding batches:
[0009] =
[0010] +
[0011] in, This indicates the start time of this boarding batch. Indicates the time of the first batch of boarding. Indicates the dynamic interval. Indicates the minimum interval between boarding batches. Indicates the base duration. Indicates the adjustment range coefficient. Indicates the queuing sensitivity coefficient. This indicates the predicted queue length for this boarding batch k. This indicates the end time of this boarding batch.
[0012] As a preferred embodiment of the present invention, step S2, which generates an identification pass code based on the boarding batch and time node and sends it to the user terminal, specifically includes: The system encrypts and binds three key pieces of information together: passenger identity information, boarding batch number, and boarding time window. Anti-counterfeiting measures and verification codes are then implemented. Any tampering with the access code information will cause verification to fail, thus ensuring system security and fraud prevention capabilities. Based on the passenger type, their boarding batch, and boarding time window, an identification access code is generated and sent to the passenger's mobile phone.
[0013] As a preferred embodiment of the present invention, step S3 specifically includes: after receiving the pass code, the user presents the pass code before entering the boarding gate, scans the pass code in real time at the boarding gate, and the gate verifies the identity and batch consistency by local decoding and the server, and records it. If the entity does not board the ship within the corresponding boarding batch time window, it is automatically rearranged to the end of the next boarding batch sequence. If the number of people in the next boarding batch is full, it is automatically sorted to the end of the next boarding batch sequence.
[0014] As a preferred embodiment of the present invention, the calculation of the predicted disembarkation time of the vehicle and its passengers in step S4 is as follows: Calculate vehicle departure time :
[0015] in, Indicates the reference time for a ship to come to a complete stop. Indicates the distance from the vehicle to the exit. This indicates the vehicle speed relative to congestion density. The function, Indicates the time required for export inspection. This represents the random error term indicating the vehicle's departure from the ship; Calculate the disembarkation time of passengers traveling in the vehicle. :
[0016] in, Indicates passenger The distance to its corresponding exit. This represents the average walking speed of passengers. Indicates the congestion impact coefficient. Indicates passenger Real-time density of the area This represents the random error term for passengers disembarking.
[0017] As a preferred embodiment of the present invention, the intelligent planning of departure batches using the spatiotemporal synchronous clustering algorithm in step S4 specifically includes: vehicle drivers and accompanying personnel are no longer strongly bound to the same batch, while accompanying personnel are still strongly bound. The goal is to redistribute departure batches based on the predicted departure times of vehicle drivers and accompanying passengers, so as to minimize the time for vehicle drivers and accompanying personnel to meet at the departure point. Specifically, as follows: Treat vehicle drivers and passengers as two independent sets; create an edge for each previously associated pair of drivers and passengers, and match each driver with a set of passengers for disembarkation batches, assigning a target cost function to each edge. The objective cost function Including time difference costs and spatial distance costs, the formula is as follows:
[0018] in, Represents the objective cost function. The weighting coefficient representing the time cost. The weighting coefficients represent the spatial distance cost. Indicates vehicle location Passenger gathering points Path distance between This indicates the batch number of the vehicle driver who disembarked. This indicates the batch number of passengers disembarking from the ship.
[0019] Secondly, the present invention also provides an intelligent passenger and vehicle management system for ferries, which is applied to the above-described method, the system comprising: Information collection and preprocessing module: used to obtain ticketing information for this ferry and classify and bind drivers and passengers of vehicles in the same trip; Boarding Batch Planning Module: Used to generate boarding batches and time points for vehicle drivers and passengers; Identification Pass Code Generation Module: Used to generate unique identification pass codes based on boarding batches and time points; Disembarkation batch planning module; used to generate disembarkation batches and time nodes for vehicle drivers and passengers; Information sending module: Used to send boarding batch information and disembarkation batch information to passenger users.
[0020] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned intelligent management method for ferry vehicles and passengers.
