CN115565379A - Method and system for replacing abnormal passenger flow data in historical passenger flow data - Google Patents
Method and system for replacing abnormal passenger flow data in historical passenger flow data Download PDFInfo
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- CN115565379A CN115565379A CN202211553355.4A CN202211553355A CN115565379A CN 115565379 A CN115565379 A CN 115565379A CN 202211553355 A CN202211553355 A CN 202211553355A CN 115565379 A CN115565379 A CN 115565379A
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
Abstract
The invention relates to the technical field of intelligent transportation, and discloses a method and a system for replacing abnormal passenger flow data in historical passenger flow data, wherein the method for replacing the abnormal passenger flow data in the historical passenger flow data comprises the steps of calling the historical passenger flow data of a subway station, calculating characteristic passenger flow data according to the historical passenger flow data, and replacing the abnormal passenger flow data in the historical passenger flow data with the characteristic passenger flow data; a system for replacing abnormal passenger flow data in historical passenger flow data comprises a data calling module, a characteristic passenger flow data calculating module and an abnormal passenger flow data replacing module. The invention can process abnormal passenger flow data in the historical passenger flow data and can generate passenger flow data with continuous time and higher quality after processing.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a method and a system for replacing abnormal passenger flow data in historical passenger flow data.
Background
At present, historical passenger flow data is generally used for passenger flow prediction of subway stations, and due to the fact that the passenger flow prediction has high quality requirements on the historical passenger flow data, abnormal passenger flow data in the historical passenger flow data needs to be processed.
Disclosure of Invention
The invention aims to provide a method and a system for replacing abnormal passenger flow data in historical passenger flow data, which not only can process the abnormal passenger flow data in the historical passenger flow data, but also can generate passenger flow data which is continuous in time and has higher quality after the abnormal passenger flow data is processed.
In order to solve the technical problem, the invention adopts the following scheme:
in one aspect, a method for replacing abnormal passenger flow data in historical passenger flow data comprises the following steps:
s1: calling historical passenger flow data of a subway station, wherein the historical passenger flow data comprises abnormal passenger flow data and normal passenger flow data;
s2: calculating characteristic passenger flow data according to the historical passenger flow data;
s3: and replacing abnormal passenger flow data in the historical passenger flow data with characteristic passenger flow data.
Further, the historical passenger flow data includes a historical time period, and an inbound passenger flow value and an outbound passenger flow value in any time period within the historical time period, the abnormal passenger flow data includes an abnormal inbound passenger flow value in the inbound passenger flow value, an abnormal outbound passenger flow value in the outbound passenger flow value, an abnormal inbound time period corresponding to the abnormal inbound passenger flow value, and an abnormal outbound time period corresponding to the abnormal outbound passenger flow value, and the normal passenger flow data includes a normal inbound passenger flow value in the inbound passenger flow value, a normal outbound passenger flow value in the outbound passenger flow value, a normal inbound time period corresponding to the normal inbound passenger flow value, and a normal outbound time period corresponding to the normal outbound passenger flow value.
Further, the characteristic passenger flow data includes a characteristic inbound value and a characteristic outbound value, and the S2 includes:
s21: calculating a characteristic inbound value according to the abnormal inbound passenger flow value, the abnormal inbound time period, the normal inbound passenger flow value and the normal inbound time period;
s22: and calculating the characteristic outbound value according to the abnormal outbound passenger flow value, the abnormal outbound time period, the normal outbound passenger flow value and the normal outbound time period.
Further, the S21 includes:
s211: dividing the historical time period into a plurality of continuous historical period time periods with the length of T of the passenger flow period;
s212: marking the normal station entering time periods corresponding to the abnormal station entering time periods in all the historical period time periods as characteristic station entering time periods, and calling the normal station entering passenger flow values of all the characteristic station entering time periods;
s213: judging whether the normal arrival passenger flow values of all the characteristic arrival time periods meet normal distribution or not;
s214: if the normal arrival passenger flow values of all the characteristic arrival time periods meet the normal distribution, calculating a first maximum likelihood value of the normal distribution met by the normal arrival passenger flow values of all the characteristic arrival time periods, and assigning the first maximum likelihood value to the characteristic arrival value; and if the normal station-entering passenger flow values of all the characteristic station-entering time periods do not meet the normal distribution, assigning the median of the normal station-entering passenger flow values of all the characteristic station-entering time periods to the characteristic station-entering value.
