Bus route optimization method, device, equipment and medium

Abstract

The invention discloses a bus route optimization method and device, equipment and a medium. The method comprises the following steps: matching candidate travel data with public transit network data to obtain target travel data meeting an initial optimization condition; judging an optimization type to which the public transit network belongs according to the target travel data; the optimization type comprises line new distribution, line customization or line adjustment; and if the optimization type is a new route, selecting a target station from the existing stations according to the travel demand quantity between the stations in the target travel data and the distance between the different stations, and generating a new route between the target stations. According to the embodiment of the invention, the public transportation network can be optimized, and the overall operation efficiency of public transportation is improved.

Description

Technical Field

[0001] The present invention relates to the field of public transportation technology, and in particular to a bus route optimization method, device, equipment and medium. Background Art

[0002] Urban public transportation is an important part of urban infrastructure and is directly related to the overall function of the city. The urban bus network is a passenger transportation network consisting of fixed routes and parking stations arranged on urban streets. The reasonable arrangement of urban bus routes and schedules is an important factor in determining the comprehensive performance of the bus system. However, the existing bus system has many problems, such as uneven station distribution, repeated station construction, low system coverage, high line duplication rate, and unreasonable line settings. Summary of the invention

[0003] The present invention provides a bus route optimization method, device, equipment and medium to solve the problems of uneven station distribution, repeated station construction, low system coverage, high line duplication rate, unreasonable line setting, etc. in the existing bus system.

[0004] According to one aspect of the present invention, a bus route optimization method is provided, comprising:

[0005] Match the candidate travel data with the bus network data to obtain the target travel data that meets the initial optimization conditions;

[0006] Determining the optimization type of the bus network according to the target travel data; the optimization type includes new line creation, line customization or line adjustment;

[0007] If the optimization type is new route construction, the target site is selected from the existing sites according to the travel demand between the sites in the target travel data and the distances between different sites, and a new route between the target sites is generated.

[0008] According to another aspect of the present invention, a bus route optimization device is provided, comprising:

[0009] The target travel data module is used to match the candidate travel data with the bus network data to obtain the target travel data that meets the initial optimization conditions;

[0010] An optimization type determination module, used to determine the optimization type of the bus network according to the target travel data; the optimization type includes new line opening, line customization or line adjustment;

[0011] The route creation module is used to select target sites from existing sites and generate new routes between the target sites based on the travel demand between the sites and the distances between different sites in the target travel data if the optimization type is route creation.

[0012] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the bus route optimization method according to any embodiment of the present invention is implemented.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and a memory in communication with the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the bus route optimization method described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the bus route optimization method described in any embodiment of the present invention when executed.

[0017] The embodiment of the present invention obtains target travel data that meets the initial optimization conditions by matching candidate travel data with bus network data; determines the optimization type of the bus network according to the target travel data; when the optimization type is new line opening, selects the target station from the bus stations involved in the bus travel routes corresponding to the target travel data in the existing bus network according to the travel demand between the stations in the target travel data and the distances between different stations, and generates new lines between the target stations. By screening the target travel data that meets the initial optimization conditions, the potential problems and improvement space of the existing bus network can be revealed, so as to optimize the bus network according to the travel demand of the target travel data, improve the overall operation efficiency of public transportation, save the time required for users' bus travel, ensure that the bus travel can be completed within a reasonable time, and meet the users' demand for bus travel efficiency.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1A is a first flow chart of the bus route optimization method provided by an embodiment of the present invention;

[0021] Figure 1B It is a schematic diagram of existing travel data provided by an embodiment of the present invention;

[0022] Figure 1C This is a schematic diagram of future travel data based on ARIMA prediction provided by an embodiment of the present invention;

[0023] Figure 2 is a second flow chart of the bus route optimization method provided by an embodiment of the present invention;

[0024] Figure 3 is a third flow chart of the bus route optimization method provided by an embodiment of the present invention;

[0025] Figure 4 It is a structural schematic diagram of a bus route optimization device provided by an embodiment of the present invention;

[0026] Figure 5 It is a schematic diagram of the structure of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Figure 1A This is the first flow chart of the bus route optimization method provided by the embodiment of the present invention. This embodiment is applicable to the case of optimizing the existing bus line network data. The method can be executed by a bus route optimization device, which can be implemented in the form of hardware and / or software. The device can be configured in an electronic device with corresponding data processing capabilities. Figure 1A As shown, the method includes:

[0030] S110, matching the candidate travel data with the bus line network data to obtain target travel data that meets the initial optimization conditions.

