CN112329989A - Bus route planning method and device based on cloud computing and storage medium - Google Patents
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Abstract
The invention relates to the field of traffic planning cloud computing, in particular to a method, equipment and a storage medium for bus route planning based on cloud computing. The bus route planning method based on the cloud computing comprises the following steps: obtaining sample information in a designated area, wherein the sample information comprises station information and getting-on/off information of a bus at each stop of the bus, and the getting-on/off information at least comprises the number of passengers getting-on and the number of passengers getting-off at each stop; setting a first passenger flow threshold value; generating associated station information according to the station information, the getting-on and getting-off information and the first passenger flow threshold; generating preset initial station information and preset terminal station information based on the associated station information; and generating a preset bus route according to the preset starting station information, the preset terminal station information and the principle of lowest running cost. The bus route planning method based on the cloud computing has the technical effect of improving the accuracy and comprehensiveness of bus route planning.
Description
The technical field is as follows:
the invention relates to the field of traffic planning cloud computing, in particular to a method, equipment and a storage medium for bus route planning based on cloud computing.
Background art:
with the acceleration of the urbanization process, a large number of workers rush into the city, so that the pressure of traffic traveling is higher and higher. And along with the expansion in city and cost of life, people's residence and place of work are more and more far away, this just causes the commute time longer and longer, if according to the set bus route in many cities, in the early peak of the station that the passenger flow volume is big and the peak period late, not only can cause serious jam still can influence people's trip because of the public transit resource is not enough, takes customized public transit for this reason and is the way of solving this problem.
At present, most of customization of bus routes are statistical determination in modes such as netizen voting, questionnaire survey and the like, but due to the mobility of personnel, holiday influence and the like, the statistical result is relatively inaccurate and random.
The invention is provided in view of the above.
The invention content is as follows:
the invention provides a bus route planning method based on cloud computing, which can effectively improve the accuracy and comprehensiveness of bus route planning.
The invention provides a bus route planning method based on cloud computing, which comprises the following steps:
obtaining sample information in a designated area, wherein the sample information comprises station information and getting-on/off information of a bus at each stop of the bus, and the getting-on/off information at least comprises the number of passengers getting-on and the number of passengers getting-off at each stop;
setting a passenger flow threshold value, wherein the passenger flow threshold value comprises a first passenger flow threshold value;
generating associated station information according to the station information, the getting-on and getting-off information and the first passenger flow threshold;
generating preset initial station information and preset terminal station information based on the associated station information;
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the principle of lowest running cost.
By adopting the scheme, the designated area can be a city or a county, the designated area is taken as an A city as an example, the bus designation is all buses operated in the A city, and because all passengers take the bus in a card swiping or code swiping mode at present, the taking information of the passengers in a ticket printing mode can be ignored. The station information comprises station names and station positions, and the number of passengers getting on the train and the number of passengers getting off the train are calculated by people.
Further, the step of obtaining sample information in the designated area includes:
setting a running time threshold;
the information of getting on or off the bus also comprises the time of getting on the bus by the passenger and the time of getting off the bus by the passenger, whether the time of getting on the bus by the passenger and the time of getting off the bus by the passenger are within the threshold range of the operation time is judged, and the number of the passengers getting on the bus and the number of the passengers getting off the bus are counted under the condition that the judgment is yes.
By adopting the scheme, the running time threshold value can be set to be 7:00-9 earlier; in the 30 early peak period, the riding time is determined by the time of card swiping and code swiping of the client, the number of passengers getting on the bus and the number of passengers getting off the bus in the whole day time period can be prevented from being counted, so that the calculation result is prevented from averaging the numerical value in the whole day time, and the calculation result is more targeted.
Further, the step of generating preset originating station information and preset destination station information based on the associated station information includes:
setting a vehicle journey threshold value;
and calculating the shortest bus distance between the associated stations, judging whether the shortest bus distance between the associated stations is within the range of a threshold value of the bus distance, and generating preset starting station information and preset destination station information under the condition of judging yes.
By adopting the scheme, the bus journey threshold value can be used for screening lines with too close associated stations, and the bus journey is a line in which the bus can run according to traffic regulations.
Preferably, the operation cost minimization principle comprises a shortest operation time principle and a highest operation efficiency principle.
