CN108564189B - Auction mechanism-based online taxi appointment real-time service vehicle resource allocation and pricing method - Google Patents

Auction mechanism-based online taxi appointment real-time service vehicle resource allocation and pricing method Download PDF

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CN108564189B
CN108564189B CN201810361626.3A CN201810361626A CN108564189B CN 108564189 B CN108564189 B CN 108564189B CN 201810361626 A CN201810361626 A CN 201810361626A CN 108564189 B CN108564189 B CN 108564189B
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张骥先
张学杰
岳昆
李伟东
张静
杨旭涛
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Abstract

The invention discloses a real-time service vehicle resource allocation and pricing method for a network car booking based on an auction mechanism. The invention realizes a dynamic price mechanism by an auction mode, improves the vehicle utilization rate and the profit, meets the vehicle using requirements of more users and reduces the vehicle using cost of the users.

Description

Auction mechanism-based online taxi appointment real-time service vehicle resource allocation and pricing method
Technical Field
The invention belongs to the technical field of online car booking, and particularly relates to an online car booking real-time service vehicle resource allocation and pricing method based on an auction mechanism.
Background
The sharing economic model has great influence on the life style of people, such as online car appointment, sharing education and sharing medical treatment, and is mainly characterized in that idle resources are integrated and provided for users as required. Wherein, the network appointment mode is a typical application of the sharing economy. In China, the market of shared automobiles is huge, and according to the statistics of a white paper analysis report of China's interconnected trip in 2017, the annual income sum of network appointment vehicle enterprises represented by Didi, China and Excellent steps reaches billions of yuan.
At present, the economic mode of the networked car reservation is a pricing mode, and at the initial stage of market development, the mode is really simple and efficient, but with the expansion of the market, the defects also gradually appear. Generally, the holding capacity of a market appointment is fixed, but the market demand varies greatly over time, which results in redundancy of vehicle resources, for example, a large number of vehicles are used during rush hours and a small number of vehicles are used during the rest of the time, in which case, if the vehicles are used at a fixed price, the user's activity is impaired and the final profit and profit are affected. The dynamic price can enable the price paid by the user to be determined according to the market supply and demand condition, and is more suitable for the current condition, and the pricing mode based on the auction mechanism is one of the dynamic prices.
The greatest difference between the online car booking service mode under the auction mechanism and the traditional online car booking service mode is that the price of the vehicle in the traditional service mode is given by a vehicle provider in a pricing mode, the final payment price is obtained by comprehensively calculating the driving time and the driving distance of the vehicle in combination with the fixed unit price, but the fixed unit price can cause no seat for the vehicle at a peak time and no people to take the vehicle at ordinary times, and the main reason is that the price does not change along with the market supply and demand conditions, and at present, although some vehicle providers can dynamically adjust the price, the decision right of pricing is mastered in the hands of the vehicle provider and is not truly dynamic. In the auction mechanism, users can comprehensively consider own travel and submit an intention valuation, and vehicle providers can determine which users are served by collecting user demands and valuation, so that the real dynamic price is realized.
In the current network appointment service mode, a real-time service mode is one of the main types, a user can order in real time, the system can assign the most suitable nearby vehicle to serve the user immediately, and a period of time is required from ordering of the user to starting of the vehicle service. At present, the network car booking reservation service mode adopts a pricing mechanism, compared with other public transportation modes, the price is much higher, the enthusiasm of users is reduced, and the idle vehicles and the resource waste are easily caused.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a network appointment real-time service vehicle resource allocation and pricing method based on an auction mechanism, a dynamic price mechanism is realized through an auction mode, the vehicle utilization rate and the profit are improved, the vehicle using requirements of more users are met, and the vehicle using cost of the users is reduced.