[0031] The existing travel data is obtained through channels such as questionnaire surveys and passenger flow OD (Origin Destination) analysis platforms. The existing travel data includes the departure point and arrival point of the trip. Determine future travel data based on the existing travel data and in combination with the navigation system or other transportation systems. In an optional implementation, the collected existing travel data is cleaned, including processing missing values, outliers, etc., and the autoregressive integrated moving average (ARIMA, AutoRegressive Integrated Moving Average) model is used to predict the travel data at future times for the processed existing travel data. The specific process is shown in the following formula (1):

[0032]

[0033] Among them, X t Indicates the departure point or arrival point of the travel data at time point t; B k X t Indicates the departure point or arrival point of the travel data at time point tk; (1-B) d X t Used to remove trends in time series and make the series stable; Indicates the influence of the travel data of the past p days on the current travel data; θ q Indicates the influence of random errors in the past q days on the current travel data; ∈ t Represents the random error at the current time point. For example, the existing travel data is as follows Figure 1B As shown in Figure 2, the future travel data based on ARIMA prediction is as follows: Figure 1C shown.

[0034] The existing travel data and the predicted future travel data are used as candidate travel data, and the candidate travel data are matched with the existing bus network data. The candidate travel data that does not match the existing bus network data are screened out as target travel data, so that the existing bus network data can be optimized according to the target travel data later.

[0035] The existing bus route optimization method uses the existing travel data to determine the optimization plan, which does not take into account that the future travel needs of residents will change over time, resulting in inaccurate bus route settings and waste of human and material resources. This embodiment predicts the future travel data of residents through the existing travel data of residents in a certain city, combines the existing travel data and the predicted future travel data as candidate travel data, matches the candidate travel data with the existing bus network data, and screens out the travel data that does not match the existing bus network data as the target travel data, so that the existing bus network data can be optimized according to the target travel data later. This embodiment not only takes into account the existing travel data but also takes into account future travel needs, so that the bus network optimization plan determined based on travel data can better meet the actual travel needs of residents and avoid waste of public transportation resources.

[0036] In an optional implementation, the candidate travel data is matched with the bus network data to obtain target travel data that meets the initial optimization conditions, including: if there are not all bus stops within a certain range of the departure point and the arrival point of the candidate travel data, then determining whether the number of transfers of the candidate travel data is less than a first value; if the number of transfers of the candidate travel data is greater than or equal to the first value, then determining whether the travel duration of the candidate travel data is less than the first duration; if the travel duration of the selected travel data is greater than or equal to the first duration, determining the candidate travel data as the target travel data.

[0037] Specifically, the candidate travel data is matched with the bus network data. If there are bus stops within a certain range of the departure point and the arrival point of the candidate travel data, the existing bus network data meets the travel needs of the candidate travel data. If there is no bus stop within a certain range of the departure point of the candidate travel data, such as within 300m, or there is no bus stop within a certain range of the arrival point of the candidate travel data, then it is determined whether the number of transfers of the candidate travel data is less than a first value, which can be set to 1; if the number of transfers of the candidate travel data is less than the first value, then the existing bus network data meets the travel needs of the candidate travel data; if there is no bus stop within a certain range of the departure point of the candidate travel data, or there is no bus stop within a certain range of the arrival point of the candidate travel data, and the number of transfers of the candidate travel data is greater than or equal to the first value, then it is determined whether the travel time of the candidate travel data is less than the first time, which can be set to 90 minutes; if the travel time of the candidate travel data is less than the first time, then the existing bus network data meets the travel needs of the candidate travel data; if the travel time of the candidate travel data is greater than or equal to the first time, then the candidate travel data is determined to be the target travel data.

[0038] By matching the candidate travel data with the bus network data, the target travel data that meets the initial optimization conditions is obtained. The bus network is optimized according to the travel demand of the target travel data. By judging whether there are bus stops within a certain range of the departure point and arrival point of the candidate travel data, it can be ensured that there are bus stops near the departure point and arrival point of the user, which greatly reduces the walking distance and time of the user and improves the convenience of travel. By limiting the number of transfers, complex bus travel routes can be avoided, reducing the inconvenience and risks that users may encounter during the transfer process, and improving the overall travel experience. By setting a limit on the duration of bus travel, it is ensured that bus travel can be completed within a reasonable time to meet the user's demand for bus travel efficiency. By screening the target travel data that meets the initial optimization conditions, the potential problems and improvement space of the existing bus network can be revealed, so as to optimize the bus network according to the travel demand of the target travel data and improve the overall operational efficiency of public transportation.

