Integrated dispatch decision-making system and method for hub supporting multi-modal transportation coordination, and device

Abstract

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

Description

A multi-modal transportation collaborative hub integrated scheduling decision-making system, method and equipment Technical Field

[0001] This invention relates to the field of integrated traffic scheduling technology, and in particular to a hub integrated scheduling decision system, method and equipment that supports multi-modal traffic coordination. Background Technology

[0002] Urban transportation hubs are crucial nodes connecting different modes of transportation within an urban transportation network. With societal development and economic progress, transportation hubs have evolved from simple to large-scale and complex. Corresponding to modern, large-scale integrated transportation hubs in cities that combine multiple modes of transportation such as aviation, high-speed rail, subway, long-distance buses, and public transport, existing multi-modal transportation modes maintain relatively independent operation and management, lacking effective integration and communication. This leads to problems such as uneven passenger flow distribution, localized hub congestion, and low transportation efficiency.

[0003] In the face of large-scale events and sudden surges in passenger flow, significant challenges exist in emergency response, customer service, and operational scheduling across various modes of transportation. For such complex hub scenarios, effectively allocating diverse transportation resources within the hub, efficiently managing and coordinating connections between different modes of transportation, rapidly responding to emergencies and safely evacuating passengers, and optimizing passenger service guidance and transfers are major industry challenges that must be overcome.

[0004] A search revealed Chinese invention patent publication number CN116923505A, which discloses a multi-system coupled energy operation and control method for urban rail transit. The method includes the following steps: Data acquisition step: collecting passenger flow, line parameters, vehicle parameters, and operation diagrams from actual lines as boundary conditions; Data storage step: storing the target energy efficiency / capacity and the full-domain data of the multi-system interactive digital simulation system; Data processing step: cleaning the full-domain data and fusing the cleaned full-domain data based on the boundary conditions to obtain new full-domain data; Strategy generation step: combining the target energy efficiency / capacity, using an adaptive droop control method to analyze the new full-domain data and generate an energy control strategy; Instruction generation step: generating control instructions based on the energy control strategy; Control step: sending the control instructions to the corresponding systems to control them to execute corresponding actions according to the control instructions. This existing patent only analyzes data from rail transit and does not include data from aviation, railway, or automotive sectors, nor does it disclose specific operation and control strategies.

[0005] How to achieve integrated hub scheduling decisions based on multi-modal transportation collaboration has become a technical problem that needs to be solved.

[0006] Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a multi-modal transportation coordination hub integrated scheduling decision system, method and equipment.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] According to one aspect of the present invention, a multi-modal transportation cooperative hub integrated scheduling decision system is provided, the system comprising:

[0010] The hub multimodal passenger flow data fusion and analysis module is used to organize and classify the collected hub multimodal passenger flow data, perform preprocessing, then convert and map it into normalized passenger flow data, and finally extract data fusion features and use the normalized transportation passenger flow model to perform multi-dimensional data fusion analysis and calculation.

[0011] The hub transportation collaborative decision-making module is used to perform capacity and volume assessment based on the fusion analysis of multimodal passenger flow data and to calculate the optimal strategy results using the optimal decision-making model.

[0012] The hub collaborative dispatching and command application module is used to provide intelligent auxiliary command functions for rail transit dispatching and command personnel based on the information access of hub-line-network and normalized transportation passenger flow model, targeting the input conditions that affect train dispatching and command.

[0013] Preferably, the conversion mapping to normalized passenger flow data specifically involves: unifying the data format of multimodal passenger flow data according to traffic type characteristic parameters, converting and mapping it to normalized passenger flow data, and establishing a historical database for real-time saving and updating.

[0014] Preferably, the extraction of data fusion features specifically involves extracting data fusion features based on spatiotemporal correlation and travel chain correlation.

[0015] Preferably, the process of establishing the normalized passenger flow model is as follows: based on the fused multimodal passenger flow data and related features, a relationship model between the hub's normalized passenger flow and various traffic influencing factors is established using statistical and machine learning models; historical data and / or simulation data are used to verify the model, and adjustments and optimizations are made based on the verification results; passenger flow information is fed back in real time, data is collected and processed in real time, and the model is updated and predicted based on the processed data.

[0016] More preferably, the traffic influencing factors include capacity allocation schemes, date type, station type, transfer channels, emergencies, large-scale events, and weather.

