CN113919586A - Bus network planning method based on taxi GPS data mining - Google Patents
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Abstract
The invention provides a taxi GPS data mining-based public transportation network planning method, which comprises the following steps of: s1, dividing the grid cells: dividing a research area into grid cells with equal sizes, and calling the grid cells containing the taxi passenger getting on and off records as hot grid cells; s2, determining candidate bus stops by using the hotspot grid unit obtained in S1 and the PDR within the radius range r, wherein the PDR represents the record of getting on and off of taxi passengers; s3, establishing a passenger flow matrix and a time matrix: according to the candidate bus stops and taxi GPS data obtained in S2, a passenger flow matrix FM and a time matrix TM between the candidate bus stops are established; and S4, forming the public transportation network by using a biological heuristic algorithm. The invention can obtain human mobile data in the real world under the conditions of lower cost and more comprehensive and accurate acquired information, and generates the public transportation network plan based on the human mobile data.
Description
Technical Field
The invention relates to the field of bus network planning, in particular to a bus network planning method based on taxi GPS data mining.
Background
Conventional public transportation network planning and design is usually based on human investigation, and a great deal of time cost and human resources are needed. In addition, the conventional method greatly depends on the experience of the designer, so that the information obtained by the manpower investigation is sometimes not accurate enough.
Nowadays, road networks of cities which are rapidly developed are continuously constructed and optimized, and planning based on investigation cannot adapt to rapid changes of road networks of large cities, but the changes can be reflected in urban resident movement modes, so that a public transportation network planning method based on a large amount of human movement data in the real world is urgently needed.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly creatively provides a bus network planning method based on taxi GPS data mining.
In order to achieve the above purpose, the invention provides a taxi GPS data mining-based public transportation network planning method, which comprises the following steps:
s1, dividing the grid cells: dividing a research area into grid cells with equal sizes, and calling the grid cells containing the taxi passenger getting on and off records as hot grid cells;
s2, determining candidate bus stops by using the hotspot grid unit obtained in S1 and the PDR within the radius range r, wherein the PDR represents the record of getting on and off of taxi passengers;
s3, establishing a passenger flow matrix and a time matrix: according to the candidate bus stops and taxi GPS data obtained in S2, a passenger flow matrix FM and a time matrix TM between the candidate bus stops are established;
and S4, forming the public transportation network by using a biological heuristic algorithm.
In a preferred embodiment of the present invention, the S2 includes the steps of:
s2-1, calculating the pdr value of each hot spot grid unit;
s2-2, sorting according to pdr value descending order, selecting the hot spot grid unit with maximum pdr value as a candidate site; putting the candidate sites into a candidate site set;
s2-3, deleting PDRs in the range by taking the candidate station generated in the step S2-2 as the center and r as the radius;
s2-4, updating pdr values of the rest hot spot grid units;
s2-5, judging whether the pdr value of the hot spot grid unit is larger than the set pdr threshold value, if so, jumping to execute the step S2-2; if not, executing the next step;
s2-6, obtaining a final candidate site set;
wherein the PDR represents the record of the taxi passenger getting on or off the taxi;
r represents a service radius of one bus station;
PDR represents the number of PDRs for a hot spot grid cell within a radius r.
In a preferred embodiment of the present invention, the S3 includes:
in the passenger flow matrix FM, each element fijA passenger flow representing candidate site i to candidate site j;
in the time matrix TM, eachAn element tijRepresents the average time from candidate station i to candidate station j;
each element value in the matrix is determined according to taxi GPS data.
In a preferred embodiment of the present invention, the S4 includes the steps of:
s4-1, taking the passenger flow matrix and the time matrix as input, and obtaining an optimal route R by utilizing a biological heuristic algorithmbest；
S4-2, updating the passenger flow matrix: in the passenger flow matrix FM, RbestThe passenger flow between the various stations is set to 0;
s4-3, judging whether the number of the generated bus routes is enough, if not, continuing to execute the step S4-1;
and S4-4, forming a public transportation network by all the generated routes.
In a preferred embodiment of the present invention, the bio-heuristic algorithm comprises the steps of:
S-A, taking A passenger flow matrix and A time matrix as input, and initializing ant colony algorithm parameters;
S-B, if the iteration number is less than or equal to itermaxExecuting the next step, otherwise, ending; wherein itermaxRepresenting the iteration times of the ant colony algorithm;
S-C, each ant generates a route: for the first ant, randomly selecting the next station according to the transfer probability, and adding the station into the current route; calculating the total time T of the current route if T < TmaxThe current ant continues to search for the next station, otherwise, the current ant stops searching, the current route is saved, and the route is switched to the next ant for route searching until m ants generate m routes; wherein T ismaxFor the time constraint of the bus route, m represents the total number of ants;
S-D, calculating the total passenger flow of each route for the generated m routes, and updating the optimal route;
and S-E, updating pheromones among the candidate sites, adding 1 to the iteration number, and executing the step S-B.
