CN115169669A - Taxi sharing method based on track big data support - Google Patents
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Abstract
The invention discloses a taxi co-taking method based on track big data support, belongs to the technical field of traffic communication, and aims to reduce the travel distance and the travel cost of passengers to the maximum extent and increase the income of taxi drivers by designing a taxi co-taking system based on taxi GPS track data. The system specifically comprises four parts, namely map grid division, dynamic carpool system construction, pricing scheme modeling and system evaluation, and the result shows that the carpool saves the distance of 25.34 percent, and the system can effectively provide real-time service for taxi users, and has very important significance for reducing the trip cost of passengers, increasing the income of drivers and relieving traffic pressure.
Description
Technical Field
The invention belongs to the technical field of traffic communication, and particularly relates to a taxi sharing method based on track big data support.
Background
Along with the rapid development of social economy and the acceleration of urbanization speed, the traffic demand shows explosive growth, the taxi can satisfy the high-efficient trip of passenger, but the difficult problem of calling the car is outstanding in peak period, and taxi seat utilization ratio is lower, the taxi is taken together and can solve above-mentioned problem well, both can reduce passenger's trip cost, can increase driver's income again, however, the taxi is taken together at present and has the problem that the route planning is difficult, the expense allocation is unreasonable etc. has, greatly influences the will that the passenger took together. The taxi carpooling is limited to the behavior of individual drivers at present, is not spread in a large scale, and lacks corresponding technical system support, so that a set of efficient dynamic carpooling system is provided, the problems of rapid planning of carpooling paths and reasonable income distribution need to be solved urgently.
Disclosure of Invention
The invention aims to solve the problems in the prior art, provides a set of efficient dynamic ride-sharing system, realizes fast planning of ride-sharing paths, reasonably distributes earnings and improves the ride-sharing rate of taxis.
In order to achieve the purpose, the technical scheme adopted by the invention is to provide a taxi carpooling method based on track big data support, which comprises the following steps:
s1: constructing road network units and generating a grid distance schedule: combining GPS data with a map, dividing the grid into grids with proper sizes according to the longitude and latitude of the region, and determining the place where the number of taxi passengers getting on and off the taxi in the grid is the largest as a central node of the grid by using the taxi GPS big data;
s2: taxi selection and passenger dynamic matching: based on GPS data of the taxi and order information sent by the passenger, processing and screening taxi data from both sides of a departure point and a destination point to find an optimal taxi meeting the demand of the passenger;
s3: planning a taxi dynamic path: a method for comparing the buffering time with the delay time is adopted to plan a co-taking path of the co-taking passenger and the taxi after the matching is successful;
s4: establishing a taxi dynamic path planning model: establishing a model by using the size relation between the order delay time and the buffer time of each subsequent arrival point;
s5: and (4) reasonable pricing: based on the principle that the difference between the passenger travel cost reduction range and the driver income increase range is small, the discount rate of different passenger groups is obtained.
Further, the specific step of S1 is to determine the distance d between two grids ij The travel time t is calculated by the distance of the central nodes of the grids from each other on the road network, and the taxi speed limit v on the road section is adopted in the invention ij And the distance and time are according to X ij ＝(d ij ,t ij ) Is stored in a grid distance schedule for subsequent computation of co-product matches.
Further, the specific step of S2 is to firstly find the grid g where the departure point is located according to the order M issued by the passenger 1 And the grid g of the destination point 5 Then the adjacent grid cells around are arranged according to t i1 、t j5 Sorting from small to large, respectively selecting a taxi set which can arrive before the latest boarding time specified by the order in the grid closest to the originating end and a taxi set which can arrive before the latest getting-off time specified in the grid closest to the destination end from the first grid selected from each side, and if the intersection is empty, proving that the selected unit grid does not have taxis meeting the conditions, and then sequentially calculating other grid units at the same time of each side; and if the intersection is not empty, stopping the selection, and then selecting the taxi in the intersection of the two sets as a matched vehicle to realize the dynamic matching of the taxi and the passenger.
