Taxi Real-time Cruising Route Recommendation System Based on Big Data
Through big data analysis, the generation and selection of efficient cruising routes is solved, and the problem of inefficient passenger carrying efficiency caused by taxi drivers relying on intuitive planning routes is achieved, and efficient taxi operation is achieved.
Patent Information
- Application Number
- CN202010451105.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2019-09-10
- Publication Date
- 2025-07-29
- Estimated Expiration
- 2039-09-10
AI Technical Summary
Taxi drivers rely on intuition and experience to plan their cruising routes, resulting in inefficiency in carrying passengers.
A taxi real-time cruise route recommendation system based on big data generates and selects efficient cruise recommended routes through candidate route acquisition modules, real-time cruise route selection modules, passenger transport center search modules, crowd density index evaluation modules, etc.
It improves the passenger efficiency of taxi drivers during the cruise process and ensures efficient operation.
Smart Images
Figure CN111798025B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of big data, and particularly to a real-time cruising route recommendation system for taxis based on big data. Background Art
[0002] With the improvement of the economic development level, people's transportation demands are continuously increasing. When people travel, they not only need relatively common and popular transportation means such as urban buses and long-distance bus lines, but sometimes also require convenient, fast, safe and comfortable passenger transportation means. Taxis are welcomed by people because of their flexible operating characteristics. Taxis do not follow fixed routes, but the cruising routes are planned by the drivers themselves. The traditional method for taxi drivers is to rely on intuition and experience to plan cruising routes, and it is sometimes very difficult to achieve efficient operation and passenger carrying with this method. Summary of the Invention
[0003] The purpose of the present invention is to provide a real-time cruising route recommendation system and method for taxis based on big data to solve the problems in the prior art.
[0004] To achieve the above purpose, the present invention provides the following technical solutions:
[0005] A real-time cruising route recommendation system for taxis based on big data, the recommendation system includes a candidate route acquisition module and a real-time cruising route selection module. The candidate route acquisition module is used to acquire multiple candidate routes for real-time cruising of taxis, and the real-time cruising route selection module is used to select a real-time cruising recommended route from the candidate routes.
[0006] Preferably, the candidate route acquisition module includes a starting position reading module, a target position input module and a candidate route generation module. The starting position reading module is used to read the current position of the taxi, the target position input module is used for the driver to input the target position, and the candidate route generation module generates several candidate routes from the current position to the target position according to the current position and the target position of the taxi.
[0007] Preferably, the real-time cruising route selection module includes a passenger transport center search module, a crowd density index evaluation module and a real-time cruising recommended route determination module. The passenger transport center search module is used to search whether there is a passenger transport center on each candidate route, the crowd density index evaluation module is used to evaluate the crowd density index on each candidate route, and the real-time cruising recommended route determination module determines the real-time cruising 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.
[0008] Preferably, the crowd density index calculation module includes an interval distance acquisition module, an interval fluctuation calculation module, a search times acquisition module, a search times calculation module, a consumption times acquisition module, a consumption times calculation module, a route length acquisition module, a crowd density index calculation module, and a candidate route sorting module. The interval distance acquisition module is used to acquire the distance between adjacent merchants on each candidate route. The interval fluctuation calculation module is used to calculate the degree of fluctuation of the distance between adjacent merchants on each candidate route. The search times acquisition module is used to acquire the number of times each merchant on each candidate route is searched on the server during the first target time period. The search times calculation module is used to calculate the average number of times each merchant on a certain candidate route is searched on the server. The consumption times acquisition module is used to acquire the number of consumption times of each merchant on each candidate route during a certain time period of each day during the second target time period. The consumption times calculation module is used to calculate the average number of consumption times of all merchants on a certain candidate route during a certain time period. The route length acquisition module is used to acquire the length of each candidate route. The crowd density index calculation module is used to calculate the crowd density index of a certain candidate route. The candidate route sorting module is used to perform sorting and evaluation according to the crowd density index of each candidate route.
[0009] A real-time cruising route recommendation method for taxis based on big data, the recommendation method includes the following steps:
[0010] S1: Obtain candidate routes;
[0011] S2: Select the real-time cruising recommendation route of the taxi from the candidate routes.
[0012] Preferably, the recommendation method further includes the following steps:
[0013] S1: Read the current location of the taxi, the driver inputs the target location, and obtain all candidate routes A1, A2, A3,... A n , where n is the number of candidate routes;
[0014] S2: Search whether there is a passenger transport center on each candidate route. If there is a passenger transport center on the candidate route, the real-time cruising recommendation route of the taxi is this candidate route. If there is no passenger transport center on the candidate route, then evaluate the crowd density index of each candidate route, and select the real-time cruising recommendation route of the taxi according to the crowd density index of each candidate route.
