An algorithm based on bus card swiping and passenger flow OD data

Abstract

This invention discloses an algorithm based on bus card swiping and passenger flow OD data. It amplifies initial card swiping data to complete passenger flow data, and then uses this amplification logic to amplify the passenger flow OD data. This amplification logic consists of two amplification coefficients, obtained from card swiping data matched to boarding stations, the ratio of all card swiping data to a fixed passenger flow, and the proportion of card payment. For different routes, it considers card swiping passenger flow and OD passenger flow at different time periods and stations, thereby obtaining the amplified results, namely, the complete passenger flow data and the amplified OD data. This invention can improve the matching degree between complete passenger flow data and actual passenger flow data, and reduce the error of operations based on complete passenger flow data.

Description

Technical Field

[0001] This invention relates to the technical field of public transport passenger flow expansion, specifically an algorithm based on bus card swiping and passenger flow OD data. Background Technology

[0002] Ground public transportation is an important component of the urban transportation system. Accurately and quantitatively evaluating the passenger flow status of ground public transportation is an urgent practical need for urban transportation planning, organization and management, and it is also the foundation for providing public transportation information services to the public.

[0003] Therefore, this invention provides an algorithm based on bus card swiping and passenger flow OD data, which expands the initial card swiping data to complete passenger flow data, and completes the expansion of passenger flow OD data based on the expansion logic, thereby providing reliable and accurate traffic passenger flow status. Summary of the Invention

[0004] The purpose of this invention is to provide an algorithm based on bus card swiping and passenger flow OD data. Based on existing card swiping and OD data, the entire expansion logic is divided into two expansion coefficients. Expansion coefficient 1 is obtained by comparing the IC card ID numbers of bus card swiping at different times, routes, and platforms with the previous period to obtain the fixed passenger flow situation for the corresponding time period, route, and platform, and combining it with the proportion of card swiping data throughout the day. The product of expansion coefficient 1 and expansion coefficient 2 is the final expansion coefficient, based on which complete passenger flow data and expanded passenger flow OD data can be obtained.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An algorithm based on bus card swiping and passenger flow OD data, comprising:

[0007] Expansion coefficient 1: The proportion of card swipe data matched to the boarding station to the total card swipe data;

[0008] Expansion coefficient 2: The proportion of fixed passenger flow in different time periods and on different platforms to the total passenger flow in the corresponding time period and on the corresponding platform.

[0009] Expansion coefficient 1:

[0010] Based on the existing card swipe data, obtain the number of non-empty boarding stations (num_swipe_notnull_i) in different time periods, and the total number of card swipe records in different time periods (num_swipe_total_i), where i represents different time periods, as detailed below:

[0011]

[0012] Therefore, the expansion coefficient F for different time periods can be obtained. dayi

[0013]

[0014] At this point, the card swipe data num_swipe_i_j matched to the boarding station and the card swipe data num_swipeOD_i_j matched to the alighting station are respectively generated from different platforms, with F dayi As the expansion coefficient, we can obtain:

[0015] (1) The card swipe data after expansion is as follows:

[0016] num_swipeExpansion_i_j=F dayi ·num_swipe_i_j

[0017] (2) The OD data after sample expansion are as follows:

[0018] num_swipeExpansionOD_i_j=F dayi ·num_swipeOD_i_j

[0019] Where j represents different platforms.

[0020] Expansion coefficient 2:

[0021] By comparing the total card swipe data for the day with the IC card ID numbers in the total card swipe data for the previous period, we can obtain the number of card swipe records with the same IC card ID number as the previous period, num_sameDayICID. Comparing this with the total number of card swipe records for the day, num_totalICID, we can obtain the proportion_samePassenger of fixed passengers for the day relative to the previous period.

[0022]

[0023] By comparing the IC card ID numbers in the card swipe data from different time periods and locations on the same day with the IC card ID numbers in the corresponding time period and location data of the previous period, the number of cards with the same IC card ID number (num_sameICID_i_j) in the current card swipe data and the corresponding time period and location data of the previous period can be obtained. Comparing this with the total card swipe data (num_totalICID_i_j) for the corresponding time period and location on the same day, the proportion of fixed passenger flow (pro_samePassenger_i_j) in the current day relative to the corresponding time period and location of the previous period can be obtained.