Further, the S22 includes:
s221: dividing the historical time period into a plurality of continuous historical period time periods with the length of T of the passenger flow period;
s222: marking the normal outbound time periods corresponding to the abnormal outbound time periods in all historical period time periods as characteristic outbound time periods, and calling the normal outbound passenger flow values of all the characteristic outbound time periods;
s223: judging whether the normal outbound passenger flow values of all the characteristic outbound time periods meet normal distribution;
s224: if the normal outbound passenger flow values of all the characteristic outbound time periods meet the normal distribution, calculating a second maximum likelihood value of the normal distribution met by the normal outbound passenger flow values of all the characteristic outbound time periods, and assigning the second maximum likelihood value to the characteristic outbound value; and if the normal outbound passenger flow values of all the characteristic outbound time periods do not meet the normal distribution, assigning the median of the normal outbound passenger flow values of all the characteristic outbound time periods to the characteristic outbound value.
Further, the passenger flow period is a minimum time interval when the total number of people coming in from the subway station is equal to the total number of people coming out from the subway station.
Further, the S3 includes:
s31: replacing the abnormal inbound passenger flow value of the abnormal inbound time period with a characteristic inbound value corresponding to the abnormal inbound time period;
s32: and replacing the abnormal outbound passenger flow value of the abnormal outbound time period with a characteristic outbound value corresponding to the abnormal outbound time period.
In another aspect, a system for replacing anomalous passenger flow data in historical passenger flow data, comprising:
a memory;
one or several processors;
one or several modules stored in the memory and configured to be executed by the one or several processors, the one or several modules comprising:
the data calling module is used for calling historical passenger flow data of the subway station;
a characteristic passenger flow data calculation module for calculating characteristic passenger flow data according to the historical passenger flow data;
and the abnormal passenger flow data replacement module is used for replacing the abnormal passenger flow data in the historical passenger flow data with the characteristic passenger flow data.
Further, the characteristic passenger flow data calculation module comprises:
a characteristic inbound value calculating module for calculating characteristic inbound values according to the abnormal inbound passenger flow value, the abnormal inbound time period, the normal inbound passenger flow value and the normal inbound time period;
and the characteristic outbound value calculating module is used for calculating a characteristic outbound value according to the abnormal outbound passenger flow value, the abnormal outbound time period, the normal outbound passenger flow value and the normal outbound time period.
Further, the module for replacing the abnormal passenger flow data in the historical passenger flow data with the characteristic passenger flow data includes:
the abnormal inbound passenger flow value replacing module is used for replacing the abnormal inbound passenger flow value of the abnormal inbound time period with the characteristic inbound value corresponding to the abnormal inbound time period;
and the abnormal outbound passenger flow value replacing module is used for replacing the abnormal outbound passenger flow value of the abnormal outbound time period with the characteristic outbound passenger flow value corresponding to the abnormal outbound time period.
The invention has the following beneficial effects:
1. the invention provides a method and a system for replacing abnormal passenger flow data in historical passenger flow data, wherein a data calling module calls the historical passenger flow data and outputs the historical passenger flow data to a characteristic passenger flow data calculation module, the characteristic passenger flow data calculation module calculates characteristic passenger flow data according to the historical passenger flow data and outputs the characteristic passenger flow data to an abnormal passenger flow data replacement module, the abnormal passenger flow data replacement module replaces the abnormal passenger flow data in the historical passenger flow data with the characteristic passenger flow data, and compared with the method and the system for eliminating the abnormal passenger flow data in the historical passenger flow data, the method and the system provided by the invention can generate passenger flow data which are continuous in time.
2. In the invention, a characteristic passenger flow data calculation module is arranged to judge whether the inbound passenger flow values and the outbound passenger flow values of all characteristic outbound time periods conform to normal distribution or not, and if the inbound passenger flow values and the outbound passenger flow values conform to the normal distribution, the maximum likelihood values corresponding to the normal distribution are assigned to the characteristic inbound passenger flow values and the characteristic outbound passenger flow values; and if the normal distribution is not met, assigning the median of the inbound passenger flow values and the outbound passenger flow values of all the characteristic outbound time periods to the characteristic inbound passenger flow values and the characteristic outbound passenger flow values correspondingly, wherein the quality of the characteristic passenger flow data obtained by the method is higher.