In order to achieve the above purpose, the auction mechanism-based online taxi appointment real-time service vehicle resource allocation and pricing method of the invention comprises the following steps:
s1: the method comprises the steps that a user requests vehicle idle information from a network car booking platform, the network car booking platform feeds back the idle vehicle information at the current moment to the user, the idle vehicle information comprises the number M of idle vehicles, and the unit time cost of the idle vehicles is recorded as tcThe unit distance cost of the idle vehicle is dcThe current position of each idle vehicle is recorded as posk，k＝1,2,…,M；
S2: the user checks the received idle vehicle information, submits vehicle using requirements including an getting-on place, a getting-off place and bidding on the journey to the network appointment platform according to the requirements of the user;
s3: the network booking platform collects all user vehicle using requirements received at the current moment t, then calculates the vehicle operation time and the vehicle operation distance from the vehicle getting-on place to the vehicle getting-off place of each user, and records the vehicle using requirements of the user i as thetai＝(t,srci,dsti,ei,di,bi) Wherein, srciIndicating the boarding location, dst, of user iiIndicating a point of alighting of the user i, eiRepresenting the vehicle running time between the boarding and disembarking points of the user i, diRepresenting the distance traveled by the vehicle between the boarding and disembarking points of the user i, biThe bid of the user i for the itinerary is shown, i is 1,2, …, and N is the number of users;
s4: according to the user vehicle using requirements, the following two matrixes are constructed and obtained:
time interval matrix CT of vehicle and user at time tt：
Figure BDA0001636121250000021
Wherein, ctkiIndicating the current position pos of the slave vehicle kkTime required to get to the boarding location of user i;
distance interval matrix CD between vehicle and user at time tt：
Figure BDA0001636121250000031
Wherein cdkiIndicating the current position pos of the slave vehicle kkDistance to the boarding location of user i;
s5: for each vehicle and user, calculating the driving cost performance of the user i served by the vehicle k according to the following formula:
Figure BDA0001636121250000032
wherein
Figure BDA0001636121250000033
Evaluating parameters for the user waiting time, wherein the calculation formula is as follows:
Figure BDA0001636121250000034
the performance price ratio of M multiplied by N running vehicles obtained by calculation
Figure BDA0001636121250000035
The set of (a) is denoted as f;
s6: the following method is adopted for vehicle resource allocation:
s6.1: initializing the yield G of the vehicle resource allocation scheme to be 0;
s6.2: searching the maximum value of the driving cost performance from the set f
Figure BDA0001636121250000036
The corresponding vehicle is k ', and the user is i';
s6.3: if t isc·(ctk′i′+ei′)+dc·(cdk′i′+di′)＜bi′If not, the step S6.4 is carried out, otherwise, the step S6.6 is carried out;
s6.4: assigning the vehicle k 'to the user i', calculating the benefit resulting therefrom
Figure BDA0001636121250000037
S6.5: the driving cost performance relevant to the vehicle k 'and the user i' is deleted from the set f, so that benefits are obtained
Figure BDA0001636121250000038
Entering step S6.7;
s6.6: cost performance of the vehicle from the set f
Figure BDA0001636121250000039
Deleting and entering step S6.7;
s6.7: determine whether to aggregate
Figure BDA00016361212500000310
If so, the vehicle resource allocation is finished, otherwise, the step S6.2 is returned;
s7: and solving the payment price of each user according to the vehicle resource allocation scheme obtained in the step S6.
The invention relates to a real-time service vehicle resource allocation and pricing method for a car booking of a network based on an auction mechanism. The invention realizes a dynamic price mechanism by an auction mode, improves the vehicle utilization rate and the profit, meets the vehicle using requirements of more users and reduces the vehicle using cost of the users.
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FIG. 1 is a flow chart of an embodiment of a real-time vehicle resource allocation and pricing method for online taxi appointment real-time service based on an auction mechanism;
FIG. 2 is a flow chart of vehicle resource allocation in the present invention;
FIG. 3 is a flow chart of a dichotomy based pricing algorithm in the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
FIG. 1 is a flow chart of an embodiment of a real-time service vehicle resource allocation and pricing method for online taxi appointment based on an auction mechanism. As shown in fig. 1, the auction-mechanism-based online taxi appointment real-time service vehicle resource allocation and pricing method of the present invention specifically includes the following steps:
s101: the user requests vehicle idle information:
the method comprises the steps that a user requests vehicle idle information from a network car booking platform, the network car booking platform feeds back the idle vehicle information at the current moment to the user, the idle vehicle information comprises the number M of idle vehicles, and the unit time cost of the idle vehicles is recorded as tcThe unit distance cost of the idle vehicle is dcThe current position of each idle vehicle is recorded as posk，k＝1,2,…,M。
S102: the user submits the vehicle using requirement:
and the user checks the received idle vehicle information and submits vehicle using requirements including an getting-on site, a getting-off site and bidding on the journey to the network appointment platform according to the requirements of the user.
