Method and device for combining travel orders of multiple tenants into ride-sharing orders

Abstract

The present invention provides a method and device for combining travel orders of multiple tenants into ride-sharing orders, and relates to the technical field of transportation. The method of the present invention comprises: determining multiple traffic nodes in pre-selected routes of each accepted order, and generating supply and demand hot and cold impact values ​​at each traffic node according to the number of orders to be received and the number of empty bus drivers; combining the supply and demand hot and cold impact values, the driving distance and time of the best ride-sharing route to generate a ride-sharing evaluation coefficient; combining the orders of the tenants to be received and the orders of the tenants received corresponding to the best ride-sharing route with the largest comprehensive ride-sharing evaluation coefficient into a ride-sharing order. The ride-sharing order combined by the method of the present invention not only has better planning in terms of the driving time and driving distance of the tenants received and the tenants to be received, but also plays a better role in alleviating the degree of hot spots in supply and demand and improving the traffic environment, thereby avoiding the problem of local traffic inconvenience in the city.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and specifically to a method and device for combining travel orders of multiple tenants into ride-sharing orders. Background Art

[0002] With the increasing tension of energy resources, transportation and environmental issues, the ride-sharing problem has been studied by more and more people. The ride-sharing problem refers to matching available vehicles with the travel needs of car-free passengers within a certain time and space range, and planning their travel routes. The effective solution to the ride-sharing problem plays an important role in improving the utilization of transportation resources, reducing transportation costs and alleviating traffic congestion. An efficient ride-sharing solution strategy aims to reduce the number of vehicles on the same or similar routes by matching driver-passenger pairs with path similarity and associating them within a fixed space and time range.

[0003] In the prior art, a two-stage matching method, system, intelligent terminal and storage medium for online car-hailing shared rides with publication number "CN116432785A" specifically relates to the field of vehicle shared rides. A mathematical model is constructed based on the travel information of passengers and drivers and preset constraints. In the reservation order matching stage, a two-stage collaborative mechanism is used to solve the mathematical model to match drivers and passengers with the goal of maximizing path similarity and driver satisfaction, and a planned path is obtained. In the real-time order matching stage, a two-stage collaborative mechanism is used to solve the mathematical model to match drivers and passengers with the goal of maximizing path distance and passenger satisfaction, and a real-time optimized path is obtained to improve the accuracy of matching drivers and passengers.

[0004] However, the existing technology still has major defects. For example, in the method of handling online car-hailing, the existing technology only takes into account the demand information of drivers and passengers and uses it to find the optimal solution, but ignores the supply and demand issues of the entire traffic environment. It may even further aggravate the severity of hot supply and demand areas (i.e., more passengers and fewer drivers) and cold supply and demand areas (i.e., more drivers and fewer passengers), causing inconvenience to urban transportation. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for combining travel orders of multiple tenants into ride-sharing orders, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for combining travel orders of multiple tenants into a ride-sharing order includes the following steps:

[0008] S1, obtaining the order origination time, order origination location, and order destination of the tenant's order, and combining the tenant's longest tolerable detour distance and longest tolerable detour time to generate the best route for the order and the alternative route for the order as the pre-selected route for the order;

[0009] S2, determining multiple transportation nodes in each pre-selected route of received orders, and obtaining the number of pending orders and the number of Airbus drivers at the transportation nodes, so as to generate the supply and demand cold and hot impact value of each transportation node;

[0010] S3, obtaining multiple waiting orders of tenants whose origins are located at traffic nodes, extracting the starting time, starting place and destination of the waiting orders of the waiting orders, generating the best path for the waiting orders, and combining the starting time, starting place, starting place and destination of the orders that have been received, generating the best path for carpooling;

[0011] S4, using the longest tolerable detour time, the longest tolerable detour distance, and the time to reach the origin of the waiting order in the optimal carpooling route of the tenants who have received the order and the tenants who are waiting to receive the order as constraint conditions, to screen out the preliminary qualified optimal carpooling route;

[0012] S5, combining the best route for received orders, the best route for pending orders, the best route for shared rides, and the cold and hot impact values ​​of supply and demand, to generate a shared ride evaluation coefficient for each shared ride best route;

[0013] S6, comparing the comprehensive evaluation coefficients of each optimal ride-sharing route, and combining the to-be-accepted tenant orders and the accepted tenant orders corresponding to the optimal ride-sharing route with the largest comprehensive evaluation coefficient into a ride-sharing order.

[0014] Furthermore, the traffic nodes include large shopping malls, residential areas, pedestrian street entrances and exits, schools, hospitals, and crossroads.

[0015] Furthermore, multiple transportation nodes in each pre-selected route for received orders are determined, and the number of pending orders and the number of Airbus drivers at the transportation nodes are obtained. The specific logic for generating the hot and cold impact value of supply and demand at each transportation node is as follows:

[0016] S21, based on the number of orders to be received and the number of Airbus drivers at the traffic node, generates the supply and demand hot and cold coefficient, and the calculation formula is as follows:

[0017]

[0018] Wherein, τ is the preset proportional coefficient, and τ≥1, represents the number of orders to be received at the jth traffic node of the i-th pre-selected path, represents the number of idle drivers at the jth traffic node of the i-th pre-selected path, represents the hot / cold coefficient of supply and demand at the jth traffic node of the i-th pre-selected path, i represents the number of different pre-selected paths, and i = 1, 2, 3, ..., m, and m represents the total number of pre-selected paths, and m∈N + , j represents the number of different traffic nodes in the same pre-selected path, and j = 1, 2, 3, ..., n i , and n i represents the total number of traffic nodes in the i-th pre-selected path, and n i ∈N + ;

[0019] S22, assigning a value to the supply and demand cooling and heating coefficient to generate a supply and demand cooling and heating impact value between 0 and 1 and positively correlated with the supply and demand cooling and heating coefficient.

