CN114037110A - Vehicle scheduling method, device, equipment and computer program product - Google Patents
Vehicle scheduling method, device, equipment and computer program product Download PDFInfo
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Abstract
The embodiment of the invention provides a vehicle scheduling method, a device, equipment and a computer program product, wherein the method comprises the following steps: acquiring the number of idle running vehicles existing at the current time in a pre-divided position area; determining the number of vehicle gaps existing in the position area in a scheduling prediction period, wherein the starting time of the scheduling prediction period is later than the current time; and generating a vehicle dispatching scheme by taking the empty running vehicles in different position areas as a target to be maximally dispatched according to the number of the vehicle gaps and the number of the empty running vehicles. In the scheme, the number of the vehicles in the empty driving range and the number of the gaps of the vehicles, which are the vehicle supply and demand conditions of each position area, are predicted in advance, and the empty driving vehicles in the universe are allocated based on the prediction result, so that the proper transportation capacity enters the correct position area in advance, the condition of supply and demand unbalance is avoided as far as possible, the scheduling effect of the vehicles is optimized, and the user experience is improved.
Description
Technical Field
The present invention relates to the field of internet technologies, and in particular, to a vehicle scheduling method, apparatus, device, and computer program product.
Background
The travel modes of people can be selected from various options, such as self-driving, public transport, subway and net appointment. In the online car booking market, a plurality of travel service providers exist to provide online car booking services for users.
In real life, if a user takes a taxi, but has no available capacity in the area around the user, the user may cancel the order due to long waiting time or automatically cancel the order after the waiting time exceeds a set threshold, thereby causing the user to lose. While at the same time, there may be some vehicles in other areas that are empty because there are no orders.
Therefore, it is desirable to provide a vehicle scheduling scheme to solve the problem of imbalance between supply and demand caused by mismatch between vehicle supply and user demand, so as to better match the user demand with available capacity.
Disclosure of Invention
The embodiment of the invention provides a vehicle scheduling method, a vehicle scheduling device, vehicle scheduling equipment and a computer program product, which are used for realizing matching between available transport capacity and user requirements and optimizing the scheduling effect of a vehicle.
In a first aspect, an embodiment of the present invention provides a vehicle scheduling method, where the method includes:
acquiring the number of idle running vehicles existing at the current time in a pre-divided position area;
determining the number of vehicle gaps existing in the position area in a scheduling prediction period, wherein the starting time of the scheduling prediction period is later than the current time;
and generating a vehicle dispatching scheme by taking the empty running vehicles in different position areas as a target to be maximally dispatched according to the number of the vehicle gaps and the number of the empty running vehicles.
In a second aspect, an embodiment of the present invention provides a vehicle dispatching device, including:
the acquisition module is used for acquiring the number of empty vehicles existing at the current time in a pre-divided position area;
the determining module is used for determining the number of vehicle gaps existing in the position area in a scheduling prediction period, and the starting time of the scheduling prediction period is later than the current time;
and the generating module is used for generating a vehicle dispatching scheme by taking the empty vehicles in different position areas as a target for maximum dispatching according to the number of the vehicle gaps and the number of the empty vehicles.
In a third aspect, an embodiment of the present invention provides an electronic device, including: a memory, a processor; wherein the memory has stored thereon executable code which, when executed by the processor, causes the processor to implement at least the vehicle scheduling method of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer program product, including: computer program which, when executed by a processor of an electronic device, makes the processor at least operable to implement the vehicle scheduling method according to the first aspect.
In the vehicle scheduling scheme provided by the embodiment of the invention, a certain geographic range is divided into a plurality of position areas in advance, and the plurality of position areas are subjected to global transport capacity scheduling at certain time intervals. Specifically, first, at the current time, for each location area, statistics of the number of empty vehicles and prediction of the number of vehicle gaps existing in each location area within the current schedule prediction period are performed. The empty vehicle is a vehicle which is continuously in an empty state and exceeds a set time length. The number of vehicle gaps is the number of orders which are not answered, and simply the number of users who can not hit the vehicle. And then, generating a vehicle dispatching scheme by taking the empty vehicles in different position areas as a target for maximum dispatching based on the obtained number of the empty vehicles and the obtained number of vehicle gaps in each position area. The aim is simply to enable more location areas lacking vehicles to be adjusted to enough empty vehicles, so that the situation that users leave orders and are not answered does not occur in the more location areas as far as possible.