[0015] Preferably, evaluating the crowd density index of each candidate route in step S2 includes the following steps:
[0016] S21: Respectively obtain the distance A between adjacent merchants on each candidate route 11=[B 11 、B 12 、B 13 、…、B 1(m1-1) 、A 21 =[B 21 、B 22 、B 23 、…、B 2(m2-1) 、A 31 =[B 31 、B 32 、B 33 、…、B 3(m3-1) 、…、A i1 =[B i1 、B i2 、B i3 、…、B ij 、…、B i(mi-1) 、…、A n1 =[B n1 、B n2 、B n3 、…、B n(mn-1) ,where A i1 is the set of distances between adjacent merchants on the i-th candidate route, B ij is the distance between the j-th and the (j + 1)-th merchants on the i-th candidate route, m1, m2, m3, …, mi, … mn are the numbers of merchants on the candidate routes A1, A2, A3, …, A i 、…、A n respectively, 1 <= i <= n,
[0017] For each candidate route, use the following formula to calculate the degree of fluctuation of the distances between adjacent merchants on the i-th candidate route:
[0018] , ,
[0019] where L i is the route distance between the first and the last merchants in the direction from the current location of the taxi to the target location on the i-th candidate route, E i is the degree of fluctuation of the distances between adjacent merchants on the i-th candidate route, mi is the total number of merchants on the i-th candidate route; the smaller the degree of fluctuation between adjacent merchants, the more evenly distributed the merchants are, indicating that the probability of a merchant picking up a passenger is relatively stable.
[0020] S22: Respectively obtain the number of times A 12 =[C 11 、C 12 、C 13, …, C 1m1 , A 22 = [C 21 , C 22 , C 23 , …, C 2m2 , A 32 = [C 31 , C 32 , C 33 , …, C 3m3 , …, A i2 = [C i1 , C i2 , C i3 , …, C ij , …, C imi , …, A n2 = [C n1 , C n2 , C n3 , …, C nmn , where A i2 is the set of the number of times each merchant on the i-th candidate route is searched on the server, and C ij is the number of times the j-th merchant on the i-th candidate route is searched on the server;
[0021] For each candidate route, use the following formula to calculate the average number of times each merchant on the i-th candidate route is searched on the server:
[0022] ; If the number of times a merchant is searched on the server is more, the potential customers of the merchant are more, so the potential passengers on the candidate route where the merchant is located are more.
[0023] S23: Obtain the number of customer visits of each merchant on each candidate route in a certain time period T3 of each day in the second target time period T2, and use the following formula to calculate the average number of customer visits of all merchants on a certain candidate route in a certain time period T3:
[0024] X i = ;
[0025] where X i represents the average number of customer visits of all merchants on the i-th candidate route in a certain time period T3, P jk is the number of customer visits of the j-th merchant on the i-th candidate route in a certain time period T3 of the k-th day in the second target time period T2, m i is the number of merchants on the i-th candidate route, and T2 represents the number of days in the second target time period; The more the average number of customer visits, the more the pedestrian flow on this candidate route. It is easier for taxi drivers to pick up passengers in places with more pedestrian flow.
[0026] S24: Obtain the lengths S1, S2, S3, …, S of each candidate route respectively n , S i is the length of the i-th candidate route. For each candidate route, calculate the crowd density index of this candidate route using the following calculation formula:
[0027] ,
[0028] where Y i is the crowd density index of the i-th candidate route, H i is the number of times each merchant on the i-th candidate route is searched on the server on average, E i is the degree of fluctuation of the distance between adjacent merchants on the i-th candidate route, X i is the average number of customer visits of all merchants on the i-th candidate route during a certain time period T3. Considering the crowd density index from multiple perspectives is conducive to more objectively evaluating the crowd density index of this candidate route.
[0029] S25: Sort the crowd density indices of all candidate routes in descending order, and select the candidate route corresponding to the first-ranked crowd density index as the real-time cruising recommended route. The higher the crowd density index, the greater the probability of the taxi picking up a passenger and the higher the frequency of picking up passengers.
[0030] Preferably, the passenger transport center includes a bus station, a railway station, and a high-speed railway station.
[0031] Compared with the prior art, the beneficial effects of the present invention are as follows: By obtaining the starting position of the taxi and using the driver target position input module to obtain all candidate routes, and then selecting the real-time cruising recommended route from the candidate routes, the taxi driver can operate and pick up passengers efficiently on the cruising recommended route. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Figure 1 is a schematic diagram of the modules of a taxi real-time cruising route recommendation system based on big data according to the present invention;
[0033] Figure 2 is a schematic flowchart of a taxi real-time cruising route recommendation method based on big data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0035] Please refer to Figures 1 to 2 In an embodiment of the present invention, a real-time cruising route recommendation system for taxis based on big data includes a candidate route acquisition module and a real-time cruising route selection module. The candidate route acquisition module is used to acquire multiple candidate routes for real-time cruising of taxis, and the real-time cruising route selection module is used to select a real-time cruising recommended route from the candidate routes.
