An intelligent transportation management system based on dynamic shared bus service scheduling

Abstract

This invention claims protection for an intelligent transportation management system based on dynamic shared bus service scheduling, comprising a system architecture, an order management module, an algorithm execution module, an operation monitoring and management module, a bus information management module, a data visualization module, and a user management module. The system architecture is primarily based on Spring Boot 2.x + Vue 2.x, combined with the Flask framework implemented in Python. Java technology handles backend data processing, condition selection, and interface services, while Vue implements the frontend display of bus and order data and API calls. Python technology mainly implements the algorithm execution module. The advantages of this invention compared to existing technologies are: it designs bus routes and scheduling schemes based on real order datasets, and establishes a comprehensive system encompassing algorithm execution, order and bus information query, statistics, and data visualization modules. This effectively improves the shared bus company's ability to manage and schedule buses and provides an intelligent software management system for vehicle scheduling and route planning management.

Description

Technical Field

[0001] This invention relates to the field of public transportation management system technology, specifically to an intelligent transportation management system for dynamic scheduling of shared bus services. The invention also provides a method for using the system. Background Technology

[0002] In recent years, with the rapid development of the global economy and driven by the goal of "carbon peaking and carbon neutrality," shared buses, as a new mode of transportation, have gradually entered the public eye and gained attention and favor in line with the era of the sharing economy. Compared with traditional public transportation, shared buses have the advantage of comfortable riding, and compared with driving or car sharing, they have the advantages of saving money, time, and effort.

[0003] However, there are currently few management systems available for shared buses. Most of the existing systems focus on vehicle and personnel management, with little emphasis on bus dispatching and route planning. Furthermore, no software system can provide route planning and dispatching solutions that balance the operating costs of shared bus companies with passenger waiting times. To promote the development and application of shared buses, the market urgently needs a software system that can provide bus dispatching and route planning solutions for shared bus companies while also considering both operating costs and passenger travel experience.

[0004] CN111127936A discloses a dynamic vehicle scheduling and route planning method for shared buses. This method abstracts the operating costs of actual public transport companies and the passenger travel experience, establishing a multi-objective optimization model. It extracts suitable traffic topologies for shared bus operation based on the actual application area and road network. Based on the extracted traffic topologies, an improved local search algorithm is used to generate a candidate route set for shared buses. An offline algorithm is designed to solve the shared bus scheduling and route planning problem in application scenarios with similar passenger flow patterns. An online algorithm is designed to solve the shared bus scheduling and route planning problem in application scenarios with dynamic and real-time passenger flow. This invention provides a novel method for dynamic scheduling and route planning of shared buses, truly resolving the contradiction between operating costs and passenger travel experience faced by shared bus companies, and providing a new and effective solution for vehicle scheduling and route planning for shared bus companies.

[0005] The methods described in the above patents cannot directly provide bus dispatching and route planning solutions for real-world shared bus companies. They are not readily usable by ordinary bus company employees, have weak operability, and present a high technical barrier. Based on these difficulties, this patent implements an intelligent traffic management system based on dynamic shared bus service dispatching, utilizing the methods described above. This system is developed with separate front-end and back-end components, featuring a visual user interface. It is easy for bus company employees to learn and highly operable, effectively improving the bus dispatching and management capabilities of shared bus companies. Compared to the algorithm solutions in the above patents, it has greater practical significance. Summary of the Invention

[0006] This invention aims to solve the problems of the prior art mentioned above. It proposes an intelligent transportation management system based on dynamic shared bus service scheduling. The technical solution of this invention is as follows:

[0007] An intelligent transportation management system based on dynamic shared bus service dispatching includes an order management module, an algorithm execution module, an operation monitoring and management module, a bus information management module, a data visualization module, and a user management module.

[0008] The order management module is used to manage passenger orders, including dynamically acquiring orders, dynamically notifying new order information, dynamically updating order status and list, viewing all order information, modifying order information, and deleting order information;

[0009] The algorithm execution module is used to analyze and process order data and combine it with the traffic topology of the shared bus company to obtain route planning and vehicle scheduling information for the shared buses. It optimizes the traffic topology, solves the candidate route set, and schedules the minimum number of buses and travels the shortest route to serve all passengers while ensuring the waiting time of passengers. It also limits the seat utilization rate of the buses to ensure the travel experience of passengers.

[0010] The operation monitoring and management module is used by users to control and display the entire process of passenger orders for the shared bus service;

[0011] The bus information management module is used to display bus operation information after algorithm scheduling;

[0012] The data visualization module is used to visualize data information, including station, bus, and passenger orders, in the form of charts.

[0013] The user management module is used to manage accounts, including an email login and registration module, an email password retrieval module, and a personal information modification module.