Drawings
FIG. 1 is a general flow diagram of a method for replacing abnormal passenger flow data in historical passenger flow data in accordance with the present invention;
FIG. 2 is a detailed flowchart of S21 in the present invention;
FIG. 3 is a detailed flowchart of S22 in the present invention;
fig. 4 is a general block diagram of a system for replacing abnormal passenger flow data in historical passenger flow data according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention/invention better understood, the present invention/invention is further described in detail below with reference to the accompanying drawings and the detailed description.
Example 1:
a method for replacing abnormal passenger flow data in historical passenger flow data as shown in fig. 1 to 3, comprising the following steps:
s1: calling historical passenger flow data of a subway station, wherein the historical passenger flow data comprises abnormal passenger flow data and normal passenger flow data;
s2: calculating characteristic passenger flow data according to the historical passenger flow data;
s3: and replacing abnormal passenger flow data in the historical passenger flow data with characteristic passenger flow data.
Specifically, the historical passenger flow data includes a historical time period, and an inbound passenger flow value and an outbound passenger flow value in any time period within the historical time period, the abnormal passenger flow data includes an abnormal inbound passenger flow value in the inbound passenger flow value, an abnormal outbound passenger flow value in the outbound passenger flow value, an abnormal inbound time period corresponding to the abnormal inbound passenger flow value, and an abnormal outbound time period corresponding to the abnormal outbound passenger flow value, and the normal passenger flow data includes a normal inbound passenger flow value in the inbound passenger flow value, a normal outbound passenger flow value in the outbound passenger flow value, a normal inbound time period corresponding to the normal inbound passenger flow value, and a normal outbound time period corresponding to the normal outbound passenger flow value.
Specifically, the characteristic passenger flow data includes a characteristic inbound value and a characteristic outbound value, and the S2 includes:
s21: calculating a characteristic inbound value according to the abnormal inbound passenger flow value, the abnormal inbound time period, the normal inbound passenger flow value and the normal inbound time period;
s22: and calculating the characteristic outbound value according to the abnormal outbound passenger flow value, the abnormal outbound time period, the normal outbound passenger flow value and the normal outbound time period.
Specifically, the S21 includes:
s211: dividing the historical time period into a plurality of continuous historical period time periods with the length of T of the passenger flow period;
s212: marking the normal station entering time periods corresponding to the abnormal station entering time periods in all the historical period time periods as characteristic station entering time periods, and calling the normal station entering passenger flow values of all the characteristic station entering time periods;
s213: judging whether the normal arrival passenger flow values of all the characteristic arrival time periods meet normal distribution;
s214: if the normal inbound passenger flow values of all the characteristic inbound time periods meet normal distribution, calculating a first maximum likelihood value of the normal distribution met by the normal inbound passenger flow values of all the characteristic inbound time periods, and assigning the first maximum likelihood value to the characteristic inbound value; and if the normal inbound passenger flow values of all the characteristic inbound time periods do not meet the normal distribution, assigning the median of the normal inbound passenger flow values of all the characteristic inbound time periods to the characteristic inbound value.
Specifically, the S22 includes:
s221: dividing the historical time period into a plurality of continuous historical period time periods with the length of T of the passenger flow period;
s222: marking the normal outbound time periods corresponding to the abnormal outbound time periods in all historical period time periods as characteristic outbound time periods, and calling the normal outbound passenger flow values of all the characteristic outbound time periods;
s223: judging whether the normal outbound passenger flow values of all the characteristic outbound time periods meet normal distribution or not;
s224: if the normal outbound passenger flow values of all the characteristic outbound time periods meet the normal distribution, calculating a second maximum likelihood value of the normal distribution met by the normal outbound passenger flow values of all the characteristic outbound time periods, and assigning the second maximum likelihood value to the characteristic outbound value; and if the normal outbound passenger flow values of all the characteristic outbound time periods do not meet the normal distribution, assigning the median of the normal outbound passenger flow values of all the characteristic outbound time periods to the characteristic outbound value.
Specifically, the passenger flow cycle is a minimum time interval in which the total number of people entering the subway station is equal to the total number of people leaving the subway station.
Specifically, the S3 includes:
s31: replacing the abnormal inbound passenger flow value of the abnormal inbound time period with a characteristic inbound value corresponding to the abnormal inbound time period;
s32: and replacing the abnormal outbound passenger flow value of the abnormal outbound time period with a characteristic outbound value corresponding to the abnormal outbound time period.
