CN112785143A - Network appointment vehicle dispatching method and system introducing passenger pick-up time satisfaction - Google Patents
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Abstract
The invention discloses a network appointment vehicle dispatching method introducing the satisfaction degree of the passenger receiving and driving time, which comprises the following steps: estimating expected pickup time and actual pickup time of passengers in different time space states by using the network appointment historical order information, and calculating to obtain the satisfaction degree of the pickup time of the passengers; the method comprises the steps that a driver group and a passenger group are subjected to type division according to space-time distribution, a bipartite graph with points and weights is constructed on the basis of the driver type group and the passenger type group, the passengers in a driver receiving and driving range have a connecting edge, and the weights of the points are the cost required by the passengers to drive a vehicle; respectively establishing linear plans of maximizing the total driver income Profit and maximizing the passenger driving receiving time satisfaction fairness PTSF; and (4) calculating off-line to obtain a fractional solution of the two targets, and performing on-line order allocation by using a KIID model. The invention can consider the fairness problem of the satisfaction degree of the passenger driving receiving time while considering the requirement of a driver on higher order income, and effectively balance the two different targets.
Description
Technical Field
The invention relates to the technical field of online network car booking intelligent scheduling, in particular to a network car booking dispatching method and system for introducing passenger driving receiving time satisfaction degree, which are used for balancing the total income of a maximized driver and the fairness of the driving receiving time satisfaction degree among the maximized passengers in an online network car booking system.
Background
At present, a plurality of technologies are used for researching the order distribution problem in the online taxi booking system, and people have higher and higher demand for rapid and convenient taxi taking along with the development of the online taxi booking at present. The traditional KIID model-based online order distribution problem can be formulated as a point weighted online bipartite graph matching problem: considering a bipartite graph G ═ (U, V, E), U denotes the set of drivers, V denotes the set of passengers, any one edge E ═ (U, V) in the set of edges E denotes that passenger V is within the pick-up range of driver U, if driver U completes the taxi order initiated by passenger V, the benefit of w (V) can be obtained, and w (V) is the weight of point V. And U is as a known set and points in V are reached online. However, this model can only accomplish automatic order dispatching within range, and cannot realize other functions.
Researchers put forward that more intelligent scheduling is carried out on network appointment car dispatching orders so as to realize more functions. Some existing methods are mainly classified into three categories: the first type is that the total income of a driver is maximized, the requirement of a passenger for short waiting time is completely ignored in the matched target, and the driving experience of the passenger is seriously influenced; the second category, minimizing the total pick-up distance, which is a scheme proposed by standing at the angle of passengers, can greatly reduce the total pick-up time of passengers, but completely violates the goal of maximizing the income of the network appointment vehicle company; in the third category, stability problems are introduced while maximizing the total driver revenue, and a stability constraint is introduced for matching, which can only reduce the time for passengers to pick up. It can be seen that most of the work is currently done at the driver and net appointment companies, and a higher overall profit is expected. Although the second category introduces passenger requirements, for example, passengers prefer less waiting time, i.e., the pickup time of the passenger needs to be as small as possible. However, there is a contradiction in the demand, especially during the rush hour of driving, and it is difficult for the passengers to satisfy the demand of all the passengers due to the huge competition of the driver for this resource. Therefore, how to improve the overall income of drivers and improve the satisfaction fairness of the passenger driving receiving time as much as possible is a problem which needs to be solved urgently at present.
Disclosure of Invention
The invention provides a network car appointment and order dispatching method for introducing the satisfaction degree of the passenger receiving and driving time, aims at the defects in the prior art, measures the fairness of the satisfaction degree of the receiving and driving time among passengers through the satisfaction degree of the passenger receiving and driving time, provides the network car appointment and order dispatching method for balancing the total income of a driver and the fairness of the receiving and driving time among the passengers, and successfully solves the balancing problem between the two contradictory target optimizations.
