CN110567474A - Taxi real-time tour route recommendation system and method based on big data - Google Patents
Taxi real-time tour route recommendation system and method based on big data Download PDFInfo
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Abstract
The invention discloses a taxi real-time tour route recommendation system and method based on big data, wherein the recommendation system comprises a candidate route acquisition module and a real-time tour route selection module, the candidate route acquisition module is used for acquiring a plurality of candidate routes for real-time tour of a taxi, the real-time tour route is used for selecting a real-time tour recommended route from the candidate routes, the candidate route acquisition module comprises an initial position reading module, a target position input module and a candidate route generation module, the initial position reading module is used for reading the current position of the taxi, the target position input module is used for inputting the target position by a driver, the candidate route generation module generates a plurality of candidate routes from the current position to the target position according to the current position and the target position of the taxi, and the real-time tour route selection module comprises a passenger traffic center search module, a real-time tour route selection module and a, The system comprises a crowd density index evaluation module and a real-time tour recommended route determination module.
Description
Technical Field
The invention relates to the field of big data, in particular to a taxi real-time tour route recommendation system and method based on the big data.
Background
With the improvement of economic development level, the traffic demand of people is continuously increased, people not only need common public transportation modes such as urban buses and long-distance line passenger transportation, but also sometimes need a convenient, quick, safe and comfortable passenger transportation mode, and taxis are popular with people due to the flexible operation characteristics of the taxis. Taxi does not follow a fixed route, but a driver plans a tour route, and the taxi driver traditionally plans the tour route by intuition and experience, so that the method is sometimes very difficult to operate efficiently.
disclosure of Invention
The invention aims to provide a taxi real-time tour route recommendation system and method based on big data, and aims to solve the problems in the prior art.
in order to achieve the purpose, the invention provides the following technical scheme:
The recommendation system comprises a candidate route acquisition module and a real-time tour route selection module, wherein the candidate route acquisition module is used for acquiring candidate routes of a plurality of taxis in real-time tour, and the real-time tour route is used for selecting a real-time tour recommendation route from the candidate routes.
Preferably, the candidate route acquiring module comprises an initial position reading module, a target position input module and a candidate route generating module, wherein the initial position reading module is used for reading the current position of the taxi, the target position input module is used for a driver to input a target position, and the candidate route generating module generates a plurality of candidate routes from the current position to the target position according to the current position and the target position of the taxi.
Preferably, the real-time tour route selecting module comprises a passenger transport center searching module, a crowd density index evaluating module and a real-time tour recommended route determining module, wherein the passenger transport center searching module is used for searching whether a passenger transport center exists on each candidate route, the crowd density index evaluating module is used for evaluating the crowd density index on each candidate route, and the real-time tour recommended route determining module determines the real-time tour recommended route according to a searching result of the passenger transport center searching module or an evaluating result of the crowd density index evaluating module.
Preferably, the crowd density index calculation module comprises an interval distance acquisition module, an interval fluctuation calculation module, a search frequency acquisition module, a search frequency calculation module, a consumer frequency acquisition module, a consumer frequency calculation module, a route length acquisition module, a crowd density index calculation module and a candidate route sorting module, wherein the interval distance acquisition module is used for acquiring the distance between adjacent merchants on each candidate route, the interval fluctuation calculation module is used for calculating the fluctuation degree of the distance between the adjacent merchants on each candidate route, the search frequency acquisition module is used for acquiring the searched times of the merchants on the server on each candidate route in a first target time period, the search frequency calculation module is used for calculating the average searched times of each merchant on the server on a certain candidate route, and the consumer frequency acquisition module is used for acquiring the searched times of each merchant on a second target time period on each candidate route in the second target time period The route calculation module is used for calculating the average number of consumers of all merchants on a certain candidate route in a certain time period, the route length acquisition module is used for acquiring the length of each candidate route, the crowd density index calculation module is used for calculating the crowd density index of the certain candidate route, and the candidate route ranking module is used for ranking and evaluating according to the crowd density index of each candidate route.
