CN112418535A - School bus scheduling planning method based on binary tree - Google Patents
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Abstract
The invention relates to a school bus scheduling planning method based on a binary tree, which comprises the following steps: step S1, collecting relevant data of school bus path planning; s2, clustering by adopting a clustering algorithm according to the obtained related data to obtain a ride-sharing site; step S3, finding out the bus stop nearest to the center point and the opposite bus stop by using the clustered ride-sharing stop and the spatial index of the bus data; step S4, traversing driving route information according to the driving time between every two points of the obtained bus stops; step S5, storing the traversed data in a database, and establishing a binary tree for the station; and step S6, obtaining the optimal path by adopting an improved path planning algorithm according to the data in the database. Based on the trip demand characteristics of school bus students, scientific and reasonable school bus route and station planning is established, and the operation efficiency of the school bus is improved.
Description
Technical Field
The invention relates to the field of school bus path planning, in particular to a school bus scheduling planning method based on a binary tree.
Background
The routine operation process of path planning, such as school bus service, is as follows: in the morning, school buses start from parking places, respectively drive to the first boarding points of all lines, travel along a preset path, pick up students at all stations along the way, send the students to schools, and the students arrive at the stations at the specified time to wait for regular buses and are out of date. If students at a certain station cannot be picked up by one bus due to the limitation of the loading capacity of the bus, the rest students are picked up by other school buses. At noon, the whole process is reversed: school buses pick up students at school, and along a predetermined route, send the students to their place of boarding in the morning, and then return to the place of parking. School's accessible customization public transit web platform provides student's trip demand, and public transit company designs the circuit and in time feeds back to school according to the demand condition. The purpose of school bus path planning is to provide a direct, safe, rapid transportation service from the student's home to the school. The line and station planning is used as the first step and the key step of the whole school bus path planning process, plays a very important role in the whole process, and the reasonable line and station planning is the basis for the correct follow-up work arrangement.
If the number of the carpooling stations passing by the route is too many, the coverage range of the stations is wider, the walking time and distance of students can be reduced, but the cost of school bus investment and operation is increased, and the discontent is caused by the fact that part of students stay on the bus for too long time. If the number of the co-riding stations is too small, the coverage range is too small, the walking distance of students is increased, the number of students willing to ride the school bus is reduced, and the advantage of picking up and delivering the school bus cannot be embodied.
Disclosure of Invention
In view of the above, the invention aims to provide a school bus scheduling planning method based on a binary tree, which constructs scientific and reasonable school bus route and station planning based on trip demand characteristics of school bus students and improves the operation efficiency of school buses.
In order to achieve the purpose, the invention adopts the following technical scheme:
a school bus scheduling planning method based on a binary tree comprises the following steps:
step S1, collecting relevant data of school bus path planning;
s2, clustering by adopting a clustering algorithm according to the obtained related data to obtain a ride-sharing site;
step S3, finding out the bus stop nearest to the center point and the opposite bus stop by using the clustered ride-sharing stop and the spatial index of the bus data;
step S4, traversing driving route information according to the driving time between every two points of the obtained bus stops;
step S5, storing the traversed data in a database, and establishing a binary tree for the station;
and step S6, obtaining the optimal path by adopting an improved path planning algorithm according to the data in the database.
Further, the school bus path planning related data comprises student home addresses, school longitude and latitude addresses, walking radius parameters, conversion rates of effective orders of stations, station effective order lower limits, preset stations, line opening effective order total lower limits, line opening effective order total upper limits, line total station number upper limits, full load rates, running time, driving mileage, clustering radius, average speed per hour and whole member conversion.
Further, the step S2 is specifically: and creating a list after the arrangement and combination of the school, the site and the opposite site based on the binary tree data structure and the school, the site and the opposite site, and sending each site combination into a TS tabu algorithm path plan one by one to select an optimal combination.
Further, the step S2 is specifically: according to the family addresses of students, walking radius parameter stations, effective order lower limit values, conversion rates of effective orders of the stations and preset stations, a plan model of the ride-sharing stations is customized and developed by adopting a clustering analysis model algorithm, and riding requirements of students with close distances and close destinations are integrated into one ride-sharing station.
Further, the step S5 is specifically:
step S51, creating a station binary tree list, traversing all path lists of the station binary tree, and inserting bus stations without opposite stations into the path lists of the station binary tree;
step S52, extracting the longitude and latitude information of the sites in the combined list, and discarding the route information of the sites not in the combined list;
and step S53, creating a list of schools, sites and opposite sites after permutation and combination.
Further, the step S6 is specifically:
step S61, obtaining the shortest path from school to all the sites
Step S62, then finding the shortest distance corresponding to the time or the distance
Step S63 is followed by finding the shortest path from the school to the rest of the sites through the TS tabu search in the rest of the sites,
and step S64, intercepting the shortest distance according with the time or the route from the shortest path, and circulating in turn until all the stations are traversed to obtain the optimal path.
Further, the step S6 is specifically: and creating a list after the arrangement and combination of the school, the site and the opposite site based on the binary tree data structure and the school, the site and the opposite site, and sending each site combination into a TS tabu algorithm path plan one by one to select an optimal combination.