[0020] Furthermore, the supply and demand cold and hot impact value at the jth traffic node of the i-th pre-selected path is calibrated as And the supply and demand cold and hot impact value The assignment criteria are as follows:

[0021] When satisfied When the supply and demand hot and cold impact value

[0022] When satisfied When the supply and demand hot and cold impact value

[0023] When satisfied When the supply and demand hot and cold impact value

[0024] When satisfied When the supply and demand hot and cold impact value

[0025] When satisfied When the supply and demand hot and cold impact value

[0026] When satisfied When the supply and demand hot and cold impact value

[0027] Furthermore, the specific logic of selecting the initially qualified optimal ride-sharing route is as follows:

[0028] The driving time and distance of the received order from the origin of the received order to the destination of the received order, the driving time and distance of the pending order from the origin of the pending order to the destination of the pending order, and the arrival time of the pending order at the origin of the pending order are extracted from each optimal carpooling route. The optimal carpooling routes that are initially qualified are screened out with the constraints that the driving time of the received order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving distance of the received order is not greater than the longest tolerable detour distance of the tenant who has received the order, and the driving time of the pending order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving distance of the pending order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving time of the pending order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving distance of the pending order is not greater than the longest tolerable detour distance of the tenant who has received the order, and the arrival time of the pending order is not greater than the time difference between the longest tolerable detour time of the tenant and the driving time of the received order.

[0029] Furthermore, the specific logic of generating the ride-sharing evaluation coefficient of each ride-sharing best path is as follows, combining the best path for received orders, the best path for waiting orders, the best path for ride-sharing, and the hot and cold impact values ​​of supply and demand:

[0030] S51, combining the travel time and travel distance of the best route for the accepted order, and the travel time and travel distance of the best route for the shared ride, to generate a first shared ride evaluation coefficient, the calculation formula of which is as follows:

[0031]

[0032] Wherein, GCpj1(k) represents the first carpooling evaluation coefficient of the kth carpooling best path among the qualified carpooling best paths, T1 represents the travel time of the accepted best path, S1 represents the travel distance of the accepted best path, T1(k) represents the travel time of the accepted order of the kth carpooling best path among the qualified carpooling best paths, S1(k) represents the travel distance of the accepted order of the kth carpooling best path among the qualified carpooling best paths, ΔT1(k) represents the time when the arrival time of the waiting order of the kth carpooling best path among the qualified carpooling best paths is ahead of the starting time of the waiting order, k represents the numbers of different qualified carpooling best paths, and k=1, 2, 3, ..., z, and z represents the total number of qualified carpooling best paths, and z∈N + ;

[0033] Wherein, λ1 and λ2 are both preset proportional coefficients, and λ1+λ2=1, and 0<λ1<1, 0<λ2<1;

[0034] S52, combining the travel time and travel distance of the best path for waiting orders, and the travel time and travel distance of the best path for carpooling, to generate a second carpooling evaluation coefficient, the calculation formula of which is as follows:

[0035]

[0036] Wherein, GCpj2(k) represents the second ride-sharing evaluation coefficient of the kth ride-sharing best path among the qualified ride-sharing best paths, T2 represents the travel time of the best path to be picked up, S2 represents the travel distance of the best path to be picked up, T2(k) represents the travel time of the kth ride-sharing best path among the qualified ride-sharing best paths, and S2(k) represents the travel distance of the kth ride-sharing best path to be picked up;

[0037] S53, generating a third ride-sharing evaluation coefficient by combining the ride time of the best route for accepted orders, the ride time of the best route for pending orders, the ride time of the best route for shared rides, and the ride time of the best route for pending orders. The calculation formula is as follows:

[0038]

[0039] Wherein, GCpj3(k) represents the third carpooling evaluation coefficient of the kth carpooling optimal path among the qualified carpooling optimal paths;

[0040] S54, generating a fourth ride-sharing evaluation coefficient by combining the driving distance of the best route for accepted orders, the driving distance of the best route for pending orders, the driving distance of the best route for shared rides, and the driving distance of the best route for pending orders. The calculation formula is as follows:

[0041]

[0042] Wherein, GCpj4(k) represents the fourth carpooling evaluation coefficient of the kth carpooling best path among the qualified carpooling best paths;

[0043] S55, combining the first ride-sharing evaluation coefficient, the second ride-sharing evaluation coefficient, the third ride-sharing evaluation coefficient, the fourth ride-sharing evaluation coefficient, and the supply and demand cold and hot impact value to generate a ride-sharing comprehensive evaluation coefficient, and the calculation formula is as follows:

[0044]

[0045] Among them, GCpj(k) represents the comprehensive evaluation coefficient of the kth best ride-sharing path among the qualified best ride-sharing paths, and f(GXlr(k)) represents the supply and demand cold and hot influence value of the transportation node where the origin of the waiting order of the kth best ride-sharing path among the qualified best ride-sharing paths is located.