S103: user vehicle utilization demand summarization:
the network booking platform collects all user vehicle using requirements received at the current moment t, then calculates the vehicle operation time and the vehicle operation distance from the vehicle getting-on place to the vehicle getting-off place of each user, and records the vehicle using requirements of the user i as thetai＝(t,srci,dsti,ei,di,bi) Wherein, srciIndicating the boarding location, dst, of user iiIndicating a point of alighting of the user i, eiRepresenting the vehicle running time between the boarding and disembarking points of the user i, diRepresenting the distance traveled by the vehicle between the boarding and disembarking points of the user i, biIndicating the bid of user i for the itinerary, i-1, 2, …, N indicating the number of users.
S104: constructing a user vehicle demand correlation matrix:
in the present invention, in order to improve the utilization rate of vehicle resources as much as possible, the following assumptions are made:
1. if the vehicle is used for serving a user from the current position, the generated cost is more than the bid of the user, and the order of the user is not accepted.
2. The vehicle will not receive a new order while in operation.
In order to enable the vehicle resource allocation result to meet the above assumption, two matrixes are constructed and obtained according to the user vehicle utilization requirement:
time interval matrix CT of vehicle and user at time tt：
Figure BDA0001636121250000051
Wherein, ctkiIndicating the current position pos of the slave vehicle kkThe time required to get on to the user i, k ═ 1,2, …, M;
distance interval matrix CD between vehicle and user at time tt：
Figure BDA0001636121250000052
Wherein cdkiIndicating the current position pos of the slave vehicle kkDistance to the boarding location of user i.
Obviously, matrix CTtAnd CDtAre all matrices of size M × N.
S105: calculating the cost performance of the travelling crane:
in order to make the vehicle resource allocation scheme more reasonable, the driving cost performance is introduced as a reference parameter during vehicle resource allocation.
Definition of
Figure BDA0001636121250000053
Evaluating parameters for the waiting time of the user for improving the influence of the waiting time of the user in evaluating the cost performance parameters of the travelling crane, wherein the calculation formula is as follows:
Figure BDA0001636121250000054
defining parameters
Figure BDA0001636121250000061
Represents the running cost performance of the vehicle k service user i,
Figure BDA0001636121250000062
a larger value of (a) indicates a more tendency to serve the user, and the calculation formula is as follows:
Figure BDA0001636121250000063
the performance price ratio of M multiplied by N running vehicles obtained by calculation
Figure BDA0001636121250000064
The set of (c) is denoted as f.
S106: vehicle resource allocation:
FIG. 2 is a flow chart of vehicle resource allocation in the present invention. As shown in fig. 2, the specific steps of allocating vehicle resources in the present invention include:
s201: initializing the yield G of the vehicle resource allocation scheme to be 0;
s202: searching the maximum driving cost performance:
searching the maximum value of the driving cost performance from the set f
Figure BDA0001636121250000065
The corresponding vehicle is k 'and the user is i'.
S203: if t isc·(ctk′i′+ei′)+dc·(cdk′i′+di′)＜bi′Step S204 is entered, otherwise step S206 is entered.
S204: distributing vehicles:
assigning the vehicle k 'to the user i', calculating the benefit resulting therefrom
Figure BDA0001636121250000066
S205: update set f and profit G:
the driving cost performance relevant to the vehicle k 'and the user i' is deleted from the set f, so that benefits are obtained
Figure BDA0001636121250000067
The process advances to step S207.
S206: updating the set f:
cost performance of the vehicle from the set f
Figure BDA0001636121250000068
Delete, proceed to step S207.
S207: determine whether to aggregate
Figure BDA0001636121250000069
If so, the vehicle resource allocation is finished, otherwise, the step S202 is returned to.
S107: calculating a payment price:
the payment price calculation is to calculate the cost to be paid by each user on the basis of the vehicle resource allocation result, and an optimal payment algorithm can be designed on the basis of a VCG (Vickrey-Clark-Groves) mechanism, and the core of the VCG mechanism is that the price finally required to be paid by the user i is unrelated to the own bid, so that the intention of the user for trying to use false quotes to make a profit is eliminated. The VCG mechanism has been proven to satisfy trustworthiness on the premise of optimal allocation. The optimal payment algorithm model based on VCG is as follows:
Figure BDA00016361212500000610
wherein,
Figure BDA0001636121250000071
for the maximum profit when the ith user is not involved, (V-b)i+tc·ei+dc·di) Subtracting the income provided by the user i from the maximum income of the ith user when participating in the ith user, wherein the price p required to be paid by the ith useriThe two parts are subtracted. In this optimal payment algorithm, since the profit is calculated, the allocation algorithm needs to be called a plurality of times to solve the profit, and the calculation complexity is O (m · n · n |), and the calculation time is unpredictable, and therefore, the optimal payment algorithm cannot be used in practice. The present embodiment therefore proposes a dichotomy-based pricing algorithm to calculate the payment price for each user.
