A digital twin smart city driverless microbus multi-line balanced scheduling method and system and medium
By using a digital twin-based smart city autonomous microbus multi-route balanced scheduling method, the capacity of bus routes is monitored and optimized in real time. A greedy algorithm is used for vehicle scheduling between routes, which solves the problem of capacity imbalance on multiple routes and improves the overall efficiency of the public transportation system and passenger service satisfaction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DONGFENG MOTOR GRP
- Filing Date
- 2023-08-17
- Publication Date
- 2026-07-03
AI Technical Summary
In existing technologies, the imbalance of capacity on multiple bus routes leads to a decline in bus utilization and passenger satisfaction with travel services. The lack of dynamic scheduling strategies when the capacity of multiple routes is out of balance with passenger demand results in low overall efficiency of the public transportation system.
By using a digital twin-based smart city autonomous microbus multi-route balanced scheduling method, an initial driving plan is generated based on historical passenger flow data. The capacity matching degree is monitored in real time, and a greedy algorithm is used to schedule capacity between routes. This enables vehicle scheduling between routes with excess capacity and routes with insufficient capacity, thereby optimizing bus route design and vehicle operation paths.
This has achieved a balanced capacity across multiple bus routes, improved the efficiency of public transportation and optimized passenger travel needs, thereby enhancing the overall efficiency of the public transportation system and passenger service satisfaction.
Smart Images
Figure CN116978211B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of unmanned bus technology, and in particular to a method, system, and medium for balanced scheduling of multi-route unmanned micro-buses in a digital twin smart city. Background Technology
[0002] In actual bus operations, imbalances in capacity across multiple routes are inevitable, negatively impacting bus utilization and passenger satisfaction. Therefore, dynamic and flexible scheduling among multiple bus routes can bring greater economic benefits to bus operators.
[0003] In actual public transport operations, imbalances in capacity across multiple routes are inevitable, negatively impacting bus utilization and passenger satisfaction. The autonomous bus multi-route balanced scheduling algorithm assesses the balance indicators of all operating routes in real time. These indicators characterize the matching degree between passenger flow and transport volume. It comprehensively considers route balance indicators, the status of empty buses already dispatched, and the transfer costs of empty buses between different routes. By transferring empty buses from routes with surplus capacity to routes with insufficient capacity through route exchange points, the algorithm maintains a balanced capacity across the multi-route system, thereby improving the overall efficiency of the multi-route public transport operation system. Based on dynamic vehicle scheduling strategies between routes, a feasible solution is proposed for flexible, demand-balanced scheduling of multiple routes.
[0004] However, most studies focus on the static scheduling problem of multiple routes, while there is less research on dynamic scheduling based on the balance of capacity between routes. There is a lack of variable route dispatch strategies for buses when there is an imbalance between capacity and passenger demand on multiple routes. Most existing multi-route bus scheduling algorithms are optimized for individual bus routes or vehicles, lacking global optimization and collaborative scheduling, resulting in low overall efficiency of the bus system. Summary of the Invention
[0005] In view of the above problems, the present invention provides a digital twin smart city unmanned micro-bus multi-route balanced scheduling method, system and medium, which not only solves the current situation of unbalanced capacity of multiple routes, which will have a negative impact on bus vehicle utilization and passenger travel service satisfaction, but also maximizes the efficiency of public transportation and optimizes the public's travel needs.
[0006] To achieve the above and other related objectives, the present invention provides the following technical solution: a method for balanced scheduling of multiple routes of driverless microbuses in a digital twin smart city, the method comprising:
[0007] Z1. Based on historical passenger flow data, an initial driving plan for each route is generated and sent to the bus terminal. Buses on each route depart and run according to the initial driving plan.
[0008] Z2. Based on the status of operating vehicles and bus stops, each route collects real-time statistics on arriving passenger flow and vehicle passenger load data, and uses a multi-route capacity balance index algorithm to process the data and output multi-route capacity balance index data.
[0009] Z3. Based on the multi-line capacity balance index data, determine whether there is an imbalance in capacity between lines. If there is no imbalance, continue to run according to the current train schedule and execute step Z2. If there is an imbalance, sort and classify each line according to the line capacity matching degree, distinguish between lines with excess capacity, normal capacity and insufficient capacity, and obtain m lines with insufficient capacity and n lines with excess capacity.
[0010] Z4. Based on the m routes with insufficient capacity and n routes with excess capacity, a greedy algorithm is used to schedule the microbuses and output multi-route microbus balanced scheduling data information.
[0011] Furthermore, in step Z2, the multi-line capacity balancing index algorithm includes:
[0012] Z21. During the operation of each minibus route, the capacity matching degree of each minibus route is monitored in real time, and a capacity matching degree function ρ is established.