[0036] The candidate route acquisition module includes a starting position reading module, a target position input module, and a candidate route generation module. The starting position reading module is used to read the current position of the taxi, the target position input module is used for the driver to input the target position, and the candidate route generation module generates several candidate routes from the current position to the target position according to the current position and the target position of the taxi.
[0037] The real-time cruising route selection module includes a passenger transport center search module, a crowd density index evaluation module, and a real-time cruising recommended route determination module. The passenger transport center search module is used to search whether there is a passenger transport center on each candidate route, the crowd density index evaluation module is used to evaluate the crowd density index on each candidate route, and the real-time cruising recommended route determination module determines the real-time cruising 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.
[0038] The crowd density index calculation module includes an interval distance acquisition module, an interval fluctuation calculation module, a search times acquisition module, a search times calculation module, a consumption person-times acquisition module, a consumption person-times calculation module, a route length acquisition module, a crowd density index calculation module, and a candidate route sorting module. The interval distance acquisition module is used to acquire the distance between adjacent merchants on each candidate route, the interval fluctuation calculation module is used to calculate the fluctuation degree of the distance between adjacent merchants on each candidate route, the search times acquisition module is used to acquire the number of times each merchant on each candidate route is searched on the server in the first target time period, the search times calculation module is used to calculate the average number of times each merchant on a certain candidate route is searched on the server, the consumption person-times acquisition module is used to acquire the consumption person-times of each merchant on each candidate route in a certain time period of each day in the second target time period, the consumption person-times calculation module is used to calculate the average consumption person-times of all merchants on a certain candidate route in a certain time period, the route length acquisition module is used to acquire the length of each candidate route, the crowd density index calculation module calculates the crowd density index of a certain candidate route, and the candidate route sorting module is used to perform sorting evaluation according to the crowd density index of each candidate route.
[0039] A real-time cruising route recommendation method for taxis based on big data, the recommendation method includes the following steps:
[0040] S1: The starting position reading module reads the current location of the taxi. The driver inputs the destination location in the destination location input module. Then, all candidate routes A1, A2, A3, … An from the current location of the taxi to the destination location are obtained from the candidate route generation module, where n is the number of candidate routes. In the actual acquisition of candidate routes, the length of the longest candidate route can be limited to be less than or equal to twice the length of the shortest candidate route, so as to prevent too many candidate routes and excessive impurities from being generated. n In fact, in the acquisition of candidate routes, the length of the longest candidate route can be limited to be less than or equal to twice the length of the shortest candidate route, so as to prevent too many candidate routes and excessive impurities from being generated.
[0041] S2: The passenger transport center search module searches whether there is a passenger transport center on each candidate route. The passenger transport center includes bus stations, railway stations, and high-speed railway stations. If there is a passenger transport center on the candidate route, the real-time cruising recommended route determination module determines the real-time cruising recommended route of the taxi as this candidate route. If there is no passenger transport center on the candidate route, the crowd density index evaluation module evaluates the crowd density index of each candidate route, and the real-time cruising recommended route determination module selects the real-time cruising recommended route of the taxi according to the crowd density index of each candidate route.
