A railway passenger flow analysis method and system based on big data

Abstract

The present invention discloses a railway passenger flow analysis method and system based on big data, which collects water and electricity consumption information, calculates the water and electricity information of a single person through the number of floating population, adopts the average water and electricity consumption of multiple days, and obtains the number of urban population according to the total electricity and water consumption, so as to facilitate the calculation of the number of floating population required during holidays according to the budgeted urban population for multiple days and the number of permanent residents, and estimates the population flow before holidays according to time nodes and the principle of long-distance priority, so as to increase train arrangements and safety guarantees and avoid passenger detention. The present invention adopts the average water and electricity consumption of multiple days to calculate the total population of the city, and uses the budgeted total population of multiple days as a reference, with a large time span and more accurate data. At the same time, statistics are made for the destination and source of floating personnel, and they are divided into long and short distances, so as to more accurately estimate the passenger flow according to the principle of long-distance priority.

Description

Technical Field

[0001] The present invention relates to the field of railway transportation technology, and in particular to a railway passenger flow analysis method, system, computer equipment and computer-readable storage medium based on big data. Background Art

[0002] Population migration between cities is an important behavior in national economic activities, especially during major holidays, when population mobility is particularly large. The railway department needs to control the passenger flow at stations based on passenger flow, transport passengers in a timely manner, avoid excessive passenger stays, and promptly grasp passenger flow information, temporarily increase trains, increase security and management personnel, and ensure corresponding temporary emergency treatment.

[0003] The above situations all require the deployment of corresponding personnel and facilities according to the size of passenger flow. At present, the main source of passenger flow information for the railway department is the online ticketing client. It is impossible to analyze the offline ticket purchase population, which leads to the railway often not having enough personnel, venues and facilities to match the passenger flow. Summary of the invention

[0004] The purpose of the present invention is to provide a railway passenger flow analysis method, system, computer equipment and computer-readable storage medium based on big data.

[0005] The technical solution to achieve the purpose of the present invention is: a railway passenger flow analysis method based on big data, comprising the following steps:

[0006] Step 1: Obtain the average water and electricity consumption of the floating population for at least 30 days and estimate the total population of the city;

[0007] Step 2: Calculate the average of the total population of the city based on at least 10 days of budget to determine the total population, and add this number to the daily floating population;

[0008] Step 3: Count the destinations and origins of the migrant population, calculate the proportion of each region, divide them into short-distance and long-distance categories, and determine the proportion of short-distance and long-distance;

[0009] Step 4: Subtract the number of local people from the total population to get the number of foreign population;

[0010] Step 5: During holidays, the number of population flows is detected 5 days in advance, and the number of foreign population minus the number of population flows in the previous 5 days is used to obtain the number of population to be transferred;

[0011] Step 6: Divide the floating population according to the proportion of destinations;

[0012] Step 7: Budget population movement before holidays based on time nodes and long-distance priority principles.

[0013] Preferably, in step one, the average water consumption of the floating population is equal to the difference in water consumption between two days divided by the floating population inflow and outflow value on that day. The floating population inflow and outflow value statistical channels include airplane, railway, and bus transportation. The total water consumption of the city on that day is divided by the average water consumption of the floating population to obtain the total population of the city.

[0014] Preferably, in step one, the average electricity consumption of the floating population is equal to the difference in electricity consumption between two days divided by the floating population inflow and outflow value on that day. The floating population inflow and outflow value statistical channels include air, railway, and bus transportation. The total electricity consumption of the city on that day is divided by the average electricity consumption of the floating population to obtain the total population of the city.

[0015] Preferably, the water and electricity consumption information in step one is obtained from a water supply company and a power supply company, and does not include commercial and industrial water and electricity consumption.

[0016] Preferably, the standard for dividing long distance and short distance in step three is greater than 100 km and less than 100 km.

[0017] A railway passenger flow analysis system based on big data realizes railway passenger flow analysis by utilizing the railway passenger flow analysis method based on big data.

[0018] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the railway passenger flow analysis method based on big data is utilized to implement railway passenger flow analysis.

[0019] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the railway passenger flow analysis method based on big data is utilized to implement railway passenger flow analysis.