In the scheme, the vehicle supply and demand conditions (namely the number of the empty vehicles and the number of the vehicle gaps) of each position area are predicted in advance, and the empty vehicles in the universe are allocated based on the prediction result, so that the proper transport capacity enters the correct position area in advance, the condition of supply and demand imbalance is avoided as much as possible, the scheduling effect of the vehicles is optimized, and the user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a vehicle scheduling method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a vehicle dispatch effect corresponding to a nearby dispatch rule;
FIG. 3 is a schematic diagram of a vehicle dispatching effect provided by an embodiment of the invention;
FIG. 4 is a flow chart of another vehicle scheduling method provided by the embodiments of the present invention;
fig. 5 is a schematic application diagram of a vehicle dispatching method according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a vehicle dispatching device according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device corresponding to the vehicle scheduling apparatus provided in the embodiment shown in fig. 6.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, the sequence of steps in each method embodiment described below is only an example and is not strictly limited.
The taxi taking and traveling mode through the taxi booking has become a generally adopted traveling mode for people at present. In real life, a user frequently calls a vehicle at a certain place without response after a delay, but in other areas, the vehicle is empty because of no order. The phenomenon of mismatching between vehicle supply and user demand easily causes imbalance of supply and demand, and the order of the user is not answered, so that the user is lost, and the situation often occurs in the peak time of vehicle use such as on-duty and off-duty.
In order to solve the problem, so that the user demand is better matched with available capacity, and the occurrence of the situation that the user orders and does not respond late is reduced, the vehicle dispatching scheme provided by the embodiment of the invention is provided. The scheme aims to bring the vehicle required by the passenger into the correct area in advance so as to avoid the occurrence of the supply and demand imbalance condition as much as possible, thereby improving the user experience.
In summary, the idea of the vehicle dispatching scheme provided by the embodiment of the invention is as follows: the method comprises the steps of dividing a certain geographical range into a plurality of position areas, predicting the vehicle supply and demand conditions of each position area in advance, and allocating the air traffic capacity according to prediction information, so that the corresponding traffic capacity enters a user order issuing range in advance, and therefore user loss caused by the lack of the traffic capacity is avoided.
In practical applications, the vehicle scheduling scheme provided by the embodiment of the present invention may be implemented by a certain travel service provider to implement scheduling of vehicles provided by the travel service provider, or may also be implemented by a management platform side that manages a plurality of travel service providers to implement scheduling of vehicles provided by the plurality of travel service providers.
The following describes in detail an implementation procedure of the vehicle scheduling method provided by the embodiment of the present invention.
Fig. 1 is a flowchart of a vehicle dispatching method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
101. and acquiring the number of empty vehicles existing at the current time in the position area divided in advance.
102. And determining the number of vehicle gaps existing in the position area in the scheduling prediction period, wherein the starting time of the scheduling prediction period is later than the current time.
103. And generating a vehicle dispatching scheme by taking the empty running vehicles in different position areas as a target to be maximally dispatched according to the number of the vehicle gaps and the number of the empty running vehicles.
In this embodiment, assuming that a vehicle scheduling scheme is executed for a vehicle providing travel service for a user in a certain city, location areas of the city may be divided in advance to obtain a plurality of location areas. For example, the grid area may be divided on the city map according to the size of the set grid, so that a plurality of grids are divided on the city map, and the geographic range corresponding to each grid forms a location area.
The vehicle dispatching scheme provided by the embodiment of the invention aims to: and dispatching the vehicles required in a certain position area to the position area in advance so as to enable the vehicles to be available for users with taxi taking requirements in the position area.
While the vehicle that can be scheduled is an empty vehicle, in the embodiment of the present invention, a vehicle that is continuously in an empty state for more than a set time period (for example, a set time period such as 10 minutes) is referred to as an empty vehicle. The number of vehicles scheduled in a certain location area is based on the number of vehicles required in the location area, and the required number of vehicles is determined according to the number of users who can not hit the vehicles, namely the number of vehicle gaps.
In addition, it should be noted that the vehicle scheduling scheme provided in this embodiment may be implemented by a certain travel service provider, and in this case, all the scheduled vehicles are vehicles provided by the travel service provider. Alternatively, the vehicle scheduling scheme provided in this embodiment may also be implemented by a management platform side that manages a plurality of travel service providers, in this case, all the scheduled vehicles are vehicles provided by the plurality of travel service providers, but it is assumed here that it is not distinguished which travel service provider the vehicle belongs to in the vehicle scheduling process, that is, the vehicle scheduling process does not distinguish between the travel service providers, that is, the preference of the user for different travel service providers is not considered in the vehicle scheduling process.