For example, the historical time period is 140 days, the length T of the passenger flow cycle is 7 days, and the historical time period is divided into 20 continuous historical cycle time periods with the length of 7 days.
The working principle of the embodiment is as follows:
according to the invention, the minimum time interval that the passenger flow period is equal to the total number of passengers entering from the subway station and the total number of passengers leaving from the subway station is defined, and a great deal of research finds that the variation curves of the entering passenger flow value and the leaving passenger flow value of most subway stations in two adjacent time periods with the length of the passenger flow period are basically consistent, so that the entering passenger flow value variation curve and the leaving passenger flow value variation curve of the subway stations in the time periods with the length of the passenger flow period can basically embody the general entering characteristics and leaving characteristics of the subway stations.
Based on the principle, the technical scheme includes that firstly, a historical time period is segmented by taking a passenger flow period as a reference, the historical time period is divided into a plurality of continuous historical period time periods with the length being the length T of the passenger flow period, although variation curves of an inbound passenger flow value and an outbound passenger flow value of a subway station in two adjacent time periods with the length being the length of the passenger flow period are basically consistent, abnormal passenger flow data with large difference with normal passenger flow data still exist, and the normal inbound passenger flow value and the normal outbound passenger flow value which are basically consistent can reflect the general inbound characteristics and the outbound characteristics of the subway station, so that normal inbound passenger flow values and normal outbound passenger flow values corresponding to the abnormal inbound passenger flow value and the abnormal outbound passenger flow value in all the historical period time periods are extracted, the inbound passenger flow value and the characteristic outbound value are calculated according to the normal inbound passenger flow value and the normal outbound passenger flow value, and finally the abnormal inbound passenger flow value and the abnormal outbound passenger flow value are generated according to the scheme, and the abnormal outbound passenger flow data are generated according to the historical data, and the abnormal outbound passenger flow data.
Example 2:
as shown in fig. 4, a system for replacing abnormal passenger flow data in historical passenger flow data comprises:
a memory;
one or several processors;
the data calling module is used for calling historical passenger flow data of the subway station;
a characteristic passenger flow data calculation module for calculating characteristic passenger flow data according to the historical passenger flow data;
and the abnormal passenger flow data replacement module is used for replacing the abnormal passenger flow data in the historical passenger flow data with the characteristic passenger flow data.
Specifically, the characteristic passenger flow data calculation module includes:
a characteristic inbound value calculating module for calculating characteristic inbound values according to the abnormal inbound passenger flow value, the abnormal inbound time period, the normal inbound passenger flow value and the normal inbound time period;
and the characteristic outbound value calculating module is used for calculating a characteristic outbound value according to the abnormal outbound passenger flow value, the abnormal outbound time period, the normal outbound passenger flow value and the normal outbound time period.
Specifically, the module for replacing the abnormal passenger flow data in the historical passenger flow data with the characteristic passenger flow data includes:
the abnormal inbound passenger flow value replacing module is used for replacing the abnormal inbound passenger flow value of the abnormal inbound time period with the characteristic inbound value corresponding to the abnormal inbound time period;
and the abnormal outbound passenger flow value replacing module is used for replacing the abnormal outbound passenger flow value of the abnormal outbound time period with the characteristic outbound passenger flow value corresponding to the abnormal outbound time period.
The working principle of the embodiment is as follows:
in the invention, a data calling module calls historical passenger flow data and outputs the historical passenger flow data to a characteristic passenger flow data calculating module; a characteristic inbound value calculation module and a characteristic outbound value calculation module in the characteristic passenger flow data calculation module calculate characteristic inbound values and characteristic outbound values according to historical passenger flow data and output the characteristic inbound values and the characteristic outbound values to an abnormal passenger flow data replacement module; an abnormal arrival passenger flow value replacing module in the abnormal passenger flow data replacing module replaces the abnormal arrival passenger flow data in the historical passenger flow data with the characteristic arrival value, an abnormal departure passenger flow value replacing module in the abnormal passenger flow data replacing module replaces the abnormal departure passenger flow data in the historical passenger flow data with the characteristic departure value, and compared with the situation that the abnormal passenger flow data in the historical passenger flow data are removed, the system can generate passenger flow data which are continuous in time.
