Route guiding strategy for guiding driver to receive order

Abstract

The invention relates to a route guiding strategy for guiding a driver to receive an order, and relates to the technical field of big data analysis. Order placing data of passengers, position information of the driver and geographic information data of a city are collected and stored in a database, and region division and supply-demand ratio calculation are performed according to the current order number and the current driver number; the city is divided into a hot area and a depression area according to the supply-demand ratio, the hot area is an area with the demand larger than the supply, the depression area is an area with the supply larger than the demand, drivers are screened, the drivers meeting the conditions are divided into a guide group and a control group, the guide group receives a recommended route, the control group is not guided, and for the drivers of the guide group, the guide group receives a recommended route; and generating a recommended route by using an optimal path algorithm according to the position and supply and demand conditions of the hot area, and preferentially guiding the driver to go to the hot area. By preferentially guiding the driver to go to the hot area, the travel time of the driver can be shortened, and the overall order receiving rate is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and more specifically, to a route guidance strategy for guiding drivers to accept orders. Background Art

[0002] In modern mobility services, orders and drivers are typically matched based on passenger demand and driver availability. When placing an order, passengers select their starting and ending points and circle available drivers near their starting point. However, this passive order-taking model has limitations, particularly when there's an imbalance between the supply and demand of drivers and orders. For example, when there are few orders near a driver's current location, they may be unable to receive orders for extended periods, resulting in reduced income and lower work efficiency. This situation not only undermines driver motivation but also diminishes the overall passenger experience, creating a vicious cycle.

[0003] During peak hours or in specific areas, passenger demand often concentrates in hotspots such as commercial centers, entertainment venues, or transportation hubs. Despite the large number of passengers waiting for service, the uneven distribution of drivers means many drivers remain in areas with fewer orders, making them unable to respond promptly to passenger requests. This imbalance in supply and demand leads to increased idle driving rates for drivers, increasing operating costs, and lengthening passenger wait times, impacting the travel experience.

[0004] To improve this situation, it's crucial to implement a strategy that proactively guides drivers to locations where they're more likely to receive orders. This strategy analyzes real-time order distribution and driver location to intelligently generate routes that are conducive to receiving orders, helping drivers navigate to high-demand areas. Summary of the Invention

[0005] In view of the technical problems existing in the prior art, the present invention provides a route guidance strategy for guiding drivers to accept orders, so as to solve the problems raised in the above background technology.

[0006] The present invention solves the above-mentioned technical problem with the following technical solution: a route guidance strategy for guiding drivers to accept orders, specifically comprising the following steps:

[0007] Step 101: Collect passenger order data, driver location information, and city geographic information data, store them in a database, and perform regional division and supply-demand ratio calculation based on the current number of orders and the number of drivers;

[0008] Step 102: Divide the city into hot and low-lying areas based on the supply-demand ratio, screen drivers, and divide qualified drivers into a guidance group and a control group. The guidance group receives recommended routes, while the control group does not receive guidance.

[0009] Step 103: Based on the location and supply and demand of the hot zone, an optimal path algorithm is used to generate a recommended route, and drivers are guided to the hot zone first.

[0010] In a preferred embodiment, in step 101, passenger order data, driver location information, and city geographic information data are collected and stored in a database. Based on the current number of orders and the number of drivers, regional division and supply-demand ratio calculation are performed. The specific steps are as follows:

[0011] Step A1: The passenger's order data includes the passenger ID, order time, departure location, and destination. The driver's location information includes the driver's current longitude and latitude, status, and driver ID. An order table and a driver information table are created. The order table stores the passenger's order data, and the driver information table stores the driver's location information.

[0012] Step A2, regional division: Based on the city's geographical characteristics and needs, in the GIS tool, set the side length of the hexagon and generate a hexagonal grid within the city area. Encode the passenger's starting point and the driver's real-time location into the corresponding hexagonal grid. Assign a unique identifier to each generated hexagon as the region ID. For each region, calculate the current number of orders expressed as O r and the number of idle drivers is denoted as D r , where r is the region ID;

[0013] Step A3: Calculate the supply-demand ratio: For each region, calculate the supply-demand ratio using the following formula:

[0014]

[0015] Among them, S r is the supply-demand ratio in region r, O r is the current order quantity of region r, D r is the current number of idle drivers in region r.