Further, the medium screening conditions of S2 are as follows:
an outgoing end: t is t i <＝M.p.l-t i1
Wherein, t i1 Indicating taxi slave grid cell g i To grid cell g 1 Travel time of t i Grid g for indicating taxi arrival i The time of (d); taxi meeting starting formula is put in set S o The preparation method comprises the following steps of (1) performing;
the destination end: t is t j <＝M.d.l-t j5
Wherein, t j5 Indicating taxi slave grid cell g j To grid cell g 5 Time of flight of (t) j Indicating taxi arrival grid g j The time of (d); taxi meeting destination formula is put in set S d In (1).
Further, the method for calculating the buffering time in S3 is as follows:
the buffering time (m.o). Bt, (m.d). Bt on the m.o, m.d sides is calculated according to the following formula:
(M.o).bt＝M.p.l-t p ；
(M.d).bt＝M.d.l-t q 。
further, the method for calculating the delay time in S3 is as follows:
delay time (T) L ): the delay time is calculated according to the following formula:
T L t (new path) -T (original path).
Further, in the step S4, the order delay time T is specifically adopted L And the buffer time M.bt of each subsequent arrival point, and the formula is as follows:
T L ≤Min{M.bt}；
wherein { m.bt } indicates the set of buffer times for subsequent arrival points in the schedule after the rental car joins a new order.
If the delay time is less than or equal to the minimum value of the buffer time set { M.bt }, the planned path is reasonable, namely, passengers agreeing to co-riding on a taxi can get on or off the taxi at the latest time or before the latest time specified by the passengers, and a path conforming to the model can be always found.
Compared with the prior art, the invention has the following beneficial effects:
(1) Based on a taxi selection and passenger dynamic matching model and a ride-sharing path planning model, a set of efficient dynamic ride-sharing system is established, the taxi ride-sharing rate is improved, and the individual requirements of passengers are met.
(2) Based on the pricing discount rate, a set of dynamic pricing system is established, and the enthusiasm of passengers and drivers for taking a ride together is improved.
Drawings
Fig. 1 is a schematic diagram of a grid cell of the present invention.
Fig. 2 is a grid distance schedule of the present invention.
Fig. 3 is a schematic diagram of the matching process of the taxi and the passenger according to the invention.
FIG. 4 is a routing diagram of the present invention.
Fig. 5 is a routing diagram of the present invention.
FIG. 6 is a graph showing the distance change after synthesis according to the present invention.
FIG. 7 is a schematic diagram showing the index variation range before and after the synthesis according to the present invention.
Detailed Description
The following examples are provided only to aid understanding of the present invention and should not be construed as further limiting the present invention.
Example (b):
the main symbols and their meanings referred to in this example are shown in table 1:
TABLE 1 Main symbols and their meanings
A taxi sharing method based on track big data support comprises the following steps:
constructing road network units and generating a grid distance schedule:
as shown in fig. 1 and 2, GPS data is combined with a map, the grid with proper size is divided according to the longitude and latitude of the region, taxi passengers get on or off the taxi in the grid by using taxi GPS big dataThe place with the most number is determined as the central node of the grid; distance d between two grids ij The travel time t is calculated by the distance of the grid center nodes from each other on the road network and the taxi limit speed v on the road section ij And the distance and the time are determined according to X ij ＝(d ij ,t ij ) Is stored in the grid distance schedule shown in fig. 2 for subsequent computation of co-product matches.
Taxi selection and passenger dynamic matching:
the co-taking of taxis refers to a behavior that a plurality of passengers having the same or similar destinations use a transportation means such as a taxi together to travel. The travel cost of the passengers is lower than that of the passengers who take the passengers alone, and the income of the drivers is increased by collecting the expenses of a plurality of people.
Based on the GPS data of the taxi and the order information sent by the passenger, the taxi data are processed and screened from the two sides of the departure point and the destination point, and the optimal taxi meeting the requirements of the passenger is found.