[0046] A device for combining travel orders of multiple tenants into a ride-sharing order, used in the above-mentioned method for combining travel orders of multiple tenants into a ride-sharing order, comprising:

[0047] The received order pre-selected path generation unit is used to obtain the received order origination time, received order origination location, and received order destination of the received tenant order, and generate the received order best path and the received order alternative path as the received order pre-selected path in combination with the tenant's longest tolerable detour distance and longest tolerable detour time;

[0048] The supply and demand cold and hot impact value generating unit is used to determine multiple transportation nodes in each pre-selected route of received orders, and obtain the number of pending orders and the number of airbus drivers at the transportation nodes, so as to generate the supply and demand cold and hot impact value of each transportation node;

[0049] The optimal ride-sharing path generation unit is used to obtain multiple waiting orders of tenants whose origins are located at traffic nodes, extract the starting time, starting place and destination of the waiting orders of the waiting orders, generate the optimal path for the waiting orders, and generate the optimal ride-sharing path by combining the starting time, starting place, starting place and destination of the orders that have been received, the starting place, the destination and destination of the orders that have been received;

[0050] The optimal ride-sharing route screening unit is used to screen out a preliminarily qualified optimal ride-sharing route based on the longest tolerable detour time and longest tolerable detour distance of the tenants who have received orders and the tenants who are waiting to receive orders, and the time to reach the origin of the waiting order in the optimal ride-sharing route as constraint conditions;

[0051] A ride-sharing evaluation coefficient calculation unit, used to generate a ride-sharing evaluation coefficient for each ride-sharing best path by combining the best path for received orders, the best path for pending orders, the best path for ride-sharing, and the cold and hot impact value of supply and demand;

[0052] The ride-sharing order generating unit is used to compare the comprehensive evaluation coefficients of each ride-sharing optimal route, and combine the waiting tenant orders and the received tenant orders corresponding to the ride-sharing optimal route with the largest comprehensive evaluation coefficient into a ride-sharing order.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The method and device for combining travel orders of multiple tenants into ridesharing orders of the present invention generates supply and demand hot and cold impact values ​​at each traffic node according to the number of waiting orders and the number of empty bus drivers, and then combines the supply and demand hot and cold impact values, the driving distance and time of the best ridesharing path to generate a ridesharing evaluation coefficient, and combines the waiting tenant orders and the accepted tenant orders corresponding to the best ridesharing path with the largest comprehensive ridesharing evaluation coefficient into a ridesharing order, so that the combined ridesharing order not only has better planning in terms of the driving time and driving distance of the accepted tenants and the waiting tenants, but also plays a better role in alleviating the degree of hot spots in supply and demand and improving the traffic environment, thereby avoiding the problem of local traffic inconvenience in the city. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart of a method for combining travel orders of multiple tenants into a ride-sharing order in the present invention;

[0056] Figure 2 A unit diagram of a device for combining travel orders of multiple tenants into a ride-sharing order in the present invention. DETAILED DESCRIPTION

[0057] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0058] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0059] Embodiment 1:

[0060] See also Figure 1 The present invention provides a method for combining travel orders of multiple tenants into a ride-sharing order according to an embodiment of the present invention, comprising the following steps:

[0061] S1, obtaining the order origination time, order origination location, and order destination of the tenant's order, and combining the tenant's longest tolerable detour distance and longest tolerable detour time to generate the best route for the order and the alternative route for the order as the pre-selected route for the order;

[0062] It should be noted that the specific method for generating the pre-selected route for an accepted order is as follows: first, the best route for an accepted order is generated based on the starting time, starting place and destination of the accepted order in the tenant's order, and the driving distance and driving time of the best route for an accepted order are extracted. The tenant manually enters or selects the longest tolerable detour distance and the longest tolerable detour time on the travel APP, and based on the driving distance and driving time of the best route for an accepted order, multiple alternative routes for an accepted order are generated with the longest tolerable detour distance and the longest tolerable detour time as restrictions, and the best route for an accepted order and the multiple alternative routes for an accepted order are used together as the pre-selected route for an accepted order for the travel order;

[0063] It should be noted that the best route for the received order can be the route with the shortest driving time, the shortest driving distance, or the route with the best distance and time. There is no limitation here. The best route for the received order and the alternative routes for the received order can be calculated and generated by the built-in algorithm program of the existing travel APP. This is the existing technology and will not be elaborated here.

[0064] Furthermore, the maximum tolerable detour distance and the maximum tolerable detour time can also be generated by default based on the driving distance and driving time of the best route for the order. For example, the maximum tolerable detour distance cannot exceed 105%, 108%, 110% and so on of the driving distance of the best route for the order; the maximum tolerable detour time cannot exceed 105%, 108%, 110% and so on of the driving time of the best route for the order. There is no restriction here.

[0065] S2, determining multiple transportation nodes in each pre-selected route for each received order, and obtaining the number of pending orders and the number of Airbus drivers at the transportation nodes, so as to generate the supply and demand cold and hot impact value of each transportation node, including the following steps:

[0066] S21, based on the number of orders to be received and the number of Airbus drivers at the traffic node, generates the supply and demand hot and cold coefficient, and the calculation formula is as follows:

[0067]

[0068] It should be noted that traffic nodes can be selected from areas with large traffic flow, such as large shopping malls, residential areas, pedestrian street entrances and exits, schools, hospitals, and crossroads;

[0069] It should be noted that represents the number of orders to be received at the jth traffic node of the i-th pre-selected path, represents the number of idle drivers at the jth traffic node of the i-th pre-selected path, represents the hot / cold coefficient of supply and demand at the jth traffic node of the i-th pre-selected path, i represents the number of different pre-selected paths, and i = 1, 2, 3, ..., m, and m represents the total number of pre-selected paths, and m∈N + , j represents the number of different traffic nodes in the same pre-selected path, and j = 1, 2, 3, ..., n i , and n i represents the total number of traffic nodes in the i-th pre-selected path, and n i ∈N + ;