[0042] Evaluating the crowd density index of each candidate route in step S2 includes the following steps:
[0043] S21: The interval distance acquisition module respectively obtains the distances between adjacent merchants on each candidate route A 11 = [B 11 , B 12 , B 13 , …, B 1(m1-1) , A 21 = [B 21 , B 22 , B 23 , …, B 2(m2-1) , A 31 = [B 31 , B 32 , B 33 , …, B 3(m3-1) , …, A i1 = [B i1 , B i2 , B i3 , …, B ij , …, B i(mi-1) , …, A n1 = [B n1 , B n2 , B n3 , …, B n(mn-1) , where A i1 is the set of distances between adjacent merchants on the i-th candidate route, and B ijis the distance between the j-th merchant and the (j + 1)-th merchant on the i-th candidate route. m1, m2, m3, …, mi, …, mn are the numbers of merchants on candidate routes A1, A2, A3, …, A i 、…、A n respectively, where 1 <= i <= n,
[0044] For each candidate route, the interval fluctuation calculation module calculates the fluctuation degree of the distances between adjacent merchants on the i-th candidate route using the following formula:
[0045] , ,
[0046] where L i is the route distance between the first merchant and the last merchant in the direction from the current location of the taxi to the target location on the i-th candidate route, E i is the fluctuation degree of the distances between adjacent merchants on the i-th candidate route, and mi is the total number of merchants on the i-th candidate route;
[0047] S22: The search times acquisition module respectively obtains the search times A 12 = [C 11 , C 12 , C 13 , …, C 1m1 , A 22 = [C 21 , C 22 , C 23 , …, C 2m2 , A 32 = [C 31 , C 32 , C 33 , …, C 3m3 , …, A i2 = [C i1 , C i2 , C i3 , …, C ij , …, C imi , …, A n2 = [C n1 , C n2 , C n3 , …, C nmn , where A i2 is the set of the search times of each merchant on the i-th candidate route on the server, and C ij is the search times of the j-th merchant on the i-th candidate route on the server;
[0048] For each candidate route, the search times calculation module calculates the number of times each merchant on the i-th candidate route is searched on the server using the following formula:
[0049] ;
[0050] S23: The consumer visit number acquisition module respectively acquires the number of consumer visits of each merchant on each candidate route during the period from 9:00 to 15:00 every day in the previous ten days. The consumer visit number calculation module calculates the average number of consumer visits of all merchants on this candidate route from 9:00 to 15:00 using the following formula:
[0051] X i = ;
[0052] Among them, X i represents the average number of consumer visits of all merchants on the i-th candidate route from 9:00 to 15:00 in the previous ten days, P jk is the number of consumer visits of the j-th merchant on the i-th candidate route during a certain period T3 on the k-th day in the previous ten days, m i is the number of merchants on the i-th candidate route. A certain period T3 can be consistent with the time when the driver can cruise. For example, if a taxi driver plans to cruise between 12:00 midnight and 4:00 am the next day, then this certain period T3 is between 12:00 midnight and 4:00 am the next day.
[0053] S24: The route length acquisition module respectively acquires the lengths S1, S2, S3,... S n , S i is the length of the i-th candidate route. For each candidate route, the crowd density index calculation module calculates the crowd density index of this candidate route using the following calculation formula:
[0054] ,
[0055] Among them, Y i is the crowd density index of the i-th candidate route, H i is the number of times each merchant on the i-th candidate route is searched on the server on average, E i is the degree of fluctuation of the distance between adjacent merchants on the i-th candidate route, X i is the average number of consumer visits of all merchants on the i-th candidate route during a certain period T3.
[0056] S25: The candidate route sorting module sorts the crowd density indices of all candidate routes in descending order, and selects the candidate route corresponding to the highest crowd density index as the real-time cruising recommendation route. The higher the crowd density index of a certain candidate route, the greater the probability of picking up a passenger on that candidate route and the higher the frequency of picking up passengers.
[0057] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.
Claims
1. A real-time cruising route recommendation system for taxis based on big data, characterized in that: The recommended system includes a candidate route acquisition module and a real-time cruising route selection module. The candidate route acquisition module is used to acquire multiple candidate routes for taxis' real-time cruising, and the real-time cruising route selection module is used to select a real-time cruising recommended route from the candidate routes. The real-time cruising route selection module includes a passenger transport center search module, a crowd density index evaluation module, and a real-time cruising recommended route determination module. The passenger transport center search module is used to search whether there is a passenger transport center on each candidate route. The crowd density index evaluation module is used to evaluate the crowd density index on each candidate route. The real-time cruising recommended route determination module determines the real-time cruising 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. The crowd density index calculation module includes an interval distance acquisition module, an interval fluctuation calculation module, a search times acquisition module, a search times calculation module, a consumption times acquisition module, a consumption times calculation module, a route length acquisition module, a crowd density index calculation module, and a candidate route sorting module. The interval distance acquisition module is used to acquire the distance between adjacent merchants on each candidate route. The interval fluctuation calculation module is used to calculate the fluctuation degree of the distance between adjacent merchants on each candidate route. The search times acquisition module is used to acquire the number of times the merchants on each candidate route are searched on the server in the first target time period. The search times calculation module is used to calculate the average number of times each merchant on a certain candidate route is searched on the server. The consumption times acquisition module is used to acquire the consumption times of each merchant on each candidate route in a certain time period of each day in the second target time period. The consumption times calculation module is used to calculate the average consumption times of all merchants on a certain candidate route in a certain time period. The route length acquisition module is used to acquire the length of each candidate route. The crowd density index calculation module calculates the crowd density index of a certain candidate route, and the candidate route sorting module is used to perform sorting evaluation according to the crowd density index of each candidate route. The method used by the system is as follows: S1: Acquire candidate routes. S2: Select a real-time cruising recommended route for the taxi from the candidate routes.