In order to uniformly schedule schedulable vehicles in a plurality of location areas according to the vehicle demand conditions in each location area in advance, a periodic scheduling mode can be adopted.
Optionally, the periodic scheduling manner may be: and executing vehicle dispatching processing at set time intervals, wherein data used in each dispatching processing process is the number of idle running vehicles and vehicle gaps existing in each position area in the dispatching cycle. The set time interval is, for example, 15 minutes, 20 minutes, or the like, for a suitable period of time.
For example, assuming that the starting scheduling time of the current scheduling period (i.e., the current time) is 10:00, and the set time interval, i.e., the scheduling period, is 15 minutes, the current scheduling prediction period may be 10:00-10: 15. The next scheduling prediction period may be: 10:15-10:30.
Specifically, assuming that the current scheduling prediction time period is a scheduling time period of 10:00-10:15, the number of empty vehicles in each position area at 10:00 can be determined, the number of vehicle gaps in each position area in a time period of 10:00-10:15 can be predicted, and then the vehicle scheduling scheme corresponding to each position area can be determined according to the obtained number of empty vehicles and the number of vehicle gaps.
That is, at the starting time of a certain scheduling cycle, the vehicle scheduling scheme can be executed to predict the vehicle scheduling scheme of each location area in a certain future time period, and the vehicle scheduling scheme can be executed to adjust the number of vehicles required in different location areas to the corresponding location areas in advance, so that the number of users who may not hit the vehicles in the corresponding time period (before the vehicle scheduling scheme is not adopted) originally can be reduced.
Specifically, assuming that the current time is 9:00, the number of empty vehicles existing in each location area at that time can be acquired. Specifically, the vehicle-mounted terminal device in the vehicle may be configured to report its service status and location information at a small time interval, where the service status may include: carrying passengers, driving without air, receiving passengers and the like. If a vehicle has been in a no-drive state for at least, say, 10 minutes before 9:00, then it may be determined that the vehicle is currently a no-drive vehicle. It can be understood that, according to the location information reported by the vehicle-mounted terminal device, it can be known in which location area the vehicle is currently located. Based on the above, the number of empty vehicles in each position area at the current time can be counted.
The method comprises the following steps of determining the number of vehicle gaps existing in each position area in the current scheduling prediction period, in general, by the following steps: and predicting the number of vehicle gaps in each position area in the current scheduling prediction time period according to the statistical information of the number of the vehicle gaps in each position area in the historical time.
Wherein, the total span of the historical time can be set according to the actual requirement, such as the past month, three months, half year, etc. In practical applications, the time of day may be pre-divided into several periods, such as: 1:00-5:00,5:00-6:00,6:00-6:30,6:30-7:00, and so on. And counting the vehicle gaps of each position area in the time period every day in historical time for each time period.
Specifically, when a user calls and places an order, the user inputs a starting and stopping point at an order placing interface and submits the order, and based on the starting and stopping point, the user in which position area can know when the order placing action is triggered. If there is a vehicle to pick up an order, it indicates that the order triggered by the user was answered, but if the user actively cancels the order after waiting for a period of time or the user is notified that his order was cancelled, it indicates that the user's order was not answered. Taking the time period of 6:30-7:00 and the location area a as an example, assuming that the user in the location area a triggers 100 orders in total in the time period of 6:30-7:00 in a certain day according to the above-mentioned manner, wherein 20 orders are not answered, the number of vehicle gaps in the location area a in the above-mentioned time period of the day is 20, that is, the number of unanswered orders, that is, the number of users who can not hit a vehicle.
Assuming that the historical time is preset to be 90 days, the number of vehicle gaps of the location area a in the period of 6:30-7:00 in the 90 days can be obtained through the above method, and further, the number of vehicle gaps of the location area a in the current scheduling prediction period can be predicted in advance based on the statistical result of the 90 days, and the scheduling prediction period is assumed to correspond to the period of 6:30-7: 00. The specific prediction method may be various, and is not limited herein. For example, the average value of the 90 days, or the maximum value is used as the prediction result. For another example, if the current scheduling prediction time interval corresponds to a holiday (e.g., double holiday), the statistical result corresponding to the same holiday can be screened out from the 90 days, and the number of vehicle gaps of the position area a in the current scheduling prediction time interval is predicted according to the screened statistical result.