A real-time taxi tour route recommendation method based on big data comprises the following steps:
s1: acquiring a candidate route;
s2: and selecting a real-time tour recommended route for renting the car from the candidate routes.
Preferably, the recommendation method further comprises the following steps:
S1: reading the current position of the taxi, inputting a target position by a driver, and acquiring all candidate routes A from the current position of the taxi to the target position1、A2、A3、…Ann is the number of candidate routes;
s2: and searching whether a passenger transport center exists on each candidate route, if so, determining the real-time tour recommended route of the taxi as the candidate route, and if not, evaluating the crowd density index of each candidate route, and selecting the real-time tour recommended route of the taxi according to the crowd density index of each candidate route.
Preferably, the step S2 of evaluating the crowd density index of each candidate route includes the following steps:
s21: respectively obtaining the distance A between adjacent merchants on each candidate route11=[B11、B12、B13、…、B1(m1-1)]、A21=[B21、B22、B23、…、B2(m2-1)]、A31=[B31、B32、B33、…、B3(m3-1)]、…、Ai1=[Bi1、Bi2、Bi3、…、Bij、…、Bi(mi-1)] 、…、An1=[Bn1、Bn2、Bn3、…、Bn(mn-1)]wherein A isi1Is a collection of distances between adjacent merchants on the ith candidate route, BijFor the distance between the jth merchant and the j +1 st merchant on the ith candidate route, m1, m2, m3, …, mi, … mn are respectively the candidate route A1、A2、A3、…、Ai、…、AnNumber of business merchants, 1<=i<=n，
For each candidate route, calculating the fluctuation degree of the distance between adjacent merchants on the ith candidate route by using the following formula:
，，
Wherein L isiThe distance of the route from the current position of the taxi to the first merchant and the last merchant in the direction from the target position on the ith candidate route, EiThe fluctuation degree of the distance between adjacent merchants on the ith candidate route is mi, and mi is the total number of the merchants on the ith candidate route; the smaller the fluctuation degree between adjacent merchants is, the more uniform the distribution of the merchants is, and the more stable the probability of the merchants carrying passengers is.
S22: respectively acquiring a first target time period T1The number A of times of searching of the merchant on the server in each candidate route12=[C11、C12、C13、…、C1m1]、A22=[C21、C22、C23、…、C2m2]、A32=[C31、C32、C33、…、C3m3]、…、Ai2=[Ci1、Ci2、Ci3、…、Cij、…、Cimi] 、…、An2=[Cn1、Cn2、Cn3、…、Cnmn]Wherein A isi2For each merchant on the ith candidate route, a collection of searched times on the server, CijSearching the server for the jth merchant on the ith candidate route for times;
for each candidate route, calculating the average number of times each merchant is searched on the server over the ith candidate route using the following formula:
(ii) a If the merchant is searched on the server more times, the more potential customers of the merchant, and therefore the more potential passengers on the candidate route of the merchant.
S23: respectively acquiring the second target time period T of each merchant on each candidate route2a certain time period T of each day3And calculating all the merchants on the candidate route in a certain time period T by using the following formula3Average number of consumers of (2):
Xi=；
Wherein, Xiindicating all merchants on the ith candidate route for a certain period of time T3Average number of consumers of (P)jkFor the jth merchant on the ith candidate route in the second target time period T2A certain of the k-th day of (1)A period of time T3Number of consuming people in (m)iis the number of merchants on the ith candidate route, T2Represents the number of days of the second target time period; the more average number of people consumed, the more the traffic of people on the candidate route is, and the more the taxi drivers in the place with much traffic of people can more easily carry the passengers.
s24: respectively obtaining the length S of each candidate route1、S2、S3、…Sn，SiFor the length of the ith candidate route, calculating the crowd density index of each candidate route by using the following calculation formula for the candidate route:
，
Wherein, YiCrowd-sourcing index for the ith candidate route, HiAveraging the number of times each merchant is searched on the server for the ith candidate route, Eias a degree of fluctuation in the distance between adjacent merchants on the ith candidate route, XiFor all merchants on the ith candidate route in a certain time period T3The average number of consumers is considered through multiple angles, and the crowd density index of the candidate route is evaluated more objectively.
