CN109409563B - Method, system and storage medium for analyzing real-time number of people in public transport operation vehicle - Google Patents
Method, system and storage medium for analyzing real-time number of people in public transport operation vehicle Download PDFInfo
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Abstract
The invention discloses a method, a system and a storage medium for analyzing the real-time number of people in a public transport operation vehicle, wherein the method comprises the following steps: acquiring historical riding data and data of site land properties; carrying out first training on the riding model according to historical riding data and site land property data, and obtaining a passenger characteristic rule base according to a first training result; acquiring boarding card swiping data in real time; calculating the getting-off probability of the user who swipes the card at each station according to the passenger characteristic rule base and the data of swiping the card for getting on the bus; and predicting the real-time number of the current vehicle according to the getting-off probability of the user who swipes the card at each station and the number of the data of swiping the card for getting on the vehicle. The invention does not need to install additional equipment on the bus, has low realization cost, does not depend on getting-off data for predicting the real-time number of people of the bus, can analyze the number of people on the current bus in time and improves the value of the data. The invention can be widely applied to the field of traffic information.
Description
Technical Field
The invention relates to the field of traffic information, in particular to a method and a system for analyzing the number of people in a public transport operation vehicle in real time and a storage medium.
Background
Along with the continuous development of the urbanization of China, the ever-increasing urban population makes the traffic pressure of large and medium cities larger and larger, and the development of large public traffic is the need of people for going out and the need of building low-carbon cities. The GPS/GPRS/4G technology and the like are popularized and applied in bus dispatching, can monitor information such as the position and the speed of a vehicle in real time, and has great effects on vehicle dispatching, intelligent stop reporting and operation safety management. However, the real-time collection of the public transportation passenger flow information is always a difficult problem in the industry, and the real-time passenger carrying information and the stop boarding and alighting information of the public transportation vehicles are accurately collected, so that the development strategy of intelligent public transportation is realized, the public transportation line is optimized, the public transportation operation efficiency is improved, the satisfaction degree of common people is improved, and the public transportation sharing rate of residents during outgoing is improved, which is a development direction for further improving the public transportation informatization and is a necessary route for finally realizing intelligent transportation.
At present, the method for acquiring the bus passenger flow information in each big city in China mainly comprises a stationing point type visual passenger flow investigation method, a manual car following statistical method and a camera judging method for increasing the position of an on-off gate, and the methods all need manual monitoring and auxiliary judging methods. In the preparation stage of the investigation, a great deal of organization work needs to be done on the investigation personnel and the distortion program judgment is needed under the condition of crowded flow. The workload of data arrangement is also large, and the data of manual investigation must be subjected to the processes of editing, arranging and data refining before use. In addition, the data quality is difficult to guarantee by manual investigation, and due to the long-time work, the investigators must stay on the vehicle all the time, so that fatigue and errors are easy to occur, for example, counting errors occur when a large number of passengers get on or off the vehicle. The manual passenger flow survey is very complicated and consumes manpower and financial resources, and is very difficult to realize the regularity and systematicness in the actual operation process. However, urban public transport is a random service system which changes with passenger flow, road conditions, climate and the like, and if information is not available or feedback is not timely and accurate, a dispatcher cannot effectively command and dispatch. Therefore, the bus operation information is the basis of the management business of the whole bus enterprise, and the information acquisition technology can provide support and guarantee for the acquisition and processing of the bus information. The comprehensive and accurate grasp of the bus passenger flow is the basis of bus management work, and not only provides a basis for daily scheduling, but also provides important reference data for network optimization.
The existing method for judging the number of people getting off the bus is basically obtained by a loading infrared scanning method, a loading video moving camera mode or a card swiping mode for getting off the bus and a manual head counting mode, wherein firstly, hardware support and equipment transformation investment need to be additionally installed, meanwhile, due to factors such as personnel movement, crossing or bee congestion, the judgment on the number of people is particularly inaccurate, the error is large, and the existing number of people getting off the bus mainly obtains real-time data of the number of people getting on the bus according to the data of people getting on the bus (card data of getting on the bus, infrared scanning of getting on the bus, identification of a camera at the door of getting on the bus, and the like) and the data of people getting off the bus (card data of getting on the bus, infrared scanning of getting on the bus, identification of a camera at the door of getting on the bus, and the like).