After the number of vehicle gaps existing in each location area in the current scheduling prediction period is obtained in the above manner, a vehicle scheduling scheme can be generated with the aim of maximizing the number of empty vehicles in different location areas to be scheduled according to the obtained number of empty vehicles existing in each location area at the current time and the obtained number of vehicle gaps.
The process of generating the vehicle scheduling scheme is a process of generating an optimization problem and solving the optimization problem. Wherein, the optimization problem is: the empty vehicles in different position areas are scheduled to the maximum, the constraint condition of the optimization problem is limited by the number of vehicle gaps existing in each position area in the current scheduling prediction side time period and the number of empty vehicles existing in each position area at the current time, and the result obtained by solving the optimization problem under the constraint condition is that: and vehicle dispatching schemes corresponding to the position areas.
In an optional embodiment, the vehicle scheduling scheme may be generated by taking the maximum scheduling of the empty vehicles in different position areas as a target under the following constraint conditions:
the number of empty vehicles dispatched from the first position area to the second position area is less than or equal to the number of empty vehicles existing in the first position area;
the total number of the empty vehicles dispatched to the second position area from each position area divided in advance is less than or equal to the number of vehicle gaps existing in the second position area;
wherein the first location area and the second location area are any two location areas of the location areas.
In another optional embodiment, the constraint condition may further include: the distance between the first position area and the second position area is less than or equal to a set threshold value.
The target for maximally scheduling the empty vehicles in different position areas is simply: allowing more location areas lacking vehicles to be tuned to enough empty vehicles. In addition, this goal also reflects: global scheduling of all location areas is required to maximize the utilization of empty vehicles.
The number of empty vehicles dispatched from the first position area to the second position area is less than or equal to the number of empty vehicles existing in the first position area, and the condition simply means that: the number of empty vehicles called out from the first location area cannot exceed the number of empty vehicles existing in the location area.
The total number of empty vehicles scheduled from each location area (actually, other location areas except the second location area) to the second location area is less than or equal to the number of vehicle gaps existing in the second location area, and this condition means simply that: the number of empty vehicles allocated to the second location area cannot exceed the number of vehicles required in the location area, i.e. the number of vehicle gaps.
Wherein, the distance between the first position area and the second position area is less than or equal to the set threshold, and the meaning of this condition is simply: if some of the empty vehicles present in the first location area are deployed into the second location area, the first location area cannot be too far away from the second location area, and the distance between the two is less than or equal to a set threshold, such as a distance of 3 location areas.
Based on this, the above optimization problem can be described as the following formula:
L(i→j)≤3
wherein x isi→jIndicating the number of empty vehicles to be dispatched from location area i to location area j in the current dispatch prediction period, N indicating a positive integer, D indicating a set of location areas consisting of all location areas, DjIndicates the number of vehicle gaps, m, existing in the current scheduling prediction period location area jiIndicating the number of empty vehicles present in the location area i during the current scheduled prediction period (which is actually the current time), and L (i → j) indicating the number of location areas separated from the location area j. s.t. represents a constraint.
The optimization problem is solved, and the obtained result is the value of the number of the empty vehicles transferred from the position area i to the position area j. It can be understood that the location areas i and j are any two location areas in the location area set, and then the scheduling of idle capacity in different location areas can be realized from a global perspective by solving the optimization problem, so that more idle capacity can be allocated to a suitable location area with a vehicle demand gap in advance, the occurrence of the condition of imbalance of supply and demand is avoided as much as possible, the macroscopic scheduling effect of the vehicle is optimized, and the user experience is improved.
In addition, in order to facilitate understanding of implementation effects of the vehicle dispatching scheme provided by the embodiment of the invention, the following example description is compared with fig. 2 and 3.
The idea of assuming a conventional vehicle dispatching scheme is: and (6) scheduling nearby. In short, if a vehicle gap exists in a certain location area, the empty transportation capacity is allocated to the location area from the nearest location area with the empty transportation capacity. In fig. 2, by way of example, assuming that 5 empty vehicles exist in the location area Q1 and the location area Q2 in the current scheduling prediction period, the number of vehicle gaps existing in the location area Q3 and the location area Q4 is 5, that is, 5 persons cannot drive the vehicle, with the location area Q1, the location area Q2, the location area Q3, and the location area Q4. When vehicle scheduling is performed based on the near scheduling rule, the scheduling result is as follows: the 5 empty vehicles in position zone Q2 were allocated in advance into position zone Q3. In this scheme, the total number of vehicles scheduled is 5 in total from a global perspective.
