CN111191922A - Riding peak period statistical method based on big data - Google Patents
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- CN111191922A CN111191922A CN201911386410.3A CN201911386410A CN111191922A CN 111191922 A CN111191922 A CN 111191922A CN 201911386410 A CN201911386410 A CN 201911386410A CN 111191922 A CN111191922 A CN 111191922A
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Abstract
The invention discloses a riding peak period statistical method based on big data, which comprises the following steps: determining time, wherein a statistical worker starts from a starting station when taking a bus, and the time is determined through a clock on the bus; determining a stop, and after the bus arrives at the stop, counting personnel confirm the serial number of the stop; determining the number of passengers getting on the bus at the station, and counting the number of passengers getting on the bus at each station by a statistical worker; corresponding data are recorded on the table, and after the time is determined by the statistical staff, the station is determined, and the number of people getting on the bus at the station is determined, the number of people is recorded to a proper position of the table. The method for counting the peak taking times can count the number of passengers at each time point and upload the number of passengers to the server, so that the peak taking times and the average number of passengers of each city at a specific time and in a specific station can be effectively known, relevant departments can reasonably move vehicles at the specific time and in a specific route, and the defect that the delay of time is caused by overlong time for passengers and the like is avoided.
Description
Technical Field
The invention relates to a riding peak period statistical method, in particular to a riding peak period statistical method based on big data, and belongs to the technical field of traffic systems.
Background
In the aspect of traffic, the two periods of time are usually eight hours to nine hours in the morning and five to seven hours in the evening. The two periods of time are the time for most companies to go on and off duty and school to go to school and leave school, and more passengers are on the bus in the period of time, so that the period of time is the peak time of taking a bus, the time for people to wait for the bus is longer in the peak time of taking a bus, the number of passengers in the same bus is more, the passengers are not only uncomfortable, but also certain safety problems are brought, the traffic jam problem usually occurs in the peak time, so part of companies adopt the elastic time to go on duty to solve the problems, public transportation tools can transport the passengers in shift at the peak time, and some cities can allocate lanes to roads with large traffic difference in entering city, even take measures such as entering city tax and limiting single-number and double-number license plates to relieve traffic flow. The peak hours of medium-length traffic are typically in the evening before the holiday and the evening from the last day of the holiday.
At present, for a riding peak period, a systematic statistical method is not available, most cities adopt an experience method to judge the number of passengers in a certain time, so that the defects that a large number of passengers cannot ride the bus or a large number of passengers are carried on the bus due to untimely vehicle scheduling in the riding peak period are caused, a certain time cost is caused to the passengers, and a certain potential hazard is caused to the safety to a certain extent. Therefore, a big data-based bus taking peak period statistical method is provided for solving the problems.
Disclosure of Invention
The invention aims to solve the problems and provide a bus taking rush hour statistical method based on big data.
The invention realizes the purpose through the following technical scheme, and provides a riding peak period statistical method based on big data, which comprises the following steps:
(1) determining time, and counting personnel take the bus from the bus starting station and recording the time;
(2) determining stations, and recording the serial numbers of the stations by a statistical worker when each station is reached;
(3) determining the number of passengers getting on the bus at the station, and recording the number of passengers getting on the bus when the bus arrives at one station;
(4) recording corresponding data on the table, and filling the data in the appropriate position of the table according to the data obtained in the steps (1), (2) and (3);
(5) calculating the average number of passengers according to the table data, and calculating and recording the average number of passengers in each time in one week according to the table;
(6) drawing a time-average people number curve graph, and drawing an average people number curve on a time-average people number coordinate according to the data recorded in the step (5);
(7) arranging the time point ranking with the highest number of passengers according to the curve graph, and arranging the time point ranking with the highest number of passengers according to the curve graph drawn in the step (6);
(8) uploading the ranking to a server and sharing data, uploading the ranking in the step (6) to the server and sharing;
(9) and completing statistics.
Preferably, the step (1) is performed when the time is recorded, and the time is recorded when the bus arrives at the stop.
Preferably, the station serial number in step (2) includes a common station serial number and a call station serial number.
Preferably, the number of people recorded in the step (3) includes the number of people with full civil performance capability and the number of people with limited civil performance capability.
Preferably, the form in step (4) includes a station number, a number of people and a time-related classification, and before filling the form, the bus number and the date need to be completely filled.
Preferably, in the step (5), when calculating the average number of people, the number of people getting on different stations at the same time from Monday to Sunday is summed up, and the average number of people is calculated.
Preferably, in the time-average people number graph in the step (6), the horizontal coordinate is a time axis, the vertical coordinate is an average people number axis, the minimum unit of the time axis is hour, and the minimum unit of the average people number axis is number.
Preferably, the step (7) is performed in such a manner that the ranking is performed from high to low according to the number of passengers at the time point when the number of passengers is the highest.
Preferably, the server in the step (8) is connected to the internet.
