Customized bus path optimization method, system, storage medium and device

Abstract

The application relates to a customized bus route optimization method and system, a storage medium and equipment, and comprises the following steps: representing a city road network by using a directed graph, constructing a customized bus route planning model, associating a starting node i and a target node n+i in the city road network with each traffic demand n, existing a service time window for each node i, the service time window representing a set formed by the earliest time and the latest time of traffic demand service, and the maximum riding time of each traffic demand n being L; constructing a target function by taking the minimum value of the sum of the total operation cost and the total user waiting time as the target, obtaining the solution of the target function through passenger service constraints, bus vehicle constraints and other constraints, and determining the optimal bus number and the optimal bus route capable of meeting the travel demand.

Description

Technical Field

[0001] This invention relates to the field of customized bus route planning technology, specifically to customized bus route optimization methods, systems, storage media, and devices. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Customized bus services are a flexible form of public transportation that falls between traditional public transportation and private cars. They collect passenger travel requests and provide customized services to passengers with similar origins, destinations, and travel times. Because they retain the characteristics of "public transportation," they operate with fixed stops but not fixed routes. In practice, due to the diverse needs of passengers, customized bus services are prone to problems such as poor punctuality and high operating costs. In some areas with comprehensive road networks but low population density, they have even encountered difficulties such as neglected routes, low occupancy rates, and insufficient applicants to operate certain routes.

[0004] Existing technologies use computer programs or mathematical methods to generate or optimize customized bus routes to balance operating costs and passenger demand. However, some current optimization methods are subjective in their response to passenger demand and lack quantitative research. Some methods assume that only a single bus is running, that passengers at the same demand point have the same travel needs, and that the vehicle capacity is infinitely large. They do not consider the actual road network structure of the city and ignore the time window requirements of passengers' travel. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a customized bus route optimization method, system, storage medium, and device. It proposes a two-stage customized bus vehicle scheduling and route optimization model under the constraints of vehicle service cost and user travel waiting time. This model generates static vehicle scheduling decisions that can cover all high-probability stops, and then generates optimized routes that can meet advance booking requirements. Finally, it determines the optimal number of buses and the optimal bus routes that can meet travel needs, thereby minimizing the total system cost.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a customized bus route optimization method, comprising the following steps:

[0008] Using a directed graph to represent the urban road network, a customized public transport route planning model is constructed. Each traffic demand n is associated with a starting node i and a target node n+i in the urban road network. Each node i has a service time window, which represents the set of the earliest and latest service times of the traffic demand. The maximum travel time for each traffic demand n is L.

[0009] An objective function is constructed with the goal of minimizing the sum of total operating costs and total user waiting time. The solution to the objective function is obtained through passenger service constraints, bus vehicle constraints, and other constraints, thereby determining the optimal number of buses and the optimal bus route that can meet travel demand.

[0010] The public transport route planning model includes:

[0011] Let G = (N, A) be a directed graph representing the city road network, where A is a directed road segment in the road network, N is a node in the road network, and n is the traffic demand of the road network. Each traffic demand is associated with a starting node i and a target node n+i, where each node i ∈ N.

[0012] Let K be the set of customized buses, where each customized bus k∈K and has a capacity of Q. k The total travel time shall not exceed T k Each node i corresponds to a vehicle load q. i and vehicle parking time d i ;

[0013] Let c be the segment cost and travel time incurred by the bus when it travels through each segment (i,j)∈A. ij t ij Each node i has a service time window [e i ,l i ], where e i and l i These represent the earliest and latest times of the transportation demand service, respectively, and L represents the maximum travel time for each transportation demand.

[0014] The bus departure node is 0, the arrival node is 2n+1, and the intermediate transition nodes include the receiving node set P = {1,...,n} and the delivery node set D = {n+1,...,2n}.

[0015] In the road network, node N is equal to the union of the set of receiving nodes, the set of delivering nodes, and the bus departure node to the arrival node, i.e., N = P∪D∪(0, 2n+1).