[0013] ,
[0014] Wherein, ρ(l i )∈[0,1],S i For line l i In the number of operating vehicles, η j To obtain the real-time load factor of vehicle j, and thus output the capacity matching data information for each route;
[0015] Z22. Based on the capacity matching data of each route, establish a multi-route capacity balancing index function. ,
[0016] ,
[0017] Where |L| represents the total number of bus routes. This represents the average capacity matching degree.
[0018] Z23. Based on the aforementioned multi-line capacity balancing index function It outputs data on the capacity balance index of multiple routes.
[0019] Furthermore, the average capacity matching degree ,
[0020] ,
[0021] Where |L| represents the total number of bus routes.
[0022] Furthermore, in step Z3, the condition for sorting and classifying the routes according to their capacity matching degree if there is an imbalance, and distinguishing between routes with excess capacity, normal capacity, and insufficient capacity, is as follows:
[0023] ,
[0024] Among them, l i Let L be the i-th bus route. sur For the excess capacity set, L ins To address the insufficient transportation capacity, L nor For normal capacity set, ρ(l) i )∈[0,1], This is a multi-line capacity balancing index function.
[0025] Furthermore, if L sur ≠Ø and L ins If ≠Ø, then both idle and busy lines exist simultaneously.
[0026] Furthermore, if L sur =Ø and L ins =Ø, then the microbus routes can achieve balanced scheduling.
[0027] Furthermore, in step Z4, the scheduling of the microbuses using a greedy algorithm includes:
[0028] Z41. Based on the m routes with insufficient capacity, obtain the microbus set M of the routes with insufficient capacity, and based on the n routes with excess capacity, obtain the microbus set N of the routes with excess capacity.
[0029] Z42. Based on the microbus data set M of the routes with insufficient capacity and the microbus data set N of the routes with excess capacity, establish a balanced scheduling function G.
[0030] ,in, Defined as a transfer operation, it dispatches microbuses from routes with excess capacity to routes with insufficient capacity.
[0031] Z43. Based on the balanced scheduling function G, output the balanced scheduling data information of multi-line microbuses.
[0032] Furthermore, the balanced scheduling function G will retrieve vehicles from idle lines that meet the constraints of being empty and having zero passengers at subsequent stations, and select the vehicle with the lowest cross-line empty running cost for cross-line scheduling.
[0033] To achieve the above and other related objectives, the present invention also provides a digital twin smart city driverless microbus multi-route balanced scheduling system, including a computer device programmed or configured to perform the steps of the digital twin smart city driverless microbus multi-route balanced scheduling method.
[0034] To achieve the above and other related objectives, the present invention also provides a computer-readable storage medium storing a computer program programmed or configured to perform the digital twin smart city driverless microbus multi-route balanced scheduling method.
[0035] The present invention has the following positive effects:
[0036] 1. This invention provides real-time evaluation of the balance index of all operating routes. This index can characterize the matching degree of passenger flow and transport volume. It comprehensively considers the route balance index, the empty vehicle status of the route, and the transfer cost of empty vehicles between different routes. It transfers empty vehicles on routes with surplus capacity to routes with shortage capacity through route exchange points, so as to maintain the capacity balance of the multi-route system and improve the overall efficiency of the multi-route bus operation system. Based on the dynamic vehicle scheduling strategy between routes, it proposes a feasible solution for flexible scheduling of demand balance of multiple routes.
[0037] 2. This invention comprehensively considers the initial bus route plan, the status of operating vehicles and bus stops, real-time statistics of arriving passenger flow and vehicle passenger load, calculation of indicators such as route capacity balance, and inter-route capacity, to determine the bus route, thus achieving optimized bus route design and real-time vehicle scheduling. This maximizes public transportation efficiency and optimizes public travel needs.
[0038] 3. This invention analyzes and processes big data from urban public transport systems to optimize them, analyzes and processes operational data of public transport vehicles to achieve optimized vehicle scheduling based on traffic big data, and realizes efficient operation and intelligent management of driverless public transport systems. Attached Figure Description
[0039] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation
[0040] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.
[0041] Example 1: As Figure 1 As shown, a method for balanced scheduling of multiple routes of driverless microbuses in a digital twin smart city is described, the method comprising:
[0042] Z1. Based on historical passenger flow data, an initial driving plan for each route is generated and sent to the bus terminal. Buses on each route depart and run according to the initial driving plan.
[0043] Z2. Based on the status of operating vehicles and bus stops, each route collects real-time statistics on arriving passenger flow and vehicle passenger load data, and uses a multi-route capacity balance index algorithm to process the data and output multi-route capacity balance index data.
