Unmanned bus customization method and system based on fixed route, and storage medium

Abstract

The invention relates to an unmanned bus customization method and system based on a fixed route and a storage medium, and the method comprises the steps: U1, obtaining the data information of the parameters of the work tasks of a plurality of groups of unmanned buses, the data information of the traffic state of a road, and the data information of the riding order of a user in real time on a preset fixed route; and U2, on the basis of the data information of the parameters of the work tasks of the multiple groups of unmanned buses, the data information of the traffic state of the road and the data information of the riding order of the user, constructing an unmanned bus user demand model based on a Spearman correlation coefficient, and representing the optimal matching information of the parameters of the unmanned bus work task and the riding order of the user. According to the invention, the real-time data processing platform automatically carries out order processing, path planning, vehicle distribution and scheduling connection, more efficient operation is realized, manual drivers are reduced or eliminated, and the labor cost is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned public transportation, and in particular to an unmanned public transportation customization method, system and storage medium based on a fixed route. Background Art

[0002] In the process of urbanization, traditional customized buses, as a supplementary mode of transportation, have a certain market demand, especially in some emerging cities or areas with inconvenient transportation. Compared with conventional buses, customized buses are characterized by their flexibility and customization. Passengers can choose different boarding and alighting points, departure times, etc. according to actual needs, and vehicles are usually arranged according to demand. However, there are also problems such as high operating costs and low service efficiency. Unmanned customized buses are a public transportation mode based on unmanned driving technology and providing personalized and flexible travel services through intelligent algorithms. Unlike traditional fixed-route buses, unmanned customized buses can automatically plan the optimal route according to user needs and real-time traffic conditions, and realize point-to-point personalized pick-up and drop-off. Usually, passengers make reservations in advance through mobile APP or other platforms, and the system will automatically match the nearest passengers and dynamically adjust the route and number of trips according to demand.

[0003] The dispatch and operation of vehicles rely on manual decision-making and real-time adjustments. Although passengers can make reservations, the flexibility and convenience are relatively low, and efficiency problems may occur during high demand. Since the vehicles need to be operated by manual drivers, labor costs are an important expense in traditional customized bus operations. Operators also need to consider issues such as driver recruitment, training, management, and rest. Drivers also need to respond to various complex traffic conditions on the road in real time, such as traffic congestion, bad weather, and emergencies. Drivers have the ability to make empirical judgments and respond to emergencies, but they may also be affected by fatigue, misjudgment, etc. In a complex traffic environment, the risk of traffic accidents is high. Summary of the invention

[0004] In view of the above problems, the present invention provides a method, system and storage medium for customizing unmanned buses based on fixed routes. Not only does the real-time data processing platform automatically perform order processing, route planning, vehicle allocation, and scheduling and docking to achieve more efficient operation, but it also reduces or eliminates manual drivers, greatly reducing labor costs.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows:

[0006] A method for customizing an unmanned bus based on a fixed route, the method comprising:

[0007] U1. On a preset fixed route, real-time acquisition of data information on the parameters of multiple groups of unmanned bus work tasks, data information on road traffic conditions, and data information on user ride orders;

[0008] U2. Based on the data information of the parameters of the multiple groups of unmanned bus work tasks, the data information of the traffic status of the roads and the data information of the user's ride order, an unmanned bus user demand model based on the Spearman correlation coefficient is constructed, and the best matching information of the parameters of the unmanned bus work tasks and the user's ride order is characterized to obtain the data information of the parameters of the unmanned bus work tasks that best matches the user's ride order;

[0009] U3. Based on the data information of the parameters of the unmanned bus task that best matches the user's ride order, the parameters of the unmanned bus task are optimized using the improved Archimedean optimization algorithm of the sine-cosine algorithm to obtain the data information of the optimized parameters of the unmanned bus task;

[0010] U4. Based on the data information of the parameters of the optimized unmanned bus work task, a satisfaction function F of the unmanned bus customization is established, the satisfaction of the unmanned bus customization is evaluated, and the data information of the satisfaction of the unmanned bus customization is obtained.

[0011] Furthermore, the method further comprises:

[0012] U5. Based on the data information of the satisfaction with the customization of the unmanned bus, a preset threshold is set. If the satisfaction with the customization of the unmanned bus is less than the preset threshold, return to step U3. If the satisfaction with the customization of the unmanned bus is greater than the preset threshold, output the data information of the parameters of the optimized unmanned bus work task.

[0013] Furthermore, the satisfaction function F of the customized unmanned bus is:

[0014]

[0015] Among them, x is the data information of the optimized parameters of the unmanned bus work task, and α1, α2 and α3 are the satisfaction reconciliation factors of the unmanned bus work task.