[0021] The beneficial effects of this invention are as follows: Through a comprehensive and intelligent process that integrates ticketing information, batch segmentation, real-time execution, and departure planning, and by matching people and vehicles, ensuring compliance with time windows, optimizing batch intervals, and controlling congestion risks during the boarding and disembarking stages, a higher level of operational efficiency, resource utilization, and passenger experience are comprehensively improved. Ultimately, it achieves effective guidance for ferry vehicles and passengers during boarding and disembarking, improving efficiency, enhancing passenger experience, and reducing traffic congestion. Attached Figure Description
[0022] Figure 1 This invention provides a flowchart illustrating the steps of an intelligent crowd control method for vehicles and passengers on a ferry. Figure 2 This invention provides a structural schematic diagram of an intelligent passenger and vehicle management system for ferries. Detailed Implementation
[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. In the following description, the expression "some embodiments" refers to a subset of all possible embodiments; however, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.
[0024] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.
[0025] It should be understood that the present invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Furthermore, the terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.
[0026] It should also be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.
[0027] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.
[0028] Firstly, please refer to the attached document. Figure 1 This invention provides an intelligent method for managing the movement of vehicles and passengers on ferries, the method comprising the following steps: Step S1: Obtain the ticketing information for this ferry and classify the drivers and passengers of vehicles on the same trip; In this embodiment, the ferry ticketing information obtained includes retrieving all ticketing records for a specified ferry trip from the ferry company's central ticketing database or ticketing system through a predetermined data interface (API) or database connection.
[0029] Step S2: Based on the ticket purchase time and the set batch interval time, the improved column generation algorithm is used to intelligently plan the boarding batches and time nodes of vehicle drivers and passengers, and generate identification access codes based on the boarding batches and time nodes and send them to the user terminal. Step S3: Scan the identification pass code at the boarding gate in real time and perform statistics. If there are any cases of failure to board within the corresponding boarding batch time window, the boarding will be automatically reassigned to the next boarding batch. Step S4: Calculate the predicted departure time of the vehicle and its passengers, and use a spatiotemporal synchronous clustering algorithm to intelligently plan the departure batches; push the departure batch information to the user terminal.
[0030] In this embodiment, by accurately classifying drivers and passengers of vehicles on the same trip based on ticket purchase information, and combining ticket purchase time and set batch intervals, an improved column generation algorithm is used to intelligently plan boarding batches and time nodes, thereby achieving scientific allocation and time decomposition of batches at the source, generating executable identification access codes, and pushing them to the user terminal in real time, ensuring that personnel and vehicles entering the boarding gate enter in an orderly manner according to the predetermined time window.
[0031] At the boarding gate, the identification pass codes are scanned and counted in real time, and verified in conjunction with the actual boarding time and the allocated time window. For those who do not board within the corresponding time window, a mechanism is automatically triggered to re-arrange them to the next boarding batch, significantly reducing the risk of queue overlap and congestion caused by time window mismatch.
[0032] After the current boarding batch is completed, based on spatiotemporal information and forward-looking capacity prediction, a spatiotemporal synchronous clustering algorithm is used to intelligently decouple the departure batches of vehicles and accompanying passengers, and to plan the departure with the shortest possible convergence time. The departure batch information is then pushed to the user terminal to ensure that the resource scheduling and departure rhythm during the departure stage are optimally matched, thereby improving overall traffic efficiency and passenger experience.
[0033] As a preferred embodiment of the present invention, step S1 specifically includes: passengers under the same order are bound into one boarding batch, and vehicle drivers and accompanying passengers are bound into one boarding batch.
[0034] In this embodiment, after receiving the raw data, it is first parsed and cleaned, including but not limited to: formatting, deduplication, and validity verification, such as verifying whether the license plate number format and mobile phone number format are correct, to ensure data quality.
[0035] First, data is aggregated on an order-by-order basis, specifically using the order number as the key, to combine multiple scattered ticket sales records into independent "order data packages." Each data package contains all the information from that purchase transaction.
[0036] Secondly, iterate through each "order data packet" and perform the following judgment: First, identify the vehicle driver entity: If a record contains a license plate number field, the system will automatically identify that the record represents a vehicle transportation unit and extract information such as the driver's name, driver's contact information, license plate number, and vehicle type, classifying it into a vehicle driver entity.
