CN109727474B - Bus station entrance and exit accurate identification method based on fusion data - Google Patents
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Abstract
A bus station entrance and exit accurate identification method based on fusion data comprises the following steps: step 1, preprocessing GPS data, and endowing the GPS data with actual running bus route numbers and station numbers; step 2, processing bus arrival data; step 3, processing the bus outbound data according to a bus inbound data processing method; step 4, fusing the incoming and outgoing data; step 5, supplementing station entering and exiting data by using GPS data; and 6, time interpolation. The invention relates to a bus in-and-out station sequential arrangement processing method based on bus scheduling data, in-and-out station reporting data, GPS data and road network data, which is used for automatically detecting and processing the bus in-and-out station data and improving the accuracy of the bus in-and-out station data.
Description
Technical Field
The invention belongs to the field of bus data analysis, and particularly relates to a method for accurately identifying the entrance and the exit of a bus based on fusion data.
Background
Urban public transport development has become an important component of urban development, and research and analysis based on public transport data is increasing. The bus station-in and station-out data is an important data component for analyzing the indexes of bus time interval passenger flow, OD, bus operation condition and the like, and is extremely important. At present, most bus station entering and exiting data are collected through a bus station reporting system, but bus station reporting is extremely dependent on manual operation, and the obtained data can be in the situations of station confusion, missed report, inaccurate time and the like.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for accurately identifying the entrance and the exit of a bus based on fusion data.
In order to achieve the purpose, the invention adopts the following technical scheme:
a bus station entrance and exit accurate identification method based on fusion data is characterized by comprising the following steps:
step 2, processing bus arrival data;
step 3, processing the bus outbound data according to a bus inbound data processing method;
step 4, fusing the incoming and outgoing data;
step 5, supplementing station entering and exiting data by using GPS data;
and 6, time interpolation.
In order to optimize the technical scheme, the specific measures adopted further comprise:
the step 1 comprises the following substeps:
step 1.1, intercepting effective data of bus shift data, wherein the effective data comprises shift date, shift number, intra-shift number, specific line number, vehicle number, actual departure time and actual travel ending time;
step 1.2, intercepting vehicle GPS effective data, including vehicle number, dotting time, longitude and latitude;
step 1.3, associating bus shift data with GPS data by using a vehicle number, and endowing the GPS data in actual departure time and actual travel ending time with a specific line number corresponding to the bus shift, wherein the GPS data comprises a shift date, a shift number, an intra-shift number, a vehicle number, dotting time, longitude, latitude and a specific line number after the step;
and step 1.4, calculating the nearest station position and the Euclidean distance of each GPS dotting position on the line number according to the specific line number, setting a threshold value, and if the distance is within the threshold value range, defaulting that the dotting position is within the station entering and exiting time range, and giving a station number to the record, wherein after the step, the GPS data comprises a shift date, a shift number, a vehicle number, a dotting time, longitude, latitude, the specific line number and the station number.
The step 2 comprises the following substeps:
step 2.1, extracting station entering data according to the shift number and the trip number, wherein the station entering data comprises date, shift number, trip number, vehicle number, line number, station serial number and station reporting time;
step 2.2, deleting repeated data, grouping according to the shift number, the trip number, the vehicle number and the line number, sequencing each group from small to large according to the station reporting time, and if the station serial number or the station reporting time recorded at present is the same as that of the previous one, determining the data as invalid data and deleting the data;
step 2.3, deleting data with abnormal station reporting sequence, grouping according to the shift number, the trip number, the vehicle number and the line number, sequencing each group from small to large according to station reporting time, and according to station reporting logic, after the station reporting time of a station with a larger station serial number is later, if the station serial number of the current record is smaller than that of the previous record, considering the data as invalid data and deleting the data;
and step 2.4, deleting data with abnormal station reporting time intervals, grouping according to the shift number, the lap number, the vehicle number and the line number, sequencing each group from small to large according to the station reporting time, setting the time threshold to be 10s, solving the difference between the current station serial number and the station reporting time and the previous recorded station serial number and the station reporting time, respectively obtaining a station serial number interval of sno and a station reporting time interval of Δ t, if the current record has no record with the front order, setting the current record to be a null value, and when the current record has no record with the front order, determining the current record to be invalid data and deleting the current record when the current record has no value of sno > =1 and the Δ t is less than 10.
The step 4 comprises the following substeps:
step 4.1, fusing and grouping the station entering and exiting data according to the shift number, the lap number, the vehicle number and the line number;
step 4.2, sorting the grouped data from small to large according to the station reporting time, wherein the sequence number of the currently recorded station is more than or equal to the previous record according to the logic of station entering time reporting and station exiting time reporting;
and 4.3, comparing the current record with the previous record, if the site sequence number interval is (Δ sno >) =0, the current record is normal, if the site sequence number interval is (Δ sno) <0, the current record is marked, and the abnormal flag field bz is added and is assigned to be 1.