In order to achieve the purpose, the invention adopts the following technical scheme:
a network appointment car dispatching method introducing passenger drive-in time satisfaction degree comprises the following steps:
s1, estimating expected pickup time and actual pickup time of passengers in different time space states by using network appointment historical order information, and calculating to obtain the satisfaction degree of the pickup time of the passengers;
s2, performing type division on a driver group and a passenger group according to space-time distribution, constructing a bipartite graph with points and weights based on the driver type group and the passenger type group, wherein the passengers in a driver driving range have a connecting edge, and the weights of the points are the cost required by the passengers to drive;
s3, respectively establishing linear plans of maximizing the driver total income Profit and maximizing the passenger driving time satisfaction fairness PTSF based on the bipartite graph in the step S2;
s4, obtaining fractional solutions of the two targets through off-line calculation based on the two linear plans; the KIID model is used for online order dispatch.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, the passenger pickup time satisfaction is the ratio of the expected pickup time of the passenger to the actual pickup time.
Further, in step S1, the step of obtaining the expected pickup time and the actual pickup time of the passenger in different time space states by estimating from the network appointment historical order information, and the step of obtaining the satisfaction degree of the pickup time of the passenger by calculation includes:
analyzing historical order information of the network taxi appointment, dividing taxi taking time periods and taxi taking areas, and calculating the average pickup time of all passengers in the history of each taxi taking area in each time period so as to calculate the expected pickup time of each taxi taking area passenger corresponding to each time period;
and estimating the time required by all other network appointment vehicles to pick up the passenger according to the time-space characteristics of any passenger who sends the taxi taking request in the online stage, and calculating the satisfaction degree of the picking up time of each network appointment vehicle corresponding to the passenger.
Further, in step S4, the process of online order assignment using the KIID model includes the following steps:
s41, based on the KIID model, using two off-line fractional solutions obtained by calculation as the input of the KIID model;
and S42, setting a dispatching strategy value a, and based on the dispatching strategy value a, balancing the maximum driver total income Profit and the maximum passenger driving time satisfaction fairness PTSF by adopting a probability selection mode.
Further, in step S42, the process of balancing the maximum driver total Profit and the maximum passenger driving time satisfaction PTSF by using a probabilistic selection method includes the following steps:
and receiving an online request v, randomly generating a number x, wherein x is larger than 0 and smaller than 1, if x is smaller than a, taking the fractional solution of the maximum driver total income Profit as the basis of order distribution, and entering a dispatching flow, otherwise, taking the fractional solution of the maximum passenger driving time satisfaction PTSF as the basis of order distribution, and entering the dispatching flow.
Further, the dispatch process includes:
s421, rounding the fractional vector related to v into an integer vector by adopting a rounding technology GKPS;
s422, taking the integer vector related to the online request v as input, and randomly arranging all edges connected with v;
s423, sequentially detecting each edge e ═ u, v, if u is unmatched and the corresponding edge e in the integer vector has a value of 1, then arranging the driver u to v, otherwise, returning to detect the next edge connected to v until one of the drivers is arranged to the online request v;
and S424, receiving a service receiving confirmation result corresponding to the online request v sent by the passenger user side, finishing the process if the passenger determines to receive the service, wherein the current order sending corresponding to the online request v is finished, otherwise, returning to the step S423 if the passenger does not receive the order sending service arranged in the step S423, and detecting the next edge connected with v.
Based on the method, the invention also provides a network appointment vehicle dispatching system introducing the satisfaction degree of the passenger pick-up time, wherein the network appointment vehicle dispatching system comprises a passenger pick-up time satisfaction degree calculation module, a dispatching model construction module, a linear programming construction module and an online order dispatching module;
the passenger pick-up time satisfaction degree calculation module is used for estimating expected pick-up time and actual pick-up time of passengers in different time space states by utilizing network appointment historical order information and calculating the satisfaction degree of the pick-up time of the passengers;
the dispatching model building module is used for carrying out type division on a driver group and a passenger group according to space-time distribution, building a bipartite graph with points with weights based on the driver type group and the passenger type group, wherein the passengers in a driver receiving and driving range have a connecting edge, and the weights of the points are the cost required by the passengers to get on the bus;
the linear programming construction module is used for respectively establishing linear programming of maximum driver total income Profit and maximum passenger driving time satisfaction fairness PTSF;
the online order allocation module is used for obtaining fractional solutions of two targets through offline calculation based on two linear plans and performing order allocation by using a KIID model.
Furthermore, the network appointment vehicle dispatching system further comprises a dispatching strategy value input interface used for setting a dispatching strategy value a, so that the online order dispatching module balances the maximum driver total income Profit and the maximum passenger driving receiving time satisfaction fairness PTSF in a probability selection mode based on the dispatching strategy value a.