[0039] S120: Determine the optimization type of the bus network according to the target travel data.

[0040] Among them, the optimization type includes new line opening, line customization or line adjustment. New line opening is to open a new bus line from the existing bus stops involved in the target travel data. In an optional implementation, judging the optimization type of the bus network according to the target travel data includes: if the time required for bus travel of the target travel data is greater than or equal to the product of the time required for driving travel of the target travel data and the first threshold, and the driving travel path length of the target travel data belongs to the first distance threshold, then the optimization type of the bus network is new line opening. The first threshold is the bus driving time ratio, that is, for any target travel data, the ratio of the time required for bus travel to the time required for driving travel. Taking the first threshold as 2 and the first distance threshold as 10KM-15KM as an example, judging the optimization type of the bus network according to the target travel data includes: if the time required for bus travel of the target travel data is greater than or equal to twice the time required for driving travel of the target travel data, and the driving travel path length of the target travel data is within the range of 10KM-15KM, then the optimization type of the bus network is new line opening.

[0041] The driving travel path length of the target travel data can reflect the actual demand length of the target travel data. When the demand length of the travel is within a reasonable range and the time required for bus travel is much longer than the time required for driving travel, it indicates that the existing bus travel routes corresponding to the target travel data are unreasonable, so as to determine the optimization type of the bus network as new route opening, so as to save the time required for users' bus travel, ensure that the bus travel can be completed within a reasonable time, and meet the users' demand for bus travel efficiency.

[0042] S130: If the optimization type is new route creation, a target site is selected from existing sites according to the travel demand between sites and the distances between different sites in the target travel data, and a new route between the target sites is generated.

[0043] According to the bus travel routes corresponding to the target travel data in the existing bus network, the existing bus stops involved in the bus travel routes are determined, and the target travel data volume passing through any two bus stops is determined as the travel demand between the stops. According to the travel demand between the stops and the distance between the stops, the target stop is selected from the existing bus stops involved in the bus travel routes, and a new route between the target stops is generated, so that the new route can meet the travel needs of users while ensuring that the new route is not too long, ensuring that the bus trip can be completed within a reasonable time, and meeting the user's demand for bus travel efficiency.

[0044] The embodiment of the present invention obtains target travel data that meets the initial optimization conditions by matching candidate travel data with bus network data; determines the optimization type of the bus network according to the target travel data; when the optimization type is new line opening, selects the target station from the bus stations involved in the bus travel routes corresponding to the target travel data in the existing bus network according to the travel demand between the stations in the target travel data and the distances between different stations, and generates new lines between the target stations. By screening the target travel data that meets the initial optimization conditions, the potential problems and improvement space of the existing bus network can be revealed, so as to optimize the bus network according to the travel demand of the target travel data, improve the overall operation efficiency of public transportation, save the time required for users' bus travel, ensure that the bus travel can be completed within a reasonable time, and meet the users' demand for bus travel efficiency.

[0045] Figure 2 2 is a second flow chart of the bus route optimization method provided by an embodiment of the present invention. This embodiment is optimized and improved on the basis of the above embodiment. Figure 2 As shown, the method includes:

[0046] S210: Match the candidate travel data with the bus network data to obtain target travel data that meets the initial optimization conditions.

[0047] S220: Determine the optimization type of the bus network according to the target travel data.

[0048] The optimization types include new line creation, line customization or line adjustment.

[0049] S230. If the optimization type is new line construction, then construct the connectivity parameters between the stations to characterize whether the stations are connected; determine the travel demand coverage based on the product of the travel demand between each station and the connectivity parameters between the stations; determine the line length based on the product of the distance between each station and the connectivity parameters between the stations.

[0050] S240, maximizing the travel demand coverage and minimizing the route length, so that the travel demand coverage of the newly opened route meets the minimum travel demand constraint and the route length of the newly opened route meets the maximum route length constraint, and obtaining the connectivity value between the sites.

[0051] S250: Obtain connected sites according to connectivity values ​​between the sites and determine a new route according to the connected sites.