Preferably, the average number of people is calculated in the step (5), the number of people getting on the same station in the week is calculated, the number of people getting on the same station in different times is summed up in the calculation, and the average number of people getting on the same station in different times in the week is calculated.
The invention has the beneficial effects that: the number of passengers at each time point can be counted and uploaded to the server, the peak riding time and the average number of passengers in a specific time and a specific station of each city can be effectively known, relevant departments can reasonably move vehicles in the specific time and a specific route, the defect that the time delay is caused by overlong waiting time of the passengers is overcome, meanwhile, after the number of passengers getting on the bus at the peak riding time is calculated, the relevant departments can also move some working personnel to assist the bus system, and the accident caused by overlarge passenger flow during riding is prevented.
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, and 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 these drawings without inventive exercise.
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a table detail of the present invention;
FIG. 3 is an enlarged view taken at A of FIG. 2 according to the present invention;
FIG. 4 is a graph of a time-average population curve according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
a bus taking peak period statistical method based on big data is characterized in that: the method for counting the peak time of the riding comprises the following steps:
(1) determining time, wherein a statistical worker takes the bus from a bus starting station and records the time, and the time is determined as 6: 00;
(2) determining stations, and recording station serial numbers and station serial numbers 1 by a statistical worker when each station is reached;
(3) determining the number of passengers getting on the bus at the station, and recording the number of passengers getting on the bus when each passenger arrives at one station, wherein the number of passengers getting on the bus is 5;
(4) recording corresponding data on a table, filling the data in a proper position of the table according to the data obtained in the steps (1), (2) and (3), and recording the number of people 5 in the table until the time 6: 00 in the table corresponding to site number 1;
(5) calculating the average number of passengers according to the table data, and calculating and recording the average number of passengers in each time in one week according to the table;
(6) drawing a time-average people number curve graph, and drawing an average people number curve on a time-average people number coordinate according to the data recorded in the step (5);
(7) arranging the time point ranking with the highest number of passengers according to the curve graph, and arranging the time point ranking with the highest number of passengers according to the curve graph drawn in the step (6);
(8) uploading the ranking to a server and sharing data, uploading the ranking in the step (6) to the server and sharing;
(9) and completing statistics.
And (2) when the time is recorded, recording when the bus arrives at the stop.
And (3) the station serial number of the step (2) comprises a common station serial number and a call station serial number.
The number of people recorded in the step (3) comprises the number of people with complete civil performance and the number of people with limited civil performance.
The form in the step (4) comprises the station serial number, the number of people and the time related classification, and before filling the form, the serial number and the date of the bus need to be completely filled, and 10 roads, 5 months, 22 days, wednesday are filled.
In the step (5), when the average number of people is calculated, the number of people getting on different stations in the same time from Monday to Sunday needs to be summed, and the average number of people is calculated.
The horizontal coordinate of the time-average people number curve chart in the step (6) is a time axis, the vertical coordinate is an average people number axis, the minimum unit of the time axis is hour, and the minimum unit of the average people number axis is number.
And (7) when the names of the passengers are arranged at the time point with the highest number of passengers, arranging according to the number of passengers from high to low.
And (4) the server in the step (8) is connected with the Internet.
And (5) calculating the average number of people, namely calculating the number of people getting on the same station in the week, summing the number of people getting on the same station in different time in the calculation process, and averagely calculating the average number of people getting on the same station in different time in the week.
The method is suitable for calculating the number of the people getting on the bus from the starting station and determining the number of the people getting on the bus from the starting station in the morning, so that related scheduling work is carried out in advance according to data, and the phenomenon of crowding and trampling caused by excessive number of people is prevented.
Example two:
a bus taking peak period statistical method based on big data is characterized in that: the method for counting the peak time of the riding comprises the following steps:
(1) determining time, wherein a statistical worker takes the bus from a bus starting station and records the time, and the time is determined as 18: 00;
(2) determining stations, and recording station serial numbers and station serial numbers 9 by a statistical worker when each station is reached;
(3) determining the number of passengers getting on the bus at the station, and recording the number of passengers getting on the bus when each passenger arrives at one station, wherein the number of passengers getting on the bus is 20;
(4) recording corresponding data on a table, filling the data in a proper position of the table according to the data obtained in the steps (1), (2) and (3), and recording the number of people 20 in the table until the time 18: 00 in the table corresponding to the station number 9;
(5) calculating the average number of passengers according to the table data, and calculating and recording the average number of passengers in each time in one week according to the table;
(6) drawing a time-average people number curve graph, and drawing an average people number curve on a time-average people number coordinate according to the data recorded in the step (5);
(7) arranging the time point ranking with the highest number of passengers according to the curve graph, and arranging the time point ranking with the highest number of passengers according to the curve graph drawn in the step (6);
(8) uploading the ranking to a server and sharing data, uploading the ranking in the step (6) to the server and sharing;
(9) and completing statistics.
And (2) when the time is recorded, recording when the bus arrives at the stop.