[0014] Furthermore, the architecture of the intelligent transportation management system is mainly based on Spring Boot 2.x + Vue 2.x, combined with the Flask framework implemented in Python. Java technology is used to implement backend data processing, condition selection and provide interface services, Vue is used to implement the frontend bus and order data display and interface calling functions, and Python technology is mainly used to implement the algorithm execution module.

[0015] Furthermore, the order management module includes a module for dynamically acquiring order information, a module for dynamically notifying new order messages, a module for dynamically updating order status, a module for dynamically updating the order list, a module for viewing all order information, a module for modifying order information, and a module for deleting order information.

[0016] The dynamic order information acquisition module is used when the user selects the date of the order dataset and clicks the start button of the operation monitoring and management module, and the system backend dynamically and automatically acquires real-time order information.

[0017] The dynamic new order notification module is used to notify the system of order information in the form of a pop-up message window.

[0018] The dynamic order status update module is used by the system to automatically identify and update the order processing status. Orders have three statuses: processed, being processed, and unprocessed.

[0019] The dynamically updated order list module is used by the system to dynamically update the different categories of order lists displayed during the operation monitoring process;

[0020] The "View All Order Information" module is used by the system to display all order information under the current dataset to the user.

[0021] The order information modification module is used by users to modify the information of a specific order in the current dataset.

[0022] The "Delete Order Information" module is used by users to delete a specific order information record in the current dataset.

[0023] Furthermore, the algorithm execution module includes a traffic topology optimization module, a candidate route set solving module, an online algorithm module, and a date selection and algorithm result acquisition module;

[0024] The traffic topology optimization module is used to optimize the actual traffic topology of shared bus company stations by taking into account historical passenger flow, road segment length between stations, and historical passenger waiting time at stations, so as to obtain a traffic topology suitable for the operation of shared buses.

[0025] The candidate route set solution module is used to solve the problem model based on the optimized traffic topology using an improved local search algorithm to generate a candidate route set.

[0026] The online algorithm module is used to solve the shared bus scheduling and route planning problems in application scenarios with dynamic and real-time passenger flow.

[0027] The "Select Date to Obtain Algorithm Result" module is used by the system backend to call the online algorithm to obtain the algorithm result—the "Bus Scheduling and Route Planning" scheme—after the user selects a date from the dataset.

[0028] Furthermore, the traffic topology optimization module is used to optimize the actual traffic topology of shared bus company stations, taking into account historical passenger flow, road segment length between stations, and historical passenger waiting time at stations, to obtain a traffic topology suitable for shared bus operation, including the following steps:

[0029] The shared bus transportation network is extracted into a directed network graph based on the actual traffic conditions;

[0030] After obtaining the directed network graph, if there are situations in the directed graph where buses cannot directly reach two stations under real traffic conditions, or where the route is too long and can be replaced by other routes, then the edges between the corresponding stations in the directed graph will be deleted.

[0031] Based on the processed directed graph, the maximum flow of the time network and the maximum flow of the passenger network are calculated using the historical order dataset and the Ford-Fulkerson algorithm. Then, the minimum values ​​of the time network flow and the passenger network flow are determined, where the time network flow T... f,e and passenger network flow P f,e Edges whose values ​​are all below the minimum of the time network flow and the passenger network flow are unimportant in the traffic routes, and the corresponding edges in the directed graph will be deleted. Among them, the time network flow T... f,e and passenger network flow P f,e The definition is as follows:

[0032]

[0033] e in the formula<u,v> d It is the length of the road segment between station u and station v. γ and ζ are obtained using the entropy method based on historical order datasets. This represents the average waiting time for passengers at this station in the historical order dataset. The maximum waiting time for passengers at this station in the historical order dataset. It represents the average number of passengers boarding at this station each day in the historical order dataset;

[0034] The improved depth-first search algorithm is used to obtain the loops in the directed graph processed in the previous step. The loops are traversed one by one, and the edges corresponding to the nodes with the largest out-degree or in-degree in the loops without origin stations are deleted to complete the loop removal operation. Finally, the optimized traffic topology suitable for bus operation is obtained.

[0035] Furthermore, the candidate route set solution module is used to solve the problem model based on the optimized traffic topology using an improved local search algorithm to generate a candidate route set;

[0036] The problem model is to minimize the number of shared buses and the average route length per passenger, and it is defined as follows:

[0037]

[0038] |P in the formula v (Φ) represents the number of passengers picked up by the shared bus at station v. p The total number of passengers using shared bus services, δ uv Φ is a binary variable representing whether an edge exists between stations u and v in a directed graph. D Given the line length, minimize the line length Φ. D And maximize the total number of passengers for the shared bus service Φ p Minimize the average line length Φ for each passenger DP It is an equivalence relation. The constraints include: Ensure that at least one shared bus is operating to serve passengers; δ uv ∈{0,1}; Ensure that the in-degree and out-degree of each station in the line are both 1; Ensure that the line length does not exceed its maximum value D. max ; Ensure that there are no loops in the route; Ensure that the seat utilization rate of buses does not exceed the upper limit. Ensure that the average waiting time for passengers does not exceed the threshold.