[0016] Furthermore, the satisfaction reconciliation factors α1, α2 and α3 of the unmanned bus task are:

[0017]

[0018] Among them, x is the data information of the parameters of the optimized unmanned bus work task, and n is a positive integer.

[0019] Furthermore, in step U2, the unmanned bus user demand model based on the Spearman correlation coefficient is constructed to characterize the best matching information between the parameters of the unmanned bus work task and the user's ride order, including:

[0020] U21. Based on the data information of the parameters of the working tasks of the multiple groups of unmanned buses, the data information of the traffic status of the roads and the data information of the ride orders of the users, a Spearman correlation function Q between the working tasks of the unmanned buses and the ride orders of the users is established.

[0021]

[0022] Among them, y1 is the data information of the parameters of multiple groups of unmanned bus work tasks, y2 is the data information of the traffic status of the road, y3 is the data information of the user's ride order, β1, β2 and β3 are weight coefficients, and the Spearman correlation between the unmanned bus work tasks and the user's ride order is characterized to obtain the data information of the Spearman correlation between the unmanned bus work tasks and the user's ride order;

[0023] U22. Based on the Spearman correlation data of the unmanned bus work task and the user's ride order, a matching function W between the parameters of the unmanned bus work task and the user's ride order is established.

[0024]

[0025] Among them, z is the data information of the Spearman correlation between the work tasks of the unmanned bus and the user's ride order, δ1, δ2 and δ3 are the matching factors between the work tasks of the unmanned bus and the user's ride order;

[0026] U23. Based on the matching function W between the parameters of the unmanned bus work task and the user's ride order, the best matching information between the parameters of the unmanned bus work task and the user's ride order is characterized to obtain data information of the parameters of the unmanned bus work task that best matches the user's ride order.

[0027] Furthermore, the constraints of the matching factors δ1, δ2 and δ3 between the work tasks of the unmanned bus and the user's ride order are:

[0028]

[0029] The constraint function f of the weight coefficients β1, β2 and β3 is,

[0030]

[0031] The value range of the constraint function f is (1,2).

[0032] Furthermore, in step U3, the improved Archimedean optimization algorithm using the sine-cosine algorithm is used to optimize the parameters of the unmanned bus task, including:

[0033] U31. Based on the data information of the parameters of the unmanned bus task that best matches the user's ride order, the Archimedean population is initialized, the population parameters and the maximum number of iterations L are determined, and the data information of the initialized Archimedean population is obtained;

[0034] U32. Based on the data information of the initialized Archimedean population, establish the fitness function R of the population individuals based on the sine factor,

[0035]

[0036] Among them, r is the data information of the initialized Archimedes population, γ1, γ2 and γ3 are the sine factors of the individuals in the Archimedes population, and the fitness values ​​of the individuals in the Archimedes population are calculated to obtain the data information of the fitness values ​​of the individuals in the population;

[0037] U33. Based on the data information of the fitness values ​​of the individuals in the population, establish the target optimization function S of the population,

[0038]

[0039] Among them, h is the data information of the fitness value of the population individual, η1, η2 and η3 are the cosine factors of population optimization, and the parameters of the unmanned bus work task are optimized to obtain the data information of the optimized parameters of the unmanned bus work task.

[0040] Furthermore, the cosine factors η1, η2 and η3 of the population optimization are:

[0041]

[0042] The sinusoidal factors γ1, γ2 and γ3 of the individuals in the Archimedean population are,

[0043]

[0044] Among them, r is the data information of the initialized Archimedean population, and h is the data information of the fitness value of the individuals in the population.

[0045] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides an unmanned bus customization system based on a fixed route, including a computer device, which is programmed or configured to execute any one of the steps of the unmanned bus customization method based on a fixed route.

[0046] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the fixed-route unmanned bus customization methods.

[0047] The present invention has the following positive effects:

[0048] 1. The present invention constructs an unmanned bus user demand model based on the Spearman correlation coefficient, characterizes the best matching information between the parameters of the unmanned bus work tasks and the user's ride order, and optimizes the parameters of the unmanned bus work tasks in combination with the improved Archimedean optimization algorithm using the sine-cosine algorithm. Not only does the real-time data processing platform automatically perform order processing, path planning, vehicle allocation, and scheduling and docking to achieve more efficient operation, but it also reduces or eliminates manual drivers, greatly reducing labor costs.