By adopting the scheme, different bus routes can be finally generated according to different operation cost types, so that the method is suitable for different traffic conditions.
Further, the step of generating the preset bus route according to the preset starting station information, the preset terminal station information and the principle of lowest operation cost comprises the following steps:
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the shortest running time principle.
By adopting the scheme, the time shortest principle can be based on the shortest bus route, and the bus route is suitable for long-distance bus routes from suburbs to urban areas or urban areas.
Specifically, the operation efficiency highest principle includes a single-line operation efficiency highest principle and a multi-line operation efficiency highest principle, and the passenger flow threshold includes a second passenger flow threshold.
Further, the step of generating the preset bus route according to the preset starting station information, the preset terminal station information and the principle of lowest operation cost comprises the following steps:
determining a designated bus according to preset starting station information, preset terminal station information and getting-on and getting-off information of the bus;
setting a stop of the appointed bus between a preset starting station and a preset terminal station as a first stop within the running time threshold;
acquiring the number of passengers getting on the bus or the number of passengers getting off the bus at each first stop;
judging whether the number of passengers at each first stop or the number of passengers getting off is within a second passenger flow threshold value, if so, marking the first stop as a first preset stop, and generating first preset stop information;
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the first preset stop station information.
Further, the step of generating the preset bus route according to the preset starting station information, the preset terminal station information and the principle of lowest operation cost comprises the following steps:
acquiring information of each bus route passing through the preset starting station and the preset terminal station;
setting a stop station of each bus between a preset starting station and a preset terminal station as a second stop station within the threshold value of the running time;
according to the formula Ci ═ Ci1+Ci2+…CijCalculating the number of passengers getting on or off each second stop, wherein i is (1,2,3, …, n) and j is (1,2,3, …, n);
judging whether the number of passengers getting on the bus or the total number of passengers getting off the bus of each second stop is within a second passenger flow threshold value, if so, marking the second stop as a second preset stop, and generating second preset stop information;
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the second preset stop station information.
The invention also protects equipment of the bus route planning method based on the cloud computing, which can effectively improve the accuracy and comprehensiveness of bus route planning and comprises the following steps: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the above method.
The invention also protects a storage medium of the bus route planning method based on the cloud computing, which can effectively improve the accuracy and comprehensiveness of bus route planning.
The invention also discloses a device of the bus route planning method based on the cloud computing, which can effectively improve the accuracy and comprehensiveness of bus route planning, and comprises the following steps:
the first information acquisition module is used for acquiring sample information in a specified area;
the first parameter setting module is used for setting a first passenger flow threshold value;
the associated site generation module is used for generating associated site information;
the first generation module is used for generating preset starting station information and preset terminal station information;
and the second generation module is used for generating a preset bus line.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
the second parameter setting module is used for setting a threshold value of the running time;
the first judgment module is used for calculating the number of passengers getting on the bus and the number of passengers getting off the bus.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
the third parameter setting module is used for setting a range threshold value;
and the second judgment module is used for generating preset starting station information and preset terminal station information.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
and the third parameter setting module is used for setting a second passenger flow threshold value.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
a third generating module for generating first preset stop information.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
a fourth generating module, configured to generate second preset stop information.
The invention has the beneficial effects that:
1. the preset bus route is generated through the sample information, the first passenger flow volume threshold value, the preset starting station information and the preset terminal station information, the technical problems of inaccurate statistics and high randomness caused by methods such as voting and investigation in the process of bus route planning are solved, and the technical effect of improving the bus route planning accuracy and comprehensiveness is achieved.
2. The first passenger flow threshold and the running time threshold are set, so that the problem of noise interference in the process of making a bus route is solved, and the technical effect of saving and efficiently utilizing bus resources is achieved.
3. The technical problem of bus resource waste in the process of making the bus line is solved by setting the single-line operation efficiency highest principle and the multi-line operation efficiency highest principle, and the technical effect of saving and efficiently utilizing the bus resource is achieved.