FIG. 3 shows that firstly, the grid g where the departure point is located is found according to the order M issued by the passenger 1 And the grid g of the destination point 5 Then the adjacent grid cells in the periphery are arranged according to t i1 、t j5 Sorting from small to large, respectively selecting a taxi set which can arrive before the latest boarding time specified by an order in the grid closest to the originating end and a taxi set which can arrive before the latest getting-off time specified by the order in the grid closest to the destination end from the first grid selected from each side, and if the intersection is empty, proving that the selected unit grid does not have a taxi meeting the condition, and then sequentially calculating other grid units at the same time on each side; and if the intersection is not empty, stopping the selection, and then selecting the taxi in the intersection of the two sets as a matched vehicle to realize the dynamic matching of the taxi and the passenger.
As shown in fig. 3, screening was then performed under the following specific conditions:
an outgoing end: t is t i <＝M.p.l-t i1 ；
Wherein，t i1 Indicating taxi slave grid cell g i To grid cell g 1 Travel time of t i Grid g for indicating taxi arrival i The time of (d); taxi meeting starting formula is put in set S o The preparation method comprises the following steps of (1) performing;
a destination end: t is t j <＝M.d.l-t j5 ；
Wherein, t j5 Indicating taxi slave grid cell g j To grid cell g 5 Time of flight of t j Grid g for indicating taxi arrival j The time of (d); taxi meeting destination formula is put in set S d In (1).
Planning a taxi dynamic path:
and (4) planning a co-taking path of the co-taking passenger and the taxi after the matching is successful by adopting a method of comparing the buffer time with the delay time.
Buffer time (m.bt): by t p And t q To indicate the time (satisfying t) when the original passenger should arrive at the departure point m.o and the destination point m.d before the passenger M is picked up p ≤M.p.l，t q M.d.l) and the buffering time (m.o).
(M.o).bt＝M.p.l-t p ；
(M.d).bt＝M.d.l-t q 。
Delay time (T) L ): the time delay due to the addition of a new passenger order, here mainly the estimated dynamic travel time of the fastest path from one location to another; the delay time is calculated according to the following formula (assuming the taxi does not arrive earlier than the earliest time required by the passenger):
T L t (new path) -T (original path).
Establishing a taxi dynamic path planning model:
delay time T with order L And the buffer time M.bt of each subsequent arrival point, and the formula is as follows:
T L ≤Min{M.bt}；
bt, among others, indicates the set of buffer times for each subsequent arrival point in the schedule after the rental car is added to the new order.
As shown in fig. 4, if the delay time is less than or equal to the minimum value of the buffer time set { m.bt }, the planned route is reasonable, that is, passengers who agree to share a taxi on a taxi can get on or off the taxi at the latest time or before the latest time specified by the passengers, and a route conforming to the model can always be found.
And (4) reasonable pricing:
taxi fee of the shared road section is shared by passengers in the same row on average, discount rates of different co-passenger numbers are different, and the discount rates increase along with the increase of the co-passenger number, so that the more co-passengers, the less taxi fee each person pays.
Based on the principle that the difference between the travel cost reduction range of the passengers and the income increase range of the drivers is small, the discount rate of the passengers in different carpools is obtained.
Determining an optimal discount rate:
determining an objective function: when the number of passengers is more than 1, the difference between the cost reduction of each passenger and the profit increase benefit of the driver is minimized to ensure the balance of the benefits of the passengers and the driver.
minN＝|N 1 -N 2 |＝|(P z -R i P z )-(iR i P z -P z )|＝|2-(1+i)R i |P z
Wherein, P z : cost (dollar) for passenger to ride alone; n is a radical of 1 : magnitude of passenger cost reduction; n is a radical of hydrogen 2 : magnitude of driver revenue increase; r is i : discount rate when i passengers ride a taxi;
R i meanwhile, the following conditions are required to be met: r 1 ＝1,
The discount rate of different passenger groups is obtained according to the formula: r 1 ＝1,R 2 ＝0.67,R 3 ＝0.5,R 4 ＝0.33。
And (3) carrying out pricing calculation:
the riding cost of each passenger can be calculated by the following formula:
the total revenue of a taxi driver for a trip can be calculated by the following formula:
wherein S is p The mileage expense of the joint riding unit; d m The total distance of the riding combination road section is realized for m passengers; d is a radical of n The total distance that m passengers do not realize the ride combination.