The step 5 comprises the following substeps:
step 5.1, grouping according to the shift number, the trip number, the vehicle number and the line number by using the GPS data acquired in the step 1;
step 5.2, taking line station data as a main part, associating GPS data and station entering and exiting station reporting data according to specific line numbers and station numbers, wherein each result of the step should comprise the specific line numbers, the station numbers, the GPS time GPS _ t of getting on and off, the time in _ t of getting on and reporting station, the time out _ t of getting off and reporting station and the abnormal mark bz;
and 5.3, supplementing data according to situations, and aiming at the same station, if the station entrance/exit time in _ t/out _ t and the abnormal mark bz meet the following conditions: (in _ t is null and out _ t is null) or (int _ t is null and out _ t is not null and bz =1), then int _ t = min (gps _ t), out _ t = max (gps _ t); if the station entering and exiting time in _ t/out _ t and the abnormal flag bz satisfy: int _ t is null and out _ t is not null and bz < > 1, then assigning the maximum gps _ t less than out _ t to in _ t; if the station entering and exiting time in _ t/out _ t meets the following conditions: int _ t is not null and out _ t is null, then assigning minimum gps _ t greater than in _ t to out _ t;
and 5.4, grouping the data according to the shift number, the trip number, the vehicle number and the line number, sequencing the data from small to large according to the time, repeating the step 4.3, carrying out data ordered check, and deleting abnormal data.
In the step 6, time interpolation is performed based on the distance weight by using the station spacing and the station reporting time at the two ends of the missing station.
The invention has the beneficial effects that: the bus station entering and exiting time sequence arrangement processing method based on the bus scheduling data, the station entering and exiting data, the GPS data and the road network data is provided, the bus station entering and exiting data are automatically detected and processed, and the accuracy of the bus station entering and exiting data is improved.
Drawings
Fig. 1 is a flow chart diagram of a creating method provided by the present invention.
Fig. 2 is a schematic diagram of a GPS data processing flow.
Fig. 3 is a schematic diagram of an ingress/egress data processing flow.
Fig. 4 is a schematic diagram of the flow of GPS data supplementing station-reporting time.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
The invention adopts various data such as GPS data, road network data, bus shift scheduling data, bus stop reporting data and the like, realizes the cleaning and supplementing of the bus stop reporting data through fusion processing, and finally obtains the relatively correct and complete bus stop reporting data. Of course, the use of the invention is not limited to these data, and various data preprocessing processes are independent, and can be combined to complete the cleaning, supplement and calculation of the bus stop report data.
As shown in fig. 1, a method for accurately identifying the entrance and the exit of a bus based on fusion data includes the following steps:
step 1.1, intercepting effective data (departure operation plan and actual operation condition of the bus) of bus shift data, wherein the effective data comprises shift date, shift number, intra-shift number, specific line number, vehicle number, actual departure time and actual travel ending time;
step 1.2, intercepting vehicle GPS effective data, including vehicle number, dotting time, longitude and latitude;
step 1.3, associating bus shift data with GPS data by using a vehicle number, and giving the GPS data in actual departure time and actual travel ending time to a specific line number corresponding to the bus shift, wherein the GPS data comprises shift date, shift number, intra-shift number, vehicle number, dotting time, longitude, latitude and the specific line number after being processed;
step 1.4, based on the specific line number, calculating the nearest station position and the Euclidean distance of each GPS dotting position on the line number, setting a threshold (generally set to be 50 m), and if the distance is within the threshold range, defaulting that the dotting position is within the time range of entering and leaving the station, and giving the station number to the record, wherein after the step, the GPS data should include the shift date, the shift number, the number of the trip in the shift, the vehicle number, the dotting time, the longitude, the latitude, the specific line number and the station number.
Step 2, bus arrival data processing, specifically referring to fig. 3, includes the following substeps:
step 2.1, extracting station entering data according to the shift number and the trip number, wherein the station entering data comprises date, shift number, trip number, vehicle number, line number, station serial number (sequencing of stations on a line), and station reporting time;
step 2.2, deleting repeated data, grouping according to the shift number, the trip number, the vehicle number and the line number, sequencing each group from small to large according to the station reporting time, and if the station serial number or the station reporting time recorded at present is the same as that of the previous one, determining the data as invalid data and deleting the data;
step 2.3, deleting data with abnormal station reporting sequence, grouping according to the shift number, the trip number, the vehicle number and the line number, sequencing each group from small to large according to station reporting time, and according to station reporting logic, after the station reporting time of a station with a larger station serial number is later, if the station serial number of the current record is smaller than that of the previous record, considering the data as invalid data and deleting the data;
and 2.4, deleting data with abnormal stop reporting time intervals, grouping according to the shift number, the lap number, the vehicle number and the line number, sequencing each group from small to large according to the stop reporting time, enabling the vehicle running time intervals among a plurality of stations to be larger than 1min according to the actual running condition of the vehicle, setting the time threshold value to be 30s, solving the difference values between the current station serial number and the stop reporting time and the previous recorded station serial number and the stop reporting time, respectively setting the station serial number intervals of sno and the stop reporting time intervals of Δ t (the unit is second), if no preamble record exists in the current record, setting the current record as a null value, and when the Δ sno > =1 and the Δ t <30, regarding the current record as invalid data and deleting the invalid data.