Description of the drawings:
in order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of one embodiment of the present invention;
FIG. 2 is a flow chart of a method for counting passengers according to an embodiment of the present invention;
FIG. 3 is a flow chart of the generation of a preset origination station and destination station in one embodiment of the present invention;
FIG. 4 is a flow chart of a process for generating a preset bus route in one embodiment of the present invention;
FIG. 5 is a flow chart of generating a preset bus route according to another embodiment of the present invention;
fig. 6 is a schematic diagram of generating a preset bus route according to another embodiment of the present invention.
The specific implementation mode is as follows:
reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
The present invention will be described in detail below by way of embodiments.
Referring to fig. 1, the invention provides a method for planning a bus route based on cloud computing, which comprises the following steps:
s100: obtaining sample information in a designated area, wherein the sample information comprises station information and getting-on/off information of a bus at each stop of the bus, and the getting-on/off information at least comprises the number of passengers getting-on and the number of passengers getting-off at each stop;
s200: setting a passenger flow threshold value, wherein the passenger flow threshold value comprises a first passenger flow threshold value;
s300: generating associated station information according to the station information, the getting-on and getting-off information and the first passenger flow threshold;
s400: generating preset initial station information and preset terminal station information based on the associated station information;
s500: and generating a preset bus route according to the preset starting station information, the preset terminal station information and the principle of lowest running cost.
By adopting the scheme, the designated area can be a city or a county, the designated area is taken as an A city as an example, the bus designation is all buses operated in the A city, and because all passengers take the bus in a card swiping or code swiping mode at present, the taking information of the passengers in a ticket printing mode can be ignored. The station information comprises station names and station positions, and the number of passengers getting on the train and the number of passengers getting off the train are calculated by people. For example, an ad 1 bus stop includes station a, station B, and station C …, a station a for 200 people, a station B for 150 people, a station 2 for 100 people, and a station B for 130 people. The passenger flow threshold may be preset to measure whether it is necessary to increase the number of buses arriving at the stop, for example, the first passenger flow threshold is that the number of passengers getting on the bus is more than 150, the number of passengers getting off the bus is more than 250, and the buses stopping at the station B have only special 1-way and special 2-way, then the number of passengers getting off the bus at the station B in the special 1-way plus 130 passengers getting off the bus at the station B in the special 2-way is equal to 280, then the stations a and B fall within the first passenger flow threshold range, and the station C does not fall within the first passenger flow threshold range, so the station A, B is determined as the associated station, and the station B, C cannot be determined as the associated station. In the two associated stations, according to the sequence of the bus stop stations, the station A is a preset starting station, and the station B is a preset terminal station, wherein the number of the passengers getting on the bus falls within the first passenger flow threshold range. Therefore, the customized bus route with the station A as the preset starting station and the station B as the preset terminal station is formed preliminarily, but in practice, a plurality of driving routes from the station A to the station B are often available, and the time or cost consumed by each driving route is different, so that different running costs can be determined through different considerations, and the running route with the lowest cost is selected as the preset bus route. And the OD matrix can be adopted to calculate the traffic flow. The references to each bus herein do not refer to a bus, such as 200 people getting on at a stop, and 15 people getting off at B stop refer to all 1-bus particular time periods, and so on.
According to the scheme, the technical problem that bus routes cannot be comprehensively and accurately increased is solved through a data calculation method, and the technical effect of optimizing the bus network setting and the travel efficiency is achieved.
Referring to fig. 2, the step of acquiring the sample information in the designated area includes:
setting a running time threshold;
the information of getting on or off the bus also comprises the time of getting on the bus by the passenger and the time of getting off the bus by the passenger, whether the time of getting on the bus by the passenger and the time of getting off the bus by the passenger are within the threshold range of the operation time is judged, and the number of the passengers getting on the bus and the number of the passengers getting off the bus are counted under the condition that the judgment is yes.
By adopting the scheme, the running time threshold value can be set to be 7:00-9 earlier; in the 30 early-peak period, the riding time is determined by the time of card swiping and code swiping of a client, for example, the number of people getting off the special 1-way bus at the station B is 200 persons per day on average, wherein the time of getting off 120 persons is 7:00-9 in the morning; 30, the number of the passengers getting off is only 120, and the same principle is applied to the passengers getting on. The passengers who get on or off the bus in the early peak period crowd the above-mentioned passenger quantity of getting on the bus and the passenger quantity of getting off the bus, can avoid counting into through the passenger quantity of getting on the bus and the passenger quantity of getting off the bus in the time quantum of whole day to make the calculation result avoid the averageness of numerical value in the time quantum of whole day, thereby it is corresponding to have more, can set up corresponding bus route specially to the time quantum of early peak or late peak this moment, can also effectively utilize bus resources.