[0024]

[0025] Based on the data num_pay_swipe obtained from card payment methods and the data num_pay_total obtained from all payment methods, the proportion of card payment data to total payment data for the day, pro_swipe_pay, can be obtained as follows:

[0026]

[0027] When obtaining complete passenger flow data based on the proportion of fixed passenger flow (2.1), (2.2) and the proportion of card swipe data (2.3), the higher the proportion of fixed passenger flow, the more stable the source of passenger flow, and the higher the proportion of card swipe data should be; conversely, the lower the proportion of fixed passenger flow, the more scattered the source of passenger flow, and the lower the proportion of card swipe data should be.

[0028] The stability or dispersion of passenger flow sources at different time periods and platforms was analyzed. Based on the analysis results, the card-swiping ratio (2.3) was adjusted to obtain the expansion coefficient 2 for different time periods and different stations: G ij .

[0029] When calculating the fixed passenger flow ratio (2.2), since the card swipe data of a certain station may be 0 within a certain time period, the result cannot be obtained when obtaining pro_samePassenger_i_j because the denominator is 0. Therefore, the actual card swipe data should be judged before the algorithm is executed. When the card swipe data of a certain station within a certain time period is 0, there is no need to calculate the expansion coefficient, and the expanded passenger flow data of the corresponding time period and the corresponding station will also be 0.

[0030] At this time, the card swipe data num_swipe_i_j matched to the boarding station and the card swipe data num_swipeOD_i_j matched to the alighting station from different platforms are respectively, in G ij As the expansion coefficient, we can obtain:

[0031] (1) The card swipe data after expansion is

[0032] num_totalExpansion_i_j=G ij ·num_swipe_i_j

[0033] (2) The OD data after amplification are

[0034] num_totalExpansionOD_i_j=G ij ·num_swipeOD_i_j.

[0035] In summary, by using the card swipe data num_swipe_i_j matched to boarding stations and the card swipe data num_swipeOD_i_j matched to alighting stations from different platforms, we can obtain complete passenger flow data and expanded OD data through data amplification. The amplification coefficient is F. dayi ·G ij By performing year-on-year expansion on both the card swipe data and the card swipe OD data, we can obtain:

[0036] (1) The complete passenger flow data after expansion is as follows:

[0037] num_totalExpansion_i_j=F dayi ·G ij ·num_swipe_i_j

[0038] (2) The OD data after sample expansion are as follows:

[0039] num_totalExpansionOD_i_j=F dayi ·G ij ·num_swipeOD_i_j.

[0040] Furthermore: different adjustment methods may result in different expansion coefficients 2, and these methods may each have their own advantages and disadvantages. Here, we propose G... ij These are three methods for calculating the expansion coefficient.

[0041] (1) Method 1

[0042] The expansion coefficients are as follows:

[0043]

[0044] The algorithm is relatively simple, has high stability, and can reflect changes in passenger flow by the proportion of a fixed passenger flow.

[0045] (2) Method Two

[0046] The expansion coefficients are as follows:

[0047]

[0048] It can be seen that when the percentage of card swipes (2.3) is low or the difference between the percentages of fixed customer flow (2.1) and (2.2) is large, the data obtained differs significantly from the actual data. Therefore, it is necessary to impose restrictions on the above formula. To ensure the validity of the data, the following restrictions are added:

[0049]

[0050] Given that pro_samePassenger_i_j≤1 always holds true, the constraint is:

[0051] pro_samePassenger_i_j-pro_samePassenger≥pro_samePassenger-1 is also:

[0052]

[0053] The constraints here are not unique and need to be set according to the actual situation of the data.

[0054] This method can be adjusted according to the actual passenger flow based on the proportion of fixed passengers, and the passenger flow data is closer to the actual situation;

[0055] (3) Method 3

[0056] The expansion coefficients are as follows:

[0057]

[0058] The card swipe ratio (2.3) is judged: when the card swipe ratio is greater than 0.5, the result is obtained directly using the algorithm; when the card swipe ratio is less than 0.5, the result is judged again. When the result meets the conditions, it is output; when it does not meet the conditions, other expansion coefficients are considered. The conditions here can be judged on the value of the expansion coefficient. Negative numbers or numbers that are too large or too small do not meet the conditions.