The invention has the beneficial effects that:
(1) the invention provides a concept of fairness capable of measuring the driving receiving time satisfaction among passengers for the first time, namely the passenger driving receiving time satisfaction, and provides a method for balancing the maximum total income of a driver and the maximum driving receiving time fairness among passengers.
(2) The present invention provides parameters that can be used by a company to adjust two goals, making the overall policy dispatching method more controllable, e.g., if one wants to focus on revenue maximization, one only needs to set the value of a bit larger, whereas if one wants to focus on PTSF maximization, one can adjust the value of a bit smaller.
(3) The method for balancing the two targets can be used for solving the problem that two contradictory target optimizations need to be balanced in other similar scenes.
Drawings
Fig. 1 is a flowchart of a network appointment car dispatching method introducing passenger drive-in time satisfaction of the invention.
Fig. 2 is a flowchart of the dual target tradeoff part of the present invention.
FIG. 3 is a diagram of a particular dispatch strategy of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
It should be noted that the terms "upper", "lower", "left", "right", "front", "back", etc. used in the present invention are for clarity of description only, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not limited by the technical contents of the essential changes.
With reference to fig. 1, the invention provides a network appointment vehicle dispatching method for balancing the fairness of the maximized driver total income and the maximized driving time satisfaction between passengers in an online network appointment vehicle system, which comprises the following steps:
firstly, estimating average receiving time of passengers in different time periods at different places through historical data provided by a network appointment vehicle company, estimating psychological expectation of newly arriving passengers on the receiving time of the passengers who get on the vehicle according to the average receiving time, estimating travel time of each driver to a specific passenger (namely actual receiving time of different drivers for receiving the passengers), and defining the satisfaction degree of the receiving time of the passengers according to the ratio of the two.
And secondly, performing type division on a driver group and passengers according to space-time distribution, and modeling the driver group and the passenger group into a bipartite graph with point weights, wherein the point weights are the cost required by the passengers to take the bus. The maximum total Profit (Profit) and the maximum driving receiving time satisfaction fairness (PTSF) are calculated off-line by two Linear Plans (LP) respectively, and two groups of corresponding fractional solutions are obtained respectively.
And thirdly, designing an online order allocation method according to the two-component solution obtained in the second step based on the KIID model.
In the first step, analyzing historical data provided by a network taxi appointment company, dividing time periods of each day and dividing taxi taking areas of a certain city, then calculating the average pickup time of all passengers in the history of the taxi taking areas of the certain time period, and corresponding to the expected pickup time of the passengers in the taxi taking areas according to the calculated value; similarly, the average time for any particular networked appointment to pick up a passenger in another particular area may be calculated for any particular time period, and based thereon, the time required for all other networked appointments to pick up the passenger upon arrival of the passenger in the online phase may be estimated. In the second step, the whole driver group and the whole passenger group are firstly classified according to the time-space state of the driver group and the passenger group (namely, the drivers (passengers) in the same region at the same time are considered as the same type of driver (passenger)), and for convenience, we collectively refer to a certain driver type as a certain driver and a certain passenger type as a certain passenger; after obtaining a driver type group and a passenger type group, establishing a bipartite graph network with points and rights, wherein a connecting edge exists for passengers in a driver pick-up range class, the weight of the points is the cost of the passengers for taking a car, and the satisfaction of the passenger pick-up time is the ratio of the expected pick-up time and the actual pick-up time of the passengers; based on the bipartite graph model, we have established that the two LPs respectively calculate the maximum total profit and the maximum PTSF offline, and respectively obtain two sets of corresponding fractional solutions. In the third step, based on a KIID model, the two offline fractional solutions obtained in the second step are used as the probability of order assignment in the online stage; in the online phase, each time a taxi taking request is reached, one side (namely, one dispatching strategy) needs to be selected to enable the total profit to be maximum or the PTSF to be maximum, the method balances the maximum profit and the maximum PTSF in a probability mode, namely, a fractional solution of the maximum profit is selected according to a probability, a fractional solution of the maximum PTSF is selected according to a probability of 1-a to be dispatched, and 0< ═ a < 1.