The existing method can analyze the number of people in the vehicle after the number of people getting on or off the vehicle is judged according to the number of people getting on or off the vehicle, basically, the number of people in the vehicle of the previous station can be calculated after the next station arrives, and belongs to post analysis, and the real-time analysis of the data information of the current vehicle cannot be realized.
In summary, the prior art has the problems of high equipment investment cost and lagging behind data requirement of people number identification.
Disclosure of Invention
To solve the above technical problems, the present invention aims to: provided are a method, a system and a storage medium for analyzing the number of real-time persons in a bus operating vehicle, which are inexpensive and capable of analyzing the number of persons in the operating vehicle in time.
The first technical scheme adopted by the invention is as follows:
a method for analyzing the number of people in a bus in real time comprises the following steps:
acquiring historical riding data and data of site land properties;
carrying out first training on the riding model according to historical riding data and site land property data, and obtaining a passenger characteristic rule base according to a first training result;
acquiring boarding card swiping data in real time;
calculating the getting-off probability of the user who swipes the card at each station according to the passenger characteristic rule base and the data of swiping the card for getting on the bus;
and predicting the real-time number of the current vehicle according to the getting-off probability of the user who swipes the card at each station and the number of the data of swiping the card for getting on the vehicle.
Further, the method also comprises the following steps:
and performing secondary training on the riding model by taking the boarding card swiping data and the getting-off probability of the card swiping user at each station as training data, and updating the passenger characteristic rule base according to the result of the secondary training.
Further, the method also comprises the following steps:
and acquiring new data of the site land property, carrying out third training on the riding model according to the new data of the site land property, and updating the passenger characteristic library according to the result of the third training.
Further, the riding model is constructed by at least one of a neural network algorithm, a decision tree algorithm, a poisson distribution algorithm and a Logistic algorithm.
Further, the step of training the riding model for the first time according to the historical riding data and the data of the site land property and obtaining a passenger characteristic rule base according to the result of the training for the first time specifically comprises the following steps:
obtaining the card swiping and coin inserting proportion of each station according to historical riding data;
obtaining the weight data of each site according to the data of the site land properties;
training a riding model for the first time according to the card swiping and coin inserting proportion of each station, the weight data of each station and historical riding data;
and obtaining a passenger characteristic rule base according to the result of the first training.
Further, the boarding card swiping data comprises a bus card ID, a boarding station code, time, longitude and latitude, a bus number and a route number.
Further, the method also comprises the following steps:
and carrying out real-time early warning according to the real-time number of the current vehicles.
The second technical scheme adopted by the invention is as follows:
an analysis system for the number of real-time persons in a bus operation vehicle, comprising:
the first acquisition module is used for acquiring historical riding data and site land property data;
the training module is used for carrying out primary training on the riding model according to the historical riding data and the data of the site land property and obtaining a passenger characteristic rule base according to the result of the primary training;
the second acquisition module is used for acquiring the boarding card swiping data in real time;
the operation module is used for calculating the getting-off probability of the user who swipes the card at each station according to the passenger characteristic rule base and the data of swiping the card for getting on the bus; and predicting the real-time number of the current vehicle according to the getting-off probability of the user who swipes the card at each station and the number of the data of swiping the card for getting on the vehicle.
The third technical scheme adopted by the invention is as follows:
an analysis system for the number of real-time persons in a bus operation vehicle, comprising:
a memory for storing a program;
and the processor is used for loading the program to execute the analysis method of the real-time number of the people in the bus operating vehicle.
The fourth technical scheme adopted by the invention is as follows:
a storage medium having stored thereon a program which, when executed by a processor, implements a method of analyzing a real-time population of a transit operator vehicle.
The invention has the beneficial effects that: the invention trains a riding model by utilizing historical riding data and station land property data to obtain a series of passenger characteristic rules to form a passenger characteristic rule base, then processes the boarding card swiping data acquired in real time according to the passenger characteristic rule base to obtain the probability of the swiping passenger getting off at each station, and calculates the current number of people of the vehicle according to the probability of the swiping passenger getting off at each station and the number of the boarding card swiping data.
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FIG. 1 is a flow chart of a method for analyzing the number of real-time people in a bus operating vehicle according to the present invention.
Detailed Description
The invention is further described with reference to the drawings and the specific examples.