[0016] Passenger service constraints are as follows:

[0017] To ensure that all travel needs are served and served only once, Let bus k travel from point i to point j, and the route (i,j)∈A, and bus k∈K, as shown in the following equation:

[0018]

[0019] Ensuring that every passenger reaches their destination according to their travel needs, i.e., the service integrity constraint, is as follows:

[0020]

[0021] The constraints for public transport vehicles are as follows:

[0022] Ensure that all vehicles within the bus depot are dispatched, as shown in the following formula:

[0023]

[0024] When a bus travels along a planned route, it must leave after entering a stop, and each bus must return to the depot after serving all travel needs, as shown in the following formula:

[0025]

[0026]

[0027] Other constraints are:

[0028] The arrival time of the bus at node j is determined by the arrival time at node i, the stop time, and the travel time, as shown in the following formula:

[0029]

[0030] The number of passengers when the bus leaves station j is determined by the number of passengers when it leaves node i and the number of passengers getting on and off at node i, as shown in the following formula:

[0031]

[0032] The travel time for passenger i on the bus route is shown in the following formula:

[0033]

[0034] The bus arrival time and load constraints are as follows:

[0035]

[0036]

[0037] The objective function is:

[0038]

[0039] In the formula, c ij For the cost of the road segment, x ij Constraints for passenger service Let α be the travel time of passenger i, and α be the average hourly wage per person.

[0040] A second aspect of the present invention provides a system for implementing the above-described method, comprising:

[0041] The customized bus route module is configured to: use a directed graph to represent the urban road network, construct a customized bus route planning model, each traffic demand n is associated with a starting node i and a target node n+i in the urban road network, each node i has a service time window, the service time window represents the set of the earliest and latest service times of the traffic demand, and the maximum travel time for each traffic demand n is L;

[0042] The objective function module is configured to construct an objective function with the goal of minimizing the sum of total operating costs and total user waiting time. The solution to the objective function is obtained through passenger service constraints, bus vehicle constraints, and other constraints, thereby determining the optimal number of buses and the optimal bus route that can meet travel demand.

[0043] A third aspect of the present invention provides a computer-readable storage medium.

[0044] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the customized bus route optimization method described above.

[0045] A fourth aspect of the present invention provides a computer device.

[0046] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the customized bus route optimization method described above.

[0047] Compared with existing technologies, one or more of the above technical solutions have the following beneficial effects:

[0048] 1. Planning problems based on time window constraints: This problem considers passengers and their travel time to meet personalized travel needs, while also constraining time and cost to reduce passenger travel delays and lower bus company fuel costs, thereby increasing passenger travel satisfaction.

[0049] 2. A two-stage customized bus vehicle scheduling and route optimization model is proposed under the constraints of vehicle service cost and user travel waiting time. First, a static vehicle scheduling decision that can cover all high-probability stops is generated. Then, an optimized route that can meet the advance reservation requirement is generated. The optimal number of buses and the optimal bus route that can meet the travel demand are determined, thereby minimizing the total system cost.

[0050] 3. In the optimal system scenario, the optimized grouping strategy can effectively reduce the scale of customized buses while achieving optimal utilization of each service vehicle. By reducing vehicle trips, the road traffic load can be reduced, traffic capacity can be improved, and traffic congestion can be alleviated.

[0051] 4. By comparing the total energy consumption with the optimal scenario for individual travel, it can be seen that customized public transportation can greatly reduce energy consumption during car travel, thereby reducing vehicle exhaust emissions and air pollution. Attached Figure Description

[0052] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0053] Figure 1 This is a flowchart of the customized bus service process provided by one or more embodiments of the present invention;

[0054] Figure 2 A system architecture diagram for customized bus route optimization provided in one or more embodiments of the present invention;

[0055] Figure 3 A schematic diagram of a bus driving route provided for one or more embodiments of the present invention;

[0056] Figure 4 This is a schematic diagram illustrating the relationship between public transportation scale and travel cost for one or more embodiments of the present invention. Detailed Implementation

[0057] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0058] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0059] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0060] As described in the background section, existing technologies use computer programs or mathematical methods to generate or optimize customized bus routes to balance operating costs and passenger demand. However, some current optimization methods are subjective in their response to passenger demand and lack quantitative research. Some methods assume that only a single bus is running, that passengers at the same demand point have the same travel needs, and that the vehicle capacity is infinitely large. They do not consider the actual road network structure of the city and ignore the time window requirements for passenger travel.

[0061] Therefore, the following embodiments provide a customized bus route optimization method, system, storage medium, and device. A two-stage customized bus vehicle scheduling and route optimization model is proposed under the constraints of vehicle service cost and user travel waiting time. The model generates a static vehicle scheduling decision that can cover all high-probability stops, and then generates an optimized route that can meet the advance reservation requirements. The optimal number of buses and the optimal bus route that can meet the travel demand are determined, thereby minimizing the total system cost.