[0070] It should be noted that, because at a traffic node, an empty car driver can pick up multiple passengers, that is, an empty car driver can receive multiple pick-up orders, so when generating the supply and demand hot and cold coefficient according to the ratio of the number of pending orders to the number of empty car drivers, the number of empty car drivers is multiplied by a preset proportional coefficient τ, so that the generated supply and demand hot and cold coefficient can better reflect the supply and demand relationship at the traffic node. The specific value of the preset proportional coefficient τ is generally determined by those skilled in the art according to actual conditions, and the preset proportional coefficient τ≥1, such as the value range of the preset proportional coefficient τ can be between 1.5-2.5;

[0071] It should be noted that the number of pending orders Number of Airbus drivers The larger the ratio is, the more passengers are waiting for their rides at the traffic node and the fewer empty cars are available for rides, that is, the more serious the shortage of supply and demand (i.e., the hot zone of supply and demand) is at the traffic node, and the hot and cold coefficient of supply and demand is The larger the number of orders to be received, the larger the number of orders to be received. Number of Airbus drivers The smaller the ratio is, the fewer passengers are waiting for boarding at the traffic node and the more empty cars are available for boarding, that is, the more serious the oversupply (i.e., the cold zone of supply and demand) is at the traffic node, and the hot and cold coefficient of supply and demand is The smaller it is;

[0072] S22, assigning a value to the supply and demand cold and hot coefficient to generate a supply and demand cold and hot impact value between 0 and 1 and positively correlated with the supply and demand cold and hot coefficient;

[0073] As an implementation method, the supply and demand cold and hot impact value is calibrated as Supply and demand hot and cold impact value The assignment criteria are as follows:

[0074]

[0075] S3, obtaining multiple waiting orders of tenants whose origins are located at traffic nodes, extracting the starting time, starting place and destination of the waiting orders of the waiting orders, generating the best path for the waiting orders, and combining the starting time, starting place, starting place and destination of the orders that have been received, generating the best path for carpooling;

[0076] It should be noted that the method for generating the best path for pending orders based on the starting time, starting place and end point of pending orders of pending tenant orders is the same as the method for generating the best path for received orders, and will not be described in detail here. The specific method for generating the best path for shared rides based on the starting time, starting place, starting place and end point of received orders is as follows: using the starting time of received orders as the departure time, the starting place of received orders as the departure place, the starting place of pending orders and the end point of pending orders (or the end point of received orders) as the intermediate places, and the end point of received orders (or the end point of pending orders) as the end point as the restriction conditions to generate the path with the shortest distance, the shortest time or the best distance and time combination, and taking the path with the shortest distance, the shortest time or the best distance and time combination as the best path for shared rides;

[0077] It should be noted that the best route for waiting orders and the best route for carpooling can both be calculated and generated through the built-in algorithm program of existing travel apps. This is existing technology and will not be elaborated here.

[0078] S4: Using the longest tolerable detour time and the longest tolerable detour distance of the tenants who have received orders and the tenants waiting for orders, as well as the time to reach the origin of the waiting orders in the optimal carpooling route as constraint conditions, a preliminary qualified optimal carpooling route is screened out, as follows:

[0079] The driving time and distance of the received order from the origin of the received order to the destination of the received order, the driving time and distance of the pending order from the origin of the pending order to the destination of the pending order, and the arrival time of the pending order at the origin of the pending order are extracted from each optimal carpooling route. The optimal carpooling routes that are initially qualified are screened out with the constraints that the driving time of the received order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving distance of the received order is not greater than the longest tolerable detour distance of the tenant who has received the order, and the driving time of the pending order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving distance of the pending order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving time of the pending order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving distance of the pending order is not greater than the longest tolerable detour distance of the tenant who has received the order, and the arrival time of the pending order is not greater than the time difference between the longest tolerable detour time of the tenant and the driving time of the received order.

[0080] S5, combining the best path for received orders, the best path for pending orders, the best path for shared rides, and the cold and hot impact values ​​of supply and demand, to generate a shared ride evaluation coefficient for each shared ride best path, including the following steps:

[0081] S51, combining the travel time and travel distance of the best route for the accepted order, and the travel time and travel distance of the best route for the shared ride, to generate a first shared ride evaluation coefficient, the calculation formula of which is as follows:

[0082]

[0083] It should be noted that GCpj1(k) represents the first carpooling evaluation coefficient of the kth carpooling best path among the qualified carpooling best paths, T1 represents the travel time of the best path that has been accepted, S1 represents the travel distance of the best path that has been accepted, T1(k) represents the travel time of the kth carpooling best path among the qualified carpooling best paths, S1(k) represents the travel distance of the kth carpooling best path among the qualified carpooling best paths, ΔT1(k) represents the time when the arrival time of the waiting order of the kth carpooling best path among the qualified carpooling best paths is ahead of the starting time of the waiting order, k represents the numbers of different qualified carpooling best paths, and k=1, 2, 3, ..., z, and z represents the total number of qualified carpooling best paths, and z∈N + ;

[0084] It should be noted that λ1 and λ2 are both preset proportional coefficients, λ1 is The influence weight in the calculation of the first carpooling evaluation coefficient GCpj1(k), λ2 is The influence weight in the calculation of the first carpooling evaluation coefficient GCpj1(k), and λ1+λ2=1, and 0<λ1<1, 0<λ2<1, and the specific values ​​of λ1 and λ2 are generally determined by those skilled in the art according to actual conditions;