[0059] Furthermore: The choice of period may have some impact on the data. Two period selection options are provided, and these options can be modified or other options can be considered depending on the actual situation. The two options are:

[0060] Option (1): Compare the card swipe records of each day with those of the previous day, using a one-day cycle.

[0061] This solution is simple and convenient. For situations where some dates are missing data or data does not match daily data due to special circumstances, the comparison can be carried forward. Monday can be compared with the Monday of the previous week, Saturday with the Saturday of the previous week, and the remaining data can still be compared with the previous day on a one-day basis.

[0062] Option (2): Compare the card swipe records of each day with the corresponding dates of the previous week, using a one-week cycle;

[0063] Set a threshold for the extension time span. When the extension time span is greater than the set threshold, combine the cycle setting of scheme (1). In order to avoid the impact of the work arrangement of single and double shifts of many companies on the data, the lines that are more affected by single and double shifts can be considered separately, and the card swiping data of these lines on Saturdays can be compared with the card swiping data of Saturdays two weeks ago.

[0064] Compared with existing technologies, the present invention can obtain complete passenger flow data for different time periods, different routes, and different stations, improve the utilization rate of bus card swiping data and OD data, improve the matching degree between complete passenger flow data and actual passenger flow data, and reduce the error of operation based on complete passenger flow data. Attached Figure Description

[0065] Figure 1 This is an architecture diagram of an algorithm based on bus card swiping and passenger flow OD data.

[0066] Figure 2 This is a logic diagram of an algorithm based on bus card swiping and passenger flow OD data. Detailed Implementation

[0067] The technical solution of this patent will be further described in detail below with reference to specific embodiments.

[0068] Please see Figure 1-2 An algorithm based on bus card swiping and passenger flow OD data, which includes

[0069] 1. Expansion coefficient 1 (the proportion of card swipe data matched to the boarding station out of the total card swipe data)

[0070] In theory, every boarding station for each card swipe record can be matched. However, due to issues with the boarding station matching logic or actual vehicle operation, not all boarding stations for card swipe records can be matched. In such cases, some card swipe records will not be considered in various calculations based on the matched boarding station records, which will affect the final data.

[0071] Based on the existing card swipe data, obtain the number of non-empty boarding stations (num_swipe_notnull_i) in different time periods, and the total number of card swipe records in different time periods (num_swipe_total_i), where i represents different time periods, as detailed below:

[0072]

[0073] Therefore, the expansion coefficient F for different time periods can be obtained. dayi

[0074]

[0075] Since the main consideration here is the proportion of card swipe data matched to the boarding station to the total card swipe data, the data for the whole day or different time periods can be analyzed to obtain the corresponding expansion coefficients. Since the deviation from the actual data is small, the expansion coefficients of card swipe data for different stations are not considered in this embodiment.

[0076] At this time, the card swipe data num_swipe_i_j matched to the boarding station and the card swipe data num_swipeOD_i_j matched to the alighting station from different platforms are respectively represented by F. dayi As the expansion coefficient, we can obtain:

[0077] (1) The card swipe data after expansion is

[0078] num_swipeExpansion_i_j=F dayi ·num_swipe_i_j

[0079] (2) The OD data after amplification are

[0080] num_swipeExpansionOD_i_j=F dayi ·num_swipeOD_i_j

[0081] Where j represents different platforms.

[0082] 2. Expansion coefficient 2 (the proportion of fixed passenger flow at different times and platforms to the total passenger flow at the corresponding time and platform).

[0083] By comparing the IC card IDs from the entire day's card swipe data with those from the previous period's entire day's card swipe data, we can obtain the number of card swipe records with the same IC card ID as the previous period's data, num_sameDayICID. Comparing this with the total number of card swipe records for the day, num_totalICID, we can obtain the proportion_samePassenger of fixed passengers for the day relative to the previous period.

[0084]

[0085] Consider comparing the IC card IDs in the card swipe data from different time periods and locations on the same day with the IC card IDs in the corresponding time period and location data from the previous period. This yields the number of cards (num_sameICID_i_j) with the same IC card ID as those in the current day's data. Comparing this number with the total number of card swipes (num_totalICID_i_j) for the corresponding time period and location on the same day, we can obtain the proportion (pro_samePassenger_i_j) of the fixed passenger flow at the corresponding time period and location on the current day relative to the previous period.