[0052] When the optimization type is new line construction, the connectivity parameters x between the stations are constructed. ijThe integer linear programming algorithm is used to determine the connectivity value between sites. The connectivity parameter is used to characterize whether two sites i and j are connected. The connectivity value of the connectivity parameter can be 0 or 1. ij When the connectivity value is 1, it means that stations i and j are connected. Specifically, according to the bus travel routes corresponding to the target travel data in the existing bus network, the existing bus stops involved in the bus travel routes are determined, and the target travel data volume passing through any two bus stops is determined as the travel demand between the stations. The travel demand coverage is determined according to the product of the travel demand between each station and the connectivity parameter between the stations. The route length is determined according to the product of the distance between each station and the connectivity parameter between the stations. As shown in the following formula 2, maximizing the travel demand coverage between stations and minimizing the route length are used as the objective functions of the integer linear programming algorithm. The minimum travel demand constraint and the maximum route length constraint are constructed as shown in the following formula 3 to ensure that the newly opened route must cover a certain number of user needs and the total length of the newly opened route does not exceed the preset maximum length to be used as a valid route.

[0053] max(αΣ (i,j) u ij x ij -β∑ (i,j0 Length ij x ij 0(2)

[0054]

[0055] Formula 2 is the objective function of the integer linear programming algorithm, and Formula 3 is the constraint condition of the integer linear programming algorithm. Among them, α and β are weight parameters used to balance travel demand coverage and route length, u ij represents the travel demand from station i to station j, x ij is the connectivity parameter between sites, indicating whether the newly opened line passes through site i to site j; Length ij represents the distance between sites i and j. min is the user demand threshold for the new line, L max It is the total length threshold of the newly created line.

[0056] By maximizing the travel demand coverage and minimizing the route length, and making the travel demand coverage of the newly opened route meet the minimum travel demand constraint and the route length of the newly opened route meet the maximum route length constraint, the connectivity values ​​between the stations are obtained, and the connected stations are obtained according to the connectivity values ​​between the stations. The connectivity value is 0 or 1, and the value is ij = 1, it means that sites i and j are connected. All connected sites are obtained, and new routes are determined based on the connected sites.

[0057] In the case where the optimization type of the embodiment of the present invention is a newly opened route, a target station is selected from the bus stations involved in the bus travel route corresponding to the target travel data in the existing bus network; by constructing connectivity parameters between stations, an integer linear programming algorithm is used to determine the connectivity values ​​between stations according to the travel demand between stations and the distance between stations, and then the connected stations are determined, and the newly opened route is determined according to the connected stations, so that the newly opened route can meet the travel needs of users while ensuring that the new travel route is not too long, ensuring that the bus trip can be completed within a reasonable time, and meeting the user's demand for bus travel efficiency.

[0058] In an optional implementation, after determining the optimization type to which the bus network belongs, the method further includes, if the optimization type is line adjustment, executing the following: if the transfer point of the target travel data is the terminal station of the bus route, extending the terminal station of the bus route to the station where the arrival point of the target travel data is located; if the transfer point of the target travel data is the starting station of the bus route, extending the starting station of the bus route to the station where the starting point of the target travel data is located; if the transfer point of the target travel data is the midway station of the first bus route before the transfer, adding the station where the arrival point of the target travel data is located to the station of the first bus route to re-plan the first bus route; if the transfer point of the target travel data is the midway station of the second bus route after the transfer, adding the station where the departure point of the target travel data is located to the station of the second bus route to re-plan the second bus route.

[0059] Among them, route adjustment is to adjust the existing bus routes. When the length of the bus transfer path of the target travel data is greater than the second distance threshold, such as 2KM, the optimization type of the bus network is determined to be route adjustment. By adopting the optimization strategy of route adjustment when the length of the bus transfer path is greater than the set second distance threshold (such as 2KM), the passenger travel efficiency is improved and the attractiveness of public transportation is enhanced; through route adjustment, public transportation resources can be allocated more reasonably, avoiding the waste of resources in some low-demand areas, while ensuring that high-demand areas receive sufficient public transportation service support.

[0060] Figure 3 is a third flow chart of the bus route optimization method provided by an embodiment of the present invention. This embodiment is optimized and improved on the basis of the above embodiments. Figure 3 As shown, the method includes:

[0061] S310: Match the candidate travel data with the bus network data to obtain target travel data that meets the initial optimization conditions.

