A demand response type bus scheduling method, device and medium
By collecting demand in real time, combining a multi-constraint dynamic insertion mechanism and heuristic algorithm optimization, a time-segmented time consumption table is established, realizing real-time response of bus dispatch and optimized allocation of vehicle resources. This resolves the contradiction between real-time performance and batch optimization in bus dispatch, and improves demand matching rate and vehicle utilization rate.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG POLICE COLLEGE (GUANGDONG PROVINCIAL PUBLIC SECURITY JUDICIAL MANAGEMENT CADRE COLLEGE)
- Filing Date
- 2026-05-26
- Publication Date
- 2026-07-31
AI Technical Summary
Existing bus dispatching technologies suffer from a contradiction between real-time performance and batch optimization. They lack multi-dimensional and systematic evaluation, vehicle routes lack continuous dynamic optimization, and road condition adaptability is insufficient, resulting in limited prediction accuracy and an inability to meet passengers' personalized and flexible travel needs.
By collecting demand in real time and combining a multi-constraint dynamic insertion mechanism with a station time consumption table, the system matches the optimal vehicle for each customer, establishes a list of vehicles to be planned, and uses a multi-objective heuristic algorithm for periodic optimization. The heuristic algorithm optimizes the vehicle execution list, calculates the estimated waiting time and travel time, and establishes a dynamic update mechanism for the time-segmented station time consumption table.
It improves the demand matching rate and satisfaction rate, ensures service quality, enhances vehicle utilization and dispatch response efficiency, and resolves the contradiction between real-time performance and batch optimization, as well as the problems of one-sided dynamic decision evaluation and insufficient road condition adaptability in existing technologies.
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Figure CN122491635A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle dispatching technology, and in particular to a demand-responsive bus dispatching method, equipment, and medium. Background Technology
[0002] With the acceleration of urbanization and the diversification of travel demands, traditional fixed-route bus models can no longer meet the personalized and flexible travel needs of passengers. Demand-responsive public transportation has emerged as a new public transportation service model. Existing technical approaches mainly fall into two categories: The first is the pre-booked customized bus model, which requires passengers to make reservations in advance. After the dispatch center collects the demand within a certain time window, it uses a static vehicle routing problem algorithm for batch planning. While this method can reduce operating costs through demand aggregation, it lacks real-time performance and cannot achieve "instant response." The second is the real-time carpooling dispatch model, which uses an instant matching strategy to allocate demand to nearby vehicles in real time. However, this model mainly targets small-capacity passenger vehicles and focuses solely on minimizing waiting time, lacking comprehensive consideration of batch demand aggregation and long-term system efficiency. Furthermore, at the algorithmic level, traditional precise algorithms have high computational complexity, making it difficult to meet real-time requirements. Heuristic algorithms still have shortcomings in terms of adaptability in dynamic environments and the stability of solution quality. Moreover, existing solutions often rely on historical average data or third-party navigation interfaces, lacking dedicated modeling and continuous learning mechanisms for travel times between bus stops.
[0003] Existing technologies face a fundamental contradiction between real-time performance and batch optimization in practical applications: First, the appointment-based model suffers from response lag, while the real-time model is prone to inefficiency in algorithm optimization, where "one vehicle is dispatched for every request." Second, the quality of dynamic insertion decision matching is unstable, lacking multi-dimensional systematic evaluation. Third, vehicle routes lack a continuous dynamic optimization mechanism, gradually deviating from the optimal state as new requests are continuously inserted. Fourth, the adaptability to road conditions is insufficient, failing to continuously correct the travel time prediction between stations in real time based on actual operating data, resulting in limited prediction accuracy. Summary of the Invention
[0004] The purpose of this application is to provide a demand-responsive public transport scheduling method, equipment, and medium that can achieve rapid response to dispersed travel demands and optimized allocation of vehicle resources.
[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a demand-responsive public transport scheduling method, including: Collect the needs of different customers in real time; A multi-constraint dynamic insertion mechanism is established based on multiple evaluation indicators, and combined with the station time consumption table, the optimal vehicle is matched to the needs of each customer. When a match fails, the customer's request is stored in a list to be planned. For the list to be planned, a multi-objective heuristic algorithm is used to perform periodic optimization and new vehicle condition judgment to obtain the optimized path and the corresponding execution list of new vehicles. When a match is successful, the customer's request is added to the execution list of the corresponding optimal vehicle; for the execution list of the optimal vehicle, a heuristic algorithm is used to optimize the order to obtain the corresponding optimized path; Based on the execution list of newly opened vehicles and the corresponding optimized routes, or the execution list of optimal vehicles and the corresponding optimized routes, the estimated waiting time and estimated travel time for the corresponding demand are calculated and fed back to the customer.
[0006] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a demand-responsive bus dispatching method.
[0007] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a demand-responsive bus dispatching method.
[0008] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application establishes a multi-constraint dynamic insertion mechanism based on multiple evaluation indicators, combined with a station time consumption table, to match the optimal vehicle for each customer's needs. This process ensures that quality screening is performed when new demands are inserted into running vehicles, avoiding service quality deterioration caused by simple nearest-neighbor matching. Furthermore, a station time consumption table containing time period information is used, rather than simply using the ratio of distance to fixed speed; this refined time calculation method makes the calculation results more accurate. When matching fails, the customer's demand is stored in a waiting-to-be-planned list, thus providing a "buffer pool" to prevent a large number of demands from being rejected due to matching failures in special circumstances, greatly improving the demand matching rate and thus improving the demand fulfillment rate. Then, by periodically transferring the demands in the waiting-to-be-planned list to customer demands and re-matching them, without changing the algorithm framework, another consumption path is cleverly provided for the "buffer pool" of the waiting-to-be-planned list, which can greatly improve the demand matching rate and thus improve the demand fulfillment rate. Upon successful matching, the customer's request is added to the corresponding optimal vehicle execution list. A heuristic evolutionary algorithm is then used to find the optimal path that meets the requirements. Compared to iterating through each route one by one, this significantly improves the speed of generating the optimal solution. Furthermore, it ensures that the vehicle maintains high path efficiency throughout the entire operating cycle, resolving the efficiency decay problem caused by local planning in existing technologies. Finally, the estimated waiting time and estimated travel time for the corresponding request are calculated and fed back to the customer.
[0009] This application constructs a four-layer scheduling mechanism of "real-time collection, dynamic matching, intelligent aggregation, and continuous evolution". Through dynamic bidirectional selection of demand and vehicles, heuristic re-optimization of the execution list, and data-driven time consumption prediction, it significantly improves vehicle utilization and scheduling response efficiency while ensuring service quality. Attached Figure Description
[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0011] Figure 1 This is a diagram illustrating the application environment of the demand-responsive bus dispatching method in one embodiment of this application.
[0012] Figure 2 This is a flowchart illustrating a demand-responsive bus dispatching method in one embodiment of this application.
[0013] Figure 3 This is a diagram illustrating the site's time consumption table and its update method.
[0014] Figure 4 This is a diagram illustrating the matching process between demand and vehicles.
[0015] Figure 5 This diagram illustrates the relationship between the three types of lists.
[0016] Figure 6 This is a flowchart of a heuristic algorithm.
[0017] Figure 7 This is the overall flowchart for an application instance.
[0018] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0020] This application collects customer needs in real time through the client and matches them with running vehicles instantly. It intelligently aggregates and periodically optimizes the routes for failed matching needs, establishes a dynamic update mechanism for time-segmented station time tables to achieve road condition adaptation, and continuously re-optimizes the execution list of matched vehicles to correct accumulated deviations. Thus, it achieves optimal global efficiency while ensuring real-time response, solving key technical problems in existing technologies such as the contradiction between real-time performance and batch optimization, one-sided dynamic decision evaluation, and insufficient road condition adaptability.
[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0022] The demand-responsive bus dispatching method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up separately, integrated into server 102, or placed in the cloud or on other servers. Terminal 101 can send different customer requests to server 102. After receiving the requests, server 102 establishes a multi-constraint dynamic insertion mechanism based on multiple evaluation indicators, and matches the optimal vehicle for each customer's request with a station time consumption table. If matching fails, the customer's request is stored in a list to be planned, and a multi-objective heuristic algorithm is used to perform periodic optimization and new vehicle condition judgment to obtain the optimized path and the corresponding execution list of new vehicles. If matching succeeds, the customer's request is added to the corresponding optimal vehicle execution list, and a heuristic algorithm is used to optimize the order to obtain the corresponding optimized path. Finally, the estimated waiting time and estimated travel time for the corresponding request are calculated. Server 102 can feed back the obtained estimated waiting time and estimated travel time to terminal 101. Furthermore, in some embodiments, the demand-responsive bus scheduling method can also be implemented separately by server 102 or terminal 101.
[0023] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. The server 102 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.
[0024] In one exemplary embodiment, such as Figure 2 As shown, a demand-responsive bus dispatching method is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1Taking server 102 as an example, the explanation includes the following steps 201 to 205.
[0025] Step 201: Collect the needs of different customers in real time; whereby a customer's needs include the starting site and the destination site, and based on this, collect the needs of customer i in real time through the customer's mobile phone or the site's equipment. , Let i be the starting point for the i-th customer's request. The target site for the i-th customer's request. For the starting site, if the data was collected using the customer's mobile phone, it is automatically set to the site closest to the current location; if the data was collected using a device at the site, the starting site is set to the current site. All customer requests are added to the matching list. middle.
[0026] Step 202: Establish a multi-constraint dynamic insertion mechanism based on multiple evaluation indicators, and combine it with the site time consumption table to match the optimal vehicle for each customer's needs.
[0027] In practical applications, the time spent traveling between stations is determined by retrieving the station time consumption table. The site time consumption table is divided into time periods, for example, represented as... , This table shows the station travel time between 6:00 AM and 7:00 AM. This table shows the station's time consumption during the period from 7:00 to 8:00 in the morning, and so on.
[0028] At the initial stage of operation, each element in the site timeout table is calculated using the following formula as its initial value: .
[0029] in, The time taken between station u and station q is represented by dis(u,q), and the actual navigation distance between station u and station q is represented by dis(u,q). This indicates the average driving speed on the corresponding city roads.
[0030] Site Time Consumption Table The scheduling is dynamically updated in real time following the movement of the vehicles. Once the scheduling method is running, it updates the schedule for each vehicle. It records the time it takes to arrive at each station in real time. Assume the vehicle... The time to arrive at station u is The time to arrive at station q is Then first determine The time period in question determines which time period's site time consumption table should be entered into. Instead of filling it in directly, it uses the element value corresponding to the current position in the table. Take the average value and then enter it. The site time consumption table and its update method are as follows: Figure 3 As shown.
[0031] In a practical application, multiple evaluation metrics include time consumption, time error, number of stations, and detour time. Based on this, such as... Figure 4 As shown, a multi-constraint dynamic insertion mechanism is established based on multiple evaluation indicators. Combined with the station time consumption table, the optimal vehicle is matched for each customer's needs, including the following steps (21)-(29).
[0032] (21) For any customer’s needs, retrieve the preset vehicle set, traverse each vehicle in the preset vehicle set, and calculate the time between the customer’s starting station and the vehicle’s next station at the current time by combining the station time table.
[0033] Let the set of vehicles currently in operation be . ,vehicle The execution list is At present The next stop is (m). The time taken from station u to station v can be retrieved from the station time table. Obtain. Iterate through each car in B. retrieval (m) and Time spent between: .
[0034] (22) If the time consumed Greater than or equal to the preset time threshold If the match fails, then the match is considered unsuccessful; where, The threshold is set manually, representing the range of vehicles that can be considered to be at the furthest acceptable distance.
[0035] (23) If the time consumed Less than the preset time threshold Then, the time taken from the starting station and the target station to the vehicle's stations, and the time taken from the starting station to the target station, are calculated, and the time difference is calculated based on triangulation consistency. During the calculation, starting from the m-th station, the process iterates through... .
[0036] (24) If the time difference is less than the preset time error, then the matching is considered successful. The time difference being less than the preset time error is expressed as: ,or, .
[0037] in, Starting station With the site The time spent between For the site With the target site The time spent between Starting station With the target site The time spent between For target site With the site The time spent between Starting station With the site The time spent between This is the preset time error.
[0038] The above formula can also be understood as: finding sites that satisfy one of the above two conditions. (n).
[0039] (25) If the time interpolation is greater than or equal to the preset time error, then count the number of stations in the execution list of the corresponding vehicle.
[0040] (26) If the number of stations is less than the preset number of stations If the match is successful, then the match is considered successful.
[0041] (27) If the number of stations is greater than or equal to the preset number of stations, then calculate the vehicle detour time after the inserted demand. When calculating the detour time, starting from the next station, continuously calculate the station time between the next station and the station after that, and the vehicle detour time... The calculation formula is: .
[0042] in, To insert the execution list of vehicles following the requirement, for Number of stations in China Here, m is the index of the next station for the vehicle at the current time in the execution list. The execution list mentioned here is also the execution list corresponding to the current time. for and The time spent between; for Number of stations in China To insert the execution list of vehicles before the requirement, for and The time spent between them.
[0043] (28) If the vehicle detour time is less than the preset detour time If the match is successful, then the match is considered successful. The threshold is set manually, representing the maximum acceptable detour time.
[0044] (29) If the vehicle detour time is greater than or equal to the preset detour time, the matching is determined to be unsuccessful.
[0045] In practical applications, if a matching failure occurs, a period of time can be waited. Then jump to step (21) to rematch. If the number of matches exceeds 5, the requirement will be determined again. If a match fails, all unsuccessful requests are added to the "Pending Planning" list, and then proceed to step 203. Upon successful matching, the successfully matched requests are added to the corresponding vehicle's execution list, and the process jumps to step 204, while continuing to match the next request. The relationship between the three lists is as follows: Figure 5 As shown.
[0046] Step 203: When matching fails, store the customer's needs in the planning list; for the planning list, use a multi-objective heuristic algorithm to perform periodic optimization and new vehicle condition judgment to obtain the optimized path and the corresponding execution list of new vehicles.
[0047] In a specific application, it is periodically determined whether the list of items to be planned meets the conditions for opening new items, such as... Figure 6 As shown, the steps are as follows: (31) Construct the first fitness function and the first constraint condition. The first fitness function is: .
[0048] ; .
[0049] ; .
[0050] ; .
[0051] Where F is the value of the first fitness function. , , All are weighting coefficients, and all are greater than zero; The total time taken for vehicle operation. For demand satisfaction rate, Let M be the total waiting time; M is the total number of stations in the feasible solution. for and The time spent between The starting point for the k-th demand or target site , For the (k+1)th request, it is either the starting or destination site. To minimize the time required to accept only one request, and The total time and total waiting time are obtained by planning the entire process according to the order in the table; For the actual set of accepted needs, For the total demand set, S represents the quantity of demand in set S; N represents the total number of demands in the list to be planned. Represents a node The index position in the path is or .
[0052] The first constraint includes: the order of getting on and off the vehicle and the vehicle capacity constraint.
[0053] Boarding and alighting sequence constraints: .
[0054] Vehicle capacity constraint: at any position k on the path, .
[0055] .
[0056] in, C represents the number of passengers on board, and C represents the maximum capacity of the vehicle.
[0057] (32) Set an initial solution set; wherein the initial solution in the initial solution set is set with a single path offset and is filled with an empty set to a fixed length.
[0058] Specifically, initial solution set , Let represent the path to the k-th feasible solution, and The following conditions must be met: .
[0059] (33) The initial solution set is evolved based on the crossover operation of random replacement and the mutation operation based on probability replacement and probability deletion to obtain the expanded solution set.
[0060] Specifically, the solution crossover operation involves randomly selecting two solutions from the initial solution set. To calculate , The parentheses represent the corresponding elements of the operand. As a basis for judgment, if Then take The element at the corresponding position in the middle, otherwise take the element at the corresponding position in the middle. The element at the corresponding position in the middle. It is a random number between (0, 1), and is sampled once for each crossover operation. This operation is performed. Next, get .
[0061] Mutation operation of solution: The probability is randomly selected from the solution set after the crossover operation. For each selected solution, the elements in that solution... and Perform the exchange. The probability is randomly selected from the solution set after the crossover operation. For each selected solution, the l-th element... Delete. Where m, n, and l are values between [0, ..., ...]. A random number between [ ] is generated, and it is sampled once for each mutation operation. .get .
[0062] (34) Based on the first constraint, the extended solution set is... The feasible solution set is obtained by filtering.
[0063] (35) For all solutions in the feasible solution set, calculate the first fitness using the first fitness function and rank them to obtain the optimized solution set. Specifically, select the top-ranked solutions with the first fitness. The solution to the name is obtained .
[0064] (36) Before reaching the maximum number of iterations If necessary, return to the step of evolving the initial solution set to obtain an expanded solution set, and repeat the process.
[0065] (37) When the maximum number of iterations is reached When the optimal solution set is obtained, the optimized solution set is taken as the optimal solution.
[0066] (38) If the number of non-empty nodes in the optimal solution is greater than the preset node number threshold, it indicates that the condition for opening a new vehicle is met. The non-empty nodes in the optimal solution are then added to the execution list of the new vehicle, and the optimized path is obtained, represented as follows: .
[0067] If the number of non-empty nodes in the optimal solution does not exceed a preset node count threshold, the requirement is returned to the planning list, and the requirements in the planning list are periodically transferred to customer requirements. This means waiting for the next periodic processing.
[0068] Step 204: Upon successful matching, add the customer's request to the corresponding optimal vehicle execution list; for the optimal vehicle execution list, use a heuristic algorithm to optimize the vehicles. Execution list The order of these steps determines the corresponding optimized path.
[0069] In practical applications, for the execution list of the optimal vehicle, the steps of optimizing the order using a heuristic algorithm to obtain the corresponding optimized path are similar to step 203 above, requiring the construction of a second fitness function and a second constraint condition.
[0070] The second fitness function is: .
[0071] The second constraint includes: boarding and alighting sequence constraint: Vehicle capacity constraints: .in, , For vehicles belonging to S The set of requirements.
[0072] When using a heuristic algorithm for processing, the steps are as follows: (1) Initial solution set ,in Let represent the path to the i-th feasible solution, and The following conditions must be met: .
[0073] (2) Evolution of solution sets.
[0074] Solution crossover operation: Randomly select two solutions from the initial solution set. To calculate , The parentheses represent the corresponding elements of the operand. As a basis for judgment, if Then take The element in, otherwise take The elements in. It is a random number between (0, 1), and is sampled once for each crossover operation. This operation is performed. Next, get .
[0075] Mutation operation of solution: The probability is randomly selected from the solution set after the crossover operation. For each selected solution, the elements in that solution... and Perform the exchange. The probability is randomly selected from the solution set after the crossover operation. For each selected solution, the l-th element... Delete. Where m, n, and l are values between [0, ..., ...]. A random number between [ ] is generated, and it is sampled once for each mutation operation. .get .
[0076] (3) Delete infeasible solutions: For From all the solutions in the set, remove the solutions that do not satisfy the second constraint to obtain the feasible solution set.
[0077] (4) Selection of the optimal solution: For all solutions in the feasible solution set, calculate the fitness using the second fitness function, and select the solution with the highest fitness ranking. The solution to the name is obtained .
[0078] (5) Repeat steps (1)-(4) above until the maximum number of iterations is reached. ,get This is the output solution, which is used to update the vehicle. Execution list .
[0079] Step 205: Based on the execution list of newly opened vehicles and the corresponding optimized routes, or the execution list of optimal vehicles and the corresponding optimized routes, calculate the estimated waiting time and estimated travel time for the corresponding demand, and provide feedback to the customer.
[0080] The estimated waiting time for: .
[0081] The estimated travel time for: .
[0082] in, for and The time consumption and demand between The allocated vehicles are The corresponding execution list is .
[0083] In practical applications, the vehicle in this application refers to a public bus, but it can also be applied to similar scenarios such as large buses, as needed.
[0084] In another application example, such as Figure 7As shown, customer needs are collected in real time through the client and then placed into a waiting-to-match list. A multi-constraint dynamic insertion algorithm is designed to match needs to running vehicles in real time based on indicators such as distance threshold, time error, and detour cost, to obtain the optimal vehicle. If the match is successful, the need is added to the execution list of the matched optimal vehicle, and a heuristic algorithm is used to recalculate the order of the execution list, and then the waiting time and travel time of all needs are calculated. If the match fails, the needs are aggregated into a waiting-to-plan list, and a multi-objective heuristic algorithm is used to periodically optimize the paths of the needs in the waiting-to-plan list and trigger a decision to add a new vehicle. When the new vehicle is added, the waiting time and travel time of all needs are calculated; when the new vehicle is not added, the need is put back into the waiting-to-plan list, and the waiting-to-plan list is periodically converted into customer needs, returning to the step of matching the optimal vehicle for the customer's needs. In addition, in the above process, a dynamic update mechanism for a time-segmented station time table is established to achieve road condition adaptation.
[0085] This application significantly improves vehicle utilization and system response efficiency while ensuring service quality through dynamic two-way selection of demand and vehicles, heuristic re-optimization of the execution list, and data-driven time consumption prediction.
[0086] Compared with the prior art, this application has the following advantages: (1) When calculating the travel time (i.e., time spent) between stations, a station time spent table containing time period information is used. Instead of simply using the ratio of distance to a fixed speed, this refined method of time calculation makes the results more accurate. Furthermore, the station time table... It is constantly updated during vehicle operation, automatically adapting to road conditions at different times such as morning and evening rush hours, becoming more and more accurate the more it runs.
[0087] (2) A multi-constraint dynamic insertion evaluation mechanism was established. Through four levels of judgment, namely distance threshold screening, time error constraint, capacity check and detour time calculation, it is ensured that when new demand is inserted into a running vehicle, it not only meets the passenger time window requirements, but also controls the detour cost within an acceptable range, thus avoiding the deterioration of service quality caused by simple nearest matching.
[0088] (3) In the event of a failed match, a "buffer pool" is set up for the unmatched requirements by creating a list of unplanned requirements. This prevents a large number of requirements from being rejected due to the failure of matching under special circumstances, which greatly improves the requirement matching rate and thus improves the requirement satisfaction rate.
[0089] A heuristic evolutionary algorithm is employed to find the optimal path that meets the new driving requirements. Compared to iterating through each path one by one, this significantly improves the speed of optimal solution generation. The initial solution uses a single path with misalignment, and is padded to a fixed length with an empty set, greatly increasing the richness of solutions and ensuring a lower bound on solution quality. The evolution of the solution set employs crossover operations based on random replacement and mutation operations based on probabilistic replacement and probabilistic deletion, ensuring continuous evolution of the solution set and avoiding getting trapped in local optima.
[0090] Meanwhile, by using a weighted normalized sum of total vehicle operating time, demand satisfaction rate, and total waiting time, we can prevent newly added vehicles from sacrificing total driving time in an attempt to greedily satisfy all demands. This ensures that the decision to add new vehicles is based on the actual demand density of the planning list, thus avoiding blindly increasing the number of vehicles.
[0091] By periodically transferring the requirements in the list to be planned to customer requirements and re-matching them, an alternative consumption path is cleverly provided for the "buffer pool" of the list to be planned without changing the algorithm framework. This can greatly improve the matching rate of requirements, thereby improving the requirement satisfaction rate.
[0092] (4) In the case of successful matching, the execution list of matched vehicles is re-optimized using a heuristic algorithm. As new requirements are continuously inserted, the accumulated path deviation is corrected through global re-optimization to ensure that the vehicles maintain high path efficiency throughout the entire operation cycle, thus solving the efficiency decay problem caused by local planning in the existing technology.
[0093] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 8 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a demand-responsive public transport scheduling method.
[0094] Those skilled in the art will understand that Figure 8The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0095] In one exemplary embodiment, a computer device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the above-described method embodiments.
[0096] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0097] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0098] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0099] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0100] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0101] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0102] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A demand-responsive public transport scheduling method, characterized in that, The method includes: Collect the needs of different customers in real time; A multi-constraint dynamic insertion mechanism is established based on multiple evaluation indicators, and combined with the station time consumption table, the optimal vehicle is matched to the needs of each customer. When a match fails, the customer's request is stored in a list to be planned. For the list to be planned, a multi-objective heuristic algorithm is used to perform periodic optimization and new vehicle condition judgment to obtain the optimized path and the corresponding execution list of new vehicles. When a match is successful, the customer's request is added to the execution list of the corresponding optimal vehicle; for the execution list of the optimal vehicle, a heuristic algorithm is used to optimize the order to obtain the corresponding optimized path; Based on the execution list of newly opened vehicles and the corresponding optimized routes, or the execution list of optimal vehicles and the corresponding optimized routes, the estimated waiting time and estimated travel time for the corresponding demand are calculated and fed back to the customer.
2. The demand-response bus dispatching method according to claim 1, characterized in that, A customer's needs include the originating site and the destination site; the time traveled between one site and another is obtained by retrieving the site time travel table. When considering multiple evaluation metrics, including travel time, time error, number of stops, and detour time, a multi-constraint dynamic insertion mechanism is established based on these metrics. Combined with a stop travel time table, the optimal vehicle is matched to each customer's needs, including: For any customer's needs, a preset vehicle set is retrieved, each vehicle in the preset vehicle set is traversed, and the time between the customer's starting station and the vehicle's next station at the current time is calculated in conjunction with the station time table. If the time consumed is greater than or equal to a preset time consumption threshold, the matching is determined to have failed; If the time consumed is less than a preset time consumption threshold, the time consumed from the starting station and the target station to the vehicle's station, and the time consumed from the starting station to the target station are calculated respectively, and the time difference is calculated based on triangulation consistency. If the time difference is less than the preset time error, the match is considered successful. If the time interpolation is greater than or equal to the preset time error, then the number of stations in the execution list of the corresponding vehicle is counted. If the number of sites is less than the preset number of sites, the match is considered successful; If the number of stations is greater than or equal to the preset number of stations, then calculate the vehicle detour time after inserting the demand; If the vehicle's detour time is less than the preset detour time, the match is considered successful. If the vehicle's detour time is greater than or equal to the preset detour time, the matching is deemed to have failed.
3. The demand-responsive bus dispatching method according to claim 2, characterized in that, The time difference being less than the preset time error is expressed as: ,or, ; in, Starting station With the site The time spent between For the site With the target site The time spent between Starting station With the target site The time spent between For target site With the site The time spent between Starting station With the site The time spent between Preset time error; Let i be the starting point for the i-th customer's request. For the target site of the i-th customer's needs; The vehicle detour time The calculation formula is: ； in, To insert the execution list of vehicles following the requirement, for Number of stations in China Here, m is the index of the next station for the vehicle at the current moment in the execution list. for and The time spent between; for Number of stations in China To insert the execution list of vehicles before the requirement, for and The time spent between them.
4. The demand-responsive bus dispatching method according to claim 1, characterized in that, For the list to be planned, a multi-objective heuristic algorithm is used to perform periodic optimization and new vehicle condition judgment to obtain the optimized path and the corresponding execution list of new vehicles, including: Construct the first fitness function and the first constraint conditions; Set an initial solution set; wherein, the initial solutions in the initial solution set are set with single path misalignment and filled with an empty set to a fixed length; The initial solution set is evolved based on crossover operations with random replacements and mutation operations with probabilistic replacements and probabilistic deletions to obtain an expanded solution set. Based on the first constraint, the expanded solution set is filtered to obtain a feasible solution set; For all solutions in the feasible solution set, the first fitness is calculated and ranked using the first fitness function to obtain the optimized solution set; If the maximum number of iterations has not been reached, return to the step of evolving the initial solution set to obtain an expanded solution set; When the maximum number of iterations is reached, the optimized solution set is taken as the optimal solution; If the number of non-empty nodes in the optimal solution is greater than the preset node number threshold, it indicates that the conditions for opening a new vehicle are met. The non-empty nodes in the optimal solution are then added to the execution list of the new vehicle, and the optimized path is obtained.
5. The demand-responsive bus dispatching method according to claim 4, characterized in that, The first fitness function is: ； ； ； ； ； ； ； Where F is the value of the first fitness function. , , All are weighting coefficients; The total time taken for vehicle operation. For demand satisfaction rate, Let M be the total waiting time; M is the total number of stations in the feasible solution. for and The time spent between The starting point for the k-th demand or target site , For the (k+1)th request, it is either the starting or destination site. To minimize the time required to accept only one request, and This represents the total time consumed and the total waiting time. For the actual set of accepted needs, For the total demand set, S represents the quantity of demand in set S; N represents the total number of demands in the list to be planned. Represents a node The index position in the path is or ; The first constraint includes: Boarding and alighting sequence constraints: ; Vehicle capacity constraints: ; in, C represents the number of passengers on board, and C represents the maximum capacity of the vehicle.
6. The demand-responsive bus dispatching method according to claim 5, characterized in that, For the execution list of the optimal vehicle, a heuristic algorithm is used to optimize the order and obtain the corresponding optimized path. In this step, a second fitness function and a second constraint condition are constructed. The second fitness function is: ； The second constraint includes: Boarding and alighting sequence constraints: ; Vehicle capacity constraints: ; in, For vehicles belonging to S The set of requirements.
7. The demand-responsive bus dispatching method according to claim 5, characterized in that, The estimated waiting time for: ； The estimated travel time for: ； Where m is the index of the vehicle's next stop in the execution list at the current moment. for and The time consumption and demand between The allocated vehicles are The corresponding execution list is k is the index of the path location.
8. The demand-responsive bus dispatching method according to claim 1, characterized in that, The station time consumption table is divided into time periods and is dynamically updated in real time as the vehicles operate; where "vehicles" refers to buses.
9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the demand-responsive bus dispatching method according to any one of claims 1-8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the demand-responsive bus dispatching method as described in any one of claims 1-8.