[0044] Z3. Based on the multi-line capacity balance index data, determine whether there is an imbalance in capacity between lines. If there is no imbalance, continue to run according to the current train schedule and execute step Z2. If there is an imbalance, sort and classify each line according to the line capacity matching degree, distinguish between lines with excess capacity, normal capacity and insufficient capacity, and obtain m lines with insufficient capacity and n lines with excess capacity.
[0045] Z4. Based on the m routes with insufficient capacity and n routes with excess capacity, a greedy algorithm is used to schedule the microbuses and output multi-route microbus balanced scheduling data information.
[0046] In this embodiment, in step Z2, the multi-line capacity balancing index algorithm includes:
[0047] Z21. During the operation of each minibus route, the capacity matching degree of each minibus route is monitored in real time, and a capacity matching degree function ρ is established.
[0048] ,
[0049] Wherein, ρ(l i )∈[0,1],S i For line l i In the number of operating vehicles, η j To obtain the real-time load factor of vehicle j, and thus output the capacity matching data information for each route;
[0050] Z22. Based on the capacity matching data of each route, establish a multi-route capacity balancing index function. ,
[0051] ,
[0052] Where |L| represents the total number of bus routes. This represents the average capacity matching degree.
[0053] Z23. Based on the aforementioned multi-line capacity balancing index function It outputs data on the capacity balance index of multiple routes.
[0054] In this embodiment, the average capacity matching degree ,
[0055] ,
[0056] Where |L| represents the total number of bus routes.
[0057] In this embodiment, in step Z3, the condition for sorting and classifying the lines according to their capacity matching degree if there is an imbalance, and distinguishing between lines with excess, normal, and insufficient capacity, is as follows:
[0058] ,
[0059] Among them, l i Let L be the i-th bus route. sur For the excess capacity set, L ins To address the insufficient transportation capacity, L nor For normal capacity set, ρ(l) i )∈[0,1], This is a multi-line capacity balancing index function.
[0060] In this embodiment, if L sur ≠Ø and L ins If ≠Ø, then both idle and busy lines exist simultaneously.
[0061] In this embodiment, if L sur =Ø and L ins =Ø, then the microbus routes can achieve balanced scheduling.
[0062] Example 2: Based on the digital twin smart city driverless microbus multi-route balanced scheduling method in Example 1, the present invention will be further explained and described below.
[0063] A method for balanced scheduling of multiple routes of driverless microbuses in a digital twin smart city, the method comprising:
[0064] Z1. Based on historical passenger flow data, an initial driving plan for each route is generated and sent to the bus terminal. Buses on each route depart and run according to the initial driving plan.
[0065] Z2. Based on the status of operating vehicles and bus stops, each route collects real-time statistics on arriving passenger flow and vehicle passenger load data, and uses a multi-route capacity balance index algorithm to process the data and output multi-route capacity balance index data.
[0066] Z3. Based on the multi-line capacity balance index data, determine whether there is an imbalance in capacity between lines. If there is no imbalance, continue to run according to the current train schedule and execute step Z2. If there is an imbalance, sort and classify each line according to the line capacity matching degree, distinguish between lines with excess capacity, normal capacity and insufficient capacity, and obtain m lines with insufficient capacity and n lines with excess capacity.
[0067] Z4. Based on the m routes with insufficient capacity and n routes with excess capacity, a greedy algorithm is used to schedule the microbuses and output multi-route microbus balanced scheduling data information.
[0068] In this embodiment, step Z4, the scheduling of the microbus using a greedy algorithm, includes:
[0069] Z41. Based on the m routes with insufficient capacity, obtain the microbus set M of the routes with insufficient capacity, and based on the n routes with excess capacity, obtain the microbus set N of the routes with excess capacity.
[0070] Z42. Based on the microbus data set M of the routes with insufficient capacity and the microbus data set N of the routes with excess capacity, establish a balanced scheduling function G.
[0071] ,in, Defined as a transfer operation, it dispatches microbuses from routes with excess capacity to routes with insufficient capacity.
[0072] Z43. Based on the balanced scheduling function G, output the balanced scheduling data information of multi-line microbuses.
[0073] In this embodiment, the balanced scheduling function G will retrieve vehicles from the idle lines that meet the constraints of being empty and having zero passengers at subsequent stations, and select the vehicle with the lowest cross-line empty running cost for cross-line scheduling.
[0074] This invention provides a digital twin smart city autonomous microbus multi-route balanced scheduling system, including a computer device that is programmed or configured to execute the steps of the digital twin smart city autonomous microbus multi-route balanced scheduling method.