[0039] Based on the problem model, an improved local search algorithm is used to obtain the candidate route set. The algorithm solution steps are as follows:

[0040] First, select a stop for the bus that first arrives at the station for route planning. There are three selection methods: ① Based on a historical experience database storing information on the best stop selections, select the next best stop using an experience-learning mechanism; ② Based on a certain probability, randomly select the next stop from the candidate stops using a random selection mechanism; ③ Based on a penalty function for a particular stop. The definition is to select the site with the least penalty as the best next site;

[0041] Secondly, regarding vehicle scheduling, there are operating bus queues and available bus queues. If the average passenger waiting time at a station exceeds a threshold, an additional bus needs to be added, and the bus to be added must be selected from the available bus queue first. If a bus reaches its final destination, it will be removed from the operating bus queue and added to the available bus queue, and its route and departure time will be saved. If the seat utilization rate of a bus reaches a threshold, it will be set so that the bus cannot serve passengers on subsequent routes.

[0042] Furthermore, the online algorithm module is used to solve the shared bus scheduling and route planning problems in application scenarios with dynamic, real-time passenger flow;

[0043] In terms of vehicle dispatching, there are operating bus queues, available bus queues, and existing bus queues. Existing bus queues include operating bus queues and bus queues with confirmed departure times and routes but which have not yet departed.

[0044] First, iterate through the current order dataset and the information of every bus in the existing bus queue. If the bus departure time meets the waiting time requirement of the earliest arriving passenger in the order dataset, assign the passenger corresponding to that order to that bus; otherwise, determine a new bus schedule based on the order information, as follows:

[0045] Based on the passenger's order information at the first arrival station, calculate the bus departure time for that passenger. Then, the candidate route set obtained from the candidate route set module is traversed to check whether the passenger waiting time at subsequent stops on the route meets the condition that the average waiting time is less than a threshold. If not, the bus departure time is modified again. Next, the average route length for each passenger on each candidate route is calculated. Select the route with the shortest average route length per passenger from the candidate route set as the route for that trip;

[0046] Once a new bus schedule is determined, a bus needs to be matched. Priority is given to selecting from the available bus queues and deleting the vehicle from them. If the available bus queues are empty, a new bus is added and added to the existing bus queue.

[0047] Secondly, iterate through each bus in the existing bus queue. If a bus is scheduled to depart at its departure time, add it to the running bus queue. Then, iterate through each bus in the running bus queue. If a bus arrives at its destination, remove it from both the existing and running bus queues and add it to the available bus queue.

[0048] Repeat the above steps until the day's bus service passenger operations are completed, and you will get a bus dispatching and route planning solution.

[0049] Furthermore, the operation monitoring and management module includes a speed control module, an order dataset selection module, a dataset time dynamic display module, a bus dynamic service passenger demonstration module, and a station information dynamic update module;

[0050] The speed control module is used for the process of monitoring the dynamic passenger operation of the bus by controlling the speed of the bus.

[0051] The order dataset selection module is used by users to select a specific day from the order dataset to obtain the results of the algorithm execution module, thereby realizing the operation monitoring function module.

[0052] The dataset time dynamic display module is used to dynamically update and display the dataset during the operation monitoring process;

[0053] The bus dynamic service passenger demonstration module is used to display the entire process of shared bus service passenger orders, including the display of maps and station routes, dynamic updates of the bus coordinates on the front-end map, and the route of the driving dispatch plan to serve passengers at each station.

[0054] The station information dynamic update module is used to dynamically update the number of people waiting at each station in real time based on dynamically acquired order information and the bus dynamic service passenger process.

[0055] Furthermore, the bus information management module includes a module for dynamically acquiring bus information, a module for dynamically displaying bus operation status, and a module for dynamically updating the bus information list;

[0056] The dynamic bus information acquisition module is used by the system to dynamically acquire bus operation information during operation monitoring.

[0057] The dynamic bus operation status notification module is used to dynamically notify the bus of special operating statuses, such as departure and arrival at the terminal station.

[0058] The dynamic bus information list update module is used to dynamically update the bus information list in the system.

[0059] Furthermore, the data visualization module is used to display dynamic line graphs of the number of people waiting at stations, dynamic scatter plots of passenger waiting times, dynamic updates of key data information, drag-and-drop x-axis line graphs of order data, dynamic competition bar charts of order data, statistical bar charts of bus running time, and pie charts of the number of passengers served by buses.