[0016] In a preferred embodiment, in step 102, the city is divided into hot and low-lying areas based on the supply-demand ratio, and drivers are screened. Qualified drivers are divided into a guidance group and a control group. The guidance group receives recommended routes, while the control group is not guided. The specific steps are as follows:

[0017] Step B1, define hot zone and depression zone: according to the supply-demand ratio S r The value of , determines the supply and demand situation of the region:

[0018] S r >1: Indicates that the demand in the area is greater than the supply, and it is necessary to increase driver dispatch and classify it as a hot zone;

[0019] S r=1: indicates that the supply and demand in the area are balanced, and the area is classified as a balanced zone;

[0020] S r <1: Indicates that the supply in this area exceeds the demand, and the driver distribution needs to be adjusted to divide it into a low-lying area;

[0021] Step B2: Driver screening: Based on the driver's status and location, the available drivers are screened to form a set D = {d j |d j Shows online status and d j In the hot zone or depression area}, where d j is the identifier of each driver, and the drivers in the hot zone are selected from the set D as the guide group, which is represented by the set G = {d j |d j ∈D and d j In the hot zone}, based on real-time supply and demand data, the best order-taking route is recommended to the guidance group drivers; the drivers in the low-lying zone in the set D are used as the control group, which is represented by the set C = {d j |d j ∈D and d j In low-lying areas, no route recommendations are provided, and the passengers are allowed to choose their own routes.

[0022] In a preferred embodiment, in step 103, an optimal path algorithm is used to generate a recommended route based on the location and supply and demand of the hot zone, and drivers are guided to the hot zone first. The specific steps are as follows:

[0023] Step C1: Construct road network model: The center coordinate of the hot zone is expressed as P h (x h ,y h ), using graph theory model, define the city's road network, vertex V represents the intersection, edge E represents the road section, edge weight w(e) represents the distance of the road section, and the driver's current position is represented by coordinates P d (x d ,y d ) indicates that the optimal path algorithm is selected from the driver's current position P d Hot zone center P h The shortest path is calculated as follows:

[0024]

[0025] Where P is the path from the starting point to the hot zone, Path(P d ,P h ) is the set of all paths from the starting point to the end point, w(e) is the weight of each edge in the path;

[0026] Step C2: Generate recommended routes: Based on the calculated optimal path, output recommended routes, including key nodes along the way and the final destination. Regularly update the recommended routes based on real-time supply and demand and traffic conditions to ensure that drivers are guided to enter hot spots in a timely manner.

[0027] The present invention has the following beneficial effects: it collects passenger order data, driver location information, and city geographic information data, stores them in a database, performs regional division and calculates the supply-demand ratio based on the current number of orders and the number of drivers, and divides the city into hot zones and low-lying zones based on the supply-demand ratio. The hot zones are areas where demand exceeds supply, and the low-lying zones are areas where supply exceeds demand. This precise regional division enables the travel platform to effectively identify high-potential service areas and help drivers optimize their order acceptance strategies. Drivers are screened and qualified drivers are divided into a guided group and a control group. The guided group receives recommended routes, while the control group does not. This can provide reliable data support for subsequent effect evaluation. Based on the location and supply-demand situation of the hot zones, an optimal path algorithm is used to generate recommended routes, prioritizing drivers to hot zones. This strategy of actively guiding drivers to high-demand areas is an effective supplement to the traditional passive order acceptance model and an important means to improve travel service efficiency and the driver and passenger experience. Through the application of technology and intelligent management, the travel service platform can achieve more efficient resource allocation, promote the sustainable development of the industry, optimize the overall travel service experience, and promote driver job satisfaction and passenger travel convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0030] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0031] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0032] Example 1

[0033] This embodiment provides Figure 1 A route guidance strategy for guiding drivers to accept orders is shown, which specifically includes the following steps:

[0034] Step 101: Collect passenger order data, driver location information, and city geographic information data, store them in a database, and perform regional division and supply-demand ratio calculation based on the current number of orders and the number of drivers;

[0035] Step 102: Divide the city into hot and low-lying areas based on the supply-demand ratio, screen drivers, and divide qualified drivers into a guidance group and a control group. The guidance group receives recommended routes, while the control group does not receive guidance.

[0036] Step 103: Based on the location and supply and demand of the hot zone, an optimal path algorithm is used to generate a recommended route, and drivers are guided to the hot zone first.