[0017] Preferably, the capacity and volume assessment specifically involves: based on historical normalized passenger flow data, combined with capacity configuration and transportation plans, analyzing and judging the matching of capacity and volume of multiple transportation modes within and between modes; establishing evaluation indicators from the perspective of capacity coordination and time connection within the hub area; and quantitatively assessing the current coordination between two modes of transportation.

[0018] Preferably, the process of using the optimal decision model to calculate the optimal strategy result includes the construction of the optimal decision model, specifically: based on the internal facility layout of the transportation hub, the alternative evacuation routes of passenger flow within it are abstracted into a network diagram composed of nodes and road segments; under the condition that the passenger flow between each origin and destination point and the alternative evacuation routes of passenger flow are known, an optimal decision model for multi-mode traffic collaborative scheduling is established according to the optimization objective of each collaborative scenario.

[0019] More preferably, the optimal decision model is solved using a designed heuristic algorithm to obtain the optimal strategy result.

[0020] More preferably, the optimal decision-making model analyzes operational data based on preset contingency plans and expert experience, predicts the passenger flow of each station on the line in the future, and generates corresponding passenger flow prediction warnings and capacity plans based on the passenger flow predictions, which are then pushed to stations in related areas to accurately implement passenger flow management.

[0021] More preferably, the process of using the optimal decision model to calculate the optimal strategy result also includes calculating the optimal strategy result, specifically: under normal operating conditions, providing passengers entering and exiting the hub with route recommendations covering their entire travel process, generating multi-transportation connection and transfer schemes within the hub, and pushing transfer guidance results to passengers upon request; under abnormal conditions, performing delay prediction and delay impact analysis of multi-modal transportation, providing cross-transportation linkage and dispatch recommendation schemes, and promptly pushing driving adjustment schemes to each other.

[0022] More preferably, the optimization objective is to minimize passenger evacuation time.

[0023] Preferably, the hub collaborative dispatch and command application module includes centralized monitoring at the station level, collaborative adjustment at the line level, and comprehensive support at the network level;

[0024] The centralized monitoring at the station level is based on normalized passenger flow data covering all transportation categories of the hub. It constructs a centralized monitoring system at the station level that covers all transportation categories of the hub and provides multi-modal transportation connection decision optimization functions through cross-professional collaboration and interactive processes.

[0025] The line-level collaborative adjustment constructs an integrated planning model for multi-scenario train operation schemes at the line level to meet complex, diverse, and time-varying passenger flow demands. It enables customized scenario operation plan preparation and collaborative adjustment, quickly generates train operation plans after line adjustments, and executes them in conjunction with the dispatching and command personnel after review and confirmation.

[0026] The comprehensive support system at the network layer establishes a vertically integrated command system that coordinates and schedules resources and facilitates business collaboration based on passenger flow demand. This system ensures the precise implementation of cross-line command decisions during major hub events or emergency failures, and tracks the effectiveness of these decisions in a closed-loop manner.

[0027] Preferably, the preprocessing includes data format conversion, cleaning, deduplication, and missing value processing, and the sorting and classification involves matching a unique identifier to each data source.

[0028] Preferably, the sources of the multi-modal passenger flow data include rail transit dispatching and command systems, railway dispatching and command systems, public transportation command systems, aviation dispatching systems, urban integrated transportation coordination and command systems, video surveillance passenger flow, venue passenger flow information, passenger terminal interaction information, location sensing, high-speed rail timetables and ticket-collecting arrival numbers, city / intercity timetables and passenger flow, flight schedules and delay information, and public transportation schedule information.

[0029] According to another aspect of the present invention, a hub integrated scheduling decision-making method for multimodal transportation cooperation is provided, the method comprising the following steps:

[0030] Step S1: Use the normalized transport passenger flow model to perform multi-modal traffic data fusion analysis on the collected hub multi-transport passenger flow data;

[0031] Step S2: Calculate the optimal hub traffic coordination decision using the optimal decision model;

[0032] Step S3: Based on the information access of multi-mode transportation and the normalized transportation passenger flow model, realize the hub coordinated scheduling and command of centralized monitoring at the station level, coordinated adjustment at the line level, and comprehensive guarantee at the network level.