[0062] S320: Determine the optimization type of the bus network according to the target travel data.

[0063] The optimization types include new route creation, route customization or route adjustment. Route customization is to determine the locations of the starting station and the terminal station of the new bus route according to the starting point and the arrival point of the target travel data and customize at least one bus route that goes directly from the starting station to the terminal station.

[0064] In an optional implementation, determining the optimization type of the bus network based on the target travel data further includes: determining a first area to which a departure point of the target travel data belongs and a second area to which an arrival point of the target travel data belongs; determining the number of target travel data from the first area to the second area as travel demand; if the distance from the first area to the second area belongs to a third distance threshold and the travel demand is greater than the second demand threshold, then the optimization type of the bus network is route customization.

[0065] Specifically, the city is divided into different areas according to a certain distance, such as 500 meters, and each area is numbered. The coordinates of the center of the area are used as the position of the current area to calculate the distance between different areas. Determine the first area to which the departure point of the target travel data belongs and the second area to which the arrival point of the target travel data belongs, and determine the number of target travel data from the first area to the second area as the travel demand. When the distance between the first area and the second area belongs to the third distance threshold, such as 5KM-20KM, and the travel demand is greater than the second demand threshold, it is determined that the optimization type of the bus network is line customization. The first distance threshold and the third distance threshold are distance ranges, and the distance range of the first distance threshold is smaller than the distance range of the third distance threshold, that is, the distance range of the third distance threshold includes the distance range of the first distance threshold, for example, the first distance threshold is 10KM-15KM, and the third distance threshold is 5KM-20KM.

[0066] By customizing bus routes between areas that meet bus travel distance restrictions and have large travel demands, it is possible to connect starting and end points with strong demand more directly and quickly, reduce the number of transfers and waiting time for passengers, and improve the travel experience of passengers; it can ensure that bus services more accurately meet the high demand between specific areas, reduce ineffective driving in non-high-demand areas, thereby improving the operating efficiency of buses and reducing operating costs; it can achieve optimal allocation of resources, help improve the overall efficiency of the public transportation system, and free up more road space for other modes of transportation (such as private cars, taxis, etc.), alleviating urban traffic congestion; the setting of customized bus routes is relatively flexible, and can be adjusted in time according to changes in travel demand, so that the bus system can better adapt to the dynamic changes of urban development and meet the diverse travel needs of residents.

[0067] S330: If the optimization type is new route creation, a target site is selected from existing sites according to the travel demand between sites in the target travel data and the distances between different sites, and a new route between the target sites is generated.

[0068] After determining the optimization type to which the bus network belongs, the method further includes:

[0069] S340: If the optimization type is route customization, determine the first area to which the departure point of the target travel data belongs, and use the existing bus stops in the first area as the initial departure cluster center point.

[0070] S350: Cluster the departure points of the target travel data according to the initial departure cluster center point, and obtain the target departure cluster center point as a candidate departure site.

[0071] Specifically, the city is divided into different areas according to a certain distance, such as 500 meters, and each area is numbered to determine the first area to which the starting point of the target travel data belongs. When the optimization type is route customization, the existing bus stops in the first area are used as the initial departure cluster center point, that is, the coordinates of each stop are selected as the initial departure cluster center point C k , C k The coordinates of Cluster the starting points of the target travel data according to the initial starting cluster center point and update the starting cluster center point. For each starting point coordinate of the target travel data Find the starting cluster center point closest to it Update the cluster assignment according to the following formula 4. Update the starting cluster center point according to the following formula 5

[0072]

[0073] in, Indicates the starting point coordinates of the target travel data With the coordinates of each starting cluster center The Euclidean distance between Indicates that the departure point of the target travel data is assigned to the departure cluster center point with the closest Euclidean distance. Update the departure cluster center point according to the coordinate mean of the departure points of all target travel data in each cluster:

[0074]

[0075] Where N k is the number of departure points of the target travel data belonging to the kth cluster.

[0076] When the departure cluster center point no longer changes, the departure cluster center point is used as the target departure cluster center point, and each target departure cluster center point is used as a candidate departure station in the first area. The candidate departure station to which the target travel data belongs is determined based on the cluster to which the departure point of the target travel data belongs.

[0077] S360: Determine the second area to which the arrival point of the target travel data belongs, and use the existing bus stops in the second area as the initial arrival cluster center point.