[0037] Preferably, in step 101, passenger order data, driver location information, and city geographic information data are collected and stored in a database. Based on the current number of orders and the number of drivers, regional division and supply-demand ratio calculation are performed to identify areas with supply-demand imbalance in advance, which can effectively reduce passenger waiting time and improve the overall user experience. The specific steps are as follows:

[0038] Step A1: The passenger's order data includes the passenger ID, order time, departure location, and destination. The driver's location information includes the driver's current longitude and latitude, status, and driver ID. An order table and a driver information table are created. The order table stores passenger order data, and the driver information table stores driver location information. By establishing structured order and driver information tables, centralized data management can be achieved, facilitating subsequent data query, analysis, and processing, and achieving more accurate order matching.

[0039] Step A2, regional division: Based on the city's geographical characteristics and needs, in the GIS tool, set the side length of the hexagon and generate a hexagonal grid within the city area. Encode the passenger's starting point and the driver's real-time location into the corresponding hexagonal grid. Assign a unique identifier to each generated hexagon as the region ID. For each region, calculate the current number of orders expressed as O r and the number of idle drivers is denoted as D r , where r is the region ID, to achieve more accurate path planning and demand analysis;

[0040] Step A3: Calculate the supply-demand ratio: For each region, calculate the supply-demand ratio using the following formula:

[0041]

[0042] Among them, S r is the supply-demand ratio in region r, O r is the current order quantity of region r, D r is the current number of idle drivers in region r.

[0043] Preferably, in step 102, the city is divided into hot areas and low-demand areas according to the supply-demand ratio, and drivers are screened. Qualified drivers are divided into a guidance group and a control group. The guidance group accepts recommended routes, while the control group is not guided. By guiding drivers to hot areas, the driver's order acceptance rate can be increased, the phenomenon of drivers driving empty in low-demand areas can be reduced, and overall operational efficiency can be improved. The specific steps are as follows:

[0044] Step B1, define hot zone and depression zone: according to the supply-demand ratio S r The value of , determines the supply and demand situation of the region:

[0045] S r >1: Indicates that the demand in the area is greater than the supply, and it is necessary to increase driver dispatch and classify it as a hot zone;

[0046] S r =1: indicates that the supply and demand in the area are balanced, and the area is classified as a balanced zone;

[0047] S r <1: Indicates that the supply in this area exceeds the demand, and the driver distribution needs to be adjusted to divide it into a low-lying area;

[0048] Step B2: Driver screening: Based on the driver's status and location, the available drivers are screened to form a set D = {d j |d j Shows online status and d j In the hot zone or depression area}, where d jis the identifier of each driver, and the drivers in the hot zone are selected from the set D as the guide group, which is represented by the set G = {d j |d j ∈D and d j In the hot zone, based on real-time supply and demand data, the optimal order-taking route is recommended to the guidance group drivers; the drivers in the low-lying zone in the set D are used as the control group, which is represented by the set C = {d j |d j ∈D and d j In low-lying areas, no route recommendations are provided, and the passengers are allowed to choose their own routes.

[0049] Preferably, in step 103, an optimal path algorithm is used to generate recommended routes based on the location and supply and demand of the hot zone, and drivers are preferentially guided to the hot zone, thereby increasing the chances of drivers receiving orders in high-demand areas and increasing the number of orders received. Drivers can quickly reach the hot zone, enabling passengers to receive service faster, thereby enhancing passenger satisfaction and loyalty, ensuring that there are sufficient drivers available in high-demand areas, and improving the availability and timeliness of the overall service. The specific steps are as follows:

[0050] Step C1: Construct road network model: The center coordinate of the hot zone is expressed as P h (x h ,y h ), using graph theory model, define the city's road network, vertex V represents the intersection, edge E represents the road section, edge weight w(e) represents the distance of the road section, and the driver's current position is represented by coordinates P d (x d ,y d ) indicates that the optimal path algorithm is selected from the driver's current position P d Hot zone center P h The shortest path is calculated as follows:

[0051]

[0052] Where P is the path from the starting point to the hot zone, Path(P d ,P h ) is the set of all paths from the starting point to the end point, w(e) is the weight of each edge in the path;

[0053] Step C2: Generate recommended routes: Based on the calculated optimal path, output recommended routes, including key nodes along the way and the final destination. Regularly update the recommended routes based on real-time supply and demand and traffic conditions to ensure that drivers are guided to enter hot spots in a timely manner.