In the vehicle scheduling scheme provided in the embodiment of the present invention, the objective is to maximize (i.e., maximize) the scheduling of the empty vehicles in different location areas, and the finally obtained vehicle scheduling scheme is as shown in fig. 3: the 5 empty vehicles in the position region Q1 were allocated into the position region Q3 in advance, and the 5 empty vehicles in the position region Q2 were allocated into the position region Q4 in advance. In this scheme, the total number of vehicles scheduled is 10 in total from a global perspective.
As described in the foregoing, in the above embodiment, it is assumed that different travel service providers are not distinguished in the vehicle scheduling process. However, in practical applications, when a user uses a network car booking service, if a certain service platform cooperates with a plurality of travel service providers, the user may select one or more preferred travel service providers when placing an order, and the service platform further selects one from the several travel service providers selected by the user according to a set screening policy, and the vehicle assigned by the service provider is used for receiving the order. In such a service mode, in the vehicle scheduling process, different travel service providers need to be further distinguished, and in this case, the scheduled vehicle is a vehicle provided by a plurality of travel service providers. The execution process of the vehicle scheduling scheme at this time is described below with reference to fig. 4.
Fig. 4 is a flowchart of another vehicle dispatching method according to an embodiment of the present invention, and as shown in fig. 4, the method may include the following steps:
401. and acquiring the number of empty vehicles existing in each travel service provider at the current time in each pre-divided location area.
402. Determining the number of vehicle gaps existing in each position area in the current scheduling prediction period, and determining the number of vehicle gaps existing in each travel service provider in each position area in the scheduling prediction period, wherein the starting time of the scheduling prediction period is later than the current time.
403. And generating a vehicle dispatching scheme by taking the empty running vehicles in different position areas as a target to be maximally dispatched according to the number of the vehicle gaps and the number of the empty running vehicles.
As can be seen from the above steps, in the present embodiment, when considering the factor of the travel service provider, mainly in the process of determining the number of empty vehicles and the number of vehicle gaps, the difference between different travel service providers is considered.
When the number of the empty vehicles is counted, the number of the empty vehicles existing in each travel service provider at the current time in each location area needs to be counted respectively. Specifically, the vehicle-mounted terminal device in the vehicle may be configured to report the service state and the location information of the vehicle and the identification information of the travel service provider to which the vehicle-mounted terminal device belongs at a small time interval, and based on this, the number of empty-running vehicles existing at the current time and in each travel service provider in each location area may be counted.
When the number of vehicle gaps is predicted, the number of vehicle gaps existing in each position area in the current scheduling prediction time period is also predicted according to different trip service providers, besides the number of vehicle gaps existing in each position area in the current scheduling prediction time period. The number of vehicle gaps existing in each position area in the current scheduling prediction time period reflects the total number of the vehicles which may not arrive in each position area in the time period; the number of vehicle gaps of each travel service provider in each location area in the current scheduling prediction period simply reflects the demand of the people who can not hit the vehicle for different travel service providers.
The two types of vehicle gap numbers can be predicted according to the statistical information of the vehicle gap numbers in each position area in the historical time.
In practical application, when a user calls and places an order, the user inputs a starting and ending point on an order placing interface, then selects one or more travel service providers to be used, and submits an order, so that the user in which position area can know when the order placing action for which travel service providers is triggered. And based on the management strategy of the service platform, selecting the travel service providers which finally provide services for the user from the travel service providers selected by the user. The end result is two kinds: firstly, determining that a certain travel service provider provides service for a user, wherein at the moment, a user order is associated with identification information of a finally selected travel service provider; secondly, each travel service provider selected by the user cannot provide services for the user, and at this time, the user order is in an unanswered state.
Based on the records of the user ordering behavior and the order state information in the historical time, the number of vehicle gaps of each travel service provider in each position area in different time periods can be counted, and of course, the number of vehicle gaps of each position area in different time periods can also be counted (different travel service providers are not distinguished). According to the statistical result, the corresponding vehicle gap number in a certain scheduling prediction time period in the future can be predicted. The statistical and predictive principles can be referred to in the description of the previous embodiments.
After the number of empty vehicles existing in each position area at the current time and each travel service provider, the total number of vehicle gaps existing in each position area at the current scheduling prediction time period and the number of vehicle gaps existing in each travel service provider in each position area at the scheduling prediction time period are obtained in the above manner, the vehicle scheduling scheme can be generated by taking the maximum number of empty vehicles in different position areas to be scheduled as a target according to the number of vehicle gaps and the number of empty vehicles.