Example 3:
the present embodiment describes in detail a method and a system for replacing abnormal passenger flow data in historical passenger flow data from an overall process perspective, as shown in fig. 1 to 4:
the technical scheme of the invention comprises a process of calling historical passenger flow data, a process of calculating characteristic passenger flow data and a process of replacing abnormal passenger flow data, and specifically comprises the following steps:
calling historical passenger flow data: the data calling module calls historical passenger flow data with a historical time period of 140 days;
calculating characteristic passenger flow data: the characteristic passenger flow data calculation module divides the historical time period into 20 continuous historical period time periods with the length of 7 days; the characteristic inbound value calculation module marks the normal inbound time periods corresponding to the abnormal inbound time periods in all historical period time periods as characteristic inbound time periods, calls normal inbound passenger flow values of all the characteristic inbound time periods, judges whether the normal inbound passenger flow values of all the characteristic inbound time periods meet normal distribution or not, calculates a first maximum likelihood value of normal distribution met by the normal inbound passenger flow values of all the characteristic inbound time periods if the normal inbound passenger flow values of all the characteristic inbound time periods meet the normal distribution, assigns the first maximum likelihood value to the characteristic inbound value, and assigns a median of the normal inbound passenger flow values of all the characteristic inbound time periods to the characteristic inbound value if the normal inbound passenger flow values of all the characteristic inbound time periods do not meet the normal distribution; similarly, the characteristic outbound value calculation module divides the historical time period into 20 continuous historical period time periods with the length of 7 days, marks the normal outbound time periods corresponding to the abnormal outbound time periods in all the historical period time periods as the characteristic outbound time periods, calls the normal outbound passenger flow values of all the characteristic outbound time periods, judges whether the normal outbound passenger flow values of all the characteristic outbound time periods meet normal distribution or not, calculates the second maximum likelihood value of the normal distribution met by the normal outbound passenger flow values of all the characteristic outbound time periods if the normal outbound passenger flow values of all the characteristic outbound time periods meet the normal distribution, and assigns the second maximum likelihood value to the characteristic outbound value; and if the normal outbound passenger flow values of all the characteristic outbound time periods do not meet the normal distribution, assigning the median of the normal outbound passenger flow values of all the characteristic outbound time periods to the characteristic outbound values.
Replacing abnormal passenger flow data: an abnormal inbound passenger flow value replacing module in the abnormal passenger flow data replacing module replaces the abnormal inbound passenger flow value in the abnormal inbound time period with a characteristic inbound value corresponding to the abnormal inbound time period; and an abnormal outbound passenger flow value replacing module in the abnormal passenger flow data replacing module replaces the abnormal outbound passenger flow value in the abnormal outbound time period with a characteristic outbound value corresponding to the abnormal outbound time period.
In the process, the characteristic passenger flow data are calculated according to the normal passenger flow data in the historical passenger flow data, and the abnormal passenger flow data in the historical passenger flow data are replaced by the characteristic passenger flow data.
It will be appreciated that the above embodiments are merely exemplary embodiments employed to illustrate the principles of the invention/invention, however, the invention/invention is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (10)
1. A method for replacing abnormal passenger flow data in historical passenger flow data is characterized by comprising the following steps:
s1: calling historical passenger flow data of a subway station, wherein the historical passenger flow data comprises abnormal passenger flow data and normal passenger flow data;
s2: calculating characteristic passenger flow data according to the historical passenger flow data;
s3: and replacing abnormal passenger flow data in the historical passenger flow data with characteristic passenger flow data.
2. The method of claim 1, wherein the historical traffic data comprises historical time periods, inbound traffic values and outbound traffic values at any time period within the historical time periods, the abnormal traffic data comprises abnormal inbound traffic values in the inbound traffic values, abnormal outbound traffic values in the outbound traffic values, abnormal inbound time periods corresponding to the abnormal inbound traffic values and abnormal outbound time periods corresponding to the abnormal outbound traffic values, and the normal traffic data comprises normal inbound traffic values in the inbound traffic values, normal outbound traffic values in the outbound traffic values, normal inbound time periods corresponding to the normal inbound traffic values and normal outbound time periods corresponding to the normal outbound traffic values.