[0054] Example 2

[0055] This embodiment provides Figure 1 A route guidance strategy for guiding drivers to accept orders is shown, which specifically includes the following steps:

[0056] Step 101: Collect passenger order data, driver location information, and city geographic information data, store them in a database, and perform regional division and supply-demand ratio calculation based on the current number of orders and the number of drivers;

[0057] Furthermore, in step 101, passenger order data, driver location information, and city geographic information data are collected and stored in a database. Based on the current number of orders and the number of drivers, regional division and supply-demand ratio calculation are performed. The specific steps are as follows:

[0058] Step A1: The passenger's order data includes the passenger ID, order time, departure location, and destination. The driver's location information includes the driver's current longitude and latitude, status, and driver ID. An order table and a driver information table are created. The order table stores the passenger's order data, and the driver information table stores the driver's location information.

[0059] Step A2, regional division: Based on the city's geographical characteristics and needs, in the GIS tool, set the side length of the hexagon and generate a hexagonal grid within the city area. Encode the passenger's starting point and the driver's real-time location into the corresponding hexagonal grid. Assign a unique identifier to each generated hexagon as the region ID. For each region, calculate the current number of orders expressed as O r and the number of idle drivers is denoted as D r , where r is the region ID;

[0060] Step A3: Calculate the supply-demand ratio: For each region, calculate the supply-demand ratio using the following formula:

[0061]

[0062] Among them, S r is the supply-demand ratio in region r, O r is the current order quantity of region r, D r is the current number of idle drivers in region r.

[0063] Step 102: Divide the city into hot and low-lying areas based on the supply-demand ratio, screen drivers, and divide qualified drivers into a guidance group and a control group. The guidance group receives recommended routes, while the control group does not receive guidance.

[0064] Furthermore, in step 102, the city is divided into hot and low-lying areas according to the supply-demand ratio, and drivers are screened. Drivers who meet the requirements are divided into a guidance group and a control group. The guidance group receives the recommended route, while the control group is not guided. The specific steps are as follows:

[0065] Step B1, define hot zone and depression zone: according to the supply-demand ratio Sr The value of , determines the supply and demand situation of the region:

[0066] S r >1: Indicates that the demand in the area is greater than the supply, and it is necessary to increase driver dispatch and classify it as a hot zone;

[0067] S r =1: indicates that the supply and demand in the area are balanced, and the area is classified as a balanced zone;

[0068] S r <1: This indicates that the supply in this area exceeds the demand, and the driver distribution needs to be adjusted to divide it into a low-lying area;

[0069] Step B2: Driver screening: Based on the driver's status and location, the available drivers are screened to form a set D = {d j |d j Shows online status and d j In the hot zone or depression area}, where d j is the identifier of each driver, and the drivers in the hot zone are selected from the set D as the guide group, which is represented by the set G = {d j |d j ∈D and d j In the hot zone, based on real-time supply and demand data, the optimal order-taking route is recommended to the guidance group drivers; the drivers in the low-lying zone in the set D are used as the control group, which is represented by the set C = {d j |d j ∈D and d j In low-lying areas, no route recommendations are provided, and the passengers are allowed to choose their own routes.

[0070] Step 103: Based on the location and supply and demand of the hot zone, an optimal path algorithm is used to generate a recommended route, prioritizing drivers to the hot zone.

[0071] Furthermore, in step 103, based on the location and supply and demand of the hot zone, an optimal path algorithm is used to generate a recommended route, and drivers are guided to the hot zone first. The specific steps are as follows:

[0072] Step C1: Construct road network model: The center coordinate of the hot zone is expressed as P h (x h ,y h ), using graph theory model, define the city's road network, vertex V represents the intersection, edge E represents the road section, edge weight w(e) represents the distance of the road section, and the driver's current position is represented by coordinates P d (x d ,y d ) indicates that the optimal path algorithm is selected from the driver's current position P d Hot zone center P h The shortest path is calculated as follows:

[0073]

[0074] Where P is the path from the starting point to the hot zone, Path(P d ,P h ) is the set of all paths from the starting point to the end point, w(e) is the weight of each edge in the path;

[0075] Step C2: Generate recommended routes: Based on the calculated optimal path, output recommended routes, including key nodes along the way and the final destination. Regularly update the recommended routes based on real-time supply and demand and traffic conditions to ensure that drivers are guided to enter hot spots in a timely manner.