The method is based on the analysis of the off-line data or the rule of learning the off-line data, and then the on-line strategy is formulated, namely the on-line order dispatching is carried out. Fig. 1 shows an analysis process of offline data of a network appointment company and a modeling process of an online algorithm. As shown in fig. 1, firstly, historical data provided by a network appointment company needs to be analyzed, expected pickup time of passengers at different time and places can be calculated through statistics, actual pickup time of the passengers on different drivers can be estimated through the historical data, and satisfaction degree of the pickup time of the passengers when the passengers on different drivers are picked up can be estimated on the basis of the actual pickup time; secondly, modeling a dispatching problem between the driver and the passenger into a point weighted bipartite graph matching problem; based on the point weighted bipartite graph, using two linear plans to formalize the objectives of maximizing the profit and maximizing the PTSF, and respectively solving two fractional solutions of the two linear plans; and finally designing an online order dispatching method based on the KIID model.
FIG. 2 is a body part of the online order matching method. The method needs to use a fractional solution obtained by two linear programming solutions in an off-line stage as an input based on a KIID model. In order to achieve the purpose of balancing maximized profit and maximized PTSF, a method based on probability selection is adopted, when an online request v comes, a number x between 0 and 1 is randomly selected, if x is smaller than a, a fractional solution of maximized profit is used as the basis for order distribution, and on the contrary, a fractional solution of maximized PTSF is used as the basis for order distribution, and in order to better make a distribution decision, a dependency rounding technology GKPS is adopted to round fractional vectors related to v into integer vectors. It is noted that a is a value between 0 and 1 and this value can be controlled by the net appointment company, and when the value of a is set to be large, the goal of matching will be biased towards a large profit, and when the value of a is small, the goal of matching will be biased towards a high PTSF. The value of a can be adjusted according to the requirements of the network appointment vehicle company by providing an interface for adjusting the parameter a. In some examples, the parameter a may also be introduced into the whole system as an automatic adjustment factor, for example, the parameter a is dynamically set according to the car-using state (such as driving peak, car-using hot door area, etc.), so as to implement a more intelligent network appointment car-dispatching method.
Fig. 3 is a detailed step of the matching strategy. Based on the trade-off strategy introduced in fig. 2, the rounding vector for maximizing the profit or the rounding vector related to the request v for maximizing the PTSF can be finally obtained. In fig. 3, first, after the integer vector associated with the online request v is used as an input, all edges connected to v are randomly arranged. Sequentially detecting each edge e connected with v as (u, v), if u is unmatched and the value of the corresponding edge e in the integer vector is 1, arranging the driver u to v, and otherwise, returning to detect the next edge connected with v; after arranging driver u to v, passenger v can choose whether to accept the service of driver u, if passenger v determines to accept the service, driver u will complete the taxi taking request of passenger v, and the decision is irretrievable, otherwise if passenger v does not accept the dispatch service, return to detecting the next edge connected with v.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (8)
1. A network appointment car dispatching method introducing passenger drive-receiving time satisfaction degree is characterized by comprising the following steps:
s1, estimating expected pickup time and actual pickup time of passengers in different time space states by using network appointment historical order information, and calculating to obtain the satisfaction degree of the pickup time of the passengers;
s2, performing type division on a driver group and a passenger group according to space-time distribution, constructing a bipartite graph with points and weights based on the driver type group and the passenger type group, wherein the passengers in a driver driving range have a connecting edge, and the weights of the points are the cost required by the passengers to drive;
s3, respectively establishing linear plans of maximizing the driver total income Profit and maximizing the passenger driving time satisfaction fairness PTSF based on the bipartite graph in the step S2;
s4, obtaining fractional solutions of the two targets through off-line calculation based on the two linear plans; the KIID model is used for online order dispatch.
2. The network appointment car dispatching method introducing passenger pickup time satisfaction as recited in claim 1, wherein the passenger pickup time satisfaction is a ratio of passenger expected pickup time to actual pickup time.
3. The network appointment car dispatching method for introducing satisfaction of passenger pickup time as claimed in claim 1, wherein in step S1, the expected pickup time and the actual pickup time of the passenger in different time space states are estimated and obtained by using historical order information of the network appointment car, and the process of calculating the satisfaction of passenger pickup time comprises:
analyzing historical order information of the network taxi appointment, dividing taxi taking time periods and taxi taking areas, and calculating the average pickup time of all passengers in the history of each taxi taking area in each time period so as to calculate the expected pickup time of each taxi taking area passenger corresponding to each time period;
and estimating the time required by all other network appointment vehicles to pick up the passenger according to the time-space characteristics of any passenger who sends the taxi taking request in the online stage, and calculating the satisfaction degree of the picking up time of each network appointment vehicle corresponding to the passenger.