[0062] Example 1:

[0063] Customized bus route optimization methods include the following steps:

[0064] Using a directed graph to represent the urban road network, a customized public transport route planning model is constructed. Each traffic demand n is associated with a starting node i and a target node n+i in the urban road network. Each node i has a service time window, which represents the set of the earliest and latest service times of the traffic demand. The maximum travel time for each traffic demand n is L.

[0065] An objective function is constructed with the goal of minimizing the sum of total operating costs and total user waiting time. The solution to the objective function is obtained through passenger service constraints, bus vehicle constraints, and other constraints, thereby determining the optimal number of buses and the optimal bus route that can meet travel demand.

[0066] Specifically:

[0067] like Figure 1As shown, customized bus services operating in cities are a differentiated, intensive, and high-quality urban public transportation service model that integrates similar travel needs such as origin and destination points and times, and provides passengers with pre-booked routes or bus services. In essence, it is still public transportation, with fixed stops but non-fixed routes, thereby providing services to travelers.

[0068] like Figure 2 As shown, this embodiment addresses the customized bus route planning problem by establishing a mathematical programming model to seek the travel option that maximizes user satisfaction, minimizes fleet size, and reduces vehicle operating costs. It also calculates and evaluates system energy consumption and travel waiting time, and solves the planning model. To verify the model's effectiveness, the proposed method forms a customized bus route planning simulation platform to determine a reasonable customized bus scale. Finally, the energy consumption under the shortest individual travel path scenario and the energy consumption under the scenario with the lowest system travel cost are compared.

[0069] Customized bus route feature analysis

[0070] To specify the actual route of customized buses in the road network, all vehicle travel needs are marked in the virtual bus network, such as... Figure 3 As shown in the diagram, to model the entire route planning, a virtual departure node *o* and a virtual return node *d* are set up. Both nodes are virtual and their locations are not fixed. All vehicles must depart from the virtual departure point and eventually return to the virtual return point. Their locations need to be determined based on the actual road network, urban morphology, etc., but this has no impact on the model's results.

[0071] Suppose a road network has seven customized bus stops (A, B, C, D, E, F, G, H), which aggregate travel demand from the surrounding area. During a certain time period, the customized bus route planning receives 33 passenger requests, with boarding points at stops A, B, C, F, and G, and alighting points at stops B, C, D, E, G, and H. To fulfill each passenger's travel needs within their booked time slot, it is necessary to plan the scale and routes of the customized bus service, while also considering system operating costs and user waiting times.

[0072] Figure 3 The travel route shown is one of the planned routes for this customized bus service. It departs from the departure station, passes through five stations (A, B, C, D, and E), and finally returns to the return station, fulfilling a total of 23 transportation needs in the system. The remaining 10 transportation needs will be served by other buses planned by the system.

[0073] Therefore, the main problem addressed in this embodiment is to optimize the scale and routes of public transportation while ensuring passenger travel needs, thereby obtaining an optimal solution that balances system costs and user requirements.

[0074] like Figure 4 As shown, with the increase in the number of customized buses, user waiting time will decrease, but operating costs will increase. However, for the research problem of finding the shortest path for individual travel, the optimal scenario for individual travel will not consider vehicle travel costs; each travel need will be served by a vehicle, and route navigation will enable users to reach their destination in the shortest time. This embodiment will consider the inherent relationship between vehicle service costs and travel waiting costs, seeking an equilibrium point to determine the optimal number of buses and the optimal bus route to meet travel needs, thus minimizing the total system cost. To solve this problem, a model will be established to achieve the following two objectives.

[0075] 1. Consider the inherent relationship between operating costs and user waiting time to determine vehicle size and travel routes.

[0076] 2. Compare the system-optimal and individual-optimal travel fuel consumption costs and operating costs.

[0077] Customized bus route planning model

[0078] Let G = (N, A) be a directed graph representing the urban road network, consisting of directed road segments A and nodes N, where n represents the traffic demand of the road network.

[0079] To represent the route of a customized bus service, let the departure node be 0 and the arrival node be 2n+1. Intermediate nodes are represented by the set of receiving nodes P = {1,...,n} and the set of delivery nodes D = {n+1,...,2n}, then N = P∪D∪(0,2n+1). Therefore, each traffic demand is associated with a starting node i and a destination node n+i.

[0080] Let K be the set of customized buses, and let Q be the capacity of each customized bus k∈K. k And the total travel time does not exceed T k At the same time, each node i∈N corresponds to a vehicle load q. i and vehicle parking time d i For example, q0 = q 2n+1 =0,q i =-q n+i (i = 1, ..., n), d0 = d 2n+1 =0.