[0060] The advantages and beneficial effects of this invention are as follows:

[0061] 1. Novel Functionality: The software system proposed in this invention enables bus companies to manage their orders through an order management module, greatly facilitating intelligent management of numerous and complex orders. The operation monitoring management module provides real-time dynamic visualization of the bus operation and passenger service process. The bus information management and data visualization modules intelligently display bus information and other important data to the bus company in the form of charts and graphs. The algorithm execution module implements bus scheduling and route planning management, truly resolving the contradiction between operating costs and passenger travel experience faced by shared bus companies from a practical perspective by providing bus scheduling and route planning solutions. This software system innovatively combines and implements dynamic bus scheduling and route planning solutions with order management and data visualization, providing a smart software management system for vehicle scheduling and route planning management of shared bus companies.

[0062] 2. Openness and stability: The software system proposed in this invention adopts the development mode of Spring Boot and Vue framework. Both have the characteristics of openness and stability, which can accelerate the development and expansion of the system. It has the advantages of simple and convenient maintenance and upgrade, low cost, data security, and real-time synchronization.

[0063] 3. Maintainability: The software system proposed in this invention is developed using a front-end and back-end separation approach. In terms of later maintenance and requirement expansion, this architecture is more developer-friendly, making it easy to add or modify functions.

[0064] 4. High efficiency: The software system proposed in this invention can efficiently acquire order data and obtain "bus scheduling and route planning" solutions in a short time. The system test shows that the average time to obtain the algorithm results "bus scheduling and route planning" on different dates of the dataset is 19.5s. Taking the operation monitoring process of a certain day as an example, the average time for the system to obtain orders is 404.83ms.

[0065] 5. Authenticity: The experimental dataset for this invention is based on a real dataset, ensuring the system's effectiveness is genuine and reliable. The dataset originates from order data collected by Panda Mobility from March 13, 2017 to September 9, 2017, for shared buses in Yongkang, Shanghai. The operational scenario involves routes between residential areas and subway stations, including nine shared bus stops. Hundreds of passengers ride the shared buses daily. The dataset includes passenger data and bus GPS data, containing nearly 50,000 ride records. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of the system functional module framework of a preferred embodiment of the present invention;

[0067] Figure 2 This is a schematic diagram of the system architecture of the present invention;

[0068] Figure 3 This is a diagram of the system operation monitoring interface of the present invention;

[0069] Figure 4 This is a schematic diagram of the order management module of the present invention;

[0070] Figure 5 This is a schematic diagram of the order management module of the present invention;

[0071] Figure 6 This is a schematic diagram of the operation monitoring and management module of the present invention;

[0072] Figure 7 This is a schematic diagram of the operation monitoring and management module of the present invention;

[0073] Figure 8 This is a schematic diagram of the user management module of the present invention;

[0074] Figure 9 This is a schematic diagram of the data visualization module of the present invention;

[0075] Figure 10 This is a schematic diagram of the data visualization module process of the present invention;

[0076] Figure 11 This is a schematic diagram of the bus information management module of the present invention;

[0077] Figure 12 This is a scatter plot of the time it takes for orders to be received on the same day according to the present invention.

[0078] Figure 13 A scatter plot of bus scheduling and route planning for different dates of this invention is obtained. Detailed Implementation

[0079] The technical solutions of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0080] The technical solution of the present invention to solve the above-mentioned technical problems is:

[0081] Example 1

[0082] This embodiment provides an intelligent transportation management system based on dynamic shared bus service scheduling. The system is based on a B / S architecture, including a browser and a server. The server contains an application server and a database server. The system runs on Windows 10, using Tomcat as the application server, MySQL as the database server, the Spring Boot server framework in Java (JDK 1.8), and Vue + ElementUI as the front-end framework. Login and access are via HTTP / HTTPS protocols to manage the intelligent transportation management system based on dynamic shared bus service scheduling.

[0083] like Figure 2 As shown, the architecture of the intelligent transportation management system based on dynamic shared bus service scheduling includes Spring MVC, Element UI, JavaScript, and HTML in the presentation layer; Spring IOC AOP, Spring Boot, and Transactional in the business logic layer; Spring Boot JPA and MyBatis-Plus in the data access layer; MySQL database in the data access layer; Flask framework in the algorithm implementation; and WebSocket and Axios in the TCP / IP data communication layer.

[0084] The following is a detailed explanation of some of the above arrangements:

[0085] 1) Spring Boot backend technology framework

[0086] Spring Boot is a new framework provided by the Pivotal team, designed to simplify the initial setup and development process of new Spring applications. This framework uses specific configuration methods, eliminating the need for developers to define boilerplate configurations. In this way, Spring Boot aims to become a leader in the rapidly growing field of rapid application development.