4. The net appointment vehicle dispatching method for passenger pickup time satisfaction according to claim 1, wherein in step S4, the process of online order dispatching using the KIID model comprises the following steps:
s41, based on the KIID model, using two off-line fractional solutions obtained by calculation as the input of the KIID model;
and S42, setting a dispatching strategy value a, and based on the dispatching strategy value a, balancing the maximum driver total income Profit and the maximum passenger driving time satisfaction fairness PTSF by adopting a probability selection mode.
5. The network appointment car dispatching method for passenger pickup time satisfaction according to claim 4, wherein in step S42, the process of balancing the maximum driver total Profit Profit and the maximum passenger pickup time satisfaction PTSF by adopting a probability selection mode comprises the following steps:
and receiving an online request v, randomly generating a number x, wherein x is larger than 0 and smaller than 1, if x is smaller than a, taking the fractional solution of the maximum driver total income Profit as the basis of order distribution, and entering a dispatching flow, otherwise, taking the fractional solution of the maximum passenger driving time satisfaction PTSF as the basis of order distribution, and entering the dispatching flow.
6. The network appointment car dispatching method for introducing passenger pickup time satisfaction of claim 5, characterized in that the dispatching process comprises:
s421, rounding the fractional vector related to v into an integer vector by adopting a rounding technology GKPS;
s422, taking the integer vector related to the online request v as input, and randomly arranging all edges connected with v;
s423, sequentially detecting each edge e ═ u, v, if u is unmatched and the corresponding edge e in the integer vector has a value of 1, then arranging the driver u to v, otherwise, returning to detect the next edge connected to v until one of the drivers is arranged to the online request v;
and S424, receiving a service receiving confirmation result corresponding to the online request v sent by the passenger user side, finishing the process if the passenger determines to receive the service, wherein the current order sending corresponding to the online request v is finished, otherwise, returning to the step S423 if the passenger does not receive the order sending service arranged in the step S423, and detecting the next edge connected with v.
7. A network appointment vehicle dispatching system introducing passenger drive-in time satisfaction based on the method of any one of claims 1-6, characterized in that the network appointment vehicle dispatching system comprises:
the passenger pick-up time satisfaction degree calculation module is used for estimating expected pick-up time and actual pick-up time of passengers in different time space states by utilizing network appointment historical order information and calculating the satisfaction degree of the pick-up time of the passengers;
the dispatching model building module is used for carrying out type division on a driver group and a passenger group according to space-time distribution, building a bipartite graph with points with weights based on the driver type group and the passenger type group, wherein the passengers in a driver receiving and driving range have a connecting edge, and the weights of the points are the cost required by the passengers to get on the bus;
the linear programming construction module is used for respectively establishing linear programming of maximum driver total income Profit and maximum passenger driving time satisfaction fairness PTSF;
and the online order dispatching module is used for obtaining fractional solutions of two targets through offline calculation based on two linear plans and utilizing a KIID model to dispatch orders.
8. The network appointment car dispatching system introducing passenger drive-on time satisfaction as claimed in claim 7, further comprising a dispatching strategy value input interface for setting a dispatching strategy value a, so that the online order dispatching module balances the maximum driver total income Profit and the maximum passenger drive-on time satisfaction fairness PTSF by means of probability selection based on the dispatching strategy value a.
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| CN113642932A (en) * | 2021-09-01 | 2021-11-12 | 首约科技(北京)有限公司 | Online taxi appointment order dispatching single-inclination strategy method |
| CN117035596A (en) * | 2023-08-03 | 2023-11-10 | 江苏华泽微福科技发展有限公司 | Commodity distribution method and system |
| CN116843166A (en) * | 2023-08-31 | 2023-10-03 | 湘江实验室 | Taxi taking method, device, equipment and medium |
| CN116843166B (en) * | 2023-08-31 | 2023-11-21 | 湘江实验室 | Taxi taking method, device, equipment and medium |
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