[0020] Compared with the prior art, the present invention has the following significant advantages: 1) It collects water and electricity consumption information through computers, and calculates the water and electricity consumption of a single person through the number of floating population, and obtains the city population based on the total electricity and water consumption information, so as to facilitate the calculation of the number of floating population required during holidays based on the city population and the permanent population, and estimates the population flow before holidays based on time nodes and the principle of long-distance priority, thereby increasing train arrangements and safety guarantees and avoiding passenger delays. 2) The total population of the city is calculated using the average water and electricity consumption of multiple days, and the budgeted total population of multiple days is used as a reference, with a large time span and more accurate data. At the same time, statistics are taken for the destinations and sources of floating personnel, and they are divided into long and short distances, so that the passenger flow is more accurately estimated based on the principle of long-distance priority. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of the railway passenger flow analysis method based on big data of the present invention. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0023] In the description of the present invention, it is necessary to understand that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0024] Example 1

[0025] A railway passenger flow analysis method based on big data includes the following steps:

[0026] Step 1: Obtain the average water consumption of floating population in 30 days and estimate the total population of the city; the average water consumption of floating population is equal to the difference between the water consumption of two days divided by the inflow and outflow value of floating population on that day. The statistical channels of the inflow and outflow value of floating population include airplane, railway and bus transportation. The total water consumption of the city on that day divided by the average water consumption of floating population can get the total population of the city;

[0027] For example, the floating population inflow and outflow value on a certain day is (+1000), and the water consumption difference between the two days is (+200m 3 ), and the average daily water consumption of floating population is 0.2m 3 / person, the average water consumption of floating population in 30 days is 0.2m 3 / people.

[0028] The total water consumption on a certain day is divided by the average water consumption of the floating population to obtain the total population of the city.

[0029] The total water consumption on that day was 20,000m 3 / 0.2m 3 = 100,000 people,

[0030] Step 2: Calculate the average of the total population of the city based on the 12-day budget to determine the total population, and add this number to the daily floating population to obtain the total population of the next day;

[0031] The total population of the city estimated on the 12th was 105,000, 10, 98, 99, 101, 102, 103, 104, 101, 10, 102 and 98,000 respectively. The average value was 101,000, and the total population was 101,000. This number was added to the daily floating population inflow and outflow value to get the total population of the next day.

[0032] Step 3: Count the destinations and origins of the floating population, calculate the proportion of each region, divide them into short-distance and long-distance categories, and determine the proportion of long-distance and short-distance. The standard for dividing long-distance and short-distance is greater than 100km and less than 100km;

[0033] For example: Among the total number of migrant population in the city, long-distance accounts for 40% and short-distance accounts for 60%.

[0034] Step 4: Subtract the number of local people from the total population of the city to get the number of foreign population. The number of local people is known data based on the household registration management system.

[0035] For example, the total population calculated by the city is 101,000 people + the floating population in and out value (+1000), which is equal to the current total population of 102,000 people. The local population is 60,000 people, and the net foreign population is 42,000 people.

[0036] Step 5: During holidays, the number of population flows is detected 5 days in advance, and the number of foreign population minus the number of population flows in the previous 5 days is used to obtain the number of population to be transferred;

[0037] For example, in the first five days before the May Day holiday, the total floating population was 12,000 people, and the number of migrants was 42,000 minus 12,000, so the number of people waiting to float was 30,000;

[0038] Step 6: Divide the floating population according to the proportion of destinations;

[0039] Since the number of people to be mobilized is 30,000, of which 60% are long-distance and 40% are short-distance, trains will be arranged according to long and short distances.

[0040] Step 7. Budget population movement before holidays and adjust train schedules based on time nodes and long-distance priority principles.

[0041] In addition, it should be noted that for holidays equal to or less than two days, no adjustment will be made to the train schedule.

[0042] Based on the destinations counted by big data and the number of existing train arrangements, analyze whether it is necessary to add trains to a certain route.

[0043] As mentioned above, water consumption information is obtained from water supply companies and does not include commercial and industrial water consumption.

[0044] Example 2

[0045] A railway passenger flow analysis method based on big data includes the following steps:

[0046] Step 1: Obtain the average electricity consumption of floating population for 30 days and estimate the total population of the city; the average electricity consumption of floating population is equal to the difference between the electricity consumption of two days divided by the floating population inflow and outflow value of the day. The statistical channels of floating population inflow and outflow value include airplane, railway and bus transportation. The total electricity consumption of the city on the day is divided by the average electricity consumption of floating population to get the total population of the city;

[0047] For example: the inflow and outflow value of floating population on a certain day is (+2000), and the difference in electricity consumption between the two days is (+4000 kWh). The average electricity consumption of floating population on a single day is 2 kWh / person, and the average electricity consumption of floating population for 30 days is calculated to be 2 kWh / person.

[0048] The total electricity consumption on a certain day is divided by the average electricity consumption of the floating population to obtain the total population of the city.

[0049] On that day, the total electricity consumption was 400,000 kWh / 2 kWh = 200,000 people.

[0050] Step 2: calculate the average of the total population of the city on the 15th to determine the total population, and add this number to the daily floating population to obtain the total population of the next day;

[0051] The total population of the city on the 15th is 205,000, 200, 196, 198, 202, 204, 206, 208, 202, 20, 204, 202, 20, 202 and 196,000 respectively. The average is 201,000, and the total population is 201,000. This number is added to the daily floating population in and out value to get the total population of the next day.