In an optional embodiment, the vehicle scheduling scheme may be generated by taking the maximum scheduling of the empty vehicles in different position areas as a target under the following constraint conditions:
the number of the empty vehicles corresponding to the target travel service provider and dispatched from the first position area to the second position area is less than or equal to the number of the empty vehicles existing in the target travel service provider in the first position area;
the total number of empty vehicles dispatched from each location area (which may actually be other location areas except the second location area) to the second location area is less than or equal to the total number of vehicle gaps existing in the second location area;
the total number of empty vehicles corresponding to the target trip service providers dispatched from each location area (actually, other location areas except the second location area) to the second location area is less than or equal to the number of vehicle gaps existing in the target trip service providers in the second location area;
the first location area and the second location area are any two location areas in the location areas, and the target travel service provider is any one of a plurality of travel service providers.
In another optional embodiment, the constraint condition may further include: the distance between the first position area and the second position area is less than or equal to a set threshold value.
As described above, the above process of generating the vehicle dispatching scheme is a process of generating an optimization problem and solving the optimization problem.
Based on this, the above optimization problem can be described as the following formula:
L(i→j)≤3
wherein the content of the first and second substances,
indicating the number of empty vehicles of s, which are travel service providers to be dispatched from location area i to location area j in the current scheduling prediction period, N indicating a positive integer, D indicating a location area set composed of all location areas, C indicating a facilitator set composed of all travel service providers, DjIndicates the number of vehicle gaps existing in the current scheduled prediction period location area j,
indicates the number of vehicle gaps that exist for travel service provider s within location area j for the current schedule prediction period,
indicating the number of empty vehicles existing for the travel service provider s in the location area i at the current time, and L (i → j) indicating the number of location areas separated from the location area j by the location area i. s.t. represents a constraint.
The optimization problem is solved, and the obtained result is the value of the number of the empty vehicles provided by the travel service provider s from the position area i to the position area j. It can be understood that the location area i and the location area j are any two location areas in the location area set, and then the scheduling of the idle capacity of different travel service providers in different location areas can be realized from a global perspective by solving the optimization problem, so that more idle capacity can be allocated to a suitable location area with a vehicle demand gap in advance according to the preference of a user on the travel service providers, the occurrence of the situation of imbalance of supply and demand can be avoided as much as possible, the macro scheduling effect of a vehicle is optimized, and the user experience is improved.
A practical application scenario of the vehicle dispatching scheme provided by the embodiment of the invention is exemplarily described below with reference to fig. 5.
In fig. 5, it is assumed that a certain service platform cooperates with a plurality of travel service providers (such as the illustrated travel service provider S1, travel service provider S2, and travel service provider S3) to provide travel services for users. The user uses the travel service through the client program provided by the service platform. In brief, when using the travel service, the user inputs a travel starting and stopping point on a scheduling interface of the client program, and then selects a required travel service provider to trigger scheduling. The service platform completes the distribution response of the order according to the set order distribution rule: the user order is cancelled because none of the trip service providers selected by the user can take the order, or a certain trip service provider selected by the user is selected, and the certain vehicle managed by the trip service provider is distributed to take the order by the trip service provider. As mentioned above, the ordering operation information (e.g. trip starting point) of the user and the status information (e.g. cancel, order taken by a certain trip service provider) of the user order are recorded by the service platform for predicting the above-mentioned various vehicle gap numbers.
In addition, a plurality of travel service providers may provide a large number of vehicles (such as the vehicle 1, the vehicle 2 · the vehicle M illustrated in fig. 5), and during the travel of the vehicles on the road, the vehicles may report their related information through the vehicle-mounted terminal, including the belonging travel service provider, the location, whether the vehicle is in an empty state, and the like. As mentioned above, the service platform will complete the above-mentioned statistics of the number of empty vehicles based on the reported information.
Based on the information provided by the user and the vehicle side and the information of each position area stored in the service platform, the service platform finally completes the determination of the vehicle gap number and the empty vehicle number, further generates a vehicle scheduling scheme, issues the vehicle scheduling schemes related to different travel service providers to corresponding travel service providers, and executes the vehicle scheduling schemes by the travel service providers to complete the scheduling of corresponding empty vehicles.