[0085] As an implementation mode, for the optimal carpooling path that takes the shortest time path as the optimal path, the value range of λ1 is 0.6-0.9, and the value range of λ2 is 0.1-0.4; for the optimal carpooling path that takes the shortest distance path as the optimal path, the value range of λ1 is 0.1-0.4, and the value range of λ2 is 0.6-0.9; for the optimal carpooling path that takes the path with the best comprehensive distance and time as the optimal path, the values ​​of λ1 and λ2 are both 0.5;

[0086] It should be noted that when the arrival time of the waiting order of the kth best ridesharing path in the qualified best ridesharing path lags behind the departure time of the waiting order (i.e., ΔT1(k)≤0), T1(k) is the total travel time of the tenant who has received the order in the entire best ridesharing path. When the arrival time of the waiting order of the kth best ridesharing path in the qualified best ridesharing path is ahead of the departure time of the waiting order (i.e., ΔT1(k)>0), it means that when the tenant who has received the order arrives at the departure place of the waiting order, the tenant who has received the order has not yet arrived at the departure place of the waiting order. The time that the tenant who has received the order needs to wait is ΔT1(k), and the total travel time of the tenant who has received the order in the entire best ridesharing path is T1(k)+ΔT1(k).

[0087] It should be noted that the larger T1(k) and ΔT1(k) are, the longer the total driving time of the tenant who has received the order in the entire carpooling optimal path is, and the worse the time planning of the carpooling optimal path is compared with the time planning of the optimal path for the received order. or As an evaluation parameter of the first carpooling evaluation coefficient GCpj1(k), the larger T1(k) and ΔT1(k) are,

[0088] and The smaller it is, the smaller the first carpooling evaluation coefficient GCpj1(k) is;

[0089] It should be noted that the larger the S1(k), the longer the total driving distance of the tenant who has received the order in the entire carpooling optimal path, and the worse the distance planning of the carpooling optimal path compared with the received order optimal path. As another evaluation parameter of the first carpooling evaluation coefficient GCpj1(k), the larger S1(k), The smaller it is, the smaller the first carpooling evaluation coefficient GCpj1(k) is;

[0090] S52, combining the travel time and travel distance of the best path for waiting orders, and the travel time and travel distance of the best path for carpooling, to generate a second carpooling evaluation coefficient, the calculation formula of which is as follows:

[0091]

[0092] It should be noted that GCpj2(k) represents the second ride-sharing evaluation coefficient of the kth ride-sharing best path among the qualified ride-sharing best paths, T2 represents the ride time of the best path to be picked up, S2 represents the ride distance of the best path to be picked up, T2(k) represents the ride time of the kth ride-sharing best path among the qualified ride-sharing best paths, and S2(k) represents the ride distance of the kth ride-sharing best path among the qualified ride-sharing best paths;

[0093] It should be noted that the larger T2(k) is, the longer the total travel time of the waiting order tenant in the entire carpooling optimal path is, and the worse the time planning of the carpooling optimal path is compared with the waiting order optimal path. As an evaluation parameter of the second carpooling evaluation coefficient GCpj2(k), the larger T2(k) is, The smaller it is, the smaller the second carpooling evaluation coefficient GCpj2(k) is;

[0094] It should be noted that the larger S2(k) is, the longer the total driving distance of the waiting order tenant in the entire carpooling optimal path is, and the worse the distance planning of the carpooling optimal path is compared with the waiting order optimal path. As another evaluation parameter of the second carpooling evaluation coefficient GCpj2(k), the larger S2(k), The smaller it is, the smaller the second carpooling evaluation coefficient GCpj2(k) is;

[0095] S53, generating a third ride-sharing evaluation coefficient by combining the ride time of the best route for accepted orders, the ride time of the best route for pending orders, the ride time of the best route for shared rides, and the ride time of the best route for pending orders. The calculation formula is as follows:

[0096]

[0097] It should be noted that GCpj3(k) represents the third carpooling evaluation coefficient of the kth carpooling optimal path among the qualified carpooling optimal paths. When ΔT1(k)≤0, for The influence weight in the calculation of the third carpooling evaluation coefficient GCpj3(k), for The influence weight in the calculation of the third ride-sharing evaluation coefficient GCpj3(k) is that the larger the T1(k), the longer the optimal ride-sharing path spends on the received tenant orders, and the more important the received tenant orders are, that is, The greater the influence weight in the calculation of the third ride-sharing evaluation coefficient GCpj3(k), the greater the T2(k), indicating that the optimal ride-sharing path spends more time on the waiting tenant orders, and the more important the waiting tenant orders are, that is, The greater the influence weight in the calculation of the third ride-sharing evaluation coefficient GCpj3(k);

[0098] When ΔT1(k)>0, for The influence weight in the calculation of the third ride-sharing evaluation coefficient GCpj3(k), for The influence weights in the calculation of the third ride-sharing evaluation coefficient GCpj3(k), the larger the T1(k) and ΔT1(k), the longer the time spent on the received tenant orders by the optimal ride-sharing path, and the more important the tenant orders are, that is, The greater the influence weight in the calculation of the third ride-sharing evaluation coefficient GCpj3(k), the greater the T2(k), indicating that the optimal ride-sharing path spends more time on the waiting tenant orders, and the more important the waiting tenant orders are, that is, The greater the influence weight in the calculation of the third ride-sharing evaluation coefficient GCpj3(k);

[0099] It should be noted that the larger T1(k) and ΔT1(k) are, the longer the total driving time of the tenant who has received the order in the entire carpooling optimal path is, and the worse the time planning of the carpooling optimal path is compared with the time planning of the optimal path for the received order. or As an evaluation parameter of the third carpooling evaluation coefficient GCpj3(k), the larger T1(k) and ΔT1(k) are, and The smaller it is, the smaller the third ride-sharing evaluation coefficient GCpj3(k) is;