Referring to fig. 1, a method for analyzing the number of real-time people in a bus operation vehicle includes the following steps:
and S1, acquiring historical riding data and data of site land properties. The historical bus taking data comprises historical bus taking card swiping data of a plurality of bus cards, the bus taking card swiping data comprises bus card IDs, bus taking station codes, time, longitude and latitude, bus numbers and route numbers, and the types of the bus cards such as common cards, student cards and old people cards can be distinguished through the bus card IDs. The data of the property of the site land refers to an application type of the site land, such as residential land, industrial land, public facility land, educational land, or commercial land, and the like. The data of land property is obtained because the attraction of different land property to passengers to get off the bus is different, for example, the shopping recreation casinos of commercial land are more, the attraction of the station close to the commercial land is stronger than that of the common station, and the number of people getting off the bus at the station is more because of the plurality of traffic hubs nearby. For example, the riding model can be optimized by using the fact that the probability of getting off the vehicle near the school is much higher for the student cards in the morning than for the student cards in other places.
And S2, training the riding model for the first time according to the historical riding data and the data of the site land property, and obtaining a passenger characteristic rule base according to the result of the training for the first time. The riding model of this embodiment may be constructed by one or more existing data mining algorithms, such as a neural network algorithm, a decision tree algorithm, a poisson distribution algorithm, and a Logistic algorithm (a generalized linear regression analysis model). The algorithms are all the prior art, and one or more of the algorithms can be flexibly selected by a person skilled in the art according to actual needs. And training the built riding model by taking the historical riding data and the data of site land properties as a training set to obtain a plurality of passenger characteristic rules, and storing the rules into one database to form a passenger characteristic rule database.
And S3, acquiring the boarding card swiping data in real time. Namely, the getting-on card swiping data of the current operation bus is obtained in real time.
And S4, calculating the getting-off probability of the user who swipes the card at each station according to the passenger characteristic rule base and the data of swiping the card for getting on the vehicle. And matching corresponding passenger characteristic rules in a passenger characteristic rule base according to the card swiping data of the user, and calculating the probability of getting off the bus of the passenger at each station according to the passenger characteristic rules and the card swiping data.
And S5, predicting the real-time number of the current vehicle according to the getting-off probability of the user who swipes the card at each station and the number of the data of swiping the card for getting on the vehicle. The expected value of the number of people getting off at each station can be calculated according to the probability of getting off at each station of the user who swipes the card, then the number of people getting on the bus can be obtained according to the number of real-time card swiping data, and the number of the real-time people on the current bus can be obtained through a simple addition and subtraction method.
As a preferred embodiment, in order to make the prediction result more accurate, the embodiment further includes the following steps:
and S6, taking the card swiping data of the vehicle and the getting-off probability of the card swiping user at each station as training data, carrying out secondary training on the riding model, and updating the passenger characteristic rule base according to the result of the secondary training. And the secondary training of the model is beneficial to improving the accuracy of the prediction result.
As a preferred embodiment, in order to make the prediction result more accurate, the embodiment further includes the following steps:
and S7, acquiring new data of the site land property, carrying out third training on the riding model according to the new data of the site land property, and updating the passenger characteristic library according to the result of the third training.
In the step, the crawler continuously crawls the data of the soil use properties around the site on the internet, once the soil use properties around the site are changed, the new data can be input into a riding model for training, and a passenger characteristic rule base is updated to form a closed-loop adaptive algorithm, so that the accuracy of the prediction result is improved.
As a preferred embodiment, in order to improve the accuracy of the model, the riding model is constructed by at least one of a neural network algorithm, a decision tree algorithm, a poisson distribution algorithm and a Logistic algorithm.
As a preferred embodiment, the step S2 specifically includes:
obtaining the card swiping and coin inserting proportion of each station according to historical riding data; because not everyone will use the traffic card while riding a car, someone may take a car in the form of a coin. Therefore, the proportion of the coins printed by the card at each station needs to be analyzed. And the card swiping and coin inserting ratio is used as a parameter for training.
Obtaining the weight data of each site according to the data of the site land properties; in the step, different weight proportions can be given to the stations with different station land properties so as to adjust the attraction of the station land properties to passengers to get off the bus. For example, the weight coefficient of the residential site is 1.0, the educational site is 0.8, and the commercial site is 1.2.
Training a riding model for the first time according to the card swiping and coin inserting proportion of each station, the weight data of each station and historical riding data;
and obtaining a passenger characteristic rule base according to the result of the first training.