[0033] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0034] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1) This invention uses a designed normalized transportation passenger flow model to normalize and fuse collected multimodal passenger flow data, ensuring the comparability of multi-source data; it establishes a historical database of normalized passenger flow data and saves and updates it in real time, providing a data foundation technology for traffic decision-making; it uses an optimal decision model to calculate the optimal strategy under various scenarios of multimodal transportation in hubs and coordinates scheduling and adjustment, improving the connectivity of various transportation modes in complex hub scenarios, enabling more rational allocation and use of various transportation resources, and also improving the efficiency of coordination and management.

[0037] 2) This invention constructs a multi-level hub collaborative scheduling and command application system covering hubs, lines, and networks, supporting multi-mode traffic collaborative scheduling decisions in normal and abnormal scenarios. In normal scenarios, it provides passengers with better connection and transfer solutions and pushes transfer guidance results upon request; in abnormal scenarios, it provides cross-transportation linkage and dispatch recommendation solutions, which can respond more quickly to sudden emergency events and safely complete the evacuation of personnel, reduce the impact of major events and local traffic failures on passenger travel, and thus improve customer satisfaction. Attached Figure Description

[0038] Figure 1 is a schematic diagram of the integrated scheduling decision system of the present invention;

[0039] Figure 2 is a schematic diagram of the processing flow of the integrated scheduling decision method of the present invention;

[0040] Figure 3 is a schematic diagram of the process for establishing the normalized passenger flow model of the present invention;

[0041] Figure 4 is a schematic diagram of the hub collaborative scheduling and command application module of the present invention;

[0042] Figure 5 is a schematic diagram of the architecture of the integrated scheduling decision system of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0044] This embodiment relates to a multi-modal transportation collaborative hub integrated dispatching and decision-making system. This system is designed for the operation management and emergency response of large urban integrated hubs. It develops and constructs a dispatching system with integrated data analysis and decision-making capabilities. It integrates data from multiple transportation modes and multiple data collection sources to form a normalized transportation passenger flow model. Based on this model, it constructs a hub collaborative dispatching and command application that is guided by passenger flow demand, integrates resource scheduling, business collaboration and linkage, and transfer connection guidance. This meets the transportation service needs of passengers for door-to-door and travel-as-you-go services, as well as the needs of complex hubs for comprehensive fault emergency management.

[0045] As shown in Figure 1, the system includes the following three modules:

[0046] 1) Hub Multimodal Passenger Flow Data Fusion and Analysis Module: This module includes data preprocessing, normalized passenger flow data, and multi-dimensional data combination analysis and calculation. It collects and preprocesses passenger flow data from multiple transportation modes entering the hub, unifies the data format based on transportation type characteristic parameters, converts and maps the multimodal passenger flow data into normalized passenger flow data, establishes a historical database, and saves and updates it in real time. It also extracts data fusion features based on spatiotemporal correlation and travel chain correlation, and uses a normalized transportation passenger flow model for multi-dimensional data fusion analysis.

[0047] 2) Hub Transportation Collaborative Decision-Making Module: This module includes capacity and passenger volume assessment, establishment of optimal decision-making models, and calculation of optimal strategy results. Based on the fusion analysis of multi-modal transportation passenger flow data, and combined with the capacity resource allocation and transportation organization plans of multi-modal transportation, capacity and passenger volume assessment is performed; the optimal objective for each collaborative scenario is identified, an optimal decision-making model adaptable to multiple collaborative scenarios is established, and the optimal strategy results for multi-transportation connections under each scenario are calculated and obtained.

[0048] 3) Hub-based collaborative dispatching and command application module: This module includes centralized monitoring at the station level, collaborative adjustment at the line level, and comprehensive support at the network level. Based on information access from the hub to the line to the network, and considering input conditions affecting train dispatching and command such as passenger flow changes, multi-mode collaboration, and emergency response, it provides intelligent auxiliary command functions for rail transit dispatching and command personnel through multi-level and multi-scenario dispatching decision-making, multi-mode hierarchical traffic dispatching and command, and multi-scenario dispatching simulation verification, as shown in Figures 4 and 5.

[0049] As shown in Figure 5, the multi-transportation passenger flow data includes: rail transit dispatch and command system, railway dispatch and command system, bus command system, aviation dispatch system, urban integrated transportation coordination and command system, video surveillance passenger flow, venue passenger flow information, passenger terminal interactive information, location perception, high-speed rail timetable and ticket collection arrival number, city / intercity timetable and passenger flow, flight time and delay information, bus schedule information, etc.