[0078] S370: Cluster the arrival points of the target travel data according to the initial arrival cluster center point, and obtain the target arrival cluster center point as a candidate arrival site.

[0079] Determine the second area to which the arrival point of the target travel data belongs, and use the existing bus stops in the second area as the initial arrival cluster center point. For each arrival point of the target travel data, find the arrival cluster center point closest to it, and update the cluster allocation according to the Euclidean distance between the arrival point of the target travel data and each arrival cluster center point. Update the arrival cluster center point according to the coordinate mean of all the arrival points of the target travel data in each cluster. When the arrival cluster center point no longer changes, the arrival cluster center point is used as the target arrival cluster center point, and each target arrival cluster center point is used as a candidate arrival station in the second area. According to the cluster to which the arrival point of the target travel data belongs, the candidate arrival station to which the target travel data belongs is determined.

[0080] S380: Determine a candidate customized route according to a candidate departure site to which the departure point of the target travel data belongs and a candidate arrival site to which the arrival point of the target travel data belongs.

[0081] A candidate customized route for the target travel data is determined according to the candidate departure site to which the target travel data belongs and the candidate arrival site to which the target travel data belongs.

[0082] S390: If the target travel data volume of the candidate customized route is greater than a first demand threshold, the candidate customized route is determined as a target customized route.

[0083] The amount of target travel data corresponding to each candidate customized route is determined as the target travel data amount of the candidate customized route. If the target travel data amount of any candidate customized route is greater than a first demand threshold, the candidate customized route is determined as the target customized route to complete route customization. The first demand threshold is less than the second demand threshold.

[0084] By dividing the city into different areas, the area where the target travel data is located is determined, and the target cluster center point is determined according to the starting point and arrival point of the target travel data and the existing bus stops in the area where the target travel data is located, and then the candidate customized route is determined. When the target travel data volume of the candidate customized route is large, the candidate customized route is determined as the target customized route to complete the route customization. By customizing bus routes between areas that meet the bus travel distance restrictions and have a large number of travel demands, it is possible to connect the starting point and the end point with strong demand more directly and quickly, reducing the number of transfers and waiting time for passengers; it can ensure that bus services meet the high demand between specific areas more accurately, and can reduce ineffective driving in non-high demand areas, thereby improving the operating efficiency of buses and reducing operating costs; it can achieve optimal allocation of resources, help improve the overall efficiency of the public transportation system, and free up more road space for other modes of transportation (such as private cars, taxis, etc.), alleviating urban traffic congestion problems; the setting of customized bus routes is relatively flexible, and can be adjusted in time according to changes in travel demand, so that the bus system can better adapt to the dynamic changes of urban development and meet the diverse travel needs of residents.

[0085] Figure 4 Schematic diagram of the structure of a bus route optimization device provided by an embodiment of the present invention. Figure 4 As shown, the device comprises:

[0086] The target travel data module 410 is used to match the candidate travel data with the bus network data to obtain the target travel data that meets the initial optimization conditions.

[0087] The optimization type determination module 420 is used to determine the optimization type of the bus network according to the target travel data.

[0088] The optimization types include new line creation, line customization or line adjustment.

[0089] The route creation module 430 is used to select target sites from existing sites according to the travel demand between sites and the distances between different sites in the target travel data, and generate a new route between the target sites if the optimization type is route creation.

[0090] The bus route optimization device provided in the embodiment of the present invention can execute the bus route optimization method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0091] Optionally, the line new module is specifically used for:

[0092] Construct connectivity parameters between sites to characterize whether the sites are connected; determine travel demand coverage according to the product of travel demand between sites and connectivity parameters between sites; determine route length according to the product of distance between sites and connectivity parameters between sites; maximize the travel demand coverage and minimize the route length, so that the travel demand coverage of the newly opened route meets the minimum travel demand constraint and the route length of the newly opened route meets the maximum route length constraint, and obtain connectivity values ​​between sites; obtain connected sites according to the connectivity values ​​between sites and determine new routes based on the connected sites.