[0100] It should be noted that the larger T2(k) is, the longer the total travel time of the waiting order tenant in the entire carpooling optimal path is, and the worse the time planning of the carpooling optimal path is compared with the waiting order optimal path. As another evaluation parameter of the third carpooling evaluation coefficient GCpj3(k), the larger T2(k) is, The smaller it is, the smaller the third ride-sharing evaluation coefficient GCpj3(k) is;

[0101] S54, generating a fourth ride-sharing evaluation coefficient by combining the driving distance of the best route for accepted orders, the driving distance of the best route for pending orders, the driving distance of the best route for shared rides, and the driving distance of the best route for pending orders. The calculation formula is as follows:

[0102]

[0103] It should be noted that GCpj4(k) represents the fourth carpooling evaluation coefficient of the kth carpooling optimal path among the qualified carpooling optimal paths. for The influence weight in the calculation of the fourth carpooling evaluation coefficient GCpj4(k), for The influence weight in the calculation of the fourth ride-sharing evaluation coefficient GCpj4(k), the larger the S1(k), the longer the distance consumed by the optimal ride-sharing path on the accepted tenant orders, and the more important the accepted tenant orders are, that is, The greater the influence weight in the calculation of the fourth ride-sharing evaluation coefficient GCpj4(k), the greater the S2(k), indicating that the distance spent on the waiting tenant orders by the optimal ride-sharing path is longer, and the waiting tenant orders are more important, that is, The greater the influence weight in the calculation of the fourth carpooling evaluation coefficient GCpj4(k);

[0104] It should be noted that the larger the S1(k), the longer the total driving distance of the tenant who has received the order in the entire carpooling optimal path, and the worse the distance planning of the carpooling optimal path compared with the received order optimal path. As an evaluation parameter of the fourth carpooling evaluation coefficient GCpj4(k), the larger S1(k), The smaller it is, the smaller the fourth carpooling evaluation coefficient GCpj4(k) is;

[0105] It should be noted that the larger S2(k) is, the longer the total driving distance of the waiting order tenant in the entire carpooling optimal path is, and the worse the distance planning of the carpooling optimal path is compared with the waiting order optimal path. As another evaluation parameter of the fourth carpooling evaluation coefficient GCpj4(k), the larger S2(k), The smaller it is, the smaller the fourth carpooling evaluation coefficient GCpj4(k) is;

[0106] S55, combining the first ride-sharing evaluation coefficient, the second ride-sharing evaluation coefficient, the third ride-sharing evaluation coefficient, the fourth ride-sharing evaluation coefficient, and the supply and demand cold and hot impact value to generate a ride-sharing comprehensive evaluation coefficient, and the calculation formula is as follows:

[0107]

[0108] It should be noted that GCpj(k) represents the comprehensive evaluation coefficient of the kth optimal ride-sharing path among the qualified optimal ride-sharing paths, f(GXlr(k)) represents the supply and demand cold and hot impact value of the traffic node where the origin of the waiting order of the kth optimal ride-sharing path among the qualified optimal ride-sharing paths is located, and

[0109]

[0110] It should be noted that the larger the GCpj1(k), GCpj2(k), GCpj3(k), and GCpj4(k), the better the planning of the optimal carpooling route in terms of the travel time and travel distance of the picked-up and waiting-for-pickup orders. The larger the f(GXlr(k)), the more urgent the need for drivers at the traffic node where the waiting-for-pickup order originates. The greater the value of alleviating the hot zone of supply and demand and improving the traffic environment through the optimal carpooling route. Therefore, As the base, let f(GXlr(k)) be the exponent to obtain the comprehensive evaluation coefficient of carpooling GCpj(k). The larger GCpj1(k), GCpj2(k), GCpj3(k), GCpj4(k), and f(GXlr(k)) are, the better the planning of the optimal carpooling route is not only in terms of riding time and driving distance, but also in terms of alleviating the degree of supply and demand hot spots and improving the traffic environment. The greater the comprehensive evaluation coefficient of carpooling GCpj(k) is.

[0111] S6, compares the comprehensive evaluation coefficients of each optimal carpooling route, and combines the waiting tenant orders and accepted tenant orders corresponding to the optimal carpooling route with the largest comprehensive evaluation coefficient into a carpooling order. In this way, the final carpooling order not only has a better planning of the riding time and riding distance of the accepted tenants and the waiting tenants, but also plays a better role in alleviating the degree of hot spots in supply and demand and improving the traffic environment, and has great progressiveness and applicability.

[0112] Embodiment 2:

[0113] See also Figure 2The present invention provides a device for combining travel orders of multiple tenants into a ride-sharing order according to an embodiment of the present invention, which is used for the method for combining travel orders of multiple tenants into a ride-sharing order, and includes:

[0114] The received order pre-selected path generation unit is used to obtain the received order origination time, received order origination location, and received order destination of the received tenant order, and generate the received order best path and the received order alternative path as the received order pre-selected path in combination with the tenant's longest tolerable detour distance and longest tolerable detour time;

[0115] The supply and demand cold and hot impact value generating unit is used to determine multiple transportation nodes in each pre-selected route of received orders, and obtain the number of pending orders and the number of airbus drivers at the transportation nodes, so as to generate the supply and demand cold and hot impact value of each transportation node;

[0116] The optimal ride-sharing path generation unit is used to obtain multiple waiting orders of tenants whose origins are located at traffic nodes, extract the starting time, starting place and destination of the waiting orders of the waiting orders, generate the optimal path for the waiting orders, and generate the optimal ride-sharing path by combining the starting time, starting place, starting place and destination of the orders that have been received, the starting place, the destination and destination of the orders that have been received;