[0075] The present invention provides a computer-readable storage medium storing a computer program programmed or configured to perform the digital twin smart city driverless microbus multi-route balanced scheduling method.
[0076] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.
[0077] In summary, this invention not only solves the problem of unbalanced capacity on multiple routes, which negatively impacts bus utilization and passenger satisfaction, but also maximizes public transportation efficiency and optimizes public travel needs.
[0078] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.
Claims
1. A method for balanced scheduling of multiple routes of driverless microbuses in a digital twin smart city, characterized in that: The method includes: Z1. Based on historical passenger flow data, an initial driving plan for each route is generated and sent to the bus terminal. Buses on each route depart and run according to the initial driving plan. Z2. Based on the status of operating vehicles and bus stops, each route collects real-time statistics on arriving passenger flow and vehicle passenger load data, and uses a multi-route capacity balance index algorithm to process the data and output multi-route capacity balance index data. Z3. Based on the multi-line capacity balance index data, determine whether there is an imbalance in capacity between lines. If there is no imbalance, continue to run according to the current train schedule and execute step Z2. If there is an imbalance, sort and classify each line according to the line capacity matching degree, distinguish between lines with excess capacity, normal capacity and insufficient capacity, and obtain m lines with insufficient capacity and n lines with excess capacity. Z4. Based on the m routes with insufficient capacity and n routes with excess capacity, a greedy algorithm is used to schedule the microbuses and output multi-route microbus balanced scheduling data information. In step Z2, the multi-line capacity balancing index algorithm includes: Z21. During the operation of each minibus route, the capacity matching degree of each minibus route is monitored in real time, and a capacity matching degree function ρ is established. ， Wherein, ρ(l i )∈[0,1],S i For line l i In the number of operating vehicles, η j To obtain the real-time load factor of vehicle j, and thus output the capacity matching data information for each route; Z22. Based on the capacity matching data of each route, establish a multi-route capacity balancing index function. , ， Where |L| represents the total number of bus routes. This represents the average capacity matching degree. Z23. Based on the aforementioned multi-line capacity balancing index function It outputs data on the balance of capacity across multiple routes.
2. The digital twin smart city unmanned micro-bus multi-route balanced scheduling method according to claim 1, characterized in that, The average capacity matching degree , ， Where |L| represents the total number of bus routes.
3. The digital twin smart city unmanned micro-bus multi-route balanced scheduling method according to claim 1, characterized in that, In step Z3, if there is an imbalance, the routes are sorted and classified according to their capacity matching degree. The criteria for distinguishing between routes with excess capacity, normal capacity, and insufficient capacity are as follows: ， Among them, l i Let L be the i-th bus route. sur For the excess capacity set, L ins To address the insufficient transportation capacity, L nor For normal capacity set, ρ(l) i )∈[0,1], This is a multi-line capacity balancing index function.
4. The digital twin smart city unmanned micro-bus multi-route balanced scheduling method according to claim 3, characterized in that: If L sur ≠Ø and L ins If ≠Ø, then both idle and busy lines exist simultaneously.
5. The digital twin smart city unmanned microbus multi-route balanced scheduling method according to claim 3, characterized in that: If L sur =Ø and L ins =Ø, then the microbus routes can achieve balanced scheduling.
6. The digital twin smart city unmanned micro-bus multi-route balanced scheduling method according to claim 1, characterized in that, In step Z4, the scheduling of the microbuses using a greedy algorithm includes: Z41. Based on the m routes with insufficient capacity, obtain the microbus set M of the routes with insufficient capacity, and based on the n routes with excess capacity, obtain the microbus set N of the routes with excess capacity. Z42. Based on the microbus data set M of the routes with insufficient capacity and the microbus data set N of the routes with excess capacity, establish a balanced scheduling function G. , in, Defined as a transfer operation, it dispatches microbuses from routes with excess capacity to routes with insufficient capacity. Z43. Based on the balanced scheduling function G, output the balanced scheduling data information of multi-line microbuses.
7. The digital twin smart city unmanned microbus multi-route balanced scheduling method according to claim 6, characterized in that: The balanced scheduling function G will retrieve vehicles from idle lines that meet the constraints of being empty and having zero passengers at subsequent stations, and select the vehicle with the lowest cross-line empty running cost for cross-line scheduling.
8. A digital twin smart city driverless microbus multi-route balanced dispatching system, comprising computer equipment, characterized in that, The computer device is programmed or configured to perform the steps of the digital twin smart city driverless microbus multi-route balanced scheduling method according to any one of claims 1 to 7.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is programmed or configured to perform the digital twin smart city driverless microbus multi-route balanced scheduling method according to any one of claims 1 to 7.