[0087] 2) Vue front-end technology framework

[0088] Vue is a progressive framework for building data-driven web interfaces. Vue.js aims to achieve reactive data binding and composable view components through the simplest possible API. It is not only easy to learn but also easy to integrate with third-party libraries or existing projects. Furthermore, Vue is fully capable of...

[0089] Provides power for complex single-page applications.

[0090] 3) JPA Standard

[0091] JPA stands for Java Persistence API, which uses JDK 5.0 annotations or XML to describe the mapping between objects and relational tables, and persists runtime entity objects to a database. Sun introduced the new JPA ORM specification for two reasons: first, to simplify the development of existing Java EE and Java SE applications; and second, Sun hoped to integrate ORM technologies and achieve a unified approach.

[0092] 4) Flask framework

[0093] Flask is a Python-based micro-framework for web development, built on Werkzeug and Jinja 2. Its advantages lie in its extreme simplicity, high flexibility, and ease of learning and application. Therefore, Flask is the best choice for Python beginners to quickly get started with web development. Furthermore, another benefit of using Flask is that you can easily integrate Python-based machine learning or data analysis algorithms into web applications.

[0094] 5) WebSocket

[0095] The WebSocket protocol was created in 2008 and became an international standard in 2011. All browsers now support it. Its biggest feature is that the server can proactively push information to the client, and the client can also proactively send information to the server, enabling truly two-way, equal dialogue. Server push technology One of them.

[0096] 6) Axios

[0097] Axios is a promise-based network request library that works in both Node.js and browsers. It is isomorphic (meaning the same code can run in both browsers and Node.js). On the server side, it uses the native Node.js http module, while on the client side (browser), it uses XMLHttpRequests.

[0098] The advantages of an intelligent transportation management system architecture based on dynamic shared bus service scheduling are summarized below:

[0099] 1) Improved system platform performance: Based on the Spring Boot and Vue frameworks, both of which have the characteristics of openness and stability, the system can accelerate development and expansion, and have the advantages of simple and convenient maintenance and upgrade, low cost, data security, and real-time synchronization.

[0100] 2) Maintainability: The software system proposed in this invention is developed using a front-end and back-end separation approach. In terms of later maintenance and requirement expansion, this architecture is more developer-friendly, making it easy to add or modify functions.

[0101] 3) Abandon the JBoss heavyweight service container and replace it with a lightweight service container (Tomcat);

[0102] 4) Project builds, dependency management (no longer using custom JAR extensions) and deployments are all managed by Maven.

[0103] 5) Use the Vue framework and Element UI to improve web compatibility and enhance the user interface's interactivity;

[0104] 6) It uses the Axios technology framework to provide a standardized and scalable communication channel protocol;

[0105] The following is a summary description of the functions of the intelligent transportation management system based on dynamic shared bus service scheduling:

[0106] like Figure 1 As shown, the intelligent transportation management system based on dynamic shared bus service scheduling includes: an order management module, an algorithm execution module, an operation monitoring and management module, a bus information management module, a data visualization module, and a user management module.

[0107] The system comprises several modules: an order management module for managing passenger orders (including dynamically acquiring and displaying order information, dynamically updating order status and lists, and providing CRUD operations); an algorithm execution module for analyzing and processing order data and combining it with the shared bus company's traffic topology to obtain route planning and vehicle dispatching information; an algorithm that can dispatch the fewest number of buses and travel the shortest routes to serve all passengers while ensuring passenger waiting time, and limiting bus seat utilization to guarantee passenger travel experience; an operations monitoring and management module for user control and display of the entire process of shared bus service for passenger orders; a bus information management module for displaying bus operation information after algorithm dispatching; a data visualization module for visualizing data such as stations, buses, and passenger orders in chart form; and a user management module for managing accounts, including an email login / registration module, an email password retrieval module, and a personal information modification module.

[0108] 1. Order Management Module

[0109] 1) Dynamic Order Information Acquisition Module: After the user selects the date of the order dataset and clicks the start button of the Operation Monitoring and Management module, the system backend dynamically and automatically acquires real-time order information.

[0110] 2) Dynamic New Order Notification Module: After the system obtains the order information, it will notify the user in the form of a pop-up message window.

[0111] 3) Dynamic order status update module: The system automatically identifies the order processing status and updates it accordingly. Orders have three statuses: processed, being processed, and unprocessed.

[0112] 4) Dynamically update order list module: The system dynamically updates the order list of different categories displayed during the operation monitoring process.

[0113] 5) View all order information module: Users can view all orders in the dataset.