[0086]

[0087] Statistical analysis of different payment methods yields the percentage of bus card swipe data for the entire day, which is a crucial component of the expansion coefficient 2. Payment methods include: coin payment, universal card payment, WeChat Pay, UnionPay contactless payment, Alipay payment, and QR code payment. By comparing the data (num_pay_swipe) obtained from card swipe payment methods with the data (num_pay_total) obtained from all payment methods, the percentage (pro_swipe_pay) of all card swipe data for the entire day can be calculated.

[0088]

[0089] When obtaining complete passenger flow data based on the fixed passenger flow ratio (2.1), (2.2) and the card swipe data ratio (2.3), the higher the fixed passenger flow ratio, the more stable the passenger flow source, and the higher the card swipe ratio should be; conversely, the lower the fixed passenger flow ratio, the more scattered the passenger flow source, and the lower the card swipe ratio should be.

[0090] The analysis of the stability or dispersion of passenger flow sources at different time periods and platforms was conducted. Based on the analysis results, the card-swiping ratio (2.3) was adjusted to obtain the expansion coefficient 2 for different time periods and different stations: G ij Different adjustment methods may result in different expansion coefficients, each with its own advantages and disadvantages. Three methods are proposed here.

[0091] (1) Method 1

[0092] The expansion coefficients are as follows:

[0093]

[0094] The algorithm is relatively simple and stable, and can reflect changes in passenger flow through the proportion of a fixed passenger flow. However, the algorithm can only increase the proportion of card swipe data based on the total daily card swipe data, resulting in a lower passenger flow figure compared to the actual passenger flow. The proportion of total daily card swipe data to payment data (pro_swipe_day) can be reduced in the expansion coefficient according to certain logic. This can be achieved by using a proportional coefficient or subtracting the result from a constant to obtain a new result. If the logic is well-developed, the obtained passenger flow data can be closer to the actual passenger flow data.

[0095] (2) Method Two

[0096] The expansion coefficients are as follows:

[0097]

[0098] It can be seen that when the percentage of card swipes (2.3) is low or the percentage of fixed customer flow (2.1) and (2.2) differs significantly from the actual data, the obtained data differs greatly from the actual data. Therefore, it is necessary to impose restrictions on the above formula. To ensure the validity of the data, the following restrictions are given here.

[0099]

[0100] Given that pro_samePassenger_i_j≤1 always holds true, the constraint is:

[0101] pro_samePassenger_i_j-pro_samePassenger≥pro_samePassenger-1, which is also...

[0102]

[0103] It should be noted that the constraints here are not unique and need to be set according to the actual situation of the data. The actual passenger flow can be adjusted according to the proportion of fixed passengers, and the passenger flow data is closer to the actual situation. When the proportion of card swiping (2.3) is low or the proportion of fixed passengers (2.1) and (2.2) is far apart, the data obtained will differ greatly from the actual data, the whole method is more complicated and less stable than the first method. This method has high requirements for the selection of constraints, so in application, it may be necessary to adjust the constraints according to the actual situation through a large amount of data.

[0104] (3) Method 3

[0105] The expansion coefficients are as follows:

[0106]

[0107] The algorithm is relatively simple, and the obtained customer flow data is close to the actual situation, and the stability is also relatively good. By default, the proportion of card swipe data to all payment methods is greater than 0.5. When the default condition is not met, the result may differ greatly from the actual data. The card swipe ratio (2.3) can be judged: when the card swipe ratio is greater than 0.5, the result can be obtained directly using the algorithm; when the card swipe ratio is less than 0.5, the result is judged again. When the result meets the condition, it is output; when it does not meet the condition, other expansion coefficients are considered. The condition here can be judged on the value of the expansion coefficient. Negative numbers or numbers that are too large or too small do not meet the condition.

[0108] It is important to note that when calculating the fixed passenger flow ratio (2.2), the card swipe data at a certain station may be 0 within a certain time period. In this case, when obtaining pro_samePassenger_i_j, the result cannot be obtained because the denominator is 0. Therefore, the actual card swipe data should be checked before the algorithm is executed. When the card swipe data at a certain station within a certain time period is 0, there is no need to calculate the expansion coefficient; the expanded passenger flow data for the corresponding time period and station will also be 0.