S25: and sequencing the crowd density indexes of all candidate routes from large to small, and taking the candidate route corresponding to the first dense index ranking as the real-time tour recommended route. The higher the crowd density index, the greater the probability that a taxi will pick up a passenger, and the more frequently it will pick up a passenger.
Preferably, the passenger transport center comprises a bus station, a railway station and a high-speed rail station.
Compared with the prior art, the invention has the beneficial effects that: according to the taxi tour guide system and the taxi tour guide method, the starting position of the taxi is obtained, the driver target position input module obtains all candidate routes, and then real-time tour recommended routes are selected from the candidate routes, so that a taxi driver can efficiently operate and carry passengers on the tour recommended routes.
drawings
Fig. 1 is a module schematic diagram of a taxi real-time tour route recommendation system based on big data according to the invention;
fig. 2 is a schematic flow chart of a real-time taxi cruising route recommendation method based on big data.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, in an embodiment of the present invention, a taxi real-time tour route recommendation system based on big data includes a candidate route acquisition module and a real-time tour route selection module, where the candidate route acquisition module is configured to acquire a plurality of candidate routes for real-time tour of a taxi, and the real-time tour route is configured to select a real-time tour recommendation route from the candidate routes.
The candidate route obtaining module comprises an initial position reading module, a target position input module and a candidate route generating module, wherein the initial position reading module is used for reading the current position of the taxi, the target position input module is used for a driver to input a target position, and the candidate route generating module generates a plurality of candidate routes from the current position to the target position according to the current position of the taxi and the target position.
The real-time tour route selection module comprises a passenger transport center searching module, a crowd density index evaluation module and a real-time tour recommended route determination module, wherein the passenger transport center searching module is used for searching whether a passenger transport center exists on each candidate route, the crowd density index evaluation module is used for evaluating the crowd density index on each candidate route, and the real-time tour recommended route determination module determines the real-time tour recommended route according to the searching result of the passenger transport center searching module or the evaluation result of the crowd density index evaluation module.
The crowd density index calculation module comprises an interval distance acquisition module, an interval fluctuation calculation module, a search frequency acquisition module, a search frequency calculation module, a consumer frequency acquisition module, a consumer frequency calculation module, a route length acquisition module, a crowd density index calculation module and a candidate route sorting module, wherein the interval distance acquisition module is used for acquiring the distance between adjacent merchants on each candidate route, the interval fluctuation calculation module is used for calculating the fluctuation degree of the distance between the adjacent merchants on each candidate route, the search frequency acquisition module is used for acquiring the searched times of the merchants on the server on each candidate route in a first target time period, the search frequency calculation module is used for calculating the average searched times of each merchant on the server on a certain candidate route, and the consumer frequency acquisition module is used for acquiring the consumer frequency of each merchant in a certain time period of each day in a second target time period on each candidate route, the route length obtaining module is used for obtaining the length of each candidate route, the crowd density index calculating module is used for calculating the crowd density index of each candidate route, and the candidate route sorting module is used for carrying out sorting evaluation according to the crowd density index of each candidate route.
A taxi real-time tour route recommendation method based on big data comprises the following steps:
s1: the starting position reading module reads the current position of the taxi, the driver inputs the target position in the target position input module, and then all candidate routes A from the current position of the taxi to the target position are obtained from the candidate route generating module1、A2、A3、…AnN is the number of candidate routes; in the actual acquisition of the route candidates, the length of the longest route candidate may be limited to be equal to or less than twice the length of the shortest route candidate, thereby preventing an excessive number of candidates from being generated and an excessive number of impurities from being generated.