[0037] Second, identify the passenger entity: For all records in the same order that do not contain license plate information or are marked as passengers, identify them as passenger entities and extract information such as passenger name, passenger contact information, and passenger type.
[0038] Finally, after completing the subject identification, based on the fact that they originate from the same order number, a binding relationship is established between the vehicle driver entity and all passenger entities in the same order, thereby binding the vehicle driver and accompanying passengers into a boarding batch.
[0039] As a preferred embodiment of the present invention, step S2, which involves planning the boarding batches and time nodes for vehicle drivers and passengers using an improved column generation algorithm based on the ticket purchase time and a set batch interval time, specifically includes: Define the objective function for the production batches to be shipped:
[0040] in, This indicates the total number of passenger entities that need to board the ship on this flight. Indicates the priority score. Indicates the total number of boarding batches. , , , All represent weighting coefficients. This indicates the number of people allocated to the current boarding batch k. This represents the number of people who purchased or reserved tickets within time t. and Both are binary decision variables, representing passenger i or passenger i. Should it be bound to the boarding batch k? This indicates the start time of the current boarding batch k. This represents the average ticket purchase time for all passengers in the current boarding batch k. The set of edges in the graph relating people and vehicles; in, , ; In the formula, Indicates the time difference in ticket purchase. Indicates the base time for scheduling planning. Indicates passenger The time for purchasing tickets, This indicates bonus points for special needs.
[0041] In this embodiment, the objective function aims to minimize the total waiting cost, minimize the batch passenger cost, and maximize the human-vehicle coordination rate, so that passengers who purchase tickets earlier are assigned to earlier boarding batches, thereby minimizing the passenger waiting time.
[0042] Specifically, The value is 0 when associated drivers and passengers are assigned to the same batch, and 1 when they are assigned to different batches. Minimizing this sum forces associated drivers and passengers to be assigned to the same batch.
[0043] For example, if passenger i's ticket purchase time priority score is Then the priority of its associated driver j is synchronized. This ensures that when optimizing the relationship between passengers and vehicles, high-priority passengers are prioritized and assigned to earlier batches alongside drivers, rather than allowing low-priority drivers to drag down passenger batches; simultaneously, when generating candidate batches, passenger priority is considered. The system sorts passengers from highest to lowest priority and prioritizes assigning them to the earliest available batch. If the current batch is full, passengers are automatically assigned to the next batch. This avoids situations where high-priority passengers are left without a batch to go to due to prioritizing low-priority passengers.
[0044] Calculate the time nodes for boarding batches:
[0045] =
[0046] +
[0047] in, This indicates the start time of this boarding batch. Indicates the time of the first batch of boarding. Indicates the dynamic interval. Indicates the minimum interval between boarding batches. Indicates the base duration. Indicates the adjustment range coefficient. Indicates the queuing sensitivity coefficient. This indicates the predicted queue length for this boarding batch k. This indicates the end time of this boarding batch.
[0048] In this embodiment, the start time of each boarding batch is calculated by taking the time of the first batch. With the calculated dynamic interval By combining these methods, the specific start time of all batches can be calculated recursively. and end time This creates a boarding time window for each batch. , Each time window corresponds to a fixed batch of passengers and vehicles. By controlling the length and interval of the time windows, it is ensured that the passenger flow to be handled within each time period is within the optimal range of the boarding gate's service capacity.
[0049] As a preferred embodiment of the present invention, step S2, which generates an identification pass code based on the boarding batch and time node and sends it to the user terminal, specifically includes: The system encrypts and binds three key pieces of information together: passenger identity information, boarding batch number, and boarding time window. It also performs anti-counterfeiting and verification code checks. Based on the passenger type and the boarding batch and time window they belong to, an identification pass code is generated and sent to the passenger's mobile phone.
[0050] As one implementation method, passenger identification information includes at least information that can uniquely identify the passenger, such as an order number or a hash value of an ID card number. The order number is preferred because it can be directly linked to the ticket purchase record.
