CN105096601A - Line load factor real-time calculating method based on bus mobile WIFI hotspot - Google Patents
Line load factor real-time calculating method based on bus mobile WIFI hotspot Download PDFInfo
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Abstract
The invention belongs to the technical field of intelligent public transport and specially relates to a line load factor real-time calculating method based on a bus mobile WIFI hotspot. The method comprises the following steps: 1) obtaining basic data of each bus in the current bus line; 2) extracting plate number ID data of each bus, and at least extracting real-time number N' of mobile phone users connected to the WIFI and corresponding to the plate number ID of each bus; 3) calculating actual total number of passengers on the single bus; 4) calculating quota carrying number of people on the single bus; 5) calculating load factor of the single bus; and 6) obtaining bus line load factor. The bus mobile WIFI hotspot is utilized as a basic technology supporting point to obtain the load factor of the current bus line so as to judge the design reasonability of the bus line and whether the bus line needs to be adjusted accordingly, and finally, the purposes of realizing bus-taking demand of each line and optimum balance of distribution and scheduling of the buses in the line are achieved.
Description
Technical Field
The invention belongs to the technical field of intelligent buses, and particularly relates to a real-time line full-load rate calculation method based on a bus mobile WIFI hotspot.
Background
The bus is a main public transportation travel mode of many domestic large cities, and bears nearly half of the travel volume of the cities. Along with the continuous increase of motor vehicles, the contradiction between supply and demand of urban traffic worsens, and corresponding policies such as public transport optimization are advocated by the nation to promote travelers to select public transport trips. Therefore, higher requirements are provided for the passenger transport capacity and the service quality of the buses, and corresponding requirements are provided for the reasonable optimization of the bus lines.
In view of the above, research on the optimal design of the bus line has been started and has been made with preliminary results. However, most of the above-mentioned results are focused on the arrangement of a positioning device or a card punching device on each bus. This is described in the chinese invention patent application with the patent name "bus dispatching control system" with the publication number "CN 103093610A: when passengers get on the bus, the passengers firstly punch cards on a card reader, a counter works, and the number of the passengers is counted. When the number of the passengers in the vehicle exceeds the full load number, the number of the passengers in the line is more, and the sending device sends a signal to the central controller. The central arithmetic unit analyzes and calculates the data fed back by the positioning device and the sending device on the bus, timely arranges the departure time interval and frequency of the bus at the bus station and reasonably schedules the bus of different lines to alternate with each other. The biggest defect of the traditional card-punching type metering overall mode is as follows: first, the overall cost is prohibitive. The centralized representation is that when optimizing aiming at the line, the above-mentioned punching positioning equipment needs to be installed on each bus which is being used or is about to be used on each line, and the maintenance and use values are not good. Secondly, the application surface is too narrow. The above document also proposes a basic premise that each person who rides a vehicle can voluntarily pay a card by targeting "all unmanned ticketing and ticket-taking ride". If no one or few people punch the card, the influence on the accuracy of the final statistical data is fatal. How to seek a bus route optimization mode with higher cost performance can ensure the accuracy of statistical data while ensuring the simplicity of the statistical process, and is a technical problem to be solved in the field for the last decade.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a more efficient and faster real-time calculation method for the line full load rate based on the bus mobile WIFI hotspot, wherein the bus mobile WIFI hotspot is used as a basic technical support point to obtain the full load rate of the current bus line to judge the design reasonability of the bus line and judge whether corresponding adjustment is needed or not, and finally the optimal balance aims of taking requirements of passengers in each line and bus distribution scheduling of the line are fulfilled; the invention has wide application range, and can ensure the accuracy requirement of statistical data while ensuring the simplicity and low cost of the statistical process.
In order to achieve the purpose, the invention adopts the following technical scheme:
a real-time line full-load rate calculation method based on a bus mobile WIFI hotspot comprises the following steps:
1) acquiring basic data of each bus in the current bus line:
the basic data at least comprise the number of WIFI of the current bus line, the license plate ID of each bus running along the current bus line, the real-time running position of each bus and the number of real-time mobile phone users connected with WIFI on each bus; counting passenger data of each bus to obtain the proportion of the number of passengers connected with WIFI hotspots by mobile phones on each bus to the total number of passengers, and counting the reciprocal number of the passengers as a sample expansion coefficient a of the bus passengers;
2) and extracting ID data of each bus license plate from the basic data, at least extracting the number of WIFI-linked real-time mobile phone users corresponding to the bus license plate ID, and recording the number as N‘；
3) Calculating the actual total number of passengers on a single bus, wherein the calculation formula is as follows:
N＝a*N′
wherein:
n is the actual total number of passengers for the bus vehicle;
n' is the total number of mobile phone users connected with the WIFI of the bus;
a is the sample expansion coefficient of the bus passenger corresponding to the bus;
4) and calculating the number of the passengers on the bus by the following calculation formula:
Nd＝Ps+S1/Ssp
wherein,
Ndthe number of the passengers on the bus;
Psis the design seat number of the bus;
S1is the area of the bus where passengers can stand;
Sspis the effective area occupied by each standing passenger in the bus;
5) and calculating the full load rate of the single bus, wherein the formula is as follows:
wherein:
r is the single bus full load rate;
6) and obtaining the bus line full load rate by the following formula:
wherein:
r is the line full load rate;
n is the total number of buses on the route;
riis the full load rate of the ith bus on the route;
liis that the ith bus on the line has the full load rate of riMileage over time.