S2: the passenger transport center searching module searches whether a passenger transport center exists on each candidate route, the passenger transport center comprises an automobile station, a railway station and a high-speed rail station, if the passenger transport center exists on the candidate route, the real-time tour recommended route determining module determines that the real-time tour recommended route of the rented automobile is the candidate route, if the passenger transport center does not exist on the candidate route, the crowd density index evaluating module evaluates the crowd density index of each candidate route, and the real-time tour recommended route determining module selects the real-time tour recommended route of the rented automobile according to the crowd density index of each candidate route.
The step of evaluating the crowd density index of each candidate route in the step S2 includes the steps of:
S21: the interval distance acquisition module respectively acquires the distance A between adjacent merchants on each candidate route11=[B11、B12、B13、…、B1(m1-1)]、A21=[B21、B22、B23、…、B2(m2-1)]、A31=[B31、B32、B33、…、B3(m3-1)]、…、Ai1=[Bi1、Bi2、Bi3、…、Bij、…、Bi(mi-1)] 、…、An1=[Bn1、Bn2、Bn3、…、Bn(mn-1)]Wherein A isi1Is a collection of distances between adjacent merchants on the ith candidate route, BijFor the distance between the jth merchant and the j +1 st merchant on the ith candidate route, m1, m2, m3, …, mi, … mn are respectively the candidate route A1、A2、A3、…、Ai、…、AnNumber of business merchants, 1<=i<=n，
For each candidate route, the interval fluctuation calculation module calculates the fluctuation degree of the distance between adjacent merchants on the ith candidate route by using the following formula:
，，
Wherein L isiThe distance of the route from the current position of the taxi to the first merchant and the last merchant in the direction from the target position on the ith candidate route, EiThe fluctuation degree of the distance between adjacent merchants on the ith candidate route is mi, and mi is the total number of the merchants on the ith candidate route;
S22: the searching times acquisition module respectively acquires the searched times A of the merchants on the server on each candidate route in the previous seven days12=[C11、C12、C13、…、C1m1]、A22=[C21、C22、C23、…、C2m2]、A32=[C31、C32、C33、…、C3m3]、…、Ai2=[Ci1、Ci2、Ci3、…、Cij、…、Cimi] 、…、An2=[Cn1、Cn2、Cn3、…、Cnmn]Wherein A isi2For each merchant on the ith candidate route, a collection of searched times on the server, CijSearching the server for the jth merchant on the ith candidate route for times;
for each candidate route, the search number calculation module calculates the number of times each merchant is searched on the server on the i-th candidate route using the following formula:
；
S23: the consumer number obtaining module is used for respectively obtaining the consumer number of each merchant in the time period from 9 to 15 points of each day in the previous ten days on each candidate route, and the consumer number calculating module is used for calculating the average consumer number of all merchants in the candidate route from 9 to 15 points by using the following formula:
Xi=；
wherein, Xirepresents the average number of consumers from 9 to 15 points of all merchants in the first ten days on the ith candidate route, PjkFor a certain time period T of the kth day of the previous ten days of the jth merchant on the ith candidate route3Number of consuming people in (m)iIs the number of merchants on the ith candidate route for a certain period of time T3may be consistent with the time a driver is cruising, e.g. a certain time period T when a taxi driver intends to cruise from 12 o 'clock in the middle of the night to 4 o' clock in the morning of the following day3I.e., 12 o 'clock in the middle of the night to 4 o' clock in the morning of the next day.
S24: the route length obtaining module obtains the length S of each candidate route respectively1、S2、S3、…Sn，SiFor the length of the ith candidate route, for each candidate route, the crowd density index calculation module calculates the crowd density index of the candidate route by using the following calculation formula:
，
Wherein, YiCrowd-sourcing index for the ith candidate route, HiAveraging the number of times each merchant is searched on the server for the ith candidate route, EiAs a degree of fluctuation in the distance between adjacent merchants on the ith candidate route, XiThe average number of consumers for all merchants on the ith candidate route over a certain time period T3.