Experimental data:
in order to verify that the system provided by the method has the advantages of high synthesis rate and reasonable cost distribution, the method calculates the total travel distance, the total travel cost, the passenger-per-person travel cost and the change range of the driver-per-person net income evaluation index under the condition of no-ride comparison and analysis by taking the taxi travel track GPS data of 7-8 points in 7 months and 3 days in Xiamen city as the basis, and obtains the following results.
Wherein the evaluation indexes are as follows:
(1) The distance that the passenger passes when going out alone is D, if several orders can satisfy the ride together simultaneously, and the total distance of the ride together route at this moment is SD, this paper uses DR to evaluate the total distance variation range after the ride together, if DR is greater than 0, prove that the ride together system is effective.
DR＝[(D-SD)/D]*100％
(2) The distance increased by each passenger due to the co-taking is S, the route increasing rate of each passenger of the taxi is represented by Dr, and if the percentage of Dr is small, the purpose that the route planned by the system is least to follow the increased travel distance is achieved.
Dr＝S/D*100％。
The experimental results are as follows:
(1) As shown in fig. 5, in the pool situation, the order is processed according to the dynamic pool system of taxis mentioned herein, and in all possible pool routes, an optimal route is found.
As shown in fig. 6, when the unit price during ride combination = the original cost of the passenger riding alone/the calendar kilometers of the passenger riding alone, and DR under each group of experiments are recorded, the line chart of fig. 6 is obtained, and it can be found that the total travel distance is necessarily reduced due to ride combination; the increased travel distance of each order M is little, and is only increased by about 3%, so that the purpose that the planned path follows the increased travel distance to the minimum is achieved.
(2) As shown in fig. 7, when traveling by the carpooling method proposed herein, the average total travel distance reduction rate DR of each taxi is about 25.34%, the total travel cost is reduced by 16.58%, the average travel cost of passengers is reduced by 16.58%, and the per-person income of drivers is increased by 29.27%. Assuming that the speed of a taxi is 50km/h, the taxi works for 8 hours, the taxi runs 400 kilometers in one day on average, after a ride-sharing scheme is adopted, the mileage of each taxi can be saved by 101.36 kilometers every day, 17,772 taxis are shared in the current buildings and cities, 14.4 kiloliters of gasoline can be reduced in one day, the directly saved economic consumption is about 90.7 ten thousand yuan, and 331 tons of carbon dioxide can be reduced in one day.
In conclusion, the taxi co-taking system provided by the invention introduces the concepts of buffer time and delay time to plan the path, can quickly meet the order sent by the passenger, and plans a more reasonable co-taking path. The taxi-sharing rate is 66%, the distance increased by all people is only 3%, and the travel distance increased by passengers is reduced to the maximum extent; the total distance is reduced by 25.34 percent on average, which generates huge social benefits on the aspects of environmental protection, energy consumption and the like, and better solves the problems of taxi utilization rate and passenger demand.
In addition, based on a pricing system provided by the dynamic discount rate, an experimental result shows that the passenger-per-person trip cost reduction rate is 16.58%, the driver-per-person net income increase rate is 29.27%, and the enthusiasm of co-riding the driver and the passengers is greatly aroused.