The main advantages of the invention are:
1) the invention breaks through the technical shackle brought by the traditional bus route optimization mode with narrow application range and high cost, and develops a new way to put the eyesight into the mature mobile WIFI hotspot at present. Due to continuous maturity of the 4G technology, all urban buses are provided with mobile WIFI hotspots for passengers to use, and mobile phone users can use flow freely by connecting to the hotspots through wireless; meanwhile, the information of the mobile phone user is also transmitted back to the background for research; this provides the best technical support for the present invention.
Compared with the traditional calculation mode, the method has the advantages that: on the one hand, the application cost is lower. Because the data sampling can be directly from the mobile WIFI hotspot background, namely the sampling pre-process can be cancelled without specially installing a data monitoring platform and equipment, the data sampling step with the highest cost is directly crossed, and the application cost of the whole optimization process is greatly reduced. On the other hand, the application range is wider. Due to the trend of surfing the internet by the mobile phone, passengers can actively carry out free data connection after getting on the bus, so that the background can obtain corresponding data. The whole sampling process can be suitable for the traditional 'unmanned ticketing and card-punching sitting' process, and can be used on all the existing bus operation lines which are inevitably provided with mobile WIFI hotspots. In addition, along with the disconnection and reconnection of the user when the passenger gets on or off the bus, the passenger data of the current bus can be refreshed timely and uninterruptedly, the real-time performance and the accuracy during the feedback of the basic data are finally guaranteed, and powerful guarantee is provided for the accuracy of the final data in the optimization calculation process.
In conclusion, the bus line full load rate calculation method and the bus line full load rate calculation device have the advantages that the bus mobile WIFI hotspot technology which is commonly adopted at present is adopted in a new way, the bus line full load rate calculation is finally realized, and reliable data support is further provided for subsequent optimization statistics of the current line. The finally obtained bus route full load rate can effectively evaluate whether the current bus route is reasonable or not, whether the bus amount and the departure time of the route even are accurate to the stop time of each bus or not meets the travel requirement of the route passenger or not, finally the optimal balance purpose of the taking requirement of each route passenger and the bus distribution scheduling of the route is achieved, and a favorable basis is provided for the planning and the adjustment of each bus route in the whole city.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
For ease of understanding, a specific implementation of the present invention is further described herein with reference to FIG. 1:
the method comprises the following steps:
(1) acquiring basic data of each bus in the current bus line;
(2) extracting WIFI connection data of a single bus;
(3) calculating the total number of passengers on the bus;
(4) calculating the number of the passengers on the bus;
(5) calculating the full load rate of the bus;
(6) and calculating the full load rate of the line.
The method comprises the steps that basic data of the mobile WIFI of the bus are acquired, wherein the basic data include the number of the WIFI, the license plate of the bus, the real-time position of the bus, mobile phone user information linked with the WIFI and the like. The mobile phone users who link WIFI are passengers on the bus. And counting and determining the sample expansion coefficient a of the bus passengers, namely, the reciprocal of the proportion of the number of the passengers connected with the WIFI hotspot by using a mobile phone to the total number of the passengers. The sampling statistics of the sample expansion coefficient a of the bus passengers can be carried out on the big data sampling and the investigation in a targeted manner according to the actual situation. Due to the homodromous tendency of the designated route, the sample expansion coefficient values of the bus passengers measured and calculated by the original statistics can be completely and directly substituted correspondingly by the calculation of a in the later period.
Secondly, extracting WIFI connection data of a single bus, wherein the WIFI connection data is mainly mobile phone user information of the WIFI connection of the single bus and is recorded as N';
thirdly, calculating the total number of passengers on the bus, wherein the calculation formula is as follows:
N＝a*N′
wherein: n is the total number of passengers in a single bus vehicle;
n' is the total number of mobile phone users connected with WIFI of a single bus;
a is the sample expansion coefficient of a single bus passenger.
And fourthly, calculating the number of the passengers carrying the bus, namely calculating standard passenger carrying information of the bus according to relevant regulations of a passenger car loading quality calculation method (GB/T12428-2005). The calculation formula is as follows:
Nd＝Ps+S1/Ssp
wherein,
Ndis the number of passengers on the bus
PsIs the design seat number of the bus;
S1is the area of the bus where passengers can stand;
Sspis the effective area occupied by each standing passenger.
Fifthly, a calculation formula of the full load rate of a single bus based on the mobile WIFI hotspot is as follows:
wherein: r is the single bus occupancy.