Referring to fig. 3, the step of generating preset origination station information and preset destination station information based on the associated station information includes:
setting a vehicle journey threshold value;
and calculating the shortest bus distance between the associated stations, judging whether the shortest bus distance between the associated stations is within the range of a threshold value of the bus distance, and generating preset starting station information and preset destination station information under the condition of judging yes.
By adopting the scheme, the bus trip threshold value can be used for screening lines with too close associated stations, the bus trip is a line on which a bus can run according to traffic regulations, for example, the calculation of the bus trip can be the calculation of the number of stations or the calculation of kilometers, taking the calculation of the number of stations as an example, the bus trip threshold value can be set to be more than or equal to 15 stations, if the shortest bus trip between the station a and the station B is 13 stations, the station a cannot be a preset starting station, the station B cannot be determined as a preset terminal station, and when the shortest bus trip between the station a and the station B is 18 stations, the station a can be a preset starting station, and the station B can be determined as a preset terminal station. The bus journey can be calculated by adopting a high-grade map or a Baidu map. Because in practice, if the bus distance between the associated stations is too short, the passenger can select a walking or riding mode, and the unreasonable setting of the bus distance threshold value can be effectively avoided.
The operation cost minimum principle comprises a shortest operation time principle and a highest operation efficiency principle.
By adopting the scheme, different bus routes can be finally generated according to different operation cost types, so that different traffic conditions are adapted, and the principle of the highest operation efficiency is that not only the bus needs to be quickly arrived from the station A to the station B, but also whether a station with large passenger flow needs to be stopped between the station A and the station B is considered.
Generating a preset bus route according to the preset starting station information, the preset terminal station information and the principle of lowest running cost, wherein the steps comprise:
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the shortest running time principle.
By adopting the scheme, the time shortest principle can be based on the shortest bus route, the bus can reach the station B from the station A at the fastest speed, and the bus route is suitable for long-distance bus routes from suburbs to urban areas or in urban areas.
The operation efficiency highest principle comprises a single-line operation efficiency highest principle and a multi-line operation efficiency highest principle, and the passenger flow threshold comprises a second passenger flow threshold.
By adopting the scheme, the single-line operation efficiency highest principle is to calculate the number of people getting on or off the bus from the station A to the station B in an extra 1 way, and the multi-line operation efficiency highest principle calculates all bus lines from the station A to the station B, such as extra 3 ways and extra 4 ways which are also from the station A to the station B.
Referring to fig. 4, the step of generating the preset bus route according to the preset starting station information, the preset terminal station information and the principle of lowest operation cost includes:
determining a designated bus according to preset starting station information, preset terminal station information and getting-on and getting-off information of the bus;
setting a stop of the appointed bus between a preset starting station and a preset terminal station as a first stop within the running time threshold;
acquiring the number of passengers getting on the bus or the number of passengers getting off the bus at each first stop;
judging whether the number of passengers at each first stop or the number of passengers getting off is within a second passenger flow threshold value, if so, marking the first stop as a first preset stop, and generating first preset stop information;
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the first preset stop station information.
By adopting the scheme, the designated bus can be one of an extra 1 way, an extra 3 way and an extra 4 way, taking the extra 1 way as an example, 7:00-9:30, the first stop stations which pass before the station a arrives at the station B include station a1, station a2, station A3, station a4 and station a5, the second passenger flow threshold can be determined according to the geographical position of the first stop station by comparing the number of passengers getting on or off obtained at each station with the second passenger flow threshold, for example, the second passenger flow threshold in urban areas can be set to be larger and the second passenger flow threshold in suburban areas can be set to be smaller, for example, only the number of passengers getting on or off at station A3 is within the second passenger flow threshold range, the first preset stop station is A3, the second passenger flow threshold range can be set to be smaller than the first passenger flow threshold, for example, the second passenger flow threshold is 100 passengers or more, the number of passengers getting on or more than 150, as a bus station for further screening of stops, then the station A is required to stop at the station A3 after the special 1 route passes through the station A, and finally the station B is reached, and the bus route A-A3-B is preset. The first preset stop station is arranged to maximize the utilization of bus resources.