[0109] At this point, the card swipe data num_swipe_i_j matched to the boarding station and the card swipe data num_swipeOD_i_j matched to the alighting station are respectively generated from different platforms, with G as the basis. ij As the expansion coefficient, we can obtain:

[0110] (1) The card swipe data after expansion is

[0111] num_totalExpansion_i_j=G ij ·num_swipe_i_j

[0112] (2) The OD data after amplification are

[0113] num_totalExpansionOD_i_j=G ij ·num_swipeOD_i_j

[0114] It should be noted that the period selection here may have some impact on the data. Two period selection options are provided here. Depending on the actual situation, these two options can be modified or other options can be considered.

[0115] (1) Using a one-day cycle, compare the card swipe records of each day with those of the previous day.

[0116] It's simple and convenient. For situations where some dates are missing or data differs from daily data due to special circumstances, you can compare data by moving backwards.

[0117] We can compare Monday with the previous week's Monday, Saturday with the previous week's Saturday, and the remaining data can still be compared with the previous day on a daily basis. Because the comparison logic for Monday and Saturday differs from other dates, there might be some data discrepancies, but these discrepancies are acceptable compared to the original method.

[0118] (2) Compare the daily card swipe records with the corresponding dates of the previous week, using a one-week cycle.

[0119] It exhibits good stability and yields relatively accurate data.

[0120] Set a time span threshold for the extension period. When the time span for the extension period is greater than the set threshold, combine the period setting of scheme (1). In order to avoid the impact of the work arrangements of many companies on the data, we can consider the lines that are more affected by the alternating work schedules of alternating work schedules to be considered separately, and compare the card swiping data of these lines on Saturday with the card swiping data of Saturday two weeks ago.

[0121] At this point, the card swipe data num_swipe_i_j matched to the boarding station from different platforms and the card swipe data num_swipeOD_i_j matched to the alighting station from different platforms are used to expand the card swipe data. Therefore, complete passenger flow data and expanded OD data can be obtained by amplifying the card swipe data. The expansion coefficient at this point is F. dayi ·G ij By performing year-on-year expansion on both the card swipe data and the card swipe OD data, we can obtain...

[0122] (1) The complete passenger flow data after expansion is

[0123] num_totalExpansion_i_j=F dayi ·G ij ·num_swipe_i_j

[0124] (2) The OD data after amplification are

[0125] num_totalExpansionOD_i_j=F dayi ·G ij ·num_swipeOD_i_j

[0126] This invention can obtain complete passenger flow data for different time periods, routes, and platforms, improve the utilization rate of bus card swiping data and OD data, increase the matching degree between complete passenger flow data and actual passenger flow data, and reduce the error of operations based on complete passenger flow data.

[0127] The preferred embodiments of this patent have been described in detail above. However, this patent is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this patent.