[0050] Multi-source, multi-format transportation data collected by sensors, smart terminals, and other devices across various transportation modes and hubs is organized and categorized. This includes high-speed rail and airplane ticketing data, rail transit AFC data, bus card swipe data, and parking lot entry and exit data. Each data source is assigned a unique identifier for identification during subsequent data fusion, ensuring data accuracy and comparability. Furthermore, since different transportation modes and data sources may use different data structures, preprocessing steps such as data format conversion, cleaning, deduplication, and missing value handling are required to ensure data quality.

[0051] The hub multi-modal passenger flow data fusion and analysis module organizes and classifies multi-source and multi-system transportation data, matching each data source with a unique identifier; preprocessing includes data format conversion, cleaning, deduplication, and missing value handling.

[0052] The multimodal traffic passenger flow data is shown in Table 1:

[0053] Table 1. Multimodal Transportation Passenger Flow Data

[0054] As shown in Figure 3, the hub multi-modal passenger flow data fusion and analysis module matches and merges multi-modal passenger flow data: data alignment and matching are performed through timestamp calibration and keyword unification, enabling different data to be compared and merged in the same time and space dimension. Considering factors such as the weight, reliability, and accuracy of each data source, multi-mode transportation passenger flow data are merged; the error between the fusion result and the true value is measured using indicators such as mean squared error and correlation coefficient, and the merged data is evaluated and verified to ensure the reliability and accuracy of the fusion result. Utilizing big data storage and processing technologies, a cluster capable of large-scale processing of massive amounts of data is constructed to store and process massive amounts of comprehensive transportation hub data. Since traffic data is mostly collected in real time, data transmission and processing latency must be considered.

[0055] Figure 3 illustrates the establishment of a normalized transportation passenger flow model: Based on the fused multi-modal transportation passenger flow data and related characteristics, statistical models and machine learning models are used to establish a relationship model between the normalized passenger flow at the hub and various influencing factors (such as capacity allocation schemes, date types, station types, transfer channels, emergencies, large-scale events, and weather). Historical data or simulation data are used to validate the model, and adjustments and optimizations are made based on the validation results to improve the model's predictive ability and adaptability. Simultaneously, to maintain the real-time performance of the transportation passenger flow model, a digital infrastructure can be used to integrate smart application devices to provide real-time feedback of passenger flow information. Combined with real-time data acquisition and processing technologies, this allows the model to promptly reflect changes in traffic conditions and update and predict based on new data.

[0056] Capacity and passenger volume assessment and trend analysis: Based on historical normalized passenger flow data, combined with capacity allocation and transportation plans, this study analyzes and assesses the matching of capacity and passenger volume across multiple transportation modes both within and between modes. From the perspectives of capacity synergy and time connectivity within the hub area, evaluation indicators are established to quantitatively assess the current synergy between different modes of transportation. Simultaneously, considering various influencing factors, the study analyzes the evolution patterns of capacity and passenger volume within the hub area over time and space, identifies risk points and capacity bottlenecks, and provides optimization suggestions.

[0057] This study investigates and constructs a dynamic passenger flow transfer model within a hub, analyzing the direction and speed of passenger flow during normal operation on various characteristic dates and time periods for all transfer routes within the hub. It also considers the impact area and time of possible abnormal events within the hub area to assess and establish possible alternative transfer routes and passenger flows when flights, high-speed trains, or other train line disruptions occur within the hub, and to estimate the increase or decrease in passenger flow at relevant transfer stations.

[0058] Establish an optimal decision-making model for multi-modal traffic collaborative scheduling at transportation hubs: Based on the internal facility layout of transportation hubs, the alternative evacuation routes for passenger flow within the hubs are abstracted into a network graph composed of nodes and road segments. Taking a sudden surge in passenger flow as an example, given the passenger flow between each origin and destination point and the alternative evacuation routes, a multi-modal traffic collaborative scheduling optimization model is established according to the optimization objective of each collaborative scenario (e.g., minimizing passenger evacuation time). A heuristic algorithm is designed to solve the model, yielding an optimized passenger evacuation scheme for multi-modal traffic collaboration.