Further, after the optimal path is obtained in step S6, the route is drawn on the map by using the longitude and latitude information of each station in the route implemented by the pyhton code, and the student information borne on each route is output and stored locally or in the cloud for the parents of students and schools to look up.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, based on the trip demand characteristics of school bus students, scientific and reasonable school bus route and station planning is constructed, and the operation efficiency of the school bus is improved.
Drawings
FIG. 1 is a flow chart of a method in one embodiment of the present invention;
FIG. 2 is a flow chart of a clustering algorithm in one embodiment of the present invention;
FIG. 3 is a schematic diagram of a front-end and back-end interaction flow in an embodiment of the invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the present invention provides a school bus scheduling planning method based on a binary tree, including the following steps:
step S1, collecting relevant data of school bus path planning;
in this embodiment, an excel table is constructed according to the obtained related data, specifically:
TABLE 1 Path planning related data
S2, clustering by adopting a clustering algorithm according to the obtained related data to obtain a ride-sharing site;
referring to fig. 2, in this embodiment, the step S2 is specifically to customize and develop a ride-sharing site planning model based on a cluster analysis model algorithm according to the home address of the student, the walking radius parameter site, the effective order lower limit value, the conversion rate of the site effective order and the preset site, and centralize the riding demands of students with close distances and close destinations to one ride-sharing site.
Preferably, the algorithm based on the cluster analysis model specifically comprises the following steps:
1. data preparation (student home address latitude and longitude, school removal, school coordinates do not participate in clustering)
2. Calculating the spherical distance between two student home addresses by using the spherical cosine law
3. Clustering algorithm parameter min _ samples: the number of min _ samples specified for aggregation is 3 (one aggregation point is at least three people)
4. Clustering algorithm parameter eps selecting 0.5 km (500 m) as a density aggregation radius parameter, wherein the distance of the geographic position is measured by using the spherical distance as the aggregation radius parameter
5. A candidate to-be-clustered center point, and the point of the center after subsequent clustering closest to the candidate to-be-clustered center point uses the candidate to-be-clustered center point as a clustering center point
6. A hybrid algorithm based on DBSCAN and Kmeans is provided: aggregating the geographical position data sets of users into a plurality of clusters according to the active radius by using the density reachable characteristic of the DBSCAN algorithm, taking the data set of each cluster as new input, solving the position of the center of mass by using iterative aggregation of the Kmeans algorithm, and setting the K value to be 1
7. And deleting the clustering centers with clustering centers smaller than min _ samples and the corresponding students (the step can be properly reserved or deleted according to actual requirements).
Step S3, finding out the bus stop nearest to the center point and the opposite bus stop by using the clustered ride-sharing stop and the spatial index of the bus data;
step S4, traversing driving route information according to the driving time between every two points of the obtained bus stops;
step S5, storing the traversed data in a database, and establishing a binary tree for the station;
in this embodiment, step S5 specifically includes:
(1) obtain the number of all sites (including original site and opposite site) + school
(2) Store opposite sites separately from only individual sites, in preparation for creating a binary tree
(3) The school longitude and latitude are placed in a double-site list for traversing all paths of the site binary tree;
(4) creating a station binary tree list, traversing all path lists of the station binary tree, and inserting bus stations without opposite stations into the path lists of the station binary tree;
(5) extracting the longitude and latitude information of the sites in the combined list, and discarding the route information of the sites which are not in the combined list; an effective stop appears at the same bus stop and the opposite stop, namely if a certain stop appears in the route, the opposite stop of the stop does not appear any more, and the uniqueness of the stop is ensured in the whole process)
(6) Create a ranked combined list of schools, sites, and opposite sites.