[0049] 2. The present invention evaluates the satisfaction of customized unmanned buses by establishing a satisfaction function F for customized unmanned buses. It is not only based on the autonomous driving technology and the intelligent dispatching platform, but also intelligently dispatches according to real-time traffic conditions, passenger needs and road conditions to improve the overall traffic efficiency and provide passengers with a smoother and more comfortable riding experience, but also saves labor costs to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0051] Figure 2 It is a flow chart of the unmanned public transportation user demand model based on the Spearman correlation coefficient of the present invention;

[0052] Figure 3 The figure is a flow chart of the improved Archimedean optimization algorithm of the sine-cosine algorithm of the present invention. DETAILED DESCRIPTION

[0053] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0054] Example 1: Figure 1 As shown, a method for customizing an unmanned bus based on a fixed route comprises:

[0055] U1. On a preset fixed route, real-time acquisition of data information on the parameters of multiple groups of unmanned bus work tasks, data information on road traffic conditions, and data information on user ride orders;

[0056] U2. Based on the data information of the parameters of the multiple groups of unmanned bus work tasks, the data information of the traffic status of the roads and the data information of the user's ride order, an unmanned bus user demand model based on the Spearman correlation coefficient is constructed, and the best matching information of the parameters of the unmanned bus work tasks and the user's ride order is characterized to obtain the data information of the parameters of the unmanned bus work tasks that best matches the user's ride order;

[0057] U3. Based on the data information of the parameters of the unmanned bus task that best matches the user's ride order, the parameters of the unmanned bus task are optimized using the improved Archimedean optimization algorithm of the sine-cosine algorithm to obtain the data information of the optimized parameters of the unmanned bus task;

[0058] U4. Based on the data information of the parameters of the optimized unmanned bus work task, a satisfaction function F of the unmanned bus customization is established, the satisfaction of the unmanned bus customization is evaluated, and the data information of the satisfaction of the unmanned bus customization is obtained.

[0059] In this embodiment, the method further includes:

[0060] U5. Based on the data information of the satisfaction with the customization of the unmanned bus, a preset threshold is set. If the satisfaction with the customization of the unmanned bus is less than the preset threshold, return to step U3. If the satisfaction with the customization of the unmanned bus is greater than the preset threshold, output the data information of the parameters of the optimized unmanned bus work task.

[0061] In this embodiment, the satisfaction function F of the customized unmanned bus is:

[0062]

[0063] Among them, x is the data information of the optimized parameters of the unmanned bus work task, and α1, α2 and α3 are the satisfaction reconciliation factors of the unmanned bus work task.

[0064] In this embodiment, the satisfaction reconciliation factors α1, α2 and α3 of the unmanned bus task are:

[0065]

[0066] Among them, x is the data information of the parameters of the optimized unmanned bus work task, and n is a positive integer.

[0067] In this embodiment, if Figure 2 As shown, in step U2, the unmanned bus user demand model based on the Spearman correlation coefficient is constructed to characterize the best matching information between the parameters of the unmanned bus work task and the user's ride order, including:

[0068] U21. Based on the data information of the parameters of the working tasks of the multiple groups of unmanned buses, the data information of the traffic status of the roads and the data information of the ride orders of the users, a Spearman correlation function Q between the working tasks of the unmanned buses and the ride orders of the users is established.

[0069]

[0070] Among them, y1 is the data information of the parameters of multiple groups of unmanned bus work tasks, y2 is the data information of the traffic status of the road, y3 is the data information of the user's ride order, β1, β2 and β3 are weight coefficients, and the Spearman correlation between the unmanned bus work tasks and the user's ride order is characterized to obtain the data information of the Spearman correlation between the unmanned bus work tasks and the user's ride order;

[0071] U22. Based on the Spearman correlation data of the unmanned bus work task and the user's ride order, a matching function W between the parameters of the unmanned bus work task and the user's ride order is established.

[0072]

[0073] Among them, z is the data information of the Spearman correlation between the work tasks of the unmanned bus and the user's ride order, δ1, δ2 and δ3 are the matching factors between the work tasks of the unmanned bus and the user's ride order;

[0074] U23. Based on the matching function W between the parameters of the unmanned bus work task and the user's ride order, the best matching information between the parameters of the unmanned bus work task and the user's ride order is characterized to obtain data information of the parameters of the unmanned bus work task that best matches the user's ride order.

[0075] In this embodiment, the constraints of the matching factors δ1, δ2 and δ3 between the work tasks of the unmanned bus and the user's ride order are:

[0076]

[0077] The constraint function f of the weight coefficients β1, β2 and β3 is,

[0078]

[0079] The value range of the constraint function f is (1,2).