[0093] Optionally, the device further comprises a route customization module, which is used to, after determining the optimization type to which the bus network belongs, if the optimization type is route customization, determine the first area to which the departure point of the target travel data belongs, and use the existing bus stops in the first area as the initial departure cluster center point;

[0094] Clustering the departure points of the target travel data according to the initial departure cluster center point, and obtaining the target departure cluster center point as a candidate departure site;

[0095] Determine a second area to which the arrival point of the target travel data belongs, and use an existing bus stop in the second area as an initial arrival cluster center point;

[0096] Clustering the arrival points of the target travel data according to the initial arrival cluster center point, and obtaining the target arrival cluster center point as a candidate arrival site;

[0097] Determine a candidate customized route according to a candidate departure site to which the departure point of the target travel data belongs and a candidate arrival site to which the arrival point of the target travel data belongs;

[0098] If the target travel data volume of the candidate customized route is greater than the first demand threshold, the candidate customized route is determined as the target customized route.

[0099] Optionally, the device further includes a line adjustment module, configured to, if the optimization type is line adjustment, perform the following:

[0100] If the transfer point of the target travel data is the terminal station of the bus route, then the terminal station of the bus route is extended to the station where the arrival point of the target travel data is located;

[0101] If the transfer point of the target travel data is the starting station of the bus route, extend the starting station of the bus route to the station where the starting point of the target travel data is located;

[0102] If the transfer point of the target travel data is an intermediate station of the first bus route before the transfer, the station where the arrival point of the target travel data is located is added to the stations of the first bus route to re-plan the first bus route;

[0103] If the transfer point of the target travel data is an intermediate station of the second bus route after the transfer, the station where the starting point of the target travel data is located is added to the stations of the second bus route to re-plan the second bus route.

[0104] Optionally, the target travel data module is specifically used for:

[0105] If there are not all bus stops within a certain range of the departure point and the arrival point of the candidate travel data, determining whether the number of transfers of the candidate travel data is less than a first value;

[0106] If the number of transfers of the candidate travel data is greater than or equal to the first value, determining whether the travel duration of the candidate travel data is less than the first duration;

[0107] If the travel duration of the selected travel data is greater than or equal to the first duration, the candidate travel data is determined to be the target travel data.

[0108] Optionally, the optimization type determination module is specifically used to:

[0109] If the time required for public transportation of the target travel data is greater than or equal to the product of the time required for driving of the target travel data and the first threshold, and the length of the driving travel path of the target travel data belongs to the first distance threshold, then the optimization type of the bus network is new line opening.

[0110] Optionally, the optimization type determination module is further used to:

[0111] Determine a first area to which a departure point of the target travel data belongs and a second area to which a arrival point of the target travel data belongs;

[0112] determining the amount of target travel data from the first area to the second area as travel demand;

[0113] If the distance from the first area to the second area belongs to a third distance threshold and the travel demand is greater than a second demand threshold, the optimization type of the bus network is route customization.

[0114] The bus route optimization device further described can also execute the bus route optimization method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0115] Figure 5A schematic diagram of an electronic device 50 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0116] like Figure 5 As shown, the electronic device 50 includes at least one processor 51, and a memory connected to the at least one processor 51 in communication, such as a read-only memory (ROM) 52, a random access memory (RAM) 53, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 51 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 52 or the computer program loaded from the storage unit 58 to the random access memory (RAM) 53. In the RAM 53, various programs and data required for the operation of the electronic device 50 can also be stored. The processor 51, the ROM 52, and the RAM 53 are connected to each other via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0117] A number of components in the electronic device 50 are connected to the I / O interface 55, including: an input unit 56, such as a keyboard, a mouse, etc.; an output unit 57, such as various types of displays, speakers, etc.; a storage unit 58, such as a disk, an optical disk, etc.; and a communication unit 59, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 59 allows the electronic device 50 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0118] The processor 51 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 51 executes the various methods and processes described above, such as the bus route optimization method.

[0119] In some embodiments, the bus route optimization method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 50 via the ROM 52 and / or the communication unit 59. When the computer program is loaded into the RAM 53 and executed by the processor 51, one or more steps of the bus route optimization method described above may be performed. Alternatively, in other embodiments, the processor 51 may be configured to execute the bus route optimization method in any other appropriate manner (e.g., by means of firmware).

[0120] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0122] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0123] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0124] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0125] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0126] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0127] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A bus route optimization method, characterized in that: The method comprises: Match the candidate travel data with the bus network data to obtain the target travel data that meets the initial optimization conditions; Determining the optimization type of the bus network according to the target travel data; the optimization type includes new line creation, line customization or line adjustment; If the optimization type is new route construction, the target site is selected from the existing sites according to the travel demand between the sites in the target travel data and the distances between different sites, and a new route between the target sites is generated.