FIG. 3 is a flow chart of a dichotomy based pricing algorithm in the present invention. As shown in fig. 3, the dichotomy-based pricing algorithm of the present invention specifically comprises the following steps:
s301: acquiring an allocated user set:
acquiring an allocated user set Z according to a vehicle resource allocation scheme;
s302, selecting the allocated users:
one user is selected from the assigned user set Z as the current user Z.
S303: initializing a payment price:
order the payment price of user zLattice pz＝bzPayment price minimum value p'z＝0，bzRepresenting a bid for user z.
S304: updating the user bid:
let user z bid bz＝(pz+p′z)/2。
S305: determine if | pz-p′zIf yes, the step S306 is executed, otherwise, the step S308 is executed.
S306: and vehicle resource allocation is carried out again:
according to the current bid b of user zzAnd if the other parameters are unchanged, vehicle resource allocation is carried out again to obtain the current vehicle allocation scheme.
S307: updating the payment price parameter:
judging the vehicle allocation plan obtained in step S306, the user z bids b at the current valuezIf it can be allocated vehicle resource usage, let pz＝bz、
Figure BDA0001636121250000072
Otherwise, let p'z＝bz、
Figure BDA0001636121250000073
The process returns to step S305.
S308: determining a payment price:
p is to bezAs the price that user Z needs to pay, it is identified that user Z has processed, deleted from the set of allocated users Z.
S309: and judging whether the distributed user set Z is empty, if so, finishing the calculation of the payment price, and otherwise, returning to the step S302.
In order to better explain the technical scheme of the invention, a specific example is adopted to describe the vehicle resource allocation process in the invention in detail. In this embodiment, the unit time cost t of the idle vehicle is setcThe unit distance cost of an idle vehicle is d 1c1. Assume that there are 2 idle vehicles A, B at time t, their initial positionsRespectively denoted as pos1And pos2In addition, 3 people of users 1,2 and 3 submit vehicle using demands to the network contract special vehicle platform, namely, M is 2 and N is 3. The car demand of 3 users is as follows:
θ1＝(t,src1,dst1,6,10,30)
θ2＝(t,src2,dst2,4,8,28)
θ3＝(t,src3,dst3,8,20,50)
initial position pos of each vehiclekAnd user boarding place srciAnd a get-off location dstiThe latitude and longitude information on the map is stored. The following matrix is constructed according to the vehicle using requirements of 3 users:
time interval matrix CT of vehicle and usert：
Figure BDA0001636121250000081
Distance separation matrix CD between vehicle and usert：
Figure BDA0001636121250000082
The evaluation parameters of the waiting time of each user are obtained by calculation as follows:
Figure BDA0001636121250000083
Figure BDA0001636121250000084
Figure BDA0001636121250000085
then calculating to obtain the following driving cost performance of each user served by each vehicle:
Figure BDA0001636121250000086
Figure BDA0001636121250000091
Figure BDA0001636121250000092
Figure BDA0001636121250000093
Figure BDA0001636121250000094
Figure BDA0001636121250000095
cost performance of the traveling crane
Figure BDA0001636121250000096
The set of (a) is marked as f, and the cost performance of the traveling crane is subjected to descending order arrangement to obtain a sequence:
Figure BDA0001636121250000097
at this time, firstly, the user 2 is served by the vehicle B, the profit is 14, the vehicle B is already distributed to the user 2 for use, the cost performance of the vehicle B related to the user 2 in the set f is deleted, and the sequence is changed into f1 A＞f3 AAt this time, the user 1 is served by the vehicle a, the profit is 8, the running cost performance of the vehicle a in the set f related to the user 1 is deleted, the set f is empty, the distribution is finished, and the total profit is 22. Users 1,2 win the allocation, serving user 1 with vehicle a, serving user 2 with vehicle B, and user 3 fails the allocation and may participate in the next round of allocation.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (2)

1. An auction mechanism-based online taxi appointment real-time service vehicle resource allocation and pricing method is characterized by comprising the following steps:
s1: the method comprises the steps that a user requests vehicle idle information from a network car booking platform, the network car booking platform feeds back the idle vehicle information at the current moment to the user, the idle vehicle information comprises the number M of idle vehicles, and the unit time cost of the idle vehicles is recorded as tcThe unit distance cost of the idle vehicle is dcThe current position of each idle vehicle is recorded as posk，k＝1,2,…,M；