In some other embodiments of the present invention, referring to fig. 5 and 6, the step of generating the preset bus route according to the preset starting station information, the preset terminal station information, and the principle of lowest operation cost includes:
acquiring information of each bus route passing through the preset starting station and the preset terminal station;
setting a stop station of each bus between a preset starting station and a preset terminal station as a second stop station within the threshold value of the running time;
according to the formula Ci ═ Ci1+Ci2+…CijCalculating the number of passengers getting on or off each second stop, wherein i is (1,2,3, …, n) and j is (1,2,3, …, n);
judging whether the number of passengers getting on the bus or the total number of passengers getting off the bus of each second stop is within a second passenger flow threshold value, if so, marking the second stop as a second preset stop, and generating second preset stop information;
generating a preset bus route according to the preset starting station information, the preset terminal station information and the second preset stop station information:
by adopting the scheme, for example, buses passing through the preset starting station and the preset terminal station comprise 5-way buses, 6-way buses and 7-way buses, and all bus stations stopped between the station A and the station B are respectively generated by acquiring the line information of the 5-way buses, the 6-way buses and the 7-way buses, so that a second stop station is determined; ci is expressed as the number of passengers getting on or off the platform with number i, and the number of the platform can be set according to practical specific rules, such as C in the formula1May be the number of passengers getting on or off station A1, C2May be the number of passengers getting on or off station A2, C3May be the number of passengers getting on or off station A3, CijThe number of passengers getting on the bus or the total number of passengers getting off the bus is shown when the bus on the same line stops at the station Ai. Take the number of persons getting on the train between 7:00 and 9:30 as an example, wherein 5 special routes pass through only the stations A1, A2, A3 and A4 in the second stop, 5 special routes pass through 50 persons getting on the train at the station A1, 300 persons getting on the train at the station A2, 400 persons getting on the train at the station A3 and 500 persons getting on the station A4, 6 special routes pass through only the stations A3, A4 and A5 in the second stop, 6 special routes pass through 200 persons getting on the train at the station A3, 300 persons getting on the train at the A4 and 400 persons getting on the train at the A5, 7 routes pass through only the stations A3 and A5 in the second stop, and 10 persons getting on the train at the station A3If the number of 0 persons and the number of 100 persons get on the bus at the station a5 are 0, then the number of C1 is 50, the number of C2 is 300, the number of C3 is 700, the number of C4 is 800, the number of C5 is 500, and the second passenger flow threshold is set to be more than 600, then the station A3 and the station a4 meet the condition of becoming a second preset stop, and the preset bus route is a-A3-a 4-B. When the number of getting-off people is calculated, the number of getting-on people can be calculated according to the number of getting-off people.
The scheme can comprehensively consider the correlation of a plurality of bus lines, thereby more comprehensively and effectively utilizing the bus resources.
The invention also provides equipment of the bus route planning method based on the cloud computing, which comprises the following steps: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the above method.
The invention also protects a storage medium of the cloud computing-based bus route planning method, wherein the storage medium comprises one or more programs, and the one or more programs can be executed by a processor to complete the method.
The invention also discloses a device of the bus route planning method based on the cloud computing, which comprises the following steps:
the first information acquisition module is used for acquiring sample information in a specified area;
the first parameter setting module is used for setting a first passenger flow threshold value;
the associated site generation module is used for generating associated site information;
the first generation module is used for generating preset starting station information and preset terminal station information;
and the second generation module is used for generating a preset bus line.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
the second parameter setting module is used for setting a threshold value of the running time;
the first judgment module is used for calculating the number of passengers getting on the bus and the number of passengers getting off the bus.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
the third parameter setting module is used for setting a range threshold value;
and the second judgment module is used for generating preset starting station information and preset terminal station information.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
and the third parameter setting module is used for setting a second passenger flow threshold value.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
a third generating module for generating first preset stop information.
The device of the bus route planning method based on the cloud computing further comprises the following steps:
a fourth generating module, configured to generate second preset stop information.