2. The method according to claim 1, characterized in that: According to the travel demand between each station in the target travel data and the distance between different stations, a target station is selected from the existing stations, and a new route between the target stations is generated, including: Constructing connectivity parameters between sites, where the connectivity parameters are used to characterize whether the sites are connected; The travel demand coverage is determined by the product of the travel demand between each station and the connectivity parameter between stations; The line length is determined based on the product of the distance between each station and the connectivity parameter between stations; Maximize the travel demand coverage and minimize the route length, so that the travel demand coverage of the newly opened route meets the minimum travel demand constraint and the route length of the newly opened route meets the maximum route length constraint, and obtain the connectivity value between the stations; According to the connectivity values ​​between the sites, connected sites are obtained and new routes are determined according to the connected sites.

3. The method according to claim 1, characterized in that After determining the optimization type of the bus network, the method further includes: If the optimization type is route customization, determining the first area to which the departure point of the target travel data belongs, and using the existing bus stops in the first area as the initial departure cluster center point; Clustering the departure points of the target travel data according to the initial departure cluster center point, and obtaining the target departure cluster center point as a candidate departure site; Determine a second area to which the arrival point of the target travel data belongs, and use an existing bus stop in the second area as an initial arrival cluster center point; Clustering the arrival points of the target travel data according to the initial arrival cluster center point, and obtaining the target arrival cluster center point as a candidate arrival site; Determine a candidate customized route according to a candidate departure site to which the departure point of the target travel data belongs and a candidate arrival site to which the arrival point of the target travel data belongs; If the target travel data volume of the candidate customized route is greater than the first demand threshold, the candidate customized route is determined as the target customized route.

4. The method according to claim 1, characterized in that After determining the optimization type of the bus network, if the optimization type is line adjustment, the following steps are performed: If the transfer point of the target travel data is the terminal station of the bus route, then the terminal station of the bus route is extended to the station where the arrival point of the target travel data is located; If the transfer point of the target travel data is the starting station of the bus route, extend the starting station of the bus route to the station where the starting point of the target travel data is located; If the transfer point of the target travel data is an intermediate station of the first bus route before the transfer, the station where the arrival point of the target travel data is located is added to the stations of the first bus route to re-plan the first bus route; If the transfer point of the target travel data is an intermediate station of the second bus route after the transfer, the station where the starting point of the target travel data is located is added to the stations of the second bus route to re-plan the second bus route.

5. The method according to any one of claims 1 to 4, characterized in that Match the candidate travel data with the bus network data to obtain the target travel data that meets the initial optimization conditions, including: If there are not all bus stops within a certain range of the departure point and the arrival point of the candidate travel data, determining whether the number of transfers of the candidate travel data is less than a first value; If the number of transfers of the candidate travel data is greater than or equal to the first value, determining whether the travel duration of the candidate travel data is less than the first duration; If the travel duration of the selected travel data is greater than or equal to the first duration, the candidate travel data is determined to be the target travel data.

6. The method according to any one of claims 1 to 4, characterized in that Determining the optimization type of the bus network according to the target travel data includes: If the time required for public transportation of the target travel data is greater than or equal to the product of the time required for driving of the target travel data and the first threshold, and the length of the driving travel path of the target travel data belongs to the first distance threshold, then the optimization type of the bus network is new line opening.

7. The method according to any one of claims 1 to 4, characterized in that Determining the optimization type of the bus network according to the target travel data further includes: Determine a first area to which a departure point of the target travel data belongs and a second area to which a arrival point of the target travel data belongs; determining the amount of target travel data from the first area to the second area as travel demand; If the distance from the first area to the second area belongs to a third distance threshold and the travel demand is greater than a second demand threshold, the optimization type of the bus network is route customization.

8. A bus route optimization device, characterized in that: The device comprises: The target travel data module is used to match the candidate travel data with the bus network data to obtain the target travel data that meets the initial optimization conditions; An optimization type determination module, used to determine the optimization type of the bus network according to the target travel data; the optimization type includes new line opening, line customization or line adjustment; The route creation module is used to select target sites from existing sites and generate new routes between the target sites based on the travel demand between the sites and the distances between different sites in the target travel data if the optimization type is route creation.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory in communication with the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the bus route optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the bus route optimization method according to any one of claims 1 to 7 when executed.

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