It is understood that, since a large number of data statistics and computation processes are involved, the service platform may specifically use a server or a server cluster provided in a cloud to perform the data statistics and computation processes.
The vehicle scheduling apparatus according to one or more embodiments of the present invention will be described in detail below. Those skilled in the art will appreciate that these means can each be constructed using commercially available hardware components and by performing the steps taught in this disclosure.
Fig. 6 is a schematic structural diagram of a vehicle dispatching device according to an embodiment of the present invention, and as shown in fig. 6, the device includes: the device comprises an acquisition module 11, a determination module 12 and a generation module 13.
The acquiring module 11 is configured to acquire the number of empty vehicles existing at the current time in the pre-divided location area.
A determining module 12, configured to determine the number of vehicle gaps existing in the location area in a scheduling prediction period, where a starting time of the scheduling prediction period is later than the current time.
And the generating module 13 is configured to generate a vehicle scheduling scheme by taking the maximum scheduling of the empty vehicles in different position areas as a target according to the number of the vehicle gaps and the number of the empty vehicles.
Optionally, the determining module 12 may be specifically configured to: and predicting the number of vehicle gaps existing in the position area in the scheduling prediction period according to the statistical information of the number of vehicle gaps in the position area in the historical time.
In an optional embodiment, the generating module 13 may specifically be configured to: under the following constraint conditions, generating a vehicle dispatching scheme by taking the maximum dispatching of the empty running vehicles in different position areas as a target:
the number of empty vehicles dispatched from a first location area to a second location area is less than or equal to the number of empty vehicles existing in the first location area; the total number of the empty vehicles dispatched from other position areas to the second position area is less than or equal to the number of vehicle gaps existing in the second position area; wherein the first location area and the second location area are any two location areas of the pre-divided location areas.
Wherein, optionally, the constraint condition further includes: the distance between the first position area and the second position area is smaller than or equal to a set threshold value.
In another optional embodiment, the scheduled vehicle includes vehicles provided by a plurality of travel service providers, and at this time, the obtaining module 11 is specifically configured to: and acquiring the number of empty vehicles existing in each travel service provider at the current time in the pre-divided position area. The determining module 12 is specifically configured to: determining the total number of vehicle gaps existing in the location area in the scheduling prediction period, and determining the number of vehicle gaps existing in each travel service provider in the location area in the scheduling prediction period. The generating module 13 is specifically configured to: under the following constraint conditions, generating a vehicle dispatching scheme by taking the maximum dispatching of the empty running vehicles in different position areas as a target:
the number of empty vehicles corresponding to a target travel service provider dispatched from a first location area to a second location area is less than or equal to the number of empty vehicles existing in the target travel service provider in the first location area; the total number of empty vehicles dispatched from other position areas to the second position area is less than or equal to the total number of vehicle gaps existing in the second position area; the total number of empty vehicles corresponding to the target trip service provider and dispatched from other position areas to the second position area is less than or equal to the number of vehicle gaps existing in the target trip service provider in the second position area; wherein the first location area and the second location area are any two location areas in the pre-divided location areas, and the target travel service provider is any one of the plurality of travel service providers.
Wherein the constraint further comprises: the distance between the first position area and the second position area is smaller than or equal to a set threshold value.
The device shown in fig. 6 may execute the vehicle dispatching method provided in the foregoing embodiment, and the detailed execution process and technical effect refer to the description in the foregoing embodiment, which are not described herein again.
In one possible design, the structure of the vehicle dispatching device shown in fig. 6 may be implemented as an electronic device, which may be a server in the cloud. As shown in fig. 7, the electronic device may include: a processor 21 and a memory 22. Wherein the memory 22 has stored thereon executable code which, when executed by the processor 21, causes the processor 21 to at least implement the vehicle scheduling method as provided in the embodiments illustrated in fig. 1 to 6 and described above.
Optionally, the electronic device may further include a communication interface 23 for communicating with other devices.
Additionally, embodiments of the present invention provide a non-transitory machine-readable storage medium having stored thereon executable code, which when executed by a processor of an electronic device, causes the processor to perform a vehicle scheduling method as provided in the foregoing embodiments.