And (3) the station serial number of the step (2) comprises a common station serial number and a call station serial number.
The number of people recorded in the step (3) comprises the number of people with complete civil performance and the number of people with limited civil performance.
The form in the step (4) comprises the station serial number, the number of people and the time related classification, and before the form is filled, the bus serial number and the date need to be completely filled, and 20 paths, 6 months, 18 days, Tuesday and the like are filled.
In the step (5), when the average number of people is calculated, the number of people getting on different stations in the same time from Monday to Sunday needs to be summed, and the average number of people is calculated.
The horizontal coordinate of the time-average people number curve chart in the step (6) is a time axis, the vertical coordinate is an average people number axis, the minimum unit of the time axis is hour, and the minimum unit of the average people number axis is number.
And (7) when the names of the passengers are arranged at the time point with the highest number of passengers, arranging according to the number of passengers from high to low.
And (4) the server in the step (8) is connected with the Internet.
And (5) calculating the average number of people, namely calculating the number of people getting on the same station in the week, summing the number of people getting on the same station in different time in the calculation process, and averagely calculating the average number of people getting on the same station in different time in the week.
The method is suitable for counting the number of the passengers getting on the bus at the midway station and determining the number of the passengers getting on the bus at the station at the evening, so that related scheduling work is carried out in advance according to data, and the phenomenon of crowding and treading caused by excessive passengers is prevented.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. A bus taking peak period statistical method based on big data is characterized in that: the method for counting the peak time of the riding comprises the following steps:
(1) determining time, and counting personnel take the bus from the bus starting station and recording the time;
(2) determining stations, and recording the serial numbers of the stations by a statistical worker when each station is reached;
(3) determining the number of passengers getting on the bus at the station, and recording the number of passengers getting on the bus when the bus arrives at one station;
(4) recording corresponding data on the table, and filling the data in the appropriate position of the table according to the data obtained in the steps (1), (2) and (3);
(5) calculating the average number of passengers according to the table data, and calculating and recording the average number of passengers in each time in one week according to the table;
(6) drawing a time-average people number curve graph, and drawing an average people number curve on a time-average people number coordinate according to the data recorded in the step (5);
(7) arranging the time point ranking with the highest number of passengers according to the curve graph, and arranging the time point ranking with the highest number of passengers according to the curve graph drawn in the step (6);
(8) uploading the ranking to a server and sharing data, uploading the ranking in the step (6) to the server and sharing;
(9) and completing statistics.
2. A big data based statistical method of peak riding time according to claim 1, wherein: and (2) when the time is recorded, recording when the bus arrives at the stop.
3. A big data based statistical method of peak riding time according to claim 1, wherein: and (3) the station serial number of the step (2) comprises a common station serial number and a call station serial number.
4. A big data based statistical method of peak riding time according to claim 1, wherein: the number of people recorded in the step (3) comprises the number of people with complete civil performance and the number of people with limited civil performance.
5. A big data based statistical method of peak riding time according to claim 1, wherein: the form in the step (4) comprises the station serial number, the number of people and the time related classification, and before filling the form, the bus serial number and the date need to be completely filled.
6. A big data based statistical method of peak riding time according to claim 1, wherein: in the step (5), when the average number of people is calculated, the number of people getting on different stations in the same time from Monday to Sunday needs to be summed, and the average number of people is calculated.
7. A big data based statistical method of peak riding time according to claim 1, wherein: the horizontal coordinate of the time-average people number curve chart in the step (6) is a time axis, the vertical coordinate is an average people number axis, the minimum unit of the time axis is hour, and the minimum unit of the average people number axis is number.
8. A big data based statistical method of peak riding time according to claim 1, wherein: and (7) when the names of the passengers are arranged at the time point with the highest number of passengers, arranging according to the number of passengers from high to low.
9. A big data based statistical method of peak riding time according to claim 1, wherein: and (4) the server in the step (8) is connected with the Internet.
10. A big data based statistical method of peak riding time according to claim 1, wherein: and (5) calculating the average number of people, namely calculating the number of people getting on the same station in the week, summing the number of people getting on the same station in different time in the calculation process, and averagely calculating the average number of people getting on the same station in different time in the week.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201911386410.3A CN111191922A (en) | 2019-12-29 | 2019-12-29 | Riding peak period statistical method based on big data |
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| CN201911386410.3A CN111191922A (en) | 2019-12-29 | 2019-12-29 | Riding peak period statistical method based on big data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112509317A (en) * | 2020-11-09 | 2021-03-16 | 广州交信投科技股份有限公司 | Bus real-time arrival prediction method, device and equipment based on machine learning algorithm |
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2019
- 2019-12-29 CN CN201911386410.3A patent/CN111191922A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112509317A (en) * | 2020-11-09 | 2021-03-16 | 广州交信投科技股份有限公司 | Bus real-time arrival prediction method, device and equipment based on machine learning algorithm |
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Application publication date: 20200522 |