Sixthly, calculating the line full load rate, namely the mileage weighted average value of the full load rate of each vehicle on the bus line, namely:
wherein:
r is the line full load rate;
n is the total number of buses on the route;
riis the full load rate of the ith bus on the route;
liis that the ith bus on the line has the full load rate of riMileage over time.
In conclusion, the invention can evaluate whether the current bus route is reasonable or not and whether the bus amount and the departure time of the route are accurate to the stop time of each bus or not according to the finally obtained bus route full load rate, so as to meet the travel demand of passengers in the route. Even if the buses on some lines are very crowded and the buses on some lines have less passenger amount in peak hours such as commuting peak hours or holiday sunrise peak hours, the method can ensure that a traffic controller holds accurate timely data through timely feedback of the basic data, so as to achieve the optimal balance effect of purposefully ensuring the passenger taking requirements of all lines and bus distribution and scheduling of the lines. The method has the advantages of low application cost, obvious effect, high and stable data measurement and calculation accuracy and high cost performance, simultaneously meets the aim of maximizing the utilization of the existing basic resources, and obviously meets the requirements of data instantaneity, accuracy and high utilization rate advocated by the existing high-efficiency intelligent transportation.
Claims (1)
1. A real-time line full-load rate calculation method based on a bus mobile WIFI hotspot is characterized by comprising the following steps:
1) acquiring basic data of each bus in the current bus line:
the basic data at least comprise the number of WIFI of the current bus line, the license plate ID of each bus running along the current bus line, the real-time running position of each bus and the number of real-time mobile phone users connected with WIFI on each bus; counting passenger data of each bus to obtain the proportion of the number of passengers connected with WIFI hotspots by mobile phones on each bus to the total number of passengers, and counting the reciprocal number of the passengers as a sample expansion coefficient a of the bus passengers;
2) extracting the ID data of each bus license plate from the basic data, and at least extracting the number of real-time mobile phone users linked with WIFI corresponding to the bus license plate ID, and recording the number as N';
3) calculating the actual total number of passengers on a single bus, wherein the calculation formula is as follows:
N＝a*N′
wherein:
n is the actual total number of passengers for the bus vehicle;
n' is the total number of mobile phone users connected with the WIFI of the bus;
a is the sample expansion coefficient of the bus passenger corresponding to the bus;
4) and calculating the number of the passengers on the bus by the following calculation formula:
Nd＝Ps+S1/Ssp
wherein,
Ndthe number of the passengers on the bus;
Psis the design seat number of the bus;
S1is the area of the bus where passengers can stand;
Sspis the effective area occupied by each standing passenger in the bus;
5) and calculating the full load rate of the single bus, wherein the formula is as follows:
wherein:
r is the single bus full load rate;
6) and obtaining the bus line full load rate by the following formula:
wherein:
r is the line full load rate;
n is the total number of buses on the route;
riis the full load rate of the ith bus on the route;
liis that the ith bus on the line has the full load rate of riMileage over time.
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| CN105046962A (en) * | 2015-08-18 | 2015-11-11 | 安徽四创电子股份有限公司 | Bus-mobile-WIFI-hot-spot-based real-time calculation method of vehicle full-load rate |
| CN106897298A (en) * | 2015-12-17 | 2017-06-27 | 北京奇虎科技有限公司 | Bus information-pushing method and device |
| CN107270919A (en) * | 2016-04-07 | 2017-10-20 | 高德信息技术有限公司 | Bus routes stage division, device and bus routes air navigation aid, device |
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| CN105046962A (en) * | 2015-08-18 | 2015-11-11 | 安徽四创电子股份有限公司 | Bus-mobile-WIFI-hot-spot-based real-time calculation method of vehicle full-load rate |
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| CN107270919A (en) * | 2016-04-07 | 2017-10-20 | 高德信息技术有限公司 | Bus routes stage division, device and bus routes air navigation aid, device |
| CN107845259A (en) * | 2017-10-24 | 2018-03-27 | 东南大学 | Public transport operation situation real-time feedback system and public transport real-time running data processing method |
| CN107901949A (en) * | 2017-11-24 | 2018-04-13 | 金绪林 | A kind of platform smart electronics protective device and method according to motor-car high ferro |
| CN109800902A (en) * | 2018-12-11 | 2019-05-24 | 华南理工大学 | A kind of unmanned public transport optimization method of uninterrupted reciprocating flexible line length |
| CN112419731A (en) * | 2021-01-22 | 2021-02-26 | 深圳市都市交通规划设计研究院有限公司 | Bus full load rate prediction method and system |
| CN112419731B (en) * | 2021-01-22 | 2021-06-22 | 深圳市都市交通规划设计研究院有限公司 | Bus full load rate prediction method and system |
| CN112907968A (en) * | 2021-02-01 | 2021-06-04 | 华录智达科技股份有限公司 | Intelligent bus non-contact novel coronavirus emergency prevention and control method |
| CN113033896A (en) * | 2021-03-25 | 2021-06-25 | 福州市电子信息集团有限公司 | Intelligent bus scheduling method and device |
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Application publication date: 20151125 |