[0117] The optimal ride-sharing route screening unit is used to screen out a preliminarily qualified optimal ride-sharing route based on the longest tolerable detour time and longest tolerable detour distance of the tenants who have received orders and the tenants who are waiting to receive orders, and the time to reach the origin of the waiting order in the optimal ride-sharing route as constraint conditions;

[0118] A ride-sharing evaluation coefficient calculation unit, used to generate a ride-sharing evaluation coefficient for each ride-sharing best path by combining the best path for received orders, the best path for pending orders, the best path for ride-sharing, and the cold and hot impact value of supply and demand;

[0119] The ride-sharing order generating unit is used to compare the comprehensive evaluation coefficients of each ride-sharing optimal route, and combine the waiting tenant orders and the received tenant orders corresponding to the ride-sharing optimal route with the largest comprehensive evaluation coefficient into a ride-sharing order.

[0120] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0121] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0122] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for combining travel orders of multiple tenants into a ride-sharing order, characterized in that: The steps include: S1, obtaining the order origination time, order origination location, and order destination of the tenant's order, and combining the tenant's longest tolerable detour distance and longest tolerable detour time to generate the best route for the order and the alternative route for the order as the pre-selected route for the order; S2, determining multiple transportation nodes in each pre-selected route of received orders, and obtaining the number of pending orders and the number of Airbus drivers at the transportation nodes, so as to generate the supply and demand cold and hot impact value of each transportation node; S3, obtaining multiple waiting orders of tenants whose origins are located at traffic nodes, extracting the starting time, starting place and destination of the waiting orders of the waiting orders, generating the best path for the waiting orders, and combining the starting time, starting place, starting place and destination of the orders that have been received, generating the best path for carpooling; S4, using the longest tolerable detour time, the longest tolerable detour distance, and the time to reach the origin of the waiting order in the optimal carpooling route of the tenants who have received the order and the tenants who are waiting to receive the order as constraint conditions, to screen out the preliminary qualified optimal carpooling route; S5, combining the best route for received orders, the best route for pending orders, the best route for shared rides, and the cold and hot impact values ​​of supply and demand, to generate a shared ride evaluation coefficient for each shared ride best route; S6, comparing the comprehensive evaluation coefficients of each optimal carpooling route, and combining the to-be-received tenant orders and the received tenant orders corresponding to the optimal carpooling route with the largest comprehensive evaluation coefficient into a carpooling order; Determine multiple transportation nodes in each pre-selected route for each received order, and obtain the number of pending orders and the number of Airbus drivers at the transportation nodes, so as to generate the specific logic of the supply and demand cold and hot impact value of each transportation node: S21, based on the number of orders to be received and the number of Airbus drivers at the traffic node, generates the supply and demand hot and cold coefficient, and the calculation formula is as follows: Wherein, τ is the preset proportional coefficient, and τ≥1, represents the number of orders to be received at the jth traffic node of the i-th pre-selected path, GXlr represents the number of idle drivers at the jth traffic node of the i-th pre-selected path, i j represents the hot / cold coefficient of supply and demand at the jth traffic node of the i-th pre-selected path, i represents the number of different pre-selected paths, and i = 1, 2, 3, ..., m, and m represents the total number of pre-selected paths, and m∈N + , j represents the number of different traffic nodes in the same pre-selected path, and j = 1, 2, 3, ..., n i , and n i represents the total number of traffic nodes in the i-th pre-selected path, and n i ∈N + ; S22, assigning a value to the supply and demand cold and hot coefficient to generate a supply and demand cold and hot impact value between 0 and 1 and positively correlated with the supply and demand cold and hot coefficient; The supply and demand cold and hot impact value at the jth traffic node of the i-th pre-selected path is calibrated as f(GXlr i j ), and the supply and demand cold and hot impact value f(GXlr i j ) is as follows: When 0≤GXlrij<0.3, the supply and demand cold and hot impact value f(GXlr i j )=0.1; When 0.3≤GXlrij<0.6, the supply and demand cold and hot impact value f(GXlr i j )=0.2; When 0.6≤GXlrij<1, the supply and demand cold and hot impact value f(GXlr i j )=0.3; When 1≤GXlrij<2 is satisfied, the supply and demand cold and hot impact value f(GXlr i j )=0.5; When 2≤GXlrij<3 is satisfied, the supply and demand cold and hot impact value f(GXlr i j )=0.7; When GXlrij≥3 is satisfied, the supply and demand cold and hot impact value f(GXlr i j )=1.

2. The method for combining travel orders of multiple tenants into a ride-sharing order according to claim 1, characterized in that: The traffic nodes include large shopping malls, residential areas, pedestrian street entrances and exits, schools, hospitals, and crossroads.

3. The method for combining travel orders of multiple tenants into a ride-sharing order according to claim 1, characterized in that: Taking the longest tolerable detour time and the longest tolerable detour distance of the tenants who have received orders and the tenants waiting for orders, as well as the time to reach the origin of the waiting orders in the optimal carpooling route as constraints, the specific logic of screening out the preliminary qualified optimal carpooling route is as follows: The driving time and distance of the received order from the origin of the received order to the destination of the received order, the driving time and distance of the pending order from the origin of the pending order to the destination of the pending order, and the arrival time of the pending order at the origin of the pending order are extracted from each optimal carpooling route. The optimal carpooling routes that are initially qualified are screened out with the constraints that the driving time of the received order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving distance of the received order is not greater than the longest tolerable detour distance of the tenant who has received the order, and the driving time of the pending order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving distance of the pending order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving time of the pending order is not greater than the longest tolerable detour time of the tenant who has received the order, and the driving distance of the pending order is not greater than the longest tolerable detour distance of the tenant who has received the order, and the arrival time of the pending order is not greater than the time difference between the longest tolerable detour time of the tenant and the driving time of the received order.