[0080] Example 2: Based on the unmanned bus customization method based on a fixed route in Example 1, the present invention is further illustrated and described below.

[0081] like Figure 1As shown, a method for customizing an unmanned bus based on a fixed route comprises:

[0082] U1. On a preset fixed route, real-time acquisition of data information on the parameters of multiple groups of unmanned bus work tasks, data information on road traffic conditions, and data information on user ride orders;

[0083] U2. Based on the data information of the parameters of the multiple groups of unmanned bus work tasks, the data information of the traffic status of the roads and the data information of the user's ride order, an unmanned bus user demand model based on the Spearman correlation coefficient is constructed, and the best matching information of the parameters of the unmanned bus work tasks and the user's ride order is characterized to obtain the data information of the parameters of the unmanned bus work tasks that best matches the user's ride order;

[0084] U3. Based on the data information of the parameters of the unmanned bus task that best matches the user's ride order, the parameters of the unmanned bus task are optimized using the improved Archimedean optimization algorithm of the sine-cosine algorithm to obtain the data information of the optimized parameters of the unmanned bus task;

[0085] U4. Based on the data information of the parameters of the optimized unmanned bus work task, a satisfaction function F of the unmanned bus customization is established, the satisfaction of the unmanned bus customization is evaluated, and the data information of the satisfaction of the unmanned bus customization is obtained.

[0086] In this embodiment, if Figure 3 As shown, in step U3, the improved Archimedean optimization algorithm using the sine-cosine algorithm is used to optimize the parameters of the unmanned bus task, including:

[0087] U31. Based on the data information of the parameters of the unmanned bus task that best matches the user's ride order, the Archimedean population is initialized, the population parameters and the maximum number of iterations L are determined, and the data information of the initialized Archimedean population is obtained;

[0088] U32. Based on the data information of the initialized Archimedean population, establish the fitness function R of the population individuals based on the sine factor,

[0089]

[0090] Among them, r is the data information of the initialized Archimedes population, γ1, γ2 and γ3 are the sine factors of the individuals in the Archimedes population, and the fitness values ​​of the individuals in the Archimedes population are calculated to obtain the data information of the fitness values ​​of the individuals in the population;

[0091] U33. Based on the data information of the fitness values ​​of the individuals in the population, establish the target optimization function S of the population,

[0092]

[0093] Among them, h is the data information of the fitness value of the population individual, η1, η2 and η3 are the cosine factors of population optimization, and the parameters of the unmanned bus work task are optimized to obtain the data information of the optimized parameters of the unmanned bus work task.

[0094] In this embodiment, the cosine factors η1, η2 and η3 of the population optimization are:

[0095]

[0096] The sinusoidal factors γ1, γ2 and γ3 of the individuals in the Archimedean population are,

[0097]

[0098] Among them, r is the data information of the initialized Archimedean population, and h is the data information of the fitness value of the individuals in the population.

[0099] In this embodiment, the present invention provides an unmanned bus customization system based on a fixed route, including a computer device, which is programmed or configured to execute the steps of any one of the unmanned bus customization methods based on a fixed route.

[0100] In this embodiment, the present invention provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the fixed-route unmanned bus customization methods.

[0101] Any reference to memory, storage, database or other media used in the embodiments provided in the present 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. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0102] In summary, the present invention not only enables the real-time data processing platform to automatically perform order processing, route planning, vehicle allocation, and scheduling and docking to achieve more efficient operation, but also reduces or eliminates manual drivers, greatly reducing labor costs.

[0103] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for customizing unmanned public transportation based on a fixed route, characterized in that: The method comprises: U1. On a preset fixed route, real-time data information on the parameters of multiple groups of unmanned bus work tasks, data information on road traffic conditions, and data information on users' ride orders; U2. Based on the data information of the parameters of the multiple groups of unmanned bus work tasks, the data information of the traffic status of the roads and the data information of the user's ride order, an unmanned bus user demand model based on the Spearman correlation coefficient is constructed, and the best matching information of the parameters of the unmanned bus work tasks and the user's ride order is characterized to obtain the data information of the parameters of the unmanned bus work tasks that best matches the user's ride order; U3. Based on the data information of the parameters of the unmanned bus task that best matches the user's ride order, the parameters of the unmanned bus task are optimized using the improved Archimedean optimization algorithm of the sine-cosine algorithm to obtain the data information of the optimized parameters of the unmanned bus task; U4. Based on the data information of the parameters of the optimized unmanned bus work task, a satisfaction function F of the unmanned bus customization is established, the satisfaction of the unmanned bus customization is evaluated, and the data information of the satisfaction of the unmanned bus customization is obtained.