[0051] The boarding batch number is specifically the batch identifier assigned to that passenger. The boarding time window refers to the specific start and end times during which boarding is permitted for that batch.
[0052] The three key pieces of information are concatenated into a string according to a predetermined format. A checksum is then calculated. To prevent tampering, this application uses a cryptographic hash function to calculate a fixed-length, unique message authentication code, also known as a checksum or hash value. Furthermore, to enhance security, a key-based hashing algorithm can be used when calculating the hash value. This involves combining the string with a private key stored only on the server side before performing the hash calculation.
[0053] The generated string is used as input, and the QR code generation component is invoked to generate a unique identification code. Then, through the communication interface of the information sending module, the generated identification code image or a text message containing a link to access the code is sent to the mobile phone number registered by the passenger when purchasing the ticket. The passenger can then view the code on their mobile phone screen.
[0054] Key information regarding boarding and disembarking batches and time points is pushed to passengers and drivers in the form of identification codes, ensuring a high degree of alignment between the individual's device and the system's scheduling, and reducing inconvenience caused by missed opportunities, misunderstandings, and repetitive operations.
[0055] In practice, scanning devices are installed at the boarding gate. When a passenger or vehicle scans the code, the system verifies the following: First, it verifies whether the flight number and batch number match the current flight. Second, it verifies whether the current time is within the valid time window. Third, it verifies whether the order has already been used to prevent duplicate boarding. If all verifications pass, the passenger is allowed to board and the order is marked as having been boarded. Otherwise, the verification fails, an error message is displayed, and staff conduct further investigation.
[0056] As a preferred embodiment of the present invention, step S3 specifically includes: after receiving the pass code, the user presents the pass code before entering the boarding gate, scans the pass code in real time at the boarding gate, the gate verifies the identity and batch consistency through local decoding and records it, if the entity does not board within the corresponding boarding batch time window, it is automatically rearranged to the end of the next boarding batch sequence, if the number of people in the next boarding batch is full, it is automatically sorted to the end of the next-next boarding batch sequence, and the valid time window of the pass code is updated to the next batch time, and the pass code is regenerated.
[0057] As a preferred embodiment of the present invention, the calculation of the predicted disembarkation time of the vehicle and its passengers in step S4 is as follows: Calculate vehicle departure time :
[0058] in, Indicates the reference time for a ship to come to a complete stop. Indicates the distance from the vehicle to the exit. This indicates the vehicle speed relative to congestion density. The function, Indicates the time required for export inspection. This represents the random error term indicating the vehicle's departure from the ship; Calculate the disembarkation time of passengers traveling in the vehicle. :
[0059] in, Indicates passenger The distance to its corresponding exit. This represents the average walking speed of passengers. Indicates the congestion impact coefficient. Indicates passenger Real-time density of the area This represents the random error term for passengers disembarking.
[0060] As a preferred embodiment of the present invention, the intelligent planning of departure batches using the spatiotemporal synchronous clustering algorithm in step S4 specifically includes: vehicle drivers and accompanying personnel are no longer strongly bound to the same batch, while accompanying personnel are still strongly bound. The goal is to redistribute departure batches based on the predicted departure times of vehicle drivers and accompanying passengers, so as to minimize the time for vehicle drivers and accompanying personnel to meet at the departure point. Specifically, as follows: Treat vehicle drivers and accompanying passengers as two independent sets; create an edge for each previously associated pair of drivers and accompanying passengers; match each driver with a set of accompanying passengers for disembarkation batches; assign a target cost function to each edge, which includes time difference cost and spatial distance cost, and its formula is as follows:
[0061] in, Represents the objective cost function. The weighting coefficient representing the time cost. The weighting coefficients represent the spatial distance cost. Indicates vehicle location Passenger gathering points Path distance between This indicates the batch number of the vehicle driver who disembarked. This indicates the batch number of passengers disembarking from the ship.
[0062] In this embodiment, all passengers belonging to the same vehicle are considered as a single group. A separate disembarkation batch is assigned to the vehicle driver and their corresponding passenger group to minimize the total rendezvous cost for the entire group. If the vehicle is assigned to the... The batch, whose passenger group was assigned to the first If batches are processed, then a pooling cost will be incurred.