Claims

1. An algorithm based on bus card swiping and passenger flow OD data, characterized in that, Expand the initial card swipe data to complete passenger flow data, and complete the expansion of passenger flow OD data based on the expansion logic; The expansion logic consists of two expansion coefficients. Expansion coefficient 1 is obtained by comparing the proportion of data that can be matched with boarding stations in different time periods to the total data. Expansion coefficient 2 is obtained by comparing the IC card ID numbers of buses swiping at different time periods, routes, and platforms with the data of the previous period to obtain the fixed passenger flow situation of the corresponding time period, route, and platform, and combining it with the card swiping data of the whole day to obtain the corresponding data proportion. The product of expansion coefficient 1 and expansion coefficient 2 is the final expansion coefficient. Based on this expansion coefficient, complete passenger flow data and expanded passenger flow OD data can be obtained. The expansion coefficient 1: Based on the existing card swipe data, obtain the number of data entries where the boarding station is not empty (num_swipe_notnull_i) for different time periods, and the total number of card swipe data records for different time periods (num_swipe_total_i), where i represents different time periods, as detailed below: It is possible to obtain the expansion coefficient F for different time periods. dayi At this point, the card swipe data num_swipe_i_j matched to the boarding station and the card swipe data num_swipeOD_i_j matched to the alighting station are respectively generated from different platforms, with F dayi Let be the expansion coefficient, from which we can obtain: (1) The card swipe data after expansion is as follows: num_swipeExpansion_i_j=F dayi ·num_swipe_i_j (2) The OD data after sample expansion are as follows: num_swipeExpansionOD_i_j=F dayi ·num_swipeOD_i_j Where j represents different platforms; The expansion coefficient 2: The card swipe data num_swipe_i_j matched to the boarding station and the card swipe data num_swipeOD_i_j matched to the alighting station are respectively generated from different platforms, with G as the base. ij Let be the expansion coefficient, from which we can obtain: (1) The card swipe data after expansion is num_totalExpansion_i_j=G ij ·num_swipe_i_j (2) The OD data after amplification are num_totalExpansionOD_i_j=G ij ·num_swipeOD_i_j。 2. The algorithm based on bus card swiping and passenger flow OD data according to claim 1, characterized in that, The card swipe data num_swipe_i_j matched to the boarding station from different platforms and the card swipe data num_swipeOD_i_j matched to the alighting station from different platforms are used to expand the card swipe data to obtain complete passenger flow data and expanded OD data. The expansion coefficient is F. dayi ·G ij By performing year-on-year amplification of both the card swipe data and the card swipe OD data, we can obtain: (1) The complete passenger flow data after expansion is as follows: num_totalExpansion_i_j=F dayi ·G ij ·num_swipe_i_j (2) The OD data after sample expansion are as follows: num_totalExpansionOD_i_j=F dayi ·G ij ·num_swipeOD_i_j。 3. The algorithm based on bus card swiping and passenger flow OD data according to claim 1, characterized in that, By comparing the total card swipe data for the day with the IC card IDs from the previous period, we can obtain the number of card swipe records (num_sameDayICID) with the same IC card ID as those from the previous period. Comparing this with the total number of card swipe records for the day (num_totalICID) yields the proportion (proportion_samePassenger) of fixed passengers relative to the previous period.

4. The algorithm based on bus card swiping and passenger flow OD data according to claim 3, characterized in that, By comparing the IC card ID numbers in the card swipe data from different time periods and locations on the same day with the IC card ID numbers in the corresponding time period and location data of the previous period, the number of cards with the same IC card ID number (num_sameICID_i_j) in the current card swipe data and the corresponding time period and location data of the previous period is obtained. Comparing this with the total card swipe data (num_totalICID_i_j) for the corresponding time period and location on the same day, the proportion (pro_samePassenger_i_j) of the fixed passenger flow in the corresponding time period and location on the current day relative to the previous period can be obtained.

5. The algorithm based on bus card swiping and passenger flow OD data according to claim 4, characterized in that, Based on the data num_pay_swipe obtained from card payment methods and the data num_pay_total obtained from all payment methods, the proportion of card payment data to total payment data for the day, pro_swipe_pay, can be obtained as follows:

6. The algorithm based on bus card swiping and passenger flow OD data according to claim 5, characterized in that, Different adjustment methods will result in different expansion coefficients 2 and G. ij The three methods for calculating the expansion coefficient are as follows: (1) Method 1 The expansion coefficients are as follows: It has high stability and can reflect changes in passenger flow by the proportion of fixed passenger flow; (2) Method Two The expansion coefficients are as follows: To ensure data validity, the following restrictions are added: Given that pro_samePassenger_i_j≤1 always holds true, the constraint is: pro_samePassenger_i_j-pro_samePassenger≥pro_samePassenger-1 is also: The constraints here are not unique and need to be set according to the actual situation of the data. (3) Method 3 The expansion coefficients are as follows:

7. The algorithm based on bus card swiping and passenger flow OD data according to claim 5, characterized in that, The card swipe percentage is judged as follows: when the card swipe percentage is greater than 0.5, the result is obtained directly using the algorithm; when the card swipe percentage is less than 0.5, the result is judged again. If the result meets the conditions, it is output; otherwise, other expansion coefficients are considered.

8. The algorithm based on bus card swiping and passenger flow OD data according to claim 1, characterized in that, The choice of period can have some impact on the data. Two period selection options are: Scheme (1) uses a one-day cycle and compares the card swipe records of each day with those of the previous day; Option (2): Compare the card swipe records of each day with the corresponding dates of the previous week, using a one-week cycle.

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