[0114] 6) Modify order information module: Users can modify order information under the dataset.

[0115] 7) Delete order information module: Users can delete order information in the dataset.

[0116] 2. Algorithm Execution Module

[0117] 1) Traffic topology optimization module: Taking into account historical passenger flow, road segment length between stations, and historical passenger waiting time at stations, the traffic topology of real shared bus company stations is optimized to obtain a traffic topology suitable for shared bus operation.

[0118] 2) Candidate route set generation module: Based on the optimized traffic topology, the improved local search algorithm is used to solve the problem model and generate the candidate route set.

[0119] 3) Online algorithm module: solves the problems of shared bus scheduling and route planning in application scenarios with dynamic and real-time passenger flow.

[0120] 4) Date Selection and Algorithm Result Module: After the user selects a date from the dataset, the system backend calls the online algorithm to obtain the algorithm result – the "Bus Scheduling and Route Planning" scheme.

[0121] 3. Operation Monitoring and Management Module

[0122] 1) Speed ​​Control Module: The process of monitoring the dynamic service of buses for passengers by controlling the speed of the bus.

[0123] 2) Order Dataset Selection Module: Users can select a specific day from the order dataset to obtain the results of the algorithm execution module, thereby enabling the operation monitoring function module.

[0124] 3) Dataset Time Dynamic Display Module: Displays the dynamic time elapsed update of the data set during the operation monitoring process.

[0125] 4) Bus Dynamic Service Passenger Demonstration Module: Displays the entire process of passenger orders for shared bus services, including map and station route display, dynamic updates of bus coordinates on the front-end map, and route scheduling to serve passengers at each station.

[0126] 5) Station Information Dynamic Update Module: Based on dynamically acquired order information and the bus dynamic service passenger process, the number of people waiting at each station is dynamically updated in real time.

[0127] 4. Bus Information Management Module

[0128] 1) Dynamic bus information acquisition module: The system dynamically acquires bus operation information during operation monitoring.

[0129] 2) Dynamic bus operation status prompt module: The system dynamically prompts the special status of bus operation, such as departure and arrival at the terminal station.

[0130] 3) Dynamically update bus information list module: The system dynamically updates the bus information list.

[0131] 5. Data Visualization Module

[0132] 1) Dynamic line graph module for waiting number at stations: The system displays the number of people waiting at each station in the form of a dynamic line graph during the operation monitoring process.

[0133] 2) Passenger Waiting Time Dynamic Scatter Plot Module: The system displays the waiting time of each passenger in the form of a dynamic scatter plot during the operation monitoring process.

[0134] 3) Iconic Data Dynamic Update Module: The system displays the waiting time of each passenger in the form of a dynamic scatter plot during the operation monitoring process.

[0135] 4) Drag-and-drop x-axis line chart module for order data: The system displays order data from different sites for all dates in the form of a draggable x-axis line chart.

[0136] 5) Order Data Dynamic Competition Bar Chart Module: The system displays the number of orders from different sites for all dates in the form of a dynamic competition bar chart.

[0137] 6) Bus running time statistics bar chart module: The system displays the running time of each bus trip in the form of a bar chart after the operation monitoring ends.

[0138] 7) Bus service passenger count pie chart module: The system displays the number of passengers served by each bus trip in the form of a pie chart after the operation monitoring ends.