In a preferred embodiment of the present invention, the formula of the transition probability in S-C is:
wherein,
representing the probability that the l-th ant transfers from candidate site i to candidate site j;
τijpheromones representing candidate sites i through j;
τikpheromones representing candidate sites i through k;
ηijrepresenting a passenger flow between candidate site i and candidate site j;
ηikrepresenting passenger flows between candidate site i and candidate site k;
alpha represents the pheromone weight value;
β represents a visibility weight value;
allowed represents the set of selectable sites for the next station.
In a preferred embodiment of the present invention, the formula in S-C for calculating the total time of the current route is:
wherein T represents the total time of the current route in both directions;
n represents the total number of stops of the route;
tm(si,si+1) Denotes from siTo si+1The average time of (d);
tm(si+1,si) Denotes from si+1To siThe average time of (d);
siindicating the ith station on the route;
si+1represents the i +1 th site on the route;
t0indicating the time at which the bus stops at each stop.
In a preferred embodiment of the present invention, the formula in S-D for calculating the total passenger flow for each route is:
where Num represents the total passenger flow for the route in both directions;
n represents the total number of stops of the route;
fm(si,sj) Denotes from siTo sjThe passenger flow of (2);
fm(sj,si) Denotes from sjTo siThe passenger flow of (2);
siindicating the ith station on the route;
sjindicating the jth stop on the route.
In a preferred embodiment of the present invention, the formula for updating pheromones between candidate sites in S-E is:
τij(t+1)＝(1-ρ)·τij(t)+Δτij
wherein, tauij(t +1) pheromones from candidate site i to candidate site j at time t + 1;
ρ represents a pheromone volatilization coefficient;
τij(t) pheromones representing candidate site i to candidate site j at time t;
Δτijpheromone increments from candidate site i to candidate site j;
m represents the total number of ants;
q is a pheromone constant;
num represents passenger flow for the route in both directions.
In summary, due to the adoption of the technical scheme, compared with the bus network plan generated based on the manpower investigation information, the method and the system can obtain the human movement data in the real world under the conditions of lower cost and more comprehensive and accurate information acquisition, and generate the bus network plan based on the human movement data.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The invention provides a taxi GPS data mining-based public transportation network planning method, which comprises the following steps of:
s1, dividing the grid cells: the study area is divided into equal sized grid cells, for example: 25m × 25m grid cells, and the grid cells containing the taxi passenger getting on and off records are called hot spot grid cells;
s2, determining candidate bus stops, namely passenger getting-on and getting-off hot spot areas by using the hot spot grid units obtained in S1 and the PDRs within the radius range of r, wherein the PDRs represent taxi getting-on and getting-off records of taxi passengers;
s3, establishing a passenger flow matrix and a time matrix: according to the candidate bus stops and taxi GPS data obtained in S2, a passenger flow matrix FM and a time matrix TM between the candidate bus stops are established;
the taxi GPS data comprises the time of getting on and off the taxi of the passenger, the longitude and latitude of the place and the like.
And S4, forming the public transportation network by using a biological heuristic algorithm.
Herein, a candidate station is a candidate bus station.
In a preferred embodiment of the present invention, the S2 includes the steps of:
s2-1, calculating the pdr value of each hot spot grid unit;
s2-2, sorting according to pdr value descending order, selecting the hot spot grid unit with maximum pdr value as a candidate site; putting the candidate sites into a candidate site set;
s2-3, deleting PDRs in the range by taking the candidate station generated in the step S2-2 as the center and r as the radius;
s2-4, updating pdr values of the rest hot spot grid units;
s2-5, judging whether the pdr value of the hot spot grid unit is larger than the set pdr threshold value, if so, jumping to execute the step S2-2; if not, executing the next step;
s2-6, obtaining a final candidate site set;
wherein the PDR represents the record of the taxi passenger getting on or off the taxi;
r represents the service radius of a bus station, typically 300m to 500 m;
PDR represents the number of PDRs for a hot spot grid cell within a radius r.
In a preferred embodiment of the present invention, the S3 includes:
in the passenger flow matrix FM, each element fijA passenger flow representing candidate site i to candidate site j; if a taxiThe trip record has a start point within a radius r of candidate site i and an end point within a radius r of candidate site j, then it represents a passenger flow from candidate site i to candidate site j.
In the time matrix TM, each element tijRepresents the average time from candidate station i to candidate station j; considering the speed difference between a taxi and a bus, the average time of the bus from the candidate station i to the candidate station j is represented by multiplying the average time of the taxi from the candidate station i to the candidate station j by lambda. λ is the ratio of the average speed of taxis to the average speed of buses in a city, and λ may be 1.5.