Step S6, obtaining an optimal path by adopting an improved path planning algorithm according to the data in the database;
in this embodiment, preferably, the path planning algorithm is: and creating a list after the arrangement and combination of the school, the site and the opposite site based on the binary tree data structure and the school, the site and the opposite site, and sending each site combination into a TS tabu algorithm path plan one by one to select an optimal combination. The basic idea of the algorithm is as follows: in the process of planning the optimal school bus by the dynamic path, the direction planning of the current station and the station closest to the school is always selected, so that the planned scheme is easy to fall into the local optimal, and in order to find the global optimal, TS tabu search is introduced, and the specific method comprises the following steps:
1. setting the length of a taboo watch: table _ len = round ((num _ city-1)/2) × 0.5), wherein num _ city represents the total number of bus stops nearest to all cluster center points and the bus stops corresponding to the cluster center points plus 1 (1 represents school)
2. Adding tabu tables one by one into initial paths corresponding to permutation and combination of schools, sites and opposite sites
3. The path length of the initial solution is calculated: the distance of the initial path corresponding to the permutation and combination is solved one by one (each path is connected with the total path of the next station in series according to the first station in sequence) and the shortest distance and the corresponding initial path scheme are solved
4. Setting the number of iterations, e.g. 50, and setting the path distance solved in the third step and the corresponding initial path plan as the initial desired path distance and the initial desired path plan
5. Finding a new solution in the whole domain, i.e. finding all neighborhood solutions corresponding to the last path solution
6. Finding the path distances of all new solutions (each path is connected in series with the total distance of the next station according to the first station in sequence)
7. Selecting a path in the new solution, selecting the shortest distance and the corresponding optimal path scheme
8. If the shortest path distance in the new solution is smaller than the expected path distance, updating two expected values (namely, the initial expected path distance and the initial expected path scheme are updated to the shortest path distance in the new solution and the corresponding optimal path scheme); if the tabu table does not have the optimal path scheme corresponding to the shortest path distance in the new solution, the optimal path scheme needs to be added into the tabu table
9. If the shortest path distance in the new solution is greater than or equal to the expected path distance, that is, the shortest path distance is not capable of improving the expectation, if the optimal path scheme corresponding to the shortest path distance in the new solution already exists in the tabu table, the shortest path distance and the corresponding optimal path scheme in the new solution greater than or equal to the expected path distance should be removed from the full-field new solution, after the shortest path distance and the corresponding optimal path scheme in the full-field new solution are removed, the shortest path distance and the corresponding optimal path scheme in the full-field new solution are recalculated, and the optimal scheme is added into the tabu table
10. If the length of the tabu table > = the length of the tabu table is set, the first path scheme of the tabu table is deleted
11. 5, 6, 7, 8, 9, 10, continuously iterating in turn until the path length lists of all new solutions are empty, or the iteration times exceed the set iteration times, ending the iteration, and outputting the optimal combination (namely the optimal path scheme)
In this embodiment, the step S6 specifically includes:
step S61, obtaining the shortest path from school to all the sites
Step S62, then finding the shortest distance corresponding to the time or the distance
Step S63 is followed by finding the shortest path from the school to the rest of the sites through the TS tabu search in the rest of the sites,
and step S64, intercepting the shortest distance according with the time or the route from the shortest path, and circulating in turn until all the stations are traversed to obtain the optimal path.
In this embodiment, the path planning algorithm adopts a tabu search algorithm, a simulated annealing algorithm, a genetic algorithm, an artificial immune algorithm, a particle swarm algorithm, an ant colony algorithm, or a hybrid algorithm thereof.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (7)
1. A school bus scheduling planning method based on a binary tree is characterized by comprising the following steps:
step S1, collecting relevant data of school bus path planning;
s2, clustering by adopting a clustering algorithm according to the obtained related data to obtain a clustering center point;
step S3, finding out the bus stop nearest to the clustering center point and the corresponding reverse bus stop by using a spatial index mode in combination with the bus stop data;
step S4, acquiring the driving distance and time between every two different stops according to each bus stop;
step S5, establishing binary tree data structure for storing the acquired distance and time data
And step S6, planning out an optimal school bus scheduling scheme by adopting an improved dynamic path planning algorithm based on the binary tree data structure and the related data.
2. The binary tree-based school bus scheduling planning method of claim 1, wherein the school bus path planning related data includes student home address, school longitude and latitude address, walking radius parameter, conversion rate of station valid orders, station valid order lower limit, preset station, line clearing valid order total lower limit, line clearing valid order total upper limit, line total station number upper limit, full load rate, running time, driving mileage, clustering radius, average speed per hour, and whole member conversion.
3. The school bus scheduling planning method based on the binary tree according to claim 1, wherein the step S2 specifically comprises: according to the family addresses of students, walking radius parameter stations, effective order lower limit values, conversion rates of effective orders of the stations and preset stations, a plan model of the ride-sharing stations is customized and developed by adopting a clustering analysis model algorithm, and riding requirements of students with close distances and close destinations are integrated into one ride-sharing station.
4. The school bus scheduling planning method based on the binary tree according to claim 2, wherein the step S5 specifically comprises:
step S51, creating a station binary tree list, traversing all path lists of the station binary tree, and inserting bus stations without opposite stations into the path lists of the station binary tree;
step S52, extracting the longitude and latitude information of the sites in the combined list, and discarding the route information of the sites not in the combined list;
and step S53, creating a list of schools, sites and opposite sites after permutation and combination.
5. The school bus scheduling planning method based on the binary tree according to claim 4, wherein the step S6 specifically comprises:
step S61, obtaining the shortest path from school to all the stations, and finding the shortest distance according with the time or the distance
Step S63 is followed by finding the shortest path from the school to the rest of the sites through the TS tabu search in the rest of the sites,
and step S64, intercepting the shortest distance according with the time or the route from the shortest path, and circulating in turn until all the stations are traversed to obtain the optimal path.
6. The school bus scheduling planning method based on the binary tree according to claim 1, wherein the step S6 specifically comprises: and creating a list after the arrangement and combination of the school, the site and the opposite site based on the binary tree data structure and the school, the site and the opposite site, and sending each site combination into a TS tabu algorithm path plan one by one to select an optimal combination.
7. The school bus scheduling planning method based on the binary tree as claimed in claim 1, wherein after the optimal path is obtained in step S6, the longitude and latitude information of each station in the route implemented by the pyhton code is used to call the high level API to draw the route on the map, and the student information carried on each route is output and stored locally or in the cloud for the parents of the students and the school to review.
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