2. The method for customizing unmanned public transportation based on a fixed route according to claim 1, characterized in that: The method further comprises: U5. Based on the data information of the satisfaction with the customization of the unmanned bus, a preset threshold is set. If the satisfaction with the customization of the unmanned bus is less than the preset threshold, return to step U3. If the satisfaction with the customization of the unmanned bus is greater than the preset threshold, output the data information of the parameters of the optimized unmanned bus work task.

3. The method for customizing unmanned public transportation based on a fixed route according to claim 1, characterized in that: The satisfaction function F of the customized unmanned bus is: Among them, x is the data information of the optimized parameters of the unmanned bus work task, and α1, α2 and α3 are the satisfaction reconciliation factors of the unmanned bus work task.

4. The method for customizing an unmanned bus based on a fixed route according to claim 3, characterized in that: The satisfaction reconciliation factors α1, α2 and α3 of the unmanned bus task are: Among them, x is the data information of the parameters of the optimized unmanned bus work task, and n is a positive integer.

5. The method for customizing unmanned public transportation based on a fixed route according to claim 1, characterized in that: In step U2, the unmanned bus user demand model based on the Spearman correlation coefficient is constructed to characterize the best matching information between the parameters of the unmanned bus work task and the user's ride order, including: U21. Based on the data information of the parameters of the working tasks of the multiple groups of unmanned buses, the data information of the traffic status of the roads and the data information of the ride orders of the users, a Spearman correlation function Q between the working tasks of the unmanned buses and the ride orders of the users is established. Among them, y1 is the data information of the parameters of multiple groups of unmanned bus work tasks, y2 is the data information of the traffic status of the road, y3 is the data information of the user's ride order, β1, β2 and β3 are weight coefficients, and the Spearman correlation between the unmanned bus work tasks and the user's ride order is characterized to obtain the data information of the Spearman correlation between the unmanned bus work tasks and the user's ride order; U22. Based on the Spearman correlation data of the unmanned bus work task and the user's ride order, a matching function W between the parameters of the unmanned bus work task and the user's ride order is established. Among them, z is the data information of the Spearman correlation between the work tasks of the unmanned bus and the user's ride order, δ1, δ2 and δ3 are the matching factors between the work tasks of the unmanned bus and the user's ride order; U23. Based on the matching function W between the parameters of the unmanned bus work task and the user's ride order, the best matching information between the parameters of the unmanned bus work task and the user's ride order is characterized to obtain data information of the parameters of the unmanned bus work task that best matches the user's ride order.

6. The method for customizing an unmanned bus based on a fixed route according to claim 5, characterized in that: The constraints of the matching factors δ1, δ2 and δ3 between the work tasks of the unmanned bus and the user's ride order are: The constraint function f of the weight coefficients β1, β2 and β3 is, The value range of the constraint function f is (1,2).

7. The method for customizing unmanned public transportation based on a fixed route according to claim 1, characterized in that: In step U3, the improved Archimedean optimization algorithm using the sine-cosine algorithm is used to optimize the parameters of the unmanned bus task, including: U31. Based on the data information of the parameters of the unmanned bus task that best matches the user's ride order, the Archimedean population is initialized, the population parameters and the maximum number of iterations L are determined, and the data information of the initialized Archimedean population is obtained; U32. Based on the data information of the initialized Archimedean population, establish the fitness function R of the population individuals based on the sine factor, Among them, r is the data information of the initialized Archimedes population, γ1, γ2 and γ3 are the sine factors of the individuals in the Archimedes population, and the fitness values ​​of the individuals in the Archimedes population are calculated to obtain the data information of the fitness values ​​of the individuals in the population; U33. Based on the data information of the fitness values ​​of the individuals in the population, establish the target optimization function S of the population, Among them, h is the data information of the fitness value of the population individual, η1, η2 and η3 are the cosine factors of population optimization, and the parameters of the unmanned bus work task are optimized to obtain the data information of the optimized parameters of the unmanned bus work task.

8. The method for customizing unmanned public transportation based on a fixed route according to claim 7, characterized in that: The cosine factors η1, η2 and η3 of the population optimization are, The sinusoidal factors γ1, γ2 and γ3 of the individuals in the Archimedean population are, Among them, r is the data information of the initialized Archimedean population, and h is the data information of the fitness value of the individuals in the population.

9. An unmanned bus customization system based on a fixed route, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the fixed-route unmanned bus customization method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that is programmed or configured to execute the fixed-route unmanned bus customization method according to any one of claims 1 to 8.

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