As a preferred embodiment, the boarding card data includes a bus card ID, a boarding station code, time, longitude and latitude, a car number, and a route number.
As a preferred embodiment, in order to provide a basis for decisions of an operation unit and a traffic management department, the embodiment further includes the following steps:
and S8, performing real-time early warning according to the real-time number of the current vehicle. The embodiment can monitor and early warn the time influencing the safe operation of the bus, and provide data support for operation units and traffic management departments.
An analysis system for the number of real-time persons in a bus operation vehicle, comprising:
the first acquisition module is used for acquiring historical riding data and site land property data;
the training module is used for carrying out primary training on the riding model according to the historical riding data and the data of the site land property and obtaining a passenger characteristic rule base according to the result of the primary training;
the second acquisition module is used for acquiring the boarding card swiping data in real time;
the operation module is used for calculating the getting-off probability of the user who swipes the card at each station according to the passenger characteristic rule base and the data of swiping the card for getting on the bus; and predicting the real-time number of the current vehicle according to the getting-off probability of the user who swipes the card at each station and the number of the data of swiping the card for getting on the vehicle.
As a preferred embodiment, in order to make the prediction result more accurate, the training module is further configured to:
and performing secondary training on the riding model by taking the boarding card swiping data and the getting-off probability of the card swiping user at each station as training data, and updating the passenger characteristic rule base according to the result of the secondary training.
As a preferred embodiment, in order to make the prediction result more accurate, the training module is further configured to:
and acquiring new data of the site land property, carrying out third training on the riding model according to the new data of the site land property, and updating the passenger characteristic library according to the result of the third training.
According to the method, the crawler continuously crawls the data of the soil use properties around the site on the internet, once the soil use properties around the site are changed, new data can be input into the riding model for training, the passenger characteristic rule base is updated, a closed-loop adaptive algorithm is formed, and therefore the accuracy of the prediction result is improved.
As a preferred embodiment, in order to improve the accuracy of the model, the riding model is constructed by at least one of a neural network algorithm, a decision tree algorithm, a poisson distribution algorithm and a Logistic algorithm.
As a preferred embodiment, the training module is specifically configured to:
obtaining the card swiping and coin inserting proportion of each station according to historical riding data; because not everyone will use the traffic card while riding a car, someone may take a car in the form of a coin. Therefore, the proportion of the coins printed by the card at each station needs to be analyzed. And the card swiping and coin inserting ratio is used as a parameter for training.
Obtaining the weight data of each site according to the data of the site land properties; in the step, different weight proportions can be given to the stations with different station land properties so as to adjust the attraction of the station land properties to passengers to get off the bus. For example, the weight coefficient of the residential site is 1.0, the educational site is 0.8, and the commercial site is 1.2.
Training a riding model for the first time according to the card swiping and coin inserting proportion of each station, the weight data of each station and historical riding data;
and obtaining a passenger characteristic rule base according to the result of the first training.
As a preferred embodiment, the boarding card data includes a bus card ID, a boarding station code, time, longitude and latitude, a car number, and a route number.
As a preferred embodiment, in order to provide a basis for decisions of an operation unit and a traffic management department, the embodiment further includes:
and the early warning module is used for carrying out real-time early warning according to the real-time number of the current vehicle. The embodiment can monitor and early warn the time influencing the safe operation of the bus, and provide data support for operation units and traffic management departments.
The embodiment discloses an analytic system of real-time number of people of public transit operation vehicle, includes:
a memory for storing a program; the memory can be a computer readable storage medium such as a U disk, an optical disk or a hard disk
And the processor is used for loading the program to execute the analysis method of the real-time number of the people in the bus operating vehicle corresponding to the figure 1.
The embodiment discloses a storage medium, wherein a program is stored on the storage medium, and when the program is executed by a processor, the method for analyzing the number of real-time people in a bus operating vehicle corresponding to the program in FIG. 1 is realized. The storage medium may be a computer readable storage medium such as a usb disk, an optical disk, or a hard disk.