The above-described apparatus embodiments are merely illustrative, wherein the units described as separate components may or may not be physically separate. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by adding a necessary general hardware platform, and of course, can also be implemented by a combination of hardware and software. With this understanding in mind, the above-described aspects and portions of the present technology which contribute substantially or in part to the prior art may be embodied in the form of a computer program product, which may be embodied on one or more computer-usable storage media having computer-usable program code embodied therein, including without limitation disk storage, CD-ROM, optical storage, and the like.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A vehicle scheduling method, comprising:
acquiring the number of idle running vehicles existing at the current time in a pre-divided position area;
determining the number of vehicle gaps existing in the position area in a scheduling prediction period, wherein the starting time of the scheduling prediction period is later than the current time;
and generating a vehicle dispatching scheme by taking the empty running vehicles in different position areas as a target to be maximally dispatched according to the number of the vehicle gaps and the number of the empty running vehicles.
2. The method of claim 1, the generating a vehicle dispatch plan with the goal of maximizing the dispatch of free-driving vehicles in different location areas based on the number of vehicle gaps and the number of free-driving vehicles, comprising:
under the following constraint conditions, generating a vehicle dispatching scheme by taking the maximum dispatching of the empty running vehicles in different position areas as a target:
the number of empty vehicles dispatched from a first location area to a second location area is less than or equal to the number of empty vehicles existing in the first location area;
the total number of the empty vehicles dispatched from other position areas to the second position area is less than or equal to the number of vehicle gaps existing in the second position area;
wherein the first location area and the second location area are any two location areas of the pre-divided location areas.
3. The method of claim 2, the constraints further comprising: the distance between the first position area and the second position area is smaller than or equal to a set threshold value.
4. The method of claim 1, the scheduled vehicles comprising a plurality of travel service provider provided vehicles;
the acquiring the number of empty vehicles existing at the current time in the pre-divided position area comprises the following steps:
acquiring the number of empty vehicles existing in each travel service provider at the current time in a pre-divided position area;
the determining the number of vehicle gaps existing in the location area in the scheduling prediction period comprises:
determining the total number of vehicle gaps existing in the location area in the scheduling prediction period, and determining the number of vehicle gaps existing in each travel service provider in the location area in the scheduling prediction period.
5. The method of claim 4, the generating a vehicle dispatch plan with the goal of maximizing the dispatch of free-driving vehicles in different location areas based on the number of vehicle gaps and the number of free-driving vehicles, comprising:
under the following constraint conditions, generating a vehicle dispatching scheme by taking the maximum dispatching of the empty running vehicles in different position areas as a target:
the number of empty vehicles corresponding to a target travel service provider dispatched from a first location area to a second location area is less than or equal to the number of empty vehicles existing in the target travel service provider in the first location area;
the total number of empty vehicles dispatched from other position areas to the second position area is less than or equal to the total number of vehicle gaps existing in the second position area;
the total number of empty vehicles corresponding to the target trip service provider and dispatched from other position areas to the second position area is less than or equal to the number of vehicle gaps existing in the target trip service provider in the second position area;
wherein the first location area and the second location area are any two location areas in the pre-divided location areas, and the target travel service provider is any one of the plurality of travel service providers.
6. The method of claim 5, the constraints further comprising: the distance between the first position area and the second position area is smaller than or equal to a set threshold value.
7. The method of any of claims 1-5, the determining a number of vehicle gaps that exist within the location area for a scheduled prediction period, comprising:
and predicting the number of vehicle gaps existing in the position area in the scheduling prediction period according to the statistical information of the number of vehicle gaps in the position area in the historical time.
8. A vehicle scheduling apparatus comprising:
the acquisition module is used for acquiring the number of empty vehicles existing at the current time in a pre-divided position area;
the determining module is used for determining the number of vehicle gaps existing in the position area in a scheduling prediction period, and the starting time of the scheduling prediction period is later than the current time;
and the generating module is used for generating a vehicle dispatching scheme by taking the empty vehicles in different position areas as a target for maximum dispatching according to the number of the vehicle gaps and the number of the empty vehicles.
9. An electronic device, comprising: a memory, a processor; wherein the memory has stored thereon executable code which, when executed by the processor, causes the processor to perform the vehicle scheduling method of any one of claims 1 to 7.
10. A non-transitory machine-readable storage medium having executable code stored thereon, which when executed by a processor of an electronic device, causes the processor to perform the vehicle scheduling method of any one of claims 1 to 7.
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| CN116343461A (en) * | 2023-04-03 | 2023-06-27 | 北京白驹易行科技有限公司 | Vehicle scheduling method, device and equipment |
| CN116343461B (en) * | 2023-04-03 | 2023-11-17 | 北京白驹易行科技有限公司 | Vehicle scheduling method, device and equipment |
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