And 3, processing the bus outbound data according to a bus inbound data processing method, specifically referring to fig. 3.
Step 4, fusing the station entering and exiting data, comprising the following substeps:
step 4.1, fusing and grouping the station entering and exiting data according to the shift number, the lap number, the vehicle number and the line number;
step 4.2, sorting the grouped data from small to large according to the station reporting time, wherein the sequence number of the currently recorded station is more than or equal to the previous record (considering the missing in-and-out station reporting record) according to the logic of in-station time reporting and out-station time reporting;
step 4.3, compare the current record with the previous record (no comparison is needed if the current record has no preamble record), if the difference between the site sequence numbers is sno > =0, then it is normal, if the site sequence number is sno <0, then mark the record, add the abnormal flag field bz, and assign 1.
And 5, supplementing station entering and exiting data by using GPS data, and particularly referring to FIG. 4, the method comprises the following substeps:
step 5.1, grouping according to the shift number, the trip number, the vehicle number and the line number by using the GPS data acquired in the step 1;
step 5.2, taking line station data (including specific line number, station number and station serial number) as a main part, associating GPS data and station reporting data of entering and exiting stations according to the specific line number and the station number, wherein each result of the step should include the specific line number, the station serial number, GPS time GPS of getting on GPS time GPS _ t, time in _ t of getting on station and reporting station, time out _ t of getting off station and exception flag bz (obtained in step 4.3);
and 5.3, supplementing data according to situations, and aiming at the same station, if the station entrance/exit time in _ t/out _ t and the abnormal mark bz meet the following conditions: (in _ t is null and out _ t is null) or (int _ t is null and out _ t is not null and bz =1), then int _ t = min (gps _ t), out _ t = max (gps _ t); if the station entering and exiting time in _ t/out _ t and the abnormal flag bz satisfy: int _ t is null and out _ t is not null and bz < > 1, then assigning maximum GPS _ t smaller than out _ t to in _ t (the GPS data used in the patent is dotted for a time interval of 15s, which is close to the average value of the stop time of the historical site); if the station entering and exiting time in _ t/out _ t meets the following conditions: int _ t is not null and out _ t is null, then assigning minimum gps _ t greater than in _ t to out _ t;
and 5.4, grouping the data according to the shift number, the trip number, the vehicle number and the line number, sequencing the data from small to large according to the time, repeating the step 4.3, carrying out data ordered check, and deleting abnormal data.
And 6, time interpolation, namely supplementing in the step 5, remaining part of station reporting time for entering and exiting the station, and performing time interpolation based on the distance weight by using the station spacing and the station reporting time at the two ends of the missing station.
It should be noted that the terms "upper", "lower", "left", "right", "front", "back", etc. used in the present invention are for clarity of description only, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not limited by the technical contents of the essential changes.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (4)
1. A bus station entrance and exit accurate identification method based on fusion data is characterized by comprising the following steps:
step 1, preprocessing GPS data, and endowing the GPS data with actual running bus route numbers and station numbers; the step 1 comprises the following substeps:
step 1.1, intercepting effective data of bus shift data, wherein the effective data comprises shift date, shift number, intra-shift number, specific line number, vehicle number, actual departure time and actual travel ending time;
step 1.2, intercepting vehicle GPS effective data, including vehicle number, dotting time, longitude and latitude;
step 1.3, associating bus shift data with GPS data by using a vehicle number, and endowing the GPS data in actual departure time and actual travel ending time with a specific line number corresponding to the bus shift, wherein the GPS data comprises a shift date, a shift number, an intra-shift number, the vehicle number, dotting time, longitude, latitude and the specific line number after the step;
step 1.4, based on the specific line number, calculating the nearest station position and Euclidean distance of each GPS dotting position on the line number, setting a threshold value, and if the distance is within the threshold value range, defaulting that the dotting position is within the time range of entering and leaving the station, and giving the station number to the record, wherein after the step, the GPS data comprises a shift date, a shift number, a vehicle number, a dotting time, longitude, latitude, the specific line number and the station number;
step 2, processing bus arrival data;
step 3, processing the bus outbound data according to a bus inbound data processing method;
step 4, fusing the incoming and outgoing data;
step 5, supplementing station entering and exiting data by using GPS data; the step 5 comprises the following substeps:
step 5.1, grouping according to the shift number, the trip number, the vehicle number and the line number by using the GPS data acquired in the step 1;