[0063] Our goal is for all ( _i, _i) Assign batch numbers to minimize total cost while satisfying batch capacity constraints. The goal is to ensure that vehicles and accompanying passengers can meet as quickly as possible at the rendezvous point after disembarking, while allowing them to be assigned to different disembarkation batches, through accurate prediction and intelligent scheduling. This minimizes the overall "people waiting for vehicles" or "vehicles waiting for people" time and alleviates traffic congestion in the exit area.
[0064] For example, according to the disembarkation batch plan, the driver is informed via mobile APP: "Your vehicle belongs to batch 3, please proceed to the vehicle deck at XX:XX"; and the passenger is informed: "Your accompanying vehicle belongs to batch 3, please wait at assembly point 3 at XX:XX", thereby achieving efficient and orderly disembarkation guidance.
[0065] Secondly, please refer to the attached document. Figure 2The present invention also provides an intelligent passenger and vehicle management system for ferries, which is applied to the above-described method, and the system includes: Information collection and preprocessing module: used to obtain ticketing information for this ferry and classify and bind drivers and passengers of vehicles in the same trip; Boarding Batch Planning Module: Used to generate boarding batches and time points for vehicle drivers and passengers; Identification Pass Code Generation Module: Used to generate unique identification pass codes based on boarding batches and time points; Disembarkation batch planning module; used to generate disembarkation batches and time nodes for vehicle drivers and passengers; Information sending module: Used to send boarding batch information and disembarkation batch information to passenger users.
[0066] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned intelligent management method for ferry vehicles and passengers.
[0067] In this embodiment, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.
[0068] Fourthly, embodiments of this application also provide an electronic device, including a processor and a memory, wherein the memory stores multiple instructions; the processor loads instructions from the memory to execute steps in any of the intelligent passenger and vehicle management methods provided in embodiments of this application.
[0069] Fifthly, embodiments of this application also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps in any of the intelligent passenger and ferry vehicle management methods provided in embodiments of this application.
[0070] In this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be accomplished by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.
[0071] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the intelligent passenger and vehicle management methods provided in embodiments of this application.
[0072] It should be noted that, through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.
[0073] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for intelligently managing the movement of vehicles and passengers on a ferry, characterized in that, The method includes the following steps: Step S1: Obtain the ticketing information for this ferry and classify the drivers and passengers of vehicles on the same trip; Step S2: Based on the ticket purchase time and the set batch interval time, the improved column generation algorithm is used to intelligently plan the boarding batches and time nodes of vehicle drivers and passengers, and generate identification access codes based on the boarding batches and time nodes and send them to the user terminal. Step S3: Scan the identification pass code at the boarding gate in real time and perform statistics. If there are any cases of failure to board within the corresponding boarding batch time window, the boarding will be automatically reassigned to the next boarding batch. Step S4: Calculate the predicted departure time of the vehicle and its passengers, and use a spatiotemporal synchronous clustering algorithm to intelligently plan the departure batches; push the departure batch information to the user terminal.
2. The intelligent passenger and vehicle management method for ferries according to claim 1, characterized in that, Step S1 specifically includes: passengers under the same order are bound into one boarding batch, and vehicle drivers and accompanying passengers are bound into one boarding batch.
3. The intelligent passenger and vehicle management method for ferries according to claim 2, characterized in that, Step S2, which involves using an improved column generation algorithm to plan the boarding batches and time nodes for vehicle drivers and passengers based on ticket purchase time and set batch interval, specifically includes: Define the objective function for the production batches to be shipped: in, This indicates the total number of passenger entities that need to board the ship on this flight. Indicates the priority score. Indicates the total number of boarding batches. , , , All represent weighting coefficients. This indicates the number of people allocated to the current boarding batch k. This represents the number of people who purchased or reserved tickets within time t. and Both are binary decision variables, representing passenger i or passenger i. Should it be bound to the boarding batch k? This indicates the start time of the current boarding batch k. This represents the average ticket purchase time for all passengers in the current boarding batch k. The set of edges in the graph relating people and vehicles; Calculate the time nodes for boarding batches: = + in, This indicates the start time of this boarding batch. Indicates the time of the first batch of boarding. Indicates the dynamic interval. Indicates the minimum interval between boarding batches. Indicates the base duration. Indicates the adjustment range coefficient. Indicates the queuing sensitivity coefficient. This indicates the predicted queue length for this boarding batch k. This indicates the end time of this boarding batch.