S25: the candidate route sorting module sorts the crowd density indexes of all candidate routes from large to small, and takes the candidate route corresponding to the first ranked crowd density index as the real-time tour recommendation route. The higher the crowd density index of a candidate route, the higher the probability of hiring a vehicle to a passenger on the candidate route, and the higher the frequency of loading the passenger.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (8)
1. The utility model provides a taxi real-time tour route recommendation system based on big data which characterized in that: the recommendation system comprises a candidate route acquisition module and a real-time tour route selection module, wherein the candidate route acquisition module is used for acquiring a plurality of candidate routes for real-time tour of the taxis, and the real-time tour route is used for selecting a real-time tour recommendation route from the candidate routes.
2. the taxi real-time tour route recommendation system based on big data according to claim 1, wherein: the candidate route obtaining module comprises an initial position reading module, a target position input module and a candidate route generating module, wherein the initial position reading module is used for reading the current position of a taxi, the target position input module is used for a driver to input a target position, and the candidate route generating module generates a plurality of candidate routes from the current position to the target position according to the current position and the target position of the taxi.
3. The taxi real-time tour route recommendation system based on big data according to claim 1, wherein: the real-time tour route selection module comprises a passenger transport center search module, a crowd density index evaluation module and a real-time tour recommended route determination module, wherein the passenger transport center search module is used for searching whether a passenger transport center exists on each candidate route, the crowd density index evaluation module is used for evaluating the crowd density index on each candidate route, and the real-time tour recommended route determination module determines the real-time tour recommended route according to the search result of the passenger transport center search module or the evaluation result of the crowd density index evaluation module.
4. The taxi real-time tour route recommendation system based on big data according to claim 3, wherein: the crowd density index calculation module comprises an interval distance acquisition module, an interval fluctuation calculation module, a search frequency acquisition module, a search frequency calculation module, a consumer frequency acquisition module, a consumer frequency calculation module, a route length acquisition module, a crowd density index calculation module and a candidate route sorting module, wherein the interval distance acquisition module is used for acquiring the distance between adjacent merchants on each candidate route, the interval fluctuation calculation module is used for calculating the fluctuation degree of the distance between the adjacent merchants on each candidate route, the search frequency acquisition module is used for acquiring the searched times of the merchants on the server on each candidate route in a first target time period, the search frequency calculation module is used for calculating the average searched times of each merchant on the server on a certain candidate route, and the consumer frequency acquisition module is used for acquiring the searched times of each merchant on each candidate route in a second target time period at a certain time each day The route ranking method comprises a consumer number calculating module, a route length obtaining module, a crowd density index calculating module and a candidate route ranking module, wherein the consumer number calculating module is used for calculating the average consumer number of all merchants on a certain candidate route in a certain time period, the route length obtaining module is used for obtaining the length of each candidate route, the crowd density index calculating module is used for calculating the crowd density index of the certain candidate route, and the candidate route ranking module is used for ranking and evaluating according to the crowd density index of each candidate route.
5. A taxi real-time tour route recommendation method based on big data is characterized by comprising the following steps: the recommendation method comprises the following steps:
S1: acquiring a candidate route;
S2: and selecting a real-time tour recommended route for renting the car from the candidate routes.
6. The method for recommending the taxi real-time tour route based on the big data according to claim 5, is characterized in that: the recommendation method further comprises the following steps:
S1: reading the current position of the taxi, inputting a target position by a driver, and acquiring all candidate routes A from the current position of the taxi to the target position1、A2、A3、…AnN is the number of candidate routes;
S2: and searching whether a passenger transport center exists on each candidate route, if so, determining the real-time tour recommended route of the taxi as the candidate route, and if not, evaluating the crowd density index of each candidate route, and selecting the real-time tour recommended route of the taxi according to the crowd density index of each candidate route.