Each element value in the matrix is determined according to taxi GPS data.
In a preferred embodiment of the present invention, the S4 includes the steps of:
s4-1, taking the passenger flow matrix and the time matrix as input, and obtaining an optimal route R by utilizing a biological heuristic algorithmbest＝[s1,s2,...sn]。
S4-2, updating the passenger flow matrix: in the passenger flow matrix FM, RbestThe passenger flow between the various stations is set to 0, i.e.:
wherein
Representing arbitrary, n represents the total number of stops of the route, f(s)i,sj) Denotes from siTo sjThe passenger flow of (2); siIndicating the ith station, s, on the routejIndicating the jth stop on the route.
S4-3, judging whether the number of the generated bus routes is enough, if not, continuing to execute the step S4-1;
and S4-4, forming a public transportation network by all the generated routes.
In a preferred embodiment of the present invention, the bio-heuristic algorithm comprises the steps of:
S-A, taking A passenger flow matrix and A time matrix as input, and initializing ant colony algorithm parameters;
S-B, if the iteration number is less than or equal to itermaxExecuting the next step, otherwise, ending;
wherein itermaxRepresenting the iteration times of the ant colony algorithm;
S-C, each ant generates a route: for the first ant, randomly selecting the next station according to the transition probability, wherein l belongs to {1,2,3 … m }, m represents the total number of ants, and adding the station into the current route; calculating the total time T of the current route if T<TmaxThe current ant continues to search for the next station, otherwise, the current ant stops searching, and saves the current route, and switches to the next ant to search for the route until m routes generated by m ants are obtained; wherein T ismaxIs a bus route time constraint;
S-D, calculating the total passenger flow of each route for the generated m routes, and updating the optimal route;
and S-E, updating pheromones among the candidate sites, adding 1 to the iteration number, and executing the step S-B.
In a preferred embodiment of the present invention, the formula of the transition probability in S-C is:
wherein,
representing the probability that the l-th ant transfers from candidate site i to candidate site j;
τijpheromones representing candidate sites i through j;
τikpheromones representing candidate sites i through k;
ηijrepresenting a passenger flow between candidate site i and candidate site j;
ηikrepresenting passenger flows, i.e. η, between candidate site i and candidate site kij＝fm(i,j)+fm(j,i)；
Alpha represents the pheromone weight value;
β represents a visibility weight value;
allowed represents the set of selectable sites for the next station.
In a preferred embodiment of the present invention, the formula in S-C for calculating the total time of the current route is:
wherein T represents the total time of the current route in both directions;
n represents the total number of stops of the route;
tm(si,si+1) Denotes from siTo si+1The average time of (d);
tm(si+1,si) Denotes from si+1To siThe average time of (d);
siindicating the ith station on the route;
si+1represents the i +1 th site on the route;
t0indicating the time at which the bus stops at each stop.
In a preferred embodiment of the present invention, the formula in S-D for calculating the total passenger flow for each route is:
where Num represents the total passenger flow for the route in both directions;
n represents the total number of stops of the route;
fm(si,sj) Denotes from siTo sjThe passenger flow of (2);
fm(sj,si) Denotes from sjTo siThe passenger flow of (2);
sirepresenting a routeThe ith station;
sjindicating the jth stop on the route.
In a preferred embodiment of the present invention, the formula for updating pheromones between candidate sites in S-E is:
τij(t+1)＝(1-ρ)·τij(t)+Δτij
wherein, tauij(t +1) pheromones from candidate site i to candidate site j at time t + 1;
ρ represents a pheromone volatilization coefficient;
τij(t) pheromones representing candidate site i to candidate site j at time t;
Δτijpheromone increments from candidate site i to candidate site j;
m represents the total number of ants;
if the kth ant passes through candidate site i to candidate site j;
q is a pheromone constant;
num represents passenger flow for the route in both directions.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (9)
1. A bus network planning method based on taxi GPS data mining is characterized by comprising the following steps:
s1, dividing the grid cells: dividing a research area into grid cells with equal sizes, and calling the grid cells containing the taxi passenger getting on and off records as hot grid cells;
s2, determining candidate bus stops by using the hotspot grid unit obtained in S1 and the PDR within the radius range r, wherein the PDR represents the record of getting on and off of taxi passengers;
s3, establishing a passenger flow matrix and a time matrix: according to the candidate bus stops and taxi GPS data obtained in S2, a passenger flow matrix FM and a time matrix TM between the candidate bus stops are established;
and S4, forming the public transportation network by using a biological heuristic algorithm.