4. The intelligent passenger and vehicle management method for ferries according to claim 3, characterized in that, The step S2, which generates an identification pass code based on the boarding batch and time point and sends it to the user terminal, specifically includes: The system encrypts and binds three key pieces of information together: passenger identity information, boarding batch number, and boarding time window. It also performs anti-counterfeiting and verification code checks. Based on the passenger type and the boarding batch and time window they belong to, an identification pass code is generated and sent to the passenger's mobile phone.
5. The intelligent passenger and vehicle management method for ferries according to claim 4, characterized in that, Step S3 specifically includes: after receiving the pass code, the user presents the pass code before entering the boarding gate, scans the pass code in real time at the boarding gate, and the gate verifies the identity and batch consistency through local decoding and records it. If the entity does not board the ship within the corresponding boarding batch time window, it is automatically rearranged to the end of the next boarding batch sequence. If the number of people in the next boarding batch is full, it is automatically sorted to the end of the next boarding batch sequence.
6. The intelligent passenger and vehicle management method for a ferry as described in claim 5, characterized in that, The calculation of the predicted departure time of the vehicle and its passengers in step S4 is as follows: Calculate vehicle departure time : in, Indicates the reference time for a ship to come to a complete stop. Indicates the distance from the vehicle to the exit. This indicates the vehicle speed relative to congestion density. The function, Indicates the time required for export inspection. This represents the random error term indicating the vehicle's departure from the ship; Calculate the disembarkation time of passengers traveling in the vehicle. : in, Indicates passenger The distance to its corresponding exit. This represents the average walking speed of passengers. Indicates the congestion impact coefficient. Indicates passenger Real-time density of the area This represents the random error term for passengers disembarking.
7. The intelligent passenger and vehicle management method for a ferry as described in claim 6, characterized in that, Step S4, which uses a spatiotemporal synchronous clustering algorithm to intelligently plan departure batches, specifically includes: vehicle drivers and accompanying passengers are no longer strongly bound to the same batch, while accompanying passengers remain strongly bound. The goal is to reallocate departure batches based on the predicted departure times of vehicle drivers and accompanying passengers, minimizing the time it takes for drivers and accompanying passengers to reunite upon departure. Specifically, as follows: Treat vehicle drivers and accompanying passengers as two independent sets; create an edge for each previously associated pair of drivers and accompanying passengers; match each driver with a set of accompanying passengers for disembarkation batches; assign a target cost function to each edge, which includes time difference cost and spatial distance cost, and its formula is as follows: in, Represents the objective cost function. The weighting coefficient representing the time cost. The weighting coefficients represent the spatial distance cost. Indicates vehicle location Passenger gathering points Path distance between This indicates the batch number of the vehicle driver who disembarked. This indicates the batch number of passengers disembarking from the ship.
8. An intelligent passenger and vehicle management system for ferries, wherein the system is applied to the method described in any one of claims 1-7, characterized in that, The system includes: Information collection and preprocessing module: used to obtain ticketing information for this ferry and classify and bind drivers and passengers of vehicles in the same trip; Boarding Batch Planning Module: Used to generate boarding batches and time points for vehicle drivers and passengers; Identification Pass Code Generation Module: Used to generate unique identification pass codes based on boarding batches and time points; Disembarkation batch planning module; used to generate disembarkation batches and time nodes for vehicle drivers and passengers; Information sending module: Used to send boarding batch information and disembarkation batch information to passenger users.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements an intelligent passenger flow management method for ferry vehicles as described in any one of claims 1-7.