[0059] Based on the optimal decision-making model, the optimal strategy for multi-transportation connections is calculated: Under normal operating conditions, route recommendations covering the entire travel process are provided for passengers entering and exiting the hub, and multi-transportation connection and transfer schemes within the hub are generated, and transfer guidance results are pushed to them upon request; Under abnormal conditions, delay prediction and delay impact analysis of multi-modal transportation are performed, cross-transportation linkage and dispatch recommendation schemes are provided, and driving adjustment schemes are pushed to each other in a timely manner.

[0060] Based on pre-set contingency plans and expert experience, analysis and decision-making are conducted to predict peak passenger flow and direction of movement at stations along the line within the region. Reasonable train operation plans are then proposed, including but not limited to extended operating hours, deployment of spare trains, and additional short-distance trains, to effectively alleviate concentrated passenger flow and improve passenger satisfaction. Corresponding passenger flow forecasts, early warnings, and capacity plans are simultaneously pushed to stations in related areas, leading to the development of corresponding station passenger flow management plans. Based on the predicted passenger flow, appropriate station operation plans are switched accordingly to ensure accurate passenger flow management.

[0061] Centralized monitoring at the station level: Based on normalized data covering all transportation categories of the hub, a centralized monitoring system covering all transportation categories of the hub is built at the station level. This system enables dispatching and command execution output interfaces covering all types of transportation participants in the station hub. Through cross-professional collaboration and interactive processes, it accurately outputs instructions, achieves closed-loop management, provides multi-mode transportation connection decision optimization functions, creates transportation connection decision optimization functions that meet the needs of passengers for door-to-door and travel-as-a-service, and improves information dissemination in collaborative scenarios.

[0062] Line-level coordinated adjustment: At the line level, an integrated planning model for multi-scenario train operation schemes is constructed to meet complex, diverse, and time-varying passenger flow demands. This enables customized scenario operation plan preparation and coordinated adjustment, accurately matches command and decision-making objectives, quickly generates train operation plans after line adjustments, and executes them in a coordinated manner after review and confirmation by dispatching and command personnel.

[0063] Comprehensive support at the network level: Establish a vertically integrated command system at the network level that is guided by passenger flow demand and coordinates resources and business operations. This system will ensure the precise implementation of cross-line command decisions during major events or emergency failures at hubs, and track the implementation results in a closed loop to reduce the impact of major events and failures on passenger travel.

[0064] The hub collaborative dispatching and command application module, based on the information access of multi-modal transportation within the hub area and the normalized passenger flow model, realizes the main functional applications of centralized monitoring at the station level, collaborative adjustment at the line level, and comprehensive support at the network level. Addressing input conditions affecting train dispatching and command, such as passenger flow changes, multi-modal collaboration, and emergency response, it expands multi-level and multi-scenario dispatching decision-making functions to construct a multi-modal transportation collaborative intelligent decision-making and dispatching system, as shown in Figure 5, providing collaborative dispatching and command auxiliary functions for operation management within the hub area.

[0065] This embodiment also relates to a hub integrated scheduling decision-making method for multi-modal transportation coordination. As shown in Figure 2, through the data fusion analysis of multi-modal transportation passenger flow, a normalized transportation passenger flow model matching the multi-modal transportation coordination of the hub is established, and the calculation of the multi-modal transportation connection and coordination scheme of large hubs is realized, thereby realizing the expansion of multi-level collaborative scheduling and command applications.

[0066] The method includes the following steps:

[0067] Step S1, multimodal traffic data fusion analysis, including data classification and preprocessing, data matching and fusion, and the establishment of a normalized transport passenger flow model.

[0068] By classifying, preprocessing, matching, and fusing passenger flow data from different transportation modes in integrated transportation hubs, a normalized transportation passenger flow model is established. This model enables the mapping and conversion of characteristic parameters of various transportation modes to passenger flow within the hub, forming normalized passenger flow data. This supports the superposition of passenger flows from multiple transportation modes and the extraction of correlation features, as well as the coordinated connection of multi-mode transportation.

[0069] Step S1 involves performing multi-modal traffic data fusion analysis on the collected hub multi-transportation passenger flow data using a normalized transport passenger flow model; this includes:

[0070] Step S101: Data classification and preprocessing;

[0071] Step S102, data matching and fusion;

[0072] Step S103: Use the normalized transport passenger flow model to perform multimodal traffic data fusion analysis.