[0052] Step 3: Count the destinations and origins of the floating population, calculate the proportion of each region, divide them into short-distance and long-distance categories, and determine the proportion of long-distance and short-distance. The standard for dividing long-distance and short-distance is greater than 100km and less than 100km;

[0053] For example: Among the total number of migrant population in the city, long-distance accounts for 40% and short-distance accounts for 60%.

[0054] Step 4: Subtract the number of local people from the total population of the city to get the number of foreign population. The number of local people is known data based on the household registration management system.

[0055] For example, the total population calculated by the city is 201,000 people + the floating population in and out value (+2,000), which is equal to the current total population of 203,000 people. The local population is 120,000 people, and the net foreign population is 83,000 people.

[0056] Step 5: During holidays, the number of population flows is detected 5 days in advance, and the number of foreign population minus the number of population flows in the previous 5 days is used to obtain the number of population to be transferred;

[0057] For example, in the first five days before the May Day holiday, the total floating population was 21,000 people. If the number of migrants was 83,000 minus 21,000, the number of people waiting to float was 62,000.

[0058] Step 6: Divide the floating population according to the proportion of destinations;

[0059] Since the number of people to be mobilized is 62,000, of which 60% are long-distance and 40% are short-distance, trains will be arranged according to long and short distances.

[0060] Step 7. Budget population movement before holidays and adjust train schedules based on time nodes and long-distance priority principles.

[0061] In addition, it should be noted that for holidays equal to or less than 2 days, no adjustment will be made to the train schedule.

[0062] Based on the destinations counted by big data and the number of existing train arrangements, analyze whether it is necessary to add trains to a certain route.

[0063] As mentioned above, the electricity consumption information is obtained from the power supply company and does not include commercial and industrial electricity consumption.

[0064] The railway passenger flow analysis method based on big data proposed in the present invention collects water and electricity consumption information through computers, and calculates the water and electricity consumption information of a single person through the number of floating population, and obtains the city population based on the total electricity and water consumption information, so as to facilitate the calculation of the number of floating population required during holidays based on the city population and the permanent population, and estimate the population flow before holidays based on time nodes and the principle of long-distance priority, thereby increasing train arrangements and safety guarantees and avoiding passenger delays.

[0065] The total population of the city is calculated using the average water and electricity consumption over multiple days, and the budgeted total population over multiple days is used as a reference. The time span is large and the data is more accurate. At the same time, statistics are taken on the destinations and sources of migrant workers, and they are divided into long and short distances. The principle of long-distance priority is used to more accurately estimate passenger flow.

[0066] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A railway passenger flow analysis method based on big data, characterized in that: The following steps are involved: Step 1: Obtain the average water and electricity consumption of floating population for 30 days and estimate the total population of the city; Step 2: Calculate the average of the total population of the city based on at least 10 days of budget to determine the total population, and add it to the daily floating population inflow and outflow value to obtain the real-time total population; Step 3: Count the destinations and origins of the migrant population, calculate the proportion of each region, divide them into short-distance and long-distance categories, and determine the proportion of short-distance and long-distance; Step 4: Subtract the local population from the real-time total population to obtain the number of foreign population; Step 5: During holidays, the number of population flows is detected 5 days in advance, and the number of foreign population minus the number of population flows in the previous 5 days is used to obtain the number of population to be transferred; Step 6: Divide the floating population according to the proportion of destinations; Step 7: Budget the population flow before holidays and adjust the train schedule according to the time nodes and the principle of long-distance priority; In the step 1, the average water consumption of the floating population is equal to the difference between the water consumption of the two days divided by the floating population inflow and outflow value on that day. The floating population inflow and outflow value statistical channels include airplane, railway, and bus transportation. The total water consumption of the city on that day is divided by the average water consumption of the floating population to obtain the total population of the city; In step 1, the average electricity consumption of the floating population is equal to the difference in electricity consumption between two days divided by the floating population inflow and outflow value on that day. The floating population inflow and outflow value statistical channels include airplane, railway, and bus transportation. The total electricity consumption of the city on that day is divided by the average electricity consumption of the floating population to obtain the total population of the city.

2. The railway passenger flow analysis method based on big data according to claim 1 is characterized by: The water and electricity consumption information in step 1 is obtained from the water supply company and the power supply company, and does not include commercial and industrial water and electricity consumption.

3. The railway passenger flow analysis method based on big data according to claim 1 is characterized by: The standard for dividing long distance and short distance in step 3 is greater than 100 km and less than 100 km.

4. A railway passenger flow analysis system based on big data, characterized in that: Railway passenger flow analysis is achieved by using the railway passenger flow analysis method based on big data as described in any one of claims 1 to 3.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the railway passenger flow analysis method based on big data described in any one of claims 1 to 3 is used to implement railway passenger flow analysis.

6. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the railway passenger flow analysis is implemented using the railway passenger flow analysis method based on big data as described in any one of claims 1 to 3.

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