[0081] Furthermore, to construct an optimization model for public transport service routes, it is necessary to define the attributes of paths and nodes in the network, including the cost of the path, the travel time of the path, and the service time of the node. Let c be the segment cost and travel time incurred by the bus when it travels through each segment (i,j)∈A. ij t ij Each node i∈N has a service time window [ei ,l i ], where e i and l i These represent the earliest and latest times of the transportation demand service, respectively, and finally, L represents the maximum travel time for each transportation demand.

[0082] To optimize bus fleet size, bus company operating costs, and bus routes, constraints need to be imposed in three aspects: passenger service constraints, bus vehicle constraints, and other constraints.

[0083] Passenger service constraints

[0084] To ensure that all travel needs are met and only require one service, Let bus k travel from point i to point j, and the route (i,j)∈A, and bus k∈K, as shown in formula (1).

[0085]

[0086] To ensure service integrity and that each passenger can reach their destination as required, service integrity constraints need to be set, as shown in formula (2).

[0087]

[0088] Bus restrictions

[0089] To meet all travel demands, maximize bus operating efficiency, and improve passenger satisfaction, it is necessary to ensure that all vehicles in the bus station are dispatched. The constraints are shown in formula (3).

[0090]

[0091] When buses travel on the planned route, the continuity of travel must be ensured. Therefore, buses must leave after entering the station, and each bus needs to return to the depot after serving all travel needs. Therefore, the relevant constraints are shown in formula (4).

[0092]

[0093] Other constraints

[0094] The time for the bus to arrive at node j is determined by the time it arrives at node i, the stop time, and the travel time, which can be derived from formula (5).

[0095]

[0096] The number of passengers when the bus leaves station j can also be determined by the number of passengers when leaving node i and the number of passengers getting on and off at node i, which can be derived from formula (6).

[0097]

[0098] Furthermore, the travel time of passenger i on the bus route can be derived from formula (7).

[0099]

[0100] Finally, the arrival time and load of the bus are constrained as shown in formulas (8) and (9).

[0101]

[0102]

[0103] Based on the constraints, the following linear programming model is established to solve the problem of determining the optimal service scale and route for customized public transportation:

[0104] The objective function is given by equation (10):

[0105]

[0106] The above formula represents the minimum sum of total operating costs and total user waiting time, which is the minimum total system cost. To standardize the calculation, multiplying time by the average hourly wage α converts it to yuan for easier calculation. This parameter is determined by factors such as regional GDP, population, and economic development. Since the social development status varies greatly from region to region, the value of this parameter needs to be determined based on the actual situation.

[0107] A two-stage customized bus scheduling and route optimization model is proposed under the constraints of vehicle service cost and user travel waiting time. First, a static vehicle scheduling decision that can cover all high-probability stops is generated. Then, an optimized route that can meet the advance reservation requirement is generated. Finally, the optimal number of buses and the optimal bus route that can meet the travel demand are determined, thereby minimizing the total system cost.

[0108] Planning problems based on time windows consider passengers and their travel time to meet personalized travel needs, while also constraining time and cost to reduce passenger travel delays and lower bus company fuel costs, thereby increasing passenger travel satisfaction.

[0109] In the optimal system scenario, the optimized grouping strategy effectively reduces the size of customized buses while maximizing the utilization rate of each service vehicle. By reducing vehicle trips, the road traffic load can be reduced, traffic capacity can be improved, and traffic congestion can be alleviated.

[0110] By comparing the total energy consumption with the optimal scenario for individual travel, it can be seen that customized public transportation can significantly reduce energy consumption during car travel, thereby reducing vehicle exhaust emissions and air pollution.

[0111] Example 2:

[0112] A system for implementing the above method includes:

[0113] The customized bus route module is configured to: use a directed graph to represent the urban road network, construct a customized bus route planning model, each traffic demand n is associated with a starting node i and a target node n+i in the urban road network, each node i has a service time window, the service time window represents the set of the earliest and latest service times of the traffic demand, and the maximum travel time for each traffic demand n is L;

[0114] The objective function module is configured to construct an objective function with the goal of minimizing the sum of total operating costs and total user waiting time. The solution to the objective function is obtained through passenger service constraints, bus vehicle constraints, and other constraints, thereby determining the optimal number of buses and the optimal bus route that can meet travel demand.

[0115] Example 3:

[0116] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the customized bus route optimization method described in Embodiment 1 above.

[0117] Example 4:

[0118] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the customized bus route optimization method described in Embodiment 1 above.