3. The method for replacing abnormal passenger flow data in historical passenger flow data according to claim 2, wherein the characteristic passenger flow data comprises a characteristic inbound value and a characteristic outbound value, the S2 comprises:
s21: calculating a characteristic inbound value according to the abnormal inbound passenger flow value, the abnormal inbound time period, the normal inbound passenger flow value and the normal inbound time period;
s22: and calculating the characteristic outbound value according to the abnormal outbound passenger flow value, the abnormal outbound time period, the normal outbound passenger flow value and the normal outbound time period.
4. The method of replacing abnormal passenger flow data in historical passenger flow data according to claim 3, wherein said S21 comprises:
s211: dividing the historical time period into a plurality of continuous historical period time periods with the length of T of the passenger flow period;
s212: marking the normal arrival time periods corresponding to the abnormal arrival time periods in all the historical period time periods as characteristic arrival time periods, and calling the normal arrival passenger flow values of all the characteristic arrival time periods;
s213: judging whether the normal arrival passenger flow values of all the characteristic arrival time periods meet normal distribution or not;
s214: if the normal inbound passenger flow values of all the characteristic inbound time periods meet normal distribution, calculating a first maximum likelihood value of the normal distribution met by the normal inbound passenger flow values of all the characteristic inbound time periods, and assigning the first maximum likelihood value to the characteristic inbound value; and if the normal inbound passenger flow values of all the characteristic inbound time periods do not meet the normal distribution, assigning the median of the normal inbound passenger flow values of all the characteristic inbound time periods to the characteristic inbound value.
5. The method of claim 4, wherein the step S22 comprises:
s221: dividing the historical time period into a plurality of continuous historical period time periods with the length of T of the passenger flow period;
s222: marking the normal outbound time periods corresponding to the abnormal outbound time periods in all historical period time periods as characteristic outbound time periods, and calling the normal outbound passenger flow values of all the characteristic outbound time periods;
s223: judging whether the normal outbound passenger flow values of all the characteristic outbound time periods meet normal distribution or not;
s224: if the normal outbound passenger flow values of all the characteristic outbound time periods meet the normal distribution, calculating a second maximum likelihood value of the normal distribution met by the normal outbound passenger flow values of all the characteristic outbound time periods, and assigning the second maximum likelihood value to the characteristic outbound value; and if the normal outbound passenger flow values of all the characteristic outbound time periods do not meet the normal distribution, assigning the median of the normal outbound passenger flow values of all the characteristic outbound time periods to the characteristic outbound values.
6. The method of claim 5, wherein the traffic cycle is a minimum time interval in which the total number of people arriving from a subway station is equal to the total number of people leaving from the subway station.
7. The method of replacing abnormal passenger flow data in historical passenger flow data according to claim 6, wherein said S3 comprises:
s31: replacing the abnormal inbound passenger flow value of the abnormal inbound time period with a characteristic inbound value corresponding to the abnormal inbound time period;
s32: and replacing the abnormal outbound passenger flow value of the abnormal outbound time period with a characteristic outbound value corresponding to the abnormal outbound time period.
8. A system for replacing anomalous passenger flow data in historical passenger flow data, comprising:
a memory;
one or several processors;
one or several modules stored in the memory and configured to be executed by the one or several processors, the one or several modules comprising:
the data calling module is used for calling historical passenger flow data of the subway station;
a characteristic passenger flow data calculation module for calculating characteristic passenger flow data according to the historical passenger flow data;
and the abnormal passenger flow data replacement module is used for replacing the abnormal passenger flow data in the historical passenger flow data with the characteristic passenger flow data.
9. The system for replacing abnormal passenger flow data in historical passenger flow data according to claim 8, wherein said characteristic passenger flow data calculation module comprises:
a characteristic inbound value calculating module for calculating a characteristic inbound value according to the abnormal inbound passenger flow value, the abnormal inbound time period, the normal inbound passenger flow value and the normal inbound time period;
and the characteristic outbound value calculating module is used for calculating a characteristic outbound value according to the abnormal outbound passenger flow value, the abnormal outbound time period, the normal outbound passenger flow value and the normal outbound time period.
10. The system for replacing abnormal passenger flow data in historical passenger flow data according to claim 9, wherein said module for replacing abnormal passenger flow data in historical passenger flow data with characteristic passenger flow data comprises:
the abnormal inbound passenger flow value replacing module is used for replacing the abnormal inbound passenger flow value of the abnormal inbound time period with the characteristic inbound value corresponding to the abnormal inbound time period;
and the abnormal outbound passenger flow value replacing module is used for replacing the abnormal outbound passenger flow value of the abnormal outbound time period with the characteristic outbound passenger flow value corresponding to the abnormal outbound time period.