The step numbers in the above method embodiments are set for convenience of illustration only, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A method for analyzing the number of people in real time in a bus operation vehicle is characterized by comprising the following steps: the method comprises the following steps:
acquiring historical riding data and data of site land properties;
carrying out first training on the riding model according to historical riding data and site land property data, and obtaining a passenger characteristic rule base according to a first training result;
acquiring boarding card swiping data in real time;
calculating the getting-off probability of the user who swipes the card at each station according to the passenger characteristic rule base and the data of swiping the card for getting on the bus;
predicting the real-time number of the current vehicles according to the getting-off probability of the users who swipe the cards at each station and the number of the data of swiping the cards for getting on the vehicles;
the method comprises the following steps of firstly training a riding model according to historical riding data and site land property data, and obtaining a passenger characteristic rule base according to a first training result, wherein the step specifically comprises the following steps:
obtaining the card swiping and coin inserting proportion of each station according to historical riding data;
obtaining the weight data of each site according to the data of the site land properties;
training a riding model for the first time according to the card swiping and coin inserting proportion of each station, the weight data of each station and historical riding data;
and obtaining a passenger characteristic rule base according to the result of the first training.
2. The method for analyzing the number of the real-time people in the bus operating vehicle as claimed in claim 1, wherein the method comprises the following steps: further comprising the steps of:
and performing secondary training on the riding model by taking the boarding card swiping data and the getting-off probability of the card swiping user at each station as training data, and updating the passenger characteristic rule base according to the result of the secondary training.
3. The method for analyzing the number of the real-time people in the bus operating vehicle as claimed in claim 1, wherein the method comprises the following steps: further comprising the steps of:
and acquiring new data of the site land property, carrying out third training on the riding model according to the new data of the site land property, and updating the passenger characteristic library according to the result of the third training.
4. The method for analyzing the number of the real-time people in the bus operating vehicle as claimed in claim 1, wherein the method comprises the following steps: the riding model is constructed by at least one of a neural network algorithm, a decision tree algorithm, a Poisson distribution algorithm and a Logistic algorithm.
5. The method for analyzing the number of the real-time people in the bus operating vehicle as claimed in claim 1, wherein the method comprises the following steps: the boarding card swiping data comprises a bus card ID, a boarding station code, time, longitude and latitude, a bus number and a route number.
6. The method for analyzing the number of people in the public transportation operation vehicle in real time according to any one of claims 1 to 5, wherein the method comprises the following steps: further comprising the steps of:
and carrying out real-time early warning according to the real-time number of the current vehicles.
7. The utility model provides an analytic system of real-time number of public transit operation vehicle which characterized in that: the method comprises the following steps:
the first acquisition module is used for acquiring historical riding data and site land property data;
the training module is used for training the riding model for the first time according to the historical riding data and the data of the site land property, obtaining a passenger characteristic rule base according to the result of the training for the first time, training the riding model for the first time according to the historical riding data and the data of the site land property, and obtaining the passenger characteristic rule base according to the result of the training for the first time, and specifically comprises:
obtaining the card swiping and coin inserting proportion of each station according to historical riding data;
obtaining the weight data of each site according to the data of the site land properties;
training a riding model for the first time according to the card swiping and coin inserting proportion of each station, the weight data of each station and historical riding data;
obtaining a passenger characteristic rule base according to the result of the first training;
the second acquisition module is used for acquiring the boarding card swiping data in real time;
the operation module is used for calculating the getting-off probability of the user who swipes the card at each station according to the passenger characteristic rule base and the data of swiping the card for getting on the bus; and predicting the real-time number of the current vehicle according to the getting-off probability of the user who swipes the card at each station and the number of the data of swiping the card for getting on the vehicle.
8. The utility model provides an analytic system of real-time number of public transit operation vehicle which characterized in that: the method comprises the following steps:
a memory for storing a program;
a processor for loading said program to perform a method of analyzing the number of real-time persons in a transit operator vehicle as claimed in any one of claims 1 to 6.
9. A storage medium having a program stored thereon, characterized in that: the program, when executed by a processor, implements a method of analyzing the number of persons in transit service vehicles in real time as claimed in any one of claims 1 to 6.
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| CN110363591B (en) * | 2019-07-19 | 2022-05-10 | 广东工业大学 | Home site identification method, device, equipment and readable storage medium |
| CN110570127B (en) * | 2019-09-12 | 2022-10-04 | 启迪数华科技有限公司 | Intelligent public transportation system, and vehicle operation scheduling method and device |
| CN111341096B (en) * | 2020-02-06 | 2020-12-18 | 长安大学 | Bus running state evaluation method based on GPS data |
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