[0073] Step S2, using the optimal decision model to calculate the optimal hub transportation coordination decision: Based on the fusion analysis of multi-modal traffic passenger flow data, combined with the multi-modal transportation capacity resource allocation and transportation organization plan, capacity and volume assessment and trend analysis are conducted within the hub area to identify the optimization objective for each coordination scenario, establish an optimal decision model adaptable to multiple coordination scenarios, and calculate and obtain the optimal multi-transportation connection strategy results for each scenario; specifically including:

[0074] Step S201: Capacity and volume assessment and trend analysis.

[0075] Step S202: Establish the optimal decision-making model for multi-mode traffic collaborative scheduling at the hub;

[0076] Step S203: Calculate the optimal strategy for multi-transportation connections based on the optimal decision model.

[0077] Step S3, based on multi-modal transportation information access and a normalized passenger flow model, realizes hub collaborative scheduling and command, including centralized monitoring at the station level, coordinated adjustment at the line level, and comprehensive support at the network level. Hub collaborative scheduling and command is used for, but is not limited to: traffic transfer guidance, optimal passenger travel routes and transfer mode planning services, regional passenger flow density safety control, optimal decision-making for multi-modal transportation plan matching and connection, and decision-making and scheduling of connecting transportation under sudden delays, as shown in Figures 4 and 5.

[0078] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0079] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0080] The processing unit executes the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 by any other suitable means (e.g., by means of firmware).

[0081] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0082] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0083] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-modal transportation collaborative hub integrated scheduling and decision-making system, characterized in that, The system includes: The hub multimodal passenger flow data fusion and analysis module is used to organize and classify the collected hub multimodal passenger flow data, perform preprocessing, then convert and map it into normalized passenger flow data, and finally extract data fusion features and use the normalized transportation passenger flow model to perform multi-dimensional data fusion analysis and calculation. The hub transportation collaborative decision-making module is used to perform capacity and volume assessment based on the fusion analysis of multimodal passenger flow data and to calculate the optimal strategy results using the optimal decision-making model. The hub collaborative dispatching and command application module is used to provide intelligent auxiliary command functions for rail transit dispatching and command personnel based on the information access of hub-line-network and normalized transportation passenger flow model, targeting the input conditions that affect train dispatching and command.

2. The hub integrated scheduling and decision-making system for multi-modal transportation coordination according to claim 1, characterized in that, The conversion and mapping to normalized passenger flow data specifically involves: unifying the data format of multimodal passenger flow data according to traffic type characteristic parameters, converting and mapping it to normalized passenger flow data, and establishing a historical database for real-time saving and updating.

3. The hub integrated scheduling and decision-making system for multi-modal transportation coordination according to claim 1, characterized in that, The extraction of data fusion features specifically involves extracting data fusion features based on spatiotemporal correlation and travel chain correlation.

4. The hub integrated scheduling and decision-making system for multi-modal transportation coordination according to claim 1, characterized in that, The process of establishing the normalized passenger flow model is as follows: based on the fused multimodal passenger flow data and related features, statistical models and machine learning models are used to establish a relationship model between the normalized passenger flow of the hub and various traffic influencing factors; historical data and / or simulation data are used to verify the model, and adjustments and optimizations are made based on the verification results; Real-time feedback of passenger flow information, real-time data collection and processing, and model updates and predictions based on the processed data.

5. A multi-modal transportation collaborative hub integrated scheduling and decision-making system according to claim 4, characterized in that, The traffic influencing factors include capacity allocation plans, date type, station type, transfer channels, emergencies, large-scale events, and weather.

6. The hub integrated scheduling and decision-making system for multi-modal transportation coordination according to claim 1, characterized in that, The capacity and volume assessment specifically involves: based on historical normalized passenger flow data, combined with capacity configuration and transportation plans, analyzing and judging the matching of capacity and volume of multiple transportation modes within and between modes; establishing evaluation indicators from the perspective of capacity coordination and time connection within the hub area; and quantitatively assessing the current coordination between two modes of transportation.