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Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103593976A (en) * | 2013-11-28 | 2014-02-19 | 青岛海信网络科技股份有限公司 | Road traffic state determining method and system based on detector |
| CN104021430A (en) * | 2014-06-12 | 2014-09-03 | 东南大学 | Method for analyzing uncertainty of passenger flow of urban mass transit terminal |
| CN105224999A (en) * | 2015-09-10 | 2016-01-06 | 北京市交通信息中心 | The real-time passenger flow forecasting of urban track traffic based on AFC data and system |
| CN107992449A (en) * | 2017-12-05 | 2018-05-04 | 北京工业大学 | A kind of subway anomalous traffic detection method based on low-rank representation |
| JP2018088225A (en) * | 2016-11-28 | 2018-06-07 | 日本コントロールシステム株式会社 | Abnormal value extraction system and method for analog waveform data using Gaussian fitting |
| CN108154342A (en) * | 2017-12-25 | 2018-06-12 | 苏州大学 | Intelligent bus data collaborative method and its system based on cloud storage |
| CN108399277A (en) * | 2018-01-24 | 2018-08-14 | 华南理工大学 | A kind of bridge damnification recognition method based on temperature with strain relevance |
| CN109543268A (en) * | 2018-11-14 | 2019-03-29 | 大连理工大学 | The recognition methods of TBM propulsive force major influence factors based on kriging model |
| CN109615226A (en) * | 2018-12-12 | 2019-04-12 | 焦点科技股份有限公司 | A kind of operation indicator method for monitoring abnormality |
| CN109726198A (en) * | 2018-12-06 | 2019-05-07 | 中科恒运股份有限公司 | Method for processing abnormal data and device |
| CN110046764A (en) * | 2019-04-12 | 2019-07-23 | 成都四方伟业软件股份有限公司 | The method and device of passenger flow forecast amount |
| US20190311301A1 (en) * | 2018-04-10 | 2019-10-10 | Ebay Inc. | Dynamically generated machine learning models and visualization thereof |
| CN110570004A (en) * | 2018-06-05 | 2019-12-13 | 上海申通地铁集团有限公司 | subway passenger flow prediction method and system |
| CN112699946A (en) * | 2020-12-31 | 2021-04-23 | 湖南工商大学 | Anomaly detection method and device for sensing monitoring data and related equipment |
| CN112734072A (en) * | 2019-10-28 | 2021-04-30 | 深圳市中电软件有限公司 | Power load prediction method, system, terminal device and medium |
| CN113076349A (en) * | 2020-01-06 | 2021-07-06 | 阿里巴巴集团控股有限公司 | Data anomaly detection method, device and system and electronic equipment |
| CN113988477A (en) * | 2021-11-26 | 2022-01-28 | 西安化奇数据科技有限公司 | Photovoltaic power short-term prediction method and device based on machine learning and storage medium |
| CN113987944A (en) * | 2021-10-29 | 2022-01-28 | 成都智元汇信息技术股份有限公司 | Subway station-entering passenger flow prediction method and device based on Prophet model |
| WO2022134692A1 (en) * | 2020-12-25 | 2022-06-30 | 卡斯柯信号有限公司 | Intelligent scheduling method and system for rail transit |
| CN115204478A (en) * | 2022-06-30 | 2022-10-18 | 北京航空航天大学 | Public traffic flow prediction method combining urban interest points and space-time causal relationship |
-
2022
- 2022-12-06 CN CN202211553355.4A patent/CN115565379B/en active Active
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103593976A (en) * | 2013-11-28 | 2014-02-19 | 青岛海信网络科技股份有限公司 | Road traffic state determining method and system based on detector |
| CN104021430A (en) * | 2014-06-12 | 2014-09-03 | 东南大学 | Method for analyzing uncertainty of passenger flow of urban mass transit terminal |
| CN105224999A (en) * | 2015-09-10 | 2016-01-06 | 北京市交通信息中心 | The real-time passenger flow forecasting of urban track traffic based on AFC data and system |