[0076] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0081] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0082] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A route guidance strategy for guiding drivers to accept orders, characterized by: The specific steps include: Step 101: Collect passenger order data, driver location information, and city geographic information data, store them in a database, and perform regional division and supply-demand ratio calculation based on the current number of orders and the number of drivers; Step 102: Divide the city into hot and low-lying areas based on the supply-demand ratio, screen drivers, and divide qualified drivers into a guidance group and a control group. The guidance group receives recommended routes, while the control group does not receive guidance. Step 103: Based on the location and supply and demand of the hot zone, an optimal path algorithm is used to generate a recommended route, and drivers are guided to the hot zone first.

2. A route guidance strategy for guiding drivers to accept orders according to claim 1, characterized in that: In step 101, passenger order data, driver location information, and city geographic information data are collected and stored in a database. Based on the current number of orders and the number of drivers, regional division and supply-demand ratio calculation are performed. The specific steps are as follows: Step A1: Create an order table and a driver information table. The order table stores passenger order data, and the driver information table stores driver location information. Step A2, regional division: Based on the city's geographical characteristics and needs, in the GIS tool, set the side length of the hexagon and generate a hexagonal grid within the city area. Encode the passenger's starting point and the driver's real-time location into the corresponding hexagonal grid. Assign a unique identifier to each generated hexagon as the region ID. For each region, calculate the current number of orders expressed as O r and the number of idle drivers is denoted as D r , where r is the region ID; Step A3: Calculate the supply-demand ratio: For each region, calculate the supply-demand ratio using the following formula: Among them, S r is the supply-demand ratio in region r, O r is the current order quantity of region r, D r is the current number of idle drivers in region r.

3. A route guidance strategy for guiding drivers to accept orders according to claim 2, characterized in that: In step 102, the city is divided into hot and low-lying areas based on the supply-demand ratio, and drivers are screened. Qualified drivers are divided into a guidance group and a control group. The guidance group receives recommended routes, while the control group is not guided. The specific steps are as follows: Step B1, define hot zone and depression zone: according to the supply-demand ratio S r The value of , determines the supply and demand situation of the region: S r >1: Indicates that the demand in the area is greater than the supply, and it is necessary to increase driver dispatch and classify it as a hot zone; S r =1: indicates that the supply and demand in the area are balanced, and the area is classified as a balanced zone; S r <1: This indicates that the supply in this area exceeds the demand, and the driver distribution needs to be adjusted to divide it into a low-lying area; Step B2: Driver screening: According to the driver's status and location, the drivers are screened to form a set D = {d j |d j Shows online status and d j In the hot zone or depression area}, where d j is the identifier of each driver, and the drivers in the hot zone are selected from the set D as the guide group, which is represented by the set G = {d j |d j ∈D and d j In the hot zone, based on real-time supply and demand data, the optimal order-taking route is recommended to the guidance group drivers; the drivers in the low-lying zone in the set D are used as the control group, which is represented by the set C = {d j |d j ∈D and d j In low-lying areas, no route recommendations are provided, and the passengers are allowed to choose their own routes.

4. A route guidance strategy for guiding drivers to accept orders according to claim 3, characterized in that: In step 103, based on the location and supply and demand of the hot zone, an optimal path algorithm is used to generate a recommended route, prioritizing drivers to the hot zone. The specific steps are as follows: Step C1: Construct road network model: The center coordinate of the hot zone is expressed as P h (x h ,y h ), using graph theory model, define the city's road network, vertex V represents the intersection, edge E represents the road section, edge weight w(e) represents the distance of the road section, and the driver's current position is represented by coordinates P d (x d ,y d ) indicates that the optimal path algorithm is selected from the driver's current position P d Hot zone center P h The shortest path; Step C2: Generate recommended routes: Based on the calculated optimal path, output recommended routes, including key nodes along the way and the final destination. Regularly update the recommended routes based on real-time supply and demand and traffic conditions to ensure that drivers are guided to enter hot spots in a timely manner.

5. A route guidance strategy for guiding drivers to accept orders according to claim 4, characterized in that: In step C1, the road network model is constructed, and the optimal path algorithm is selected from the driver's current position P d Hot zone center P h The shortest path is calculated as follows: Where P is the path from the starting point to the hot zone, Path(P d ,P h ) is the set of all paths from the starting point to the end point, and w(e) is the weight of each edge in the path.

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