S2: the user checks the received idle vehicle information, submits vehicle using requirements including an getting-on place, a getting-off place and bidding on the journey to the network appointment platform according to the requirements of the user;
s3: the network booking platform collects all user vehicle using requirements received at the current moment t, then calculates the vehicle operation time and the vehicle operation distance from the vehicle getting-on place to the vehicle getting-off place of each user, and records the vehicle using requirements of the user i as thetai＝(t,srci,dsti,ei,di,bi) Wherein, srciIndicating the boarding location, dst, of user iiIndicating a point of alighting of the user i, eiRepresenting the vehicle running time between the boarding and disembarking points of the user i, diRepresenting the distance traveled by the vehicle between the boarding and disembarking points of the user i, biThe bid of the user i for the itinerary is shown, i is 1,2, …, and N is the number of users;
s4: according to the user vehicle using requirements, the following two matrixes are constructed and obtained:
time interval matrix CT of vehicle and user at time tt：
Figure FDA0002950495110000011
Wherein, ctkiIndicating the current position pos of the slave vehicle kkTime required to get to the boarding location of user i;
distance interval matrix CD between vehicle and user at time tt：
Figure FDA0002950495110000012
Wherein cdkiIndicating the current position pos of the slave vehicle kkDistance to the boarding location of user i;
s5: for each vehicle and user, calculating the driving cost performance of the user i served by the vehicle k according to the following formula:
Figure FDA0002950495110000013
wherein
Figure FDA0002950495110000021
Evaluating parameters for the user waiting time, wherein the calculation formula is as follows:
Figure FDA0002950495110000022
calculating the performance price ratio f of M multiplied by N traveling vehiclesi kThe set of (a) is denoted as f;
s6: the following method is adopted for vehicle resource allocation:
s6.1: initializing the yield G of the vehicle resource allocation scheme to be 0;
s6.2: searching the most cost-effective driving vehicle from the set fHigh value
Figure FDA0002950495110000023
The corresponding vehicle is k ', and the user is i';
s6.3: if t isc·(ctk′i′+ei′)+dc·(cdk′i′+di′)＜bi′If not, the step S6.4 is carried out, otherwise, the step S6.6 is carried out;
s6.4: assigning the vehicle k 'to the user i', calculating the benefit resulting therefrom
Figure FDA0002950495110000024
S6.5: the driving cost performance relevant to the vehicle k 'and the user i' is deleted from the set f, so that benefits are obtained
Figure FDA0002950495110000025
Entering step S6.7;
s6.6: cost performance of the vehicle from the set f
Figure FDA0002950495110000026
Deleting and entering step S6.7;
s6.7: determine whether to aggregate
Figure FDA0002950495110000027
If so, the vehicle resource allocation is finished, otherwise, the step S6.2 is returned;
s7: and solving the payment price of each user according to the vehicle resource allocation scheme obtained in the step S6.
2. The auction mechanism based online car appointment real-time service vehicle resource allocation and pricing method according to claim 1, wherein the payment price of the user is calculated by the following method:
s7.1: acquiring an allocated user set Z according to a vehicle resource allocation scheme;
s7.2: selecting one user from the distributed user set Z as a current user Z;
s7.3: let user z pay price pz＝bzPayment price minimum value p'z＝0，bzRepresenting a bid for user z;
s7.4: let user z bid bz＝(pz+p′z)/2；
S7.5: determine if | pz-p′zIf | > epsilon, epsilon represents a preset threshold, if yes, the step S7.6 is carried out, otherwise, the step S7.8 is carried out;
s7.6: according to the current bid b of user zzIf the other parameters are unchanged, vehicle resource allocation is carried out again to obtain the current vehicle allocation scheme;
s7.7: determining the vehicle allocation made in step S7.6 that user z is currently bidding for bzIf it can be allocated vehicle resource usage, let pz＝bz、
Figure FDA0002950495110000031
Otherwise, let p'z＝bz、
Figure FDA0002950495110000032
Returning to the step S7.5;
s7.8: p is to bezAs the price needed to be paid by the user Z, identifying that the user Z has processed, and deleting the user Z from the allocated user set Z;
s7.9: and judging whether the distributed user set Z is empty, if so, finishing the calculation of the payment price, and otherwise, returning to the step S7.1.
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CN107368904A (en) * 2017-07-12 2017-11-21 乐山易通天下网络科技有限公司 A kind of net of trip in time about car order allocation method and system
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