[0139] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0140] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0141] The above embodiments should be understood as illustrative only and not as limiting the scope of protection of the present invention. After reading the description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. An intelligent transportation management system based on dynamic shared bus service scheduling, characterized in that: It includes an order management module, an algorithm execution module, an operations monitoring and management module, a bus information management module, a data visualization module, and a user management module. The order management module is used to manage passenger orders, including dynamically acquiring orders, dynamically notifying new order information, dynamically updating order status and list, viewing all order information, modifying order information, and deleting order information; The algorithm execution module is used to analyze and process order data and combine it with the traffic topology of the shared bus company to obtain route planning and vehicle scheduling information for the shared buses. It optimizes the traffic topology, solves the candidate route set, and schedules the minimum number of buses and travels the shortest route to serve all passengers while ensuring the waiting time of passengers. It also limits the seat utilization rate of the buses to ensure the travel experience of passengers. The operation monitoring and management module is used by users to control and display the entire process of passenger orders for the shared bus service; The bus information management module is used to display bus operation information after algorithm scheduling; The data visualization module is used to visualize data information, including station, bus, and passenger orders, in the form of charts. The user management module is used to manage accounts, including an email login and registration module, an email password retrieval module, and a personal information modification module; The algorithm execution module includes a traffic topology optimization module, a candidate route set solving module, an online algorithm module, and a date selection and algorithm result acquisition module. The traffic topology optimization module is used to optimize the actual traffic topology of shared bus company stations by taking into account historical passenger flow, road segment length between stations, and historical passenger waiting time at stations, so as to obtain a traffic topology suitable for the operation of shared buses. The candidate route set solution module is used to solve the problem model based on the optimized traffic topology using an improved local search algorithm to generate a candidate route set. The online algorithm module is used to solve the shared bus scheduling and route planning problems in application scenarios with dynamic and real-time passenger flow. The "Select Date to Obtain Algorithm Result" module is used by the system backend to call the online algorithm to obtain the algorithm result—the "Bus Scheduling and Route Planning" scheme—after the user selects a date from the dataset. The traffic topology optimization module is used to optimize the actual traffic topology of shared bus company stations, taking into account historical passenger flow, road segment length between stations, and historical passenger waiting time at stations, to obtain a traffic topology suitable for shared bus operation. This includes the following steps: The shared bus transportation network is extracted into a directed network graph based on the actual traffic conditions; After obtaining the directed network graph, if there are situations in the directed graph where buses cannot directly reach two stations under real traffic conditions, or where the route is too long and can be replaced by other routes, then the edges between the corresponding stations in the directed graph will be deleted. Based on the processed directed graph, the maximum flow of the time network and the maximum flow of the passenger network are calculated using the historical order dataset and the Ford-Fulkerson algorithm. Then, the minimum values ​​of the time network flow and the passenger network flow are determined, where the time network flow T... f,e and passenger network flow P f,e Edges whose values ​​are both below the minimum of the time network flow and the passenger network flow are unimportant in the traffic route, and the corresponding edges in the directed graph will be deleted; where the time network flow T f,e and passenger network flow P f,e The definition is as follows: e in the formula<u,v> d It is the length of the road segment between station u and station v. γ and ζ are obtained using the entropy method based on historical order datasets. This represents the average waiting time for passengers at this station in the historical order dataset. The maximum waiting time for passengers at this station in the historical order dataset. It represents the average number of passengers boarding at this station each day in the historical order dataset; The improved depth-first search algorithm is used to obtain the loops in the directed graph processed in the previous step. The loops are traversed one by one, and the edges corresponding to the nodes with the largest out-degree or in-degree in the loops without origin stations are deleted to complete the loop removal operation. Finally, the optimized traffic topology suitable for bus operation is obtained. The candidate route set solution module is used to solve the problem model based on the optimized traffic topology using an improved local search algorithm to generate a candidate route set. The problem model is to minimize the number of shared buses and the average route length per passenger, and it is defined as follows: |P in the formula v (Φ) represents the number of passengers picked up by the shared bus at station v. p The total number of passengers using shared bus services, δ uv Φ is a binary variable representing whether an edge exists between stations u and v in a directed graph. D Given the line length, minimize the line length Φ. D And maximize the total number of passengers for the shared bus service Φ p Minimize the average line length Φ per passenger DP It is an equivalence relation; the constraints are: N B ≥1, Ensure that at least one shared bus is operating to serve passengers; δ uv ∈{0,1}; u∈φ∧u≠J, v∈φ∧v≠J, ensuring that the in-degree and out-degree of each station in the line are both 1; Φ D ≤D max , Ensure that the line length does not exceed its maximum value D. max ; Ensure that there are no loops in the line; L f ∈[0,+∞), ensuring that the seat utilization rate of the bus does not exceed the upper limit. Ensure that the average waiting time for passengers does not exceed the threshold. Based on the problem model, an improved local search algorithm is used to obtain the candidate route set. The algorithm solution steps are as follows: First, select a stop for the bus that first arrives at the station for route planning. There are three selection methods: ① Based on a historical experience database storing information on the best stop selections, select the next best stop using an experience-learning mechanism; ② Based on a certain probability, randomly select the next stop from the candidate stops using a random selection mechanism; ③ Based on a penalty function for a particular stop. The definition is to select the site with the least penalty as the best next site; Secondly, in terms of vehicle scheduling, there are operating bus queues and available bus queues. If the average waiting time for passengers at a certain station exceeds a threshold, an additional bus needs to be added, and the additional bus should be selected first from the available bus queue. If a bus reaches its final destination, it will be removed from the operating bus queue and added to the available bus queue, and the bus's route and departure time will be saved. If the seat utilization rate of a bus reaches a threshold, it will be set so that the bus cannot serve passengers on subsequent routes.

2. The intelligent transportation management system based on dynamic shared bus service scheduling according to claim 1, characterized in that, The architecture of the intelligent transportation management system is based on Spring Boot 2.x + Vue 2.x, combined with the Flask framework implemented in Python. Java technology is used to implement backend data processing, condition selection and provide interface services, Vue is used to implement frontend bus and order data display and API call functions, and Python technology is used to implement the algorithm execution module.