7. The method for recommending the taxi real-time tour route based on the big data according to claim 6, is characterized in that: the step S2 of evaluating the crowd density index of each candidate route includes the following steps:
S21: respectively obtaining the distance A between adjacent merchants on each candidate route11=[B11、B12、B13、…、B1(m1-1)]、A21=[B21、B22、B23、…、B2(m2-1)]、A31=[B31、B32、B33、…、B3(m3-1)]、…、Ai1=[Bi1、Bi2、Bi3、…、Bij、…、Bi(mi-1)] 、…、An1=[Bn1、Bn2、Bn3、…、Bn(mn-1)]Wherein A isi1Is a collection of distances between adjacent merchants on the ith candidate route, BijFor the distance between the jth merchant and the j +1 st merchant on the ith candidate route, m1, m2, m3, …, mi, … mn are respectively the candidate route A1、A2、A3、…、Ai、…、AnNumber of business merchants, 1<=i<=n，
For each candidate route, calculating the fluctuation degree of the distance between adjacent merchants on the ith candidate route by using the following formula:
，，
Wherein L isithe distance of the route from the current position of the taxi to the first merchant and the last merchant in the direction from the target position on the ith candidate route, Eithe fluctuation degree of the distance between adjacent merchants on the ith candidate route is mi, and mi is the total number of the merchants on the ith candidate route;
S22: respectively acquiring a first target time period T1The number A of times of searching of the merchant on the server in each candidate route12=[C11、C12、C13、…、C1m1]、A22=[C21、C22、C23、…、C2m2]、A32=[C31、C32、C33、…、C3m3]、…、Ai2=[Ci1、Ci2、Ci3、…、Cij、…、Cimi] 、…、An2=[Cn1、Cn2、Cn3、…、Cnmn]Wherein A isi2for each merchant on the ith candidate route, a collection of searched times on the server, CijSearching the server for the jth merchant on the ith candidate route for times;
For each candidate route, calculating the average number of times each merchant is searched on the server over the ith candidate route using the following formula:
；
S23: respectively acquiring the second target time period T of each merchant on each candidate route2a certain time period T of each day3And calculating all the merchants on the candidate route in a certain time period T by using the following formula3Average number of consumers of (2):
Xi=；
Wherein, XiIndicating all merchants on the ith candidate route for a certain period of time T3average number of consumers of (P)jkfor the jth merchant on the ith candidate route in the second target time period T2A certain time period T of the k-th day3Number of consuming people in (m)iIs the number of merchants on the ith candidate route, T2Represents the number of days of the second target time period,
s24: respectively obtaining the length S of each candidate route1、S2、S3、…Sn，SiFor the length of the ith candidate route, calculating the crowd density index of each candidate route by using the following calculation formula for the candidate route:
，
Wherein, YiCrowd-sourcing index for the ith candidate route, HiAveraging the number of times each merchant is searched on the server for the ith candidate route, Eias a degree of fluctuation in the distance between adjacent merchants on the ith candidate route, XiFor all merchants on the ith candidate route in a certain time period T3average number of consumers of (1);
s25: and sequencing the crowd density indexes of all candidate routes from large to small, and taking the candidate route corresponding to the first dense index ranking as the real-time tour recommended route.
8. The taxi real-time tour route recommendation system and method based on big data according to claim 5 are characterized in that: the passenger transport center comprises bus stations, railway stations and high-speed rail stations.
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| CN112070563A (en) * | 2020-09-25 | 2020-12-11 | 汪洋 | Market intelligent shopping guide system and method based on big data |
| CN112732858A (en) * | 2021-01-25 | 2021-04-30 | 腾讯科技（深圳）有限公司 | Path planning method and device, computer equipment and storage medium |
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| CN112732858A (en) * | 2021-01-25 | 2021-04-30 | 腾讯科技（深圳）有限公司 | Path planning method and device, computer equipment and storage medium |
| CN112732858B (en) * | 2021-01-25 | 2022-06-07 | 腾讯科技（深圳）有限公司 | Path planning method and device, computer equipment and storage medium |
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| CN110567474B (en) | 2020-07-14 |
| CN111798025A (en) | 2020-10-20 |
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