It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the protection scope of the claims of the present invention.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
It should be understood that the technical problems can be solved by combining and combining the features of the embodiments from the claims.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein, the foregoing description of the disclosed embodiments being directed to enabling one skilled in the art to make and use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A bus route planning method based on cloud computing is characterized by comprising the following steps:
obtaining sample information in a designated area, wherein the sample information comprises station information and getting-on/off information of a bus at each stop of the bus, and the getting-on/off information at least comprises the number of passengers getting-on and the number of passengers getting-off at each stop;
setting a first passenger flow threshold value;
generating associated station information according to the station information, the getting-on and getting-off information and the first passenger flow threshold;
generating preset initial station information and preset terminal station information based on the associated station information;
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the principle of lowest running cost.
2. The cloud-computing-based bus route planning method according to claim 1, wherein the step of obtaining sample information in the designated area comprises:
setting a running time threshold;
the information of getting on or off the bus also comprises the time of getting on the bus by the passenger and the time of getting off the bus by the passenger, whether the time of getting on the bus by the passenger and the time of getting off the bus by the passenger are within the threshold range of the operation time is judged, and the number of the passengers getting on the bus and the number of the passengers getting off the bus are counted under the condition that the judgment is yes.
3. The cloud-computing-based bus route planning method according to claim 2, wherein the step of generating preset starting station information and preset terminal station information based on the associated station information comprises:
setting a vehicle journey threshold value;
and calculating the shortest bus distance between the associated stations, judging whether the shortest bus distance between the associated stations is within the range of a threshold value of the bus distance, and generating preset starting station information and preset destination station information under the condition of judging yes.
4. The cloud-computing-based bus route planning method according to any one of claims 1 to 3, wherein the operation cost minimization principle comprises a shortest operation time principle and a highest operation efficiency principle.
5. The cloud-computing-based bus route planning method according to claim 4, wherein the step of generating the preset bus route according to the preset starting station information, the preset terminal station information and the operation cost minimum principle comprises:
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the shortest running time principle.
6. The cloud-computing-based bus route planning method according to claim 4, wherein the operation efficiency maximization principle comprises a single-line operation efficiency maximization principle and a multi-line operation efficiency maximization principle, and the passenger flow threshold comprises a second passenger flow threshold.
7. The cloud-computing-based bus route planning method according to claim 6, wherein the step of generating the preset bus route according to the preset starting station information, the preset terminal station information and the operation cost minimum principle comprises:
determining a designated bus according to preset starting station information, preset terminal station information and getting-on and getting-off information of the bus;
setting a stop of the appointed bus between a preset starting station and a preset terminal station as a first stop within the running time threshold;
acquiring the number of passengers getting on the bus or the number of passengers getting off the bus at each first stop;
judging whether the number of passengers at each first stop or the number of passengers getting off is within a second passenger flow threshold value, if so, marking the first stop as a first preset stop, and generating first preset stop information;
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the first preset stop station information.
8. The cloud-computing-based bus route planning method according to claim 6, wherein the step of generating the preset bus route according to the preset starting station information, the preset terminal station information and the operation cost minimum principle comprises:
acquiring information of each bus route passing through the preset starting station and the preset terminal station;
setting a stop station of each bus between a preset starting station and a preset terminal station as a second stop station within the threshold value of the running time;
according to the formula Ci ═ Ci1+Ci2+…CijCalculating the number of passengers getting on or off each second stop, wherein i is (1,2,3, …, n) and j is (1,2,3, …, n);
judging whether the number of passengers getting on the bus or the total number of passengers getting off the bus of each second stop is within a second passenger flow threshold value, if so, marking the second stop as a second preset stop, and generating second preset stop information;
and generating a preset bus route according to the preset starting station information, the preset terminal station information and the second preset stop station information.
9. A bus route planning device based on cloud computing is characterized by comprising: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the method of any of the preceding claims 1 to 8.