3. The intelligent transportation management system based on dynamic shared bus service scheduling according to claim 1, characterized in that, The order management module includes a module for dynamically acquiring order information, a module for dynamically notifying new order messages, a module for dynamically updating order status, a module for dynamically updating the order list, a module for viewing all order information, a module for modifying order information, and a module for deleting order information. The dynamic order information acquisition module is used when the user selects the date of the order dataset and clicks the start button of the operation monitoring and management module, and the system backend dynamically and automatically acquires real-time order information. The dynamic new order notification module is used to notify the system of order information in the form of a pop-up message window. The dynamic order status update module is used by the system to automatically identify and update the order processing status. Orders have three statuses: processed, being processed, and unprocessed. The dynamically updated order list module is used by the system to dynamically update the different categories of order lists displayed during the operation monitoring process; The "View All Order Information" module is used by the system to display all order information under the current dataset to the user. The order information modification module is used by users to modify the information of a specific order in the current dataset. The "Delete Order Information" module is used by users to delete a specific order information record in the current dataset.

4. The intelligent transportation management system based on dynamic shared bus service scheduling according to claim 1, characterized in that, The online algorithm module is used to solve the shared bus scheduling and route planning problems in application scenarios with dynamic and real-time passenger flow. In terms of vehicle dispatching, there are operating bus queues, available bus queues, and existing bus queues. Existing bus queues include operating bus queues and bus queues with confirmed departure times and routes but which have not yet departed. First, iterate through the current order dataset and the information of every bus in the existing bus queue. If the bus departure time meets the waiting time requirement of the earliest arriving passenger in the order dataset, assign the passenger corresponding to that order to that bus; otherwise, determine a new bus schedule based on the order information, as follows: Based on the passenger's order information at the first arrival station, calculate the bus departure time for that passenger. Then, the candidate route set obtained from the candidate route set module is traversed to check whether the passenger waiting time at subsequent stops on the route meets the condition that the average waiting time is less than a threshold. If not, the bus departure time is modified again. Next, the average route length for each passenger on each candidate route is calculated. Select the route with the shortest average route length per passenger from the candidate route set as the route for that trip; Once a new bus schedule is determined, a bus needs to be matched. Priority is given to selecting from the available bus queues and deleting the vehicle from them. If the available bus queues are empty, a new bus is added and added to the existing bus queue. Secondly, iterate through each bus in the existing bus queue. If a bus is scheduled to depart at its departure time, add it to the running bus queue. Then, iterate through each bus in the running bus queue. If a bus arrives at its destination, remove it from both the existing and running bus queues and add it to the available bus queue. Repeat the above steps until the day's bus service passenger operations are completed, and you will get a bus dispatching and route planning solution.

5. The intelligent transportation management system based on dynamic shared bus service scheduling according to claim 1, characterized in that, The operation monitoring and management module includes a speed control module, an order dataset selection module, a dataset time dynamic display module, a bus dynamic service passenger demonstration module, and a station information dynamic update module. The speed control module is used for the process of monitoring the dynamic passenger operation of the bus by controlling the speed of the bus. The order dataset selection module is used by users to select a specific day from the order dataset to obtain the results of the algorithm execution module, thereby realizing the operation monitoring function module. The dataset time dynamic display module is used to dynamically update and display the dataset during the operation monitoring process; The bus dynamic service passenger demonstration module is used to display the entire process of shared bus service passenger orders, including the display of maps and station routes, dynamic updates of the bus coordinates on the front-end map, and the route of the driving dispatch plan to serve passengers at each station. The station information dynamic update module is used to dynamically update the number of people waiting at each station in real time based on dynamically acquired order information and the bus dynamic service passenger process.

6. The intelligent transportation management system based on dynamic shared bus service scheduling according to claim 1, characterized in that, The bus information management module includes a module for dynamically acquiring bus information, a module for dynamically displaying bus operation status, and a module for dynamically updating the bus information list. The dynamic bus information acquisition module is used by the system to dynamically acquire bus operation information during operation monitoring. The dynamic bus operation status notification module is used to dynamically notify the bus of special operating statuses, such as departure and arrival at the terminal station. The dynamic bus information list update module is used to dynamically update the bus information list in the system.

7. The intelligent transportation management system based on dynamic shared bus service scheduling according to claim 1, characterized in that, The data visualization module is used to display dynamic line graphs of waiting numbers at stations, dynamic scatter plots of passenger waiting times, dynamic updates of key data information, drag-and-drop x-axis line graphs of order data, dynamic competition bar charts of order data, statistical bar charts of bus running times, and pie charts of the number of passengers served by buses.

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