7. A multi-modal transportation collaborative hub integrated scheduling and decision-making system according to claim 1, characterized in that, The process of using the optimal decision model to calculate the optimal strategy result includes the construction of the optimal decision model, specifically: based on the internal facility layout of the transportation hub, the alternative evacuation routes of passenger flow within it are abstracted into a network diagram composed of nodes and road segments; under the condition that the passenger flow between each origin and destination point and the alternative evacuation routes of passenger flow are known, an optimal decision model for multi-mode traffic collaborative scheduling is established according to the optimization objective of each collaborative scenario.

8. A multi-modal transportation collaborative hub integrated scheduling and decision-making system according to claim 7, characterized in that, The optimal decision model is solved using the designed heuristic algorithm to obtain the optimal strategy result.

9. A multi-modal transportation collaborative hub integrated scheduling and decision-making system according to claim 7, characterized in that, The optimal decision-making model analyzes operational data based on preset contingency plans and expert experience, predicts passenger flow at stations along the line in the region in the future, and generates corresponding passenger flow forecasts, early warnings, and capacity plans based on the passenger flow forecasts, which are then simultaneously pushed to stations in related areas to accurately implement passenger flow management.

10. A multi-modal transportation collaborative hub integrated scheduling and decision-making system according to claim 7, characterized in that, The process of using the optimal decision model to calculate the optimal strategy result also includes calculating the optimal strategy result, specifically: under normal operating conditions, providing route recommendations covering the entire travel process for passengers entering and leaving the hub, generating multi-transportation connection and transfer schemes within the hub, and pushing transfer guidance results to passengers upon request; under abnormal conditions, performing delay prediction and delay impact analysis of multi-modal transportation, providing cross-transportation linkage and dispatch recommendation schemes, and promptly pushing driving adjustment schemes to each other.

11. A multi-modal transportation collaborative hub integrated scheduling and decision-making system according to claim 7, characterized in that, The optimization objective is to minimize passenger evacuation time.

12. A multi-modal transportation collaborative hub integrated scheduling and decision-making system according to claim 1, characterized in that, The hub collaborative dispatch and command application module includes centralized monitoring at the station level, collaborative adjustment at the line level, and comprehensive support at the network level. The centralized monitoring at the station level is based on normalized passenger flow data covering all transportation categories of the hub. It constructs a centralized monitoring system at the station level that covers all transportation categories of the hub and provides multi-modal transportation connection decision optimization functions through cross-professional collaboration and interactive processes. The aforementioned route-level collaborative adjustment constructs an integrated planning model for multi-scenario driving schemes at the route level, meeting complex, diverse, and time-varying passenger flow demands. This enables customized scenario operation plan creation and collaborative adjustment, and rapid generation of routes. The adjusted train operation plan is implemented in a coordinated manner after being reviewed and confirmed by the dispatching and command personnel; The comprehensive support system at the network layer establishes a vertically integrated command system that coordinates and schedules resources and promotes business collaboration based on passenger flow demand. This system ensures the precise implementation of cross-line command decisions during major hub events or emergency failures, and tracks the effectiveness of these decisions in a closed loop.

13. A multi-modal transportation coordination hub integrated scheduling and decision-making system according to claim 1, characterized in that, The preprocessing includes data format conversion, cleaning, deduplication, and missing value handling, while the sorting and classification involves matching a unique identifier to each data source.

14. A multi-modal transportation collaborative hub integrated scheduling and decision-making system according to claim 1, characterized in that, The sources of the multimodal passenger flow data include rail transit dispatch and command systems, railway dispatch and command systems, public transportation command systems, aviation dispatch systems, urban integrated transportation coordination and command systems, video surveillance passenger flow, venue passenger flow information, passenger terminal interaction information, location sensing, high-speed rail timetables and ticket-collecting arrival numbers, city / intercity timetables and passenger flow, flight schedules and delay information, and public transportation schedule information.

15. A method for a hub integrated scheduling and decision-making system employing multi-modal transportation coordination as described in claim 1, the method comprising the following steps: Step S1: Use the normalized transport passenger flow model to perform multi-modal traffic data fusion analysis on the collected hub multi-transport passenger flow data; Step S2: Calculate the optimal hub traffic coordination decision using the optimal decision model; Step S3: Based on the information access of multi-mode transportation and the normalized transportation passenger flow model, realize the hub coordinated scheduling and command of centralized monitoring at the station level, coordinated adjustment at the line level, and comprehensive guarantee at the network level.

16. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 15.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 15.

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