As used herein, the phrase "based on" should not be read as referring to a closed condition set. For example, an exemplary step described as "based on condition a" may be based on both condition a and condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" should be interpreted in the same manner as the phrase "based, at least in part, on.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. The terms "example" or "exemplary" throughout this disclosure indicate an example or instance and do not imply or require any preference for the mentioned example. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A taxi sharing method based on track big data support is characterized by comprising the following steps:
s1: constructing road network units and generating a grid distance schedule: combining GPS data with a map, dividing into grids with proper sizes according to the longitude and latitude of the region, and determining the place where the number of taxi passengers getting on and off the taxi in the grids is the largest as a central node of a grid by using taxi GPS big data;
s2: taxi selection and passenger dynamic matching: based on GPS data of the taxi and order information sent by the passenger, processing and screening taxi data from both sides of a departure point and a destination point to find an optimal taxi meeting the demand of the passenger;
s3: planning a taxi dynamic path: a method for comparing the buffering time with the delay time is adopted to plan a co-taking path of the co-taking passenger and the taxi after the matching is successful;
s4: establishing a taxi dynamic path planning model: establishing a model by using the size relation between the order delay time and the buffer time of each subsequent arrival point;
s5: and (3) reasonable pricing: based on the principle that the difference between the travel cost reduction range of the passengers and the income increase range of the drivers is small, the discount rate of the passengers in different carpools is obtained.
2. The taxi sharing method based on track big data support according to claim 1, wherein the method comprises the following steps: the specific step of S1 is that the distance d between two grids ij The travel time t is calculated by the distance of the grid center nodes from each other on the road network and the taxi limit speed v on the road section ij And the distance and the time are determined according to X ij ＝(d ij ,t ij ) Is stored in a grid distance schedule for subsequent computation of co-product matches.
3. The taxi sharing method based on track big data support according to claim 1, wherein the taxi sharing method comprises the following steps: s2, firstly, searching the grid g where the starting point is located according to the order M sent by the passenger 1 And the grid g where the destination point is located 5 Then the adjacent grid cells in the periphery are arranged according to t i1 、t j5 Sorting from small to large, respectively selecting a taxi set which can arrive before the latest boarding time specified by an order in the grid closest to the originating end and a taxi set which can arrive before the latest getting-off time specified by the order in the grid closest to the destination end from the first grid selected from each side, and if the intersection is empty, proving that the selected unit grid does not have a taxi meeting the condition, and then sequentially calculating other grid units at the same time on each side; and if the intersection is not empty, stopping the selection, and then selecting the taxi in the intersection of the two sets as a matched vehicle to realize the dynamic matching of the taxi and the passenger.
4. The taxi sharing method based on track big data support according to claim 1, wherein the method comprises the following steps: the medium screening conditions of S2 are as follows:
an outgoing end: t is t i <＝M.p.l-t i1
Wherein, t i1 Indicating taxi slave grid cell g i To grid cell g 1 Travel time of t i Grid g for indicating taxi arrival i The time of (d); taxi set S meeting starting end formula o Performing the following steps;
the destination end: t is t j <＝M.d.l-t j5
Wherein, t j5 Indicating taxi slave grid cell g j To grid cell g 5 Time of flight of t j Grid g for indicating taxi arrival j The time of (d); taxi meeting destination formula is put in set S d In (1).
5. The taxi sharing method based on track big data support according to claim 1, wherein the method comprises the following steps: the calculation method of the buffering time in the S3 comprises the following steps:
the buffering times (m.o). Bt, (m.d). Bt on the m.o, m.d sides are calculated according to the following formulas:
(M.o).bt＝M.p.l-t p ；
(M.d).bt＝M.d.l-t q 。
6. the taxi sharing method based on track big data support according to claim 1, wherein the taxi sharing method comprises the following steps: the method for calculating the delay time in the step S3 is as follows:
delay time (T) L ): the delay time is calculated according to the following formula:
T L t (new path) -T (original path).
7. The taxi sharing method based on track big data support according to claim 1, wherein the method comprises the following steps: the step S4 specifically adopts the order delay time T L And the buffer time M.bt of each subsequent arrival point, and the formula is as follows:
T L ≤Min{M.bt}；
bt, among others, indicates the set of buffer times for each subsequent arrival point in the schedule after the rental car is added to the new order.
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