4. The method for combining travel orders of multiple tenants into a ride-sharing order according to claim 1, characterized in that: The specific logic for generating the ride-sharing evaluation coefficient of each ride-sharing best path is as follows: S51, combining the travel time and travel distance of the best route for the accepted order, and the travel time and travel distance of the best route for the shared ride, to generate a first shared ride evaluation coefficient, the calculation formula of which is as follows: Wherein, GCpj1(k) represents the first carpooling evaluation coefficient of the kth carpooling best path among the qualified carpooling best paths, T1 represents the travel time of the accepted best path, S1 represents the travel distance of the accepted best path, T1(k) represents the travel time of the accepted order of the kth carpooling best path among the qualified carpooling best paths, S1(k) represents the travel distance of the accepted order of the kth carpooling best path among the qualified carpooling best paths, ΔT1(k) represents the time when the arrival time of the waiting order of the kth carpooling best path among the qualified carpooling best paths is ahead of the starting time of the waiting order, k represents the numbers of different qualified carpooling best paths, and k=1, 2, 3, ..., z, and z represents the total number of qualified carpooling best paths, and z∈N + ; Wherein, λ1 and λ2 are both preset proportional coefficients, and λ1+λ2=1, and 0<λ1<1, 0<λ2<1; S52, combining the travel time and travel distance of the best path for waiting orders, and the travel time and travel distance of the best path for carpooling, to generate a second carpooling evaluation coefficient, the calculation formula of which is as follows: Wherein, GCpj2(k) represents the second ride-sharing evaluation coefficient of the kth ride-sharing best path among the qualified ride-sharing best paths, T2 represents the travel time of the best path to be picked up, S2 represents the travel distance of the best path to be picked up, T2(k) represents the travel time of the kth ride-sharing best path among the qualified ride-sharing best paths, and S2(k) represents the travel distance of the kth ride-sharing best path to be picked up; S53, generating a third ride-sharing evaluation coefficient by combining the ride time of the best route for accepted orders, the ride time of the best route for pending orders, the ride time of the best route for shared rides, and the ride time of the best route for pending orders. The calculation formula is as follows: Wherein, GCpj3(k) represents the third carpooling evaluation coefficient of the kth carpooling optimal path among the qualified carpooling optimal paths; S54, generating a fourth ride-sharing evaluation coefficient by combining the driving distance of the best route for accepted orders, the driving distance of the best route for pending orders, the driving distance of the best route for shared rides, and the driving distance of the best route for pending orders. The calculation formula is as follows: Wherein, GCpj4(k) represents the fourth carpooling evaluation coefficient of the kth carpooling best path among the qualified carpooling best paths; S55, combining the first ride-sharing evaluation coefficient, the second ride-sharing evaluation coefficient, the third ride-sharing evaluation coefficient, the fourth ride-sharing evaluation coefficient, and the supply and demand cold and hot impact value to generate a ride-sharing comprehensive evaluation coefficient, and the calculation formula is as follows: Among them, GCpj(k) represents the comprehensive evaluation coefficient of the kth best ride-sharing path among the qualified best ride-sharing paths, and f(GXlr(k)) represents the supply and demand cold and hot influence value of the transportation node where the origin of the waiting order of the kth best ride-sharing path among the qualified best ride-sharing paths is located.

5. A device for combining travel orders of multiple tenants into a ride-sharing order, used in the method for combining travel orders of multiple tenants into a ride-sharing order as described in any one of claims 1 to 4, characterized in that: include: The received order pre-selected path generation unit is used to obtain the received order origination time, received order origination location, and received order destination of the received tenant order, and generate the received order best path and the received order alternative path as the received order pre-selected path in combination with the tenant's longest tolerable detour distance and longest tolerable detour time; The supply and demand cold and hot impact value generating unit is used to determine multiple transportation nodes in each pre-selected route of received orders, and obtain the number of pending orders and the number of airbus drivers at the transportation nodes, so as to generate the supply and demand cold and hot impact value of each transportation node; The optimal ride-sharing path generation unit is used to obtain multiple waiting orders of tenants whose origins are located at traffic nodes, extract the starting time, starting place and destination of the waiting orders of the waiting orders, generate the optimal path for the waiting orders, and generate the optimal ride-sharing path by combining the starting time, starting place, starting place and destination of the orders that have been received, the starting place, the destination and destination of the orders that have been received; The optimal ride-sharing route screening unit is used to screen out a preliminarily qualified optimal ride-sharing route based on the longest tolerable detour time and longest tolerable detour distance of the tenants who have received orders and the tenants who are waiting to receive orders, and the time to reach the origin of the waiting order in the optimal ride-sharing route as constraint conditions; A ride-sharing evaluation coefficient calculation unit, used to generate a ride-sharing evaluation coefficient for each ride-sharing best path by combining the best path for received orders, the best path for pending orders, the best path for ride-sharing, and the cold and hot impact value of supply and demand; The ride-sharing order generating unit is used to compare the comprehensive evaluation coefficients of each ride-sharing optimal route, and combine the waiting tenant orders and the received tenant orders corresponding to the ride-sharing optimal route with the largest comprehensive evaluation coefficient into a ride-sharing order.

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