2. The method for planning a bus network based on taxi GPS data mining as claimed in claim 1, wherein the S2 comprises the steps of:
s2-1, calculating the pdr value of each hot spot grid unit;
s2-2, sorting according to pdr value descending order, selecting the hot spot grid unit with maximum pdr value as a candidate site; putting the candidate sites into a candidate site set;
s2-3, deleting PDRs in the range by taking the candidate station generated in the step S2-2 as the center and r as the radius;
s2-4, updating pdr values of the rest hot spot grid units;
s2-5, judging whether the pdr value of the hot spot grid unit is larger than the set pdr threshold value, if so, jumping to execute the step S2-2; if not, executing the next step;
s2-6, obtaining a final candidate site set;
wherein the PDR represents the record of the taxi passenger getting on or off the taxi;
r represents a service radius of one bus station;
PDR represents the number of PDRs for a hot spot grid cell within a radius r.
3. The method for planning a bus network based on taxi GPS data mining as claimed in claim 1, wherein the S3 includes:
in the passenger flow matrix FM, each element fijA passenger flow representing candidate site i to candidate site j;
in the time matrix TM, each element tijRepresents the average time from candidate station i to candidate station j;
each element value in the matrix is determined according to taxi GPS data.
4. The method for planning a bus network based on taxi GPS data mining as claimed in claim 1, wherein the S4 comprises the steps of:
s4-1, taking the passenger flow matrix and the time matrix as input, and obtaining an optimal route R by utilizing a biological heuristic algorithmbest；
S4-2, updating the passenger flow matrix: in the passenger flow matrix FM, RbestThe passenger flow between the various stations is set to 0;
s4-3, judging whether the number of the generated bus routes is enough, if not, continuing to execute the step S4-1;
and S4-4, forming a public transportation network by all the generated routes.
5. The method for bus network planning based on taxi GPS data mining as claimed in claim 1, wherein the bio-heuristic algorithm comprises the following steps:
S-A, taking A passenger flow matrix and A time matrix as input, and initializing ant colony algorithm parameters;
S-B, if the iteration number is less than or equal to itermaxExecuting the next step, otherwise, ending; wherein itermaxRepresenting the iteration times of the ant colony algorithm;
S-C, each ant generates a route: for the first ant, randomly selecting the next station according to the transition probability, and adding the station into the first antA front route; calculating the total time T of the current route if T < TmaxThe current ant continues to search for the next station, otherwise, the current ant stops searching, the current route is saved, and the route is switched to the next ant for route searching until m ants generate m routes; wherein T ismaxFor the time constraint of the bus route, m represents the total number of ants;
S-D, calculating the total passenger flow of each route for the generated m routes, and updating the optimal route;
and S-E, updating pheromones among the candidate sites, adding 1 to the iteration number, and executing the step S-B.
6. The method for planning a bus network based on taxi GPS data mining of claim 5, wherein the formula of the transfer probability in S-C is as follows:
wherein,
representing the probability that the l-th ant transfers from candidate site i to candidate site j;
τijpheromones representing candidate sites i through j;
τikpheromones representing candidate sites i through k;
ηijrepresenting a passenger flow between candidate site i and candidate site j;
ηikrepresenting passenger flows between candidate site i and candidate site k;
alpha represents the pheromone weight value;
β represents a visibility weight value;
allowed represents the set of selectable sites for the next station.
7. The method for bus network planning based on taxi GPS data mining as claimed in claim 5, wherein the formula for calculating the total time of the current route in S-C is as follows:
wherein T represents the total time of the current route in both directions;
n represents the total number of stops of the route;
tm(si,si+1) Denotes from siTo si+1The average time of (d);
tm(si+1,si) Denotes from si+1To siThe average time of (d);
siindicating the ith station on the route;
si+1represents the i +1 th site on the route;
t0indicating the time at which the bus stops at each stop.
8. The method for bus network planning based on taxi GPS data mining as claimed in claim 5, wherein the formula for calculating the total passenger flow of each route in S-D is as follows:
where Num represents the total passenger flow for the route in both directions;
n represents the total number of stops of the route;
fm(si,sj) Denotes from siTo sjThe passenger flow of (2);
fm(sj,si) Denotes from sjTo siThe passenger flow of (2);
siindicating the ith station on the route;
sjindicating the jth stop on the route.
9. The method for bus network planning based on taxi GPS data mining as claimed in claim 5, wherein the formula for updating pheromones between candidate stations in S-E is as follows:
τij(t+1)＝(1-ρ)·τij(t)+Δτij
wherein, tauij(t +1) pheromones from candidate site i to candidate site j at time t + 1;
ρ represents a pheromone volatilization coefficient;
τij(t) pheromones representing candidate site i to candidate site j at time t;
Δτijpheromone increments from candidate site i to candidate site j;
m represents the total number of ants;
q is a pheromone constant;
num represents passenger flow for the route in both directions.
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