| JP2018088225A (en) * | 2016-11-28 | 2018-06-07 | 日本コントロールシステム株式会社 | Abnormal value extraction system and method for analog waveform data using Gaussian fitting |
| CN107992449A (en) * | 2017-12-05 | 2018-05-04 | 北京工业大学 | A kind of subway anomalous traffic detection method based on low-rank representation |
| CN108154342A (en) * | 2017-12-25 | 2018-06-12 | 苏州大学 | Intelligent bus data collaborative method and its system based on cloud storage |
| CN108399277A (en) * | 2018-01-24 | 2018-08-14 | 华南理工大学 | A kind of bridge damnification recognition method based on temperature with strain relevance |
| US20190311301A1 (en) * | 2018-04-10 | 2019-10-10 | Ebay Inc. | Dynamically generated machine learning models and visualization thereof |
| CN110570004A (en) * | 2018-06-05 | 2019-12-13 | 上海申通地铁集团有限公司 | subway passenger flow prediction method and system |
| CN109543268A (en) * | 2018-11-14 | 2019-03-29 | 大连理工大学 | The recognition methods of TBM propulsive force major influence factors based on kriging model |
| CN109726198A (en) * | 2018-12-06 | 2019-05-07 | 中科恒运股份有限公司 | Method for processing abnormal data and device |
| CN109615226A (en) * | 2018-12-12 | 2019-04-12 | 焦点科技股份有限公司 | A kind of operation indicator method for monitoring abnormality |
| CN110046764A (en) * | 2019-04-12 | 2019-07-23 | 成都四方伟业软件股份有限公司 | The method and device of passenger flow forecast amount |
| CN112734072A (en) * | 2019-10-28 | 2021-04-30 | 深圳市中电软件有限公司 | Power load prediction method, system, terminal device and medium |
| CN113076349A (en) * | 2020-01-06 | 2021-07-06 | 阿里巴巴集团控股有限公司 | Data anomaly detection method, device and system and electronic equipment |
| WO2022134692A1 (en) * | 2020-12-25 | 2022-06-30 | 卡斯柯信号有限公司 | Intelligent scheduling method and system for rail transit |
| CN112699946A (en) * | 2020-12-31 | 2021-04-23 | 湖南工商大学 | Anomaly detection method and device for sensing monitoring data and related equipment |
| CN113987944A (en) * | 2021-10-29 | 2022-01-28 | 成都智元汇信息技术股份有限公司 | Subway station-entering passenger flow prediction method and device based on Prophet model |
| CN113988477A (en) * | 2021-11-26 | 2022-01-28 | 西安化奇数据科技有限公司 | Photovoltaic power short-term prediction method and device based on machine learning and storage medium |
| CN115204478A (en) * | 2022-06-30 | 2022-10-18 | 北京航空航天大学 | Public traffic flow prediction method combining urban interest points and space-time causal relationship |
Non-Patent Citations (12)
| Title |
|---|
| XIAO WANG: "The Short-Term Passenger Flow Forecasting of Urban Rail Transit Based on Holt-Winters" Seasonal Method", 《2019 4TH INTERNATIONAL CONFERENCE ON ELECTROMECHANICAL CONTROL TECHNOLOGY AND TRANSPORTATION (ICECTT)》 * |
| 刘杰等: "城市交通枢纽短期客流量的组合预测模型", 《交通信息与安全》 * |
| 卫铮铮: "基于客运大数据平台的铁路客流预测系统" * |
| 周庆梅: "基于组合特征的地铁短时乘客流量预测", 《中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑)》 * |
| 孙同心等: "大数据背景下的高速公路流量预测实现", 《中国公共安全》 * |
| 张敏: "Meta回归模型的异常值识别", 《中国博士学位论文全文数据库 (基础科学辑)》 * |
| 张见: "AFC动态数据仓库应用系统的关键技术研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》 * |
| 张豪杰: "基于HHT的电力负荷影响因素分析及预测" * |
| 张豪杰: "基于HHT的电力负荷影响因素分析及预测", 《中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑)》 * |
| 李瀚文: "基于时序分解和LSTM神经网络的城市轨道交通短时客流预测" * |
| 王佳琳: "基于深度学习模型的城市轨道交通短时客流预测方法研究", 《中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑)》 * |
| 蒲斌等: "基于神经网络的海量GPS数据交通流量预测", 《云南大学学报(自然科学版)》 * |
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