10. A storage medium for bus route planning based on cloud computing is characterized in that: the storage medium includes one or more programs that are executable by a processor to perform the method of any of claims 1-8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114936724A (en) * | 2022-07-25 | 2022-08-23 | 四川语璐科技有限公司 | Route prediction recommendation method and system based on big data |
| CN115731734A (en) * | 2022-11-09 | 2023-03-03 | 支付宝(杭州)信息技术有限公司 | Method and system for planning travel route based on traffic travel data processing |
| CN116433308A (en) * | 2023-06-13 | 2023-07-14 | 西南交通大学 | Multi-system track traffic dynamic pricing method based on arrival and arrival time |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101807222A (en) * | 2010-02-09 | 2010-08-18 | 武汉大学 | Station-based urban public traffic network optimized configuration method |
| CN107330559A (en) * | 2017-07-03 | 2017-11-07 | 华南理工大学 | A kind of hybrid customization public bus network planing method of many terminus multi-vehicle-types |
| CN108053062A (en) * | 2017-12-11 | 2018-05-18 | 北京航空航天大学 | A kind of customization public bus network generation method based on multi-source data |
| CN108805356A (en) * | 2018-06-11 | 2018-11-13 | 苏州规划设计研究院股份有限公司 | Zonule public bus network optimization method and system |
| CN108831149A (en) * | 2018-06-14 | 2018-11-16 | 重庆同济同枥信息技术有限公司 | One kind starting method and system based on history OD information customization public bus network |
| CN109753694A (en) * | 2018-12-13 | 2019-05-14 | 东南大学 | Small and medium-sized cities Transit Network Design method based on overall process trip detecting period |
| CN110084397A (en) * | 2019-03-11 | 2019-08-02 | 昆明理工大学 | A kind of subway straightforward line planing method |
| US20200211140A1 (en) * | 2018-12-28 | 2020-07-02 | Proterra Inc. | Dynamic bus routing |
| CN111461489A (en) * | 2020-03-05 | 2020-07-28 | 支付宝(杭州)信息技术有限公司 | Route generation method and device, electronic equipment and readable storage medium |
-
2020
- 2020-10-19 CN CN202011118117.1A patent/CN112329989A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101807222A (en) * | 2010-02-09 | 2010-08-18 | 武汉大学 | Station-based urban public traffic network optimized configuration method |
| CN107330559A (en) * | 2017-07-03 | 2017-11-07 | 华南理工大学 | A kind of hybrid customization public bus network planing method of many terminus multi-vehicle-types |
| CN108053062A (en) * | 2017-12-11 | 2018-05-18 | 北京航空航天大学 | A kind of customization public bus network generation method based on multi-source data |
| CN108805356A (en) * | 2018-06-11 | 2018-11-13 | 苏州规划设计研究院股份有限公司 | Zonule public bus network optimization method and system |
| CN108831149A (en) * | 2018-06-14 | 2018-11-16 | 重庆同济同枥信息技术有限公司 | One kind starting method and system based on history OD information customization public bus network |
| CN109753694A (en) * | 2018-12-13 | 2019-05-14 | 东南大学 | Small and medium-sized cities Transit Network Design method based on overall process trip detecting period |
| US20200211140A1 (en) * | 2018-12-28 | 2020-07-02 | Proterra Inc. | Dynamic bus routing |
| CN110084397A (en) * | 2019-03-11 | 2019-08-02 | 昆明理工大学 | A kind of subway straightforward line planing method |
| CN111461489A (en) * | 2020-03-05 | 2020-07-28 | 支付宝(杭州)信息技术有限公司 | Route generation method and device, electronic equipment and readable storage medium |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114936724A (en) * | 2022-07-25 | 2022-08-23 | 四川语璐科技有限公司 | Route prediction recommendation method and system based on big data |
| CN114936724B (en) * | 2022-07-25 | 2022-09-20 | 四川语璐科技有限公司 | Route prediction recommendation method and system based on big data |
| CN115731734A (en) * | 2022-11-09 | 2023-03-03 | 支付宝(杭州)信息技术有限公司 | Method and system for planning travel route based on traffic travel data processing |
| CN116433308A (en) * | 2023-06-13 | 2023-07-14 | 西南交通大学 | Multi-system track traffic dynamic pricing method based on arrival and arrival time |
| CN116433308B (en) * | 2023-06-13 | 2023-08-15 | 西南交通大学 | Multi-system track traffic dynamic pricing method based on arrival and arrival time |
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