step 5.2, taking line station data as a main part, associating GPS data and station entering and exiting station reporting data according to specific line numbers and station numbers, wherein each result of the step should comprise the specific line numbers, the station numbers, the GPS time GPS _ t of getting on and off, the time in _ t of getting on and reporting station, the time out _ t of getting off and reporting station and the abnormal mark bz;
and 5.3, supplementing data according to situations, and aiming at the same station, if the station entrance/exit time in _ t/out _ t and the abnormal mark bz meet the following conditions: (in _ t is null and out _ t is null) or (int _ t is null and out _ t is not null and bz =1), then int _ t = min (gps _ t), out _ t = max (gps _ t); if the station entering and exiting time in _ t/out _ t and the abnormal flag bz satisfy: int _ t is null and out _ t is not null and bz < > 1, then assigning the maximum gps _ t less than out _ t to in _ t; if the station entering and exiting time in _ t/out _ t meets the following conditions: int _ t is not null and out _ t is null, then assigning minimum gps _ t greater than in _ t to out _ t;
step 5.4, grouping the data according to the shift number, the trip number, the vehicle number and the line number, sequencing the data from small to large according to time, repeating the step 4.3, carrying out data ordered check, and deleting abnormal data;
and 6, time interpolation.
2. The method for accurately identifying the entrance and the exit of the bus based on the fusion data as claimed in claim 1, wherein: the step 2 comprises the following substeps:
step 2.1, extracting station entering data according to the shift number and the trip number, wherein the station entering data comprises date, shift number, trip number, vehicle number, line number, station serial number and station reporting time;
step 2.2, deleting repeated data, grouping according to the shift number, the trip number, the vehicle number and the line number, sequencing each group from small to large according to the station reporting time, and if the station serial number or the station reporting time recorded at present is the same as that of the previous one, determining the data as invalid data and deleting the data;
step 2.3, deleting data with abnormal station reporting sequence, grouping according to the shift number, the trip number, the vehicle number and the line number, sequencing each group from small to large according to station reporting time, and according to station reporting logic, after the station reporting time of a station with a larger station serial number is later, if the station serial number of the current record is smaller than that of the previous record, considering the data as invalid data and deleting the data;
and step 2.4, deleting data with abnormal station reporting time intervals, grouping according to the shift number, the lap number, the vehicle number and the line number, sequencing each group from small to large according to the station reporting time, setting the time threshold to be 10s, solving the difference between the current station serial number and the station reporting time and the previous recorded station serial number and the station reporting time, respectively obtaining a station serial number interval of sno and a station reporting time interval of Δ t, if the current record has no record with the front order, setting the current record to be a null value, and when the current record has no record with the front order, determining the current record to be invalid data and deleting the current record when the current record has no value of sno > =1 and the Δ t is less than 10.
3. The method for accurately identifying the entrance and the exit of the bus based on the fusion data as claimed in claim 2, wherein: the step 4 comprises the following substeps:
step 4.1, fusing and grouping the station entering and exiting data according to the shift number, the lap number, the vehicle number and the line number;
step 4.2, sorting the grouped data from small to large according to the station reporting time, wherein the sequence number of the currently recorded station is more than or equal to the previous record according to the logic of station entering time reporting and station exiting time reporting;
and 4.3, comparing the current record with the previous record, if the site sequence number interval is (Δ sno >) =0, the current record is normal, if the site sequence number interval is (Δ sno) <0, the current record is marked, and the abnormal flag field bz is added and is assigned to be 1.
4. The method for accurately identifying the entrance and the exit of the bus based on the fusion data as claimed in claim 1, wherein: in the step 6, time interpolation is performed based on the distance weight by using the station spacing and the station reporting time at the two ends of the missing station.
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| CN110992726B (en) * | 2019-10-28 | 2021-08-27 | 上海城市交通设计院有限公司 | Method for identifying arrival of bus and dividing up-down movement and shift of bus |
| CN111125293B (en) * | 2019-12-31 | 2021-07-09 | 南京市城市与交通规划设计研究院股份有限公司 | Automatic generation method and device of public transport network model and electronic equipment |
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| CN108154698B (en) * | 2018-01-05 | 2020-12-29 | 上海元卓信息科技有限公司 | Bus arrival and departure accurate time calculation method based on GPS track big data |
| CN108230724B (en) * | 2018-01-31 | 2020-05-22 | 华南理工大学 | Urban public transport system vehicle-mounted station reporting missing data repairing method based on maximum probability estimation |
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