[0119] The steps or modules involved in Embodiments 2 to 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0120] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A customized bus route optimization method, characterized by: Includes the following steps: Using directed graphs to represent the urban road network, a customized public transport route planning model is constructed, considering each traffic demand. They are all associated with a starting node in a city road network. and target node Each node There exists a service time window, which represents the set of earliest and latest times for traffic demand to be served. Each traffic demand... The maximum travel time is ; The objective function is constructed with the goal of minimizing the sum of total operating cost and total user waiting time. The solution to the objective function is obtained through passenger service constraints, bus vehicle constraints and other constraints, and the optimal number of buses and the optimal bus route that can meet travel demand are determined. The public transport route planning model includes: Given a directed graph For the urban road network, A directed road segment in the road network. For nodes in the road network, For road network traffic demand, each traffic demand is associated with a starting node. and target node Each node ; set up For the purpose of assembling customized buses, each customized bus And the capacity is The total travel time shall not exceed Each node Each corresponds to a vehicle load. and vehicle parking time ; Assume the bus passes through each section of the road The resulting road segment costs and travel time are respectively , Each node There is a service time window ,in and These represent the earliest and latest times of traffic demand services, respectively. This indicates the maximum travel time for each type of transportation demand. The constraints on the public transport vehicles are as follows: Ensure that all vehicles within the bus depot are dispatched, as shown in the following formula: ； When a bus travels along a planned route, it must leave after entering a stop, and each bus must return to the depot after serving all travel needs, as shown in the following formula: ； 。 2. The customized bus route optimization method as described in claim 1, characterized in that: The public transport route planning model also includes: The bus departure node is 0, and the arrival node is... The intermediate transition nodes include a set of receiving nodes. and delivery node set Nodes in the road network It equals the union of the set of receiving nodes, the set of delivering nodes, and the bus departure node to the arrival node, i.e. .

3. The customized bus route optimization method as described in claim 1, characterized in that: The passenger service constraints are as follows: To ensure that all travel needs are served and served only once, For public transportation From point Drive to point and the road section Bus As shown in the following formula: ； Ensuring that every passenger reaches their destination according to their travel needs, i.e., the service integrity constraint, is as follows: 。 4. The customized bus route optimization method as described in claim 1, characterized in that: The other constraints are: Bus arrival point j Time is determined by the arrival time at the node i The time, stop time, and travel time are determined as follows: ； The bus leaves the station j The number of passengers at the time of departure from the node i Passenger numbers at the time and at the node i The number of passengers getting on and off the bus is determined as shown in the following formula: ； On bus routes, passengers i The travel time is shown in the following formula: ； The bus arrival time and load constraints are as follows: ； 。 5. The customized bus route optimization method as described in claim 1, characterized in that: The objective function is: In the formula, For road segment costs, Constraints for passenger service For passenger i, the travel time. The average hourly wage per person.

6. A customized bus route optimization system, characterized by: include: The customized bus route module is configured to: use a directed graph to represent the urban road network, construct a customized bus route planning model, and determine the route for each traffic demand. They are all associated with a starting node in a city road network. and target node Each node There exists a service time window, which represents the set of earliest and latest times for traffic demand to be served. Each traffic demand... The maximum travel time is ; The objective function module is configured to construct an objective function with the goal of minimizing the sum of total operating costs and total user waiting time. The solution to the objective function is obtained through passenger service constraints, bus constraints, and other constraints, thereby determining the optimal number of buses and the optimal bus route that can meet travel demand. The public transport route planning model includes: Given a directed graph For the urban road network, A directed road segment in the road network. For nodes in the road network, For road network traffic demand, each traffic demand is associated with a starting node. and target node Each node ; set up For the purpose of assembling customized buses, each customized bus And the capacity is The total travel time shall not exceed Each node Each corresponds to a vehicle load. and vehicle parking time ; Assume the bus passes through each section of the road The resulting road segment costs and travel time are respectively , Each node There is a service time window ,in and These represent the earliest and latest times of traffic demand services, respectively. This indicates the maximum travel time for each type of transportation demand. The constraints on the public transport vehicles are as follows: Ensure that all vehicles within the bus depot are dispatched, as shown in the following formula: ； When a bus travels along a planned route, it must leave after entering a stop, and each bus must return to the depot after serving all travel needs, as shown in the following formula: ； 。 7. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the customized bus route optimization method as described in any one of claims 1-5.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